research on chemical properties

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Physical and Chemical Properties : Home

• CRC Handbook of Chemistry and Physics Almost every data search on a chemical property should begin in the CRC. (Online or print) Steenbock Ref: QD65 H32 AMP Ref: QD65 H32 more... less... The CRC Handbook of Chemistry and Physics is a basic reference book for chemical and physical data. Material is updated and revised and new material included in each annual. Sections include basic constants, units conversion factors and mathematical tables, chemical and physics terminology, organic and inorganic compounds, biochemistry, analytical chemistry, properties of atoms, particles, solids, fluids, polymers, and sections on geophysics, astronomy, and laboratory safety.
• Lange's Handbook of Chemistry Another major reference work for chemists. (Print only) Chem Ref: QD65 L362 2005 Steenbock: QD65 L362 1999 more... less... Lange's lists the properties of over 4000 organic and 1400 inorganic compounds. The new edition features new tables covering viscosity, thermal conductivity, critical constants, explosion limits, and vapor density, and more.
• Kirk-Othmer Encyclopedia of Chemical Technology. Regularly updated encyclopedia with entries on chemicals, technology, and industrial processes. (Online) more... less... The Kirk-Othmer Encyclopedia of Chemical Technology presents a wide scope of articles on chemical substances—including their properties, manufacturing, and uses. It also focuses on industrial processes and unit operations in chemical engineering, as well as covering fundamentals and scientific subjects related to the field. Additionally, environmental and health issues concerning chemical technology are also addressed. Comparable to Ullmann's Encyclopedia.
• Merck Index Chem Ref: 14th Edition, 2006 Older editions available at other locations. more... less... The Merck Index contains over 10,000 monographs with information relating to compounds of significance in research, commerce and environmental impact.
• SpringerMaterials SpringerMaterials provides access to curated data on 3000+ physical and chemical properties of 250,000+ materials and chemical systems (mixtures, alloys, etc) more... less... Relevant to research in the fields of Physics, Chemistry, Materials Science and most Engineering disciplines. Data sources currently include the Landolt-Börnstein New Series, the Linus Pauling Files and specialized databases on thermophysical properties, polymer thermodynamics, adsorption isotherms, and 32,000+ substance profiles. 1883 – present.
• eEROS: Encyclopedia of Reagents for Organic Synthesis Online encyclopedia containing articles and information on more than 5,100 chemical reagents and catalysts.
• SciFinder Requires registration using your @wisc email. Search by chemical name, identification #, chemical structure, or molecular formula. Make sure you are looking at Experimental Properties, not the Predicted Properties (usually have to scroll past the Predicted Properties). more... less... SciFinder is used to access information in selected Chemical Abstracts Service (CAS) databases: CAPlus, CARegistry, CAREACTS, CHEMCATS, and CHEMLISTS, plus Medline (1946-present). It permits searching in a variety of ways: author name, research topic, substance identifier (CAS Registry Number, chemical name), chemical structure/substructure, or chemical reaction.
• Reaxys Requires users to create an account before using. Includes data from the Beilstein/Gmelin databases as well as patent information. Search by chemical name, identification #, chemical structure, or molecular formula. Good place to find data for organic compounds. more... less... Reaxys is a web-based search and retrieval system for chemical compounds, bibliographic data and chemical reactions. Combined data from three sources: The Beilstein Database, The Gmelin Database, and The Patent Chemistry Database.
• Organic Chemistry Data Collection Originally curated by UW-Madison professor Hans Reich. Topics include structural information, organic reactions, nomenclature, physical properties, and spectroscopic data.
• NIST Chemistry WebBook Provides thermodynamic properties and various spectra for a large array of compounds. Compiled by the National Institute of Standards and Technology (NIST).
• Additional NIST Databases List of free databases from NIST - includes physical constants, thermodynamic properties and the JANAF Tables, atomic spectra, phase diagrams, and more

Other Reference Sources

• DIPPR Data The Design Institute for Physical Property (DIPPR) Data Project published critically evaluated data on mostly pure organic compounds. UW-Madison owns 14 of their publications in print. more... less... Created in 1978 under the auspices of the American Institute of Chemical Engineers the Design Institute for Physical Properties (DIPPR) has become the premier research collaboration on physical property data to satisfy process engineering needs.
• Journal of Physical and Chemical Reference Data This journal provides critically evaluated physical and chemical property data, fully documented as to the original sources and the criteria used for evaluation, preferably with uncertainty analysis. UW-Madison does not have subscription access to this journal, so full text can be obtained in the following ways: -Request via Interlibrary Loan (ILL) ; we will scan our print copies or get scans from another library - 1972-2012 volumes : Some are freely available online - may not contain every article from each volume - Monographs and Supplements from the JPCRD

About this Guide

This site is an index to physical and chemical properties data located primarily in the UW-Madison Libraries online with some print resources included.

For assistance locating property data, contact [email protected] .

• Last Updated: Mar 5, 2024 2:16 PM
• URL: https://researchguides.library.wisc.edu/propertiesguide

1.3 Physical and Chemical Properties

Learning objectives.

By the end of this section, you will be able to:

• Identify properties of and changes in matter as physical or chemical
• Identify properties of matter as extensive or intensive

The characteristics that distinguish one substance from another are called properties. A physical property is a characteristic of matter that is not associated with a change in its chemical composition. Familiar examples of physical properties include density, color, hardness, melting and boiling points, and electrical conductivity. Some physical properties, such as density and color, may be observed without changing the physical state of the matter. Other physical properties, such as the melting temperature of iron or the freezing temperature of water, can only be observed as matter undergoes a physical change. A physical change is a change in the state or properties of matter without any accompanying change in the chemical identities of the substances contained in the matter. Physical changes are observed when wax melts, when sugar dissolves in coffee, and when steam condenses into liquid water ( Figure 1.18 ). Other examples of physical changes include magnetizing and demagnetizing metals (as is done with common antitheft security tags) and grinding solids into powders (which can sometimes yield noticeable changes in color). In each of these examples, there is a change in the physical state, form, or properties of the substance, but no change in its chemical composition.

The ability to change from one type of matter into another (or the inability to change) is a chemical property . Examples of chemical properties include flammability, toxicity, acidity, and many other types of reactivity. Iron, for example, combines with oxygen in the presence of water to form rust; chromium does not oxidize ( Figure 1.19 ). Nitroglycerin is very dangerous because it explodes easily; neon poses almost no hazard because it is very unreactive.

A chemical change always produces one or more types of matter that differ from the matter present before the change. The formation of rust is a chemical change because rust is a different kind of matter than the iron, oxygen, and water present before the rust formed. The explosion of nitroglycerin is a chemical change because the gases produced are very different kinds of matter from the original substance. Other examples of chemical changes include reactions that are performed in a lab (such as copper reacting with nitric acid), all forms of combustion (burning), and food being cooked, digested, or rotting ( Figure 1.20 ).

Properties of matter fall into one of two categories. If the property depends on the amount of matter present, it is an extensive property . The mass and volume of a substance are examples of extensive properties; for instance, a gallon of milk has a larger mass than a cup of milk. The value of an extensive property is directly proportional to the amount of matter in question. If the property of a sample of matter does not depend on the amount of matter present, it is an intensive property . Temperature is an example of an intensive property. If the gallon and cup of milk are each at 20 °C (room temperature), when they are combined, the temperature remains at 20 °C. As another example, consider the distinct but related properties of heat and temperature. A drop of hot cooking oil spattered on your arm causes brief, minor discomfort, whereas a pot of hot oil yields severe burns. Both the drop and the pot of oil are at the same temperature (an intensive property), but the pot clearly contains much more heat (extensive property).

Chemistry in Everyday Life

Hazard diamond.

You may have seen the symbol shown in Figure 1.21 on containers of chemicals in a laboratory or workplace. Sometimes called a “fire diamond” or “hazard diamond,” this chemical hazard diamond provides valuable information that briefly summarizes the various dangers of which to be aware when working with a particular substance.

The National Fire Protection Agency (NFPA) 704 Hazard Identification System was developed by NFPA to provide safety information about certain substances. The system details flammability, reactivity, health, and other hazards. Within the overall diamond symbol, the top (red) diamond specifies the level of fire hazard (temperature range for flash point). The blue (left) diamond indicates the level of health hazard. The yellow (right) diamond describes reactivity hazards, such as how readily the substance will undergo detonation or a violent chemical change. The white (bottom) diamond points out special hazards, such as if it is an oxidizer (which allows the substance to burn in the absence of air/oxygen), undergoes an unusual or dangerous reaction with water, is corrosive, acidic, alkaline, a biological hazard, radioactive, and so on. Each hazard is rated on a scale from 0 to 4, with 0 being no hazard and 4 being extremely hazardous.

While many elements differ dramatically in their chemical and physical properties, some elements have similar properties. For example, many elements conduct heat and electricity well, whereas others are poor conductors. These properties can be used to sort the elements into three classes: metals (elements that conduct well), nonmetals (elements that conduct poorly), and metalloids (elements that have intermediate conductivities).

The periodic table is a table of elements that places elements with similar properties close together ( Figure 1.22 ). You will learn more about the periodic table as you continue your study of chemistry.

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Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

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• Authors: Paul Flowers, Klaus Theopold, Richard Langley, William R. Robinson, PhD
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• Publication date: Feb 14, 2019
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Chemical Properties

The Combined Chemical Dictionary Contains descriptive and numerical data on chemical, physical and biological properties of compounds; systematic and common names of compounds; literature references; and more.

CRC Handbook of Chemistry and Physics Coverage includes physical constants of organic compounds, properties of the elements and inorganic compounds, thermochemistry, electrochemistry and kinetics, fluid properties, biochemistry, analytical chemistry, molecular structure and spectroscopy, atomic, molecular and optical physics, nuclear and particle physics, properties of solids, polymer properties, geophysics, astronomy, and acoustics, practical laboratory data, health and safety information. Searchable by text, and by substance/property.

Design Institute for Physical Properties (DIPPR) Database Evaluated process design data of physical, thermodynamic, and transport properties for industrially important chemicals used in chemical process and equipment design.

International Critical Tables of Numerical Data, Physics, Chemistry and Technology Physical, thermodynamic, mechanical, and other key properties of inorganic and organic compounds, and pure substances.

Knovel Chemistry and Chemical Engineering Reference Books A searchable collection of reference books in chemistry and chemical engineering.

Reaxys Locate property data by a substance search. Reaxys includes combined data the Beilstein Database, the Gmelin Database, and the Patent Chemistry Database.

SciFinder Scholar Explore substances for property data from the chemical literature.

SpringerMaterials Access to data on the properties of materials and chemical systems relevant to research in the fields of physics, chemistry, and materials science. Data sources include the Landolt-Börnstein New Series, the Linus Pauling Files and specialized databases on thermophysical properties, polymer thermodynamics, adsorption isotherms, and substance profiles.

Yaws' Critical Property Data for Chemical Engineers and Chemists This database contains over 290,000 data records covering physical, thermophysical, thermodynamic, transport, safety, and environmental properties for over 5,000 inorganic substances and over 35,000 organic substances.

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Chemical and Physical Property Information

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Subject Guide

Searching for a Compound

There are many ways you can describe a compound in order to search for it within a database. Consider your options and what types of descriptions the particular database will accept. If you get zero results, try another way before moving on to another resource.

Chemical name/synonyms Common/trivial name Molecular formula Empirical formula Structure CAS Registry Number

For example, consider ethanol :

Start with these online resources

These resources collect information on the most commonly encountered compounds and their most basic properties. If you are not finding an entry under the first chemical name that you try, try another synonym for that same compound or in the case of some of the sources, draw the structure of the compound you are looking for.  Then try one of the more comprehensive sources (other tabs) or contact Jeremy (contact info on the right) if you are still having trouble finding the data you need.

• CHEMnetBASE A collection of more robust resources targeted toward specific compounds (natural products, drugs, organic compounds, polymers, etc.) or you can choose to search the Combined Chemical Dictionary of over 500,000 substances. [Allows structure searching.]

• CRC Handbook of Chemistry and Physics A good place to start. Besides data, find basic constants, units and conversion factors; general health and safety information; and mathematical tables, etc. Search substance name, molecular formula, or full-text, or browse text and tables. [Allows structure searching.]
• Knovel Knovel provides numerical and tabular data from over 900 leading engineering and science handbooks and resources. Some tables and graphs are interactive.
• Merck Index Descriptive information on 10,000+ chemicals, drugs (human and veterinary), and biologicals. Each entry lists synonyms for drug names (trade, chemical, generic, and research codes), CAS Registry Numbers, physical data, patent information, uses, toxicity, and bibliographic citations on synthesis, pharmacology, and toxicology. [Allows structure searching.]
• NIST Chemistry WebBook Database of critically-evaluated physical property data from collections maintained by the National Institute of Standards (NIST) Standard Reference Data Program and outside contributors. [Allows structure searching.] You can search for data on specific compounds based on name, chemical formula, CAS registry number, molecular weight, chemical structure, or selected ion energetics and spectral properties.
• Sigma-Aldrich Chem Product Central Sales information, but also basic property data (including, often, various spectra) and key references to other important data resources, including the Beilstein Handbuch. Search in the top right. [Allows structure searching.]

And what about Wikipedia and Wolfram Alpha?

Both Wikipedia and Wolfram Alpha can be quick and easy to seach when it come to chemical and physical properties.  But before you use these resources, please consider the following when searching each source.

• Wikipedia: WikiProject Chemicals This WikiProject is meant to standardize information about chemicals found on Wikipedia and to improve the quality of the information available. A compounds rating indicates how reliable the information is. For example, for the over 8000 compounds found in Wikipedia, nearly 2500 are unassessed, meaning the information cannot be verified. Common compounds may have reliable info, but it is best to verify the data found with another source listed above, especially for more uncommon substance more... less... Also note that currently (Sept 2010) only 17 compounds meet the highest level of quality by Wikipedia standards! Make sure the databox for your compound has a green check, meaning the data has been validated. Also note that many of the Wikipedia articles cite the suggested resources listed above, so why not start with them in the first place?
• Wolfram Alpha WA contains a wide variety of data on many chemical compounds. However, WA's major flaw is that it DOES NOT tell you the specific source of the data presented. WA claims to have a proprietary database of data gathered from various sources. While these sources are often listed, you can not be certain where a particular boiling point or heat capacity came from. When you need to cite an exact source or justify your data from a reliable & credible source, try one of the other resource above instead
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A Research Guide of Chemistry Sources

• Library Instruction - Chemistry
• Chemistry Databases
• Major Database: SciFinder
• Major Databases: REAXYS
• Major Database: KnowItAll U spectra database
• Open-Access Publishing
• ChemRxiv: The Preprint Server for Chemistry
• Major Database: Science of Synthesis
• CAS Registry Number
• Chemical and Physical Properties of Substances
• Specialized Chemistry Sources
• Webinars for Chemistry, Geosciences and Environmental Studies: upcoming and previously recorded
• ACS Author module for Canvas
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• 2021 Nobel in Chemistry, Library Research for asymmetric organocatalysis
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Sources for Chemical and Physical Properties of Substances

Compilations of chemical and physical properties are often found in secondary sources in the literature, such as handbooks like the crc handbook of chemistry and physics, reference books like organic solvents: physical properties and methods of purification, web resources and databases. however, it is useful to note that science database searching is far more powerful than it was in the past, so that the need for specialized reference books has greatly diminished..

CRC Handbook of Chemistry and Physics  – Electronic access via  https://library.princeton.edu/resource/3825   

NIST Chemistry WebBook Access  -  https://webbook.nist.gov/chemistry/ This site provides thermochemical, thermophysical, and ion energetics data compiled by NIST under the Standard Reference Data Program. Only includes roughly 3000 0f the most common chemical compounds.

Material Safety Datasheets Access  -  https://hazard.com//msds/index.php MSDSs can be a place to find basic property information as well as safety data.

REAXYS  – for property info Access  https://library.princeton.edu/resource/title/reaxys Once you have conducted your search, limit to your specific compound, then navigate through the hit set via the facet limiters on the left of the screen.

SciFinder  – for property info Access –  https://library.princeton.edu/resource/4683 In the record for a chemical compound, look for a link to Experimental Properties OR Predicted Properties. CAS Registry number search will get you to your compound faster. You can also do a natural language for your compound and the property you want “formaldehyde and melting point”.

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Physical and Chemical Properties

Sigma-Aldrich Lists common physical properties (boiling point, molecular weight, etc.) for chemical compounds that are available through the company.

ChemSpider Provides access to experimental and predicted chemical properties data from hundreds of sources for millions of structures.

Handbook of Chemistry and Physics Contains physical property data for many compounds. Can search on substance name, formula, CAS Registry Number, or chemical structure, and search can be combined with a request for a desired property (e.g., viscosity of benzene as a function of temperature).

Merck Index Encyclopedia of chemicals, drugs, and biologicals that contains more than 11,500 monographs. Each monograph in this authoritative reference source is a concise description of a single substance or a small group of closely related compounds.

Knovel Scientific and Engineering Databases Provides information on several chemical and physical properties for chemicals, esp. industrial chemicals. The interactive graphs feature also allows easy ways to manipulate data.

NIST Chemistry Webbook Provides a number of physical properties such as thermochemical, thermophysical, and ion energetics data compiled by NIST under the Standard Reference Data Program.

SpringerMaterials Encompasses all volumes of Landolt-Börnstein New Series (largest and most respected compilation of data in physics and chemistry), the complete Linus Pauling Files (properties of inorganic solid phases), a subset of the Dortmund Database of Software and Separation Technology (thermophysical properties of pure liquids and binary mixtures), and other specialized databases.

Reaxys Search for your compound and locate the Physical Data section to see a list of physical properties available for your compound, along with references for the data.

SciFinder-n Under Substances, click on Substance Identifier. Click on the CAS number for a result to see detailed results.

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• Background & Starting Resources
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Chemical Abstracts Service (CAS) Registry Numbers

Property information: online, property information: in print, hazmat information.

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• CAS Registry Numbers How to look up a CAS Registry Number (CASRN) for a specific chemical compound.
• Merck Index How to search the Merck Index in print.
• CRC Handbook of Chemistry & Physics How to search the print version of the CRC.
• Searching the Combined Chemical Dictionary (CCD) Search the Combined Chemical Dictionary online.
• Searching for Spectral Data: SDBS Use the Spectral Database for Organic Compounds to find IR and NMR spectra.
• SciFinder Registration How to register to use SciFinder Scholar.
• SciFinder Searching: Topics and Authors Search SciFinder for research topics or for papers by specific authors.

The fastest and most reliable way to find information about a compound in a resource is to use the Chemical Abstracts Service Registry Number (CASRN). This is like a social security number for compounds and is often used as an indexing tool in many chemistry resources. Look for it as a series of three numbers, often inside of square brackets:  [XXXX-XX-X].

To lookup a compound's CASRN, search one of the below resources using the compound's name.

• ChemID plus
• NIST Chemistry WebBook
• CHEMnetBASE This link opens in a new window Try the Combined Chemical Dictionary (CCD) for basic property information for both common and esoteric compounds. CHEMnetBASE also contains the online version of the CRC Handbook of Chemistry & Physics .

• Sigma Aldrich Material Safety Data Sheets (MSDS)
• NIOSH Pocket Guide to Chemical Hazards

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Thermodynamics Research Center

Trc supplies thermodynamic properties tables, thermophysical properties data, models, standards and research for industry, public health and safety, and the environment., contact information.

Group Leader: Chris Muzny NIST, 647.01 325 Broadway Boulder, CO 80305-3337 Phone : (303) 497-5549 Fax : (303) 497-5044 [email protected]

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Electronic Data Products

• T hermo D ata E ngine (TDE) 103a - Pure Compounds only
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• Catalog of all NIST Databases
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For over 70 years, TRC has produced a great number of periodical compilations and electronic databases that have become source data for scientific research and industrial process design. Subject areas include thermophysical properties and transport properties of pure compounds, binary mixtures, ternary mixtures, and chemical reactions, as well as properties for classes of materials representing particular needs in academia, industry, and government such as clathrate hydrates , and ionic liquids . The goals of TRC are to establish a comprehensive archive of experimental data of well-defined composition and be a comprehensive source of critically evaluated data.

• compiles and evaluates experimental data
• develops tools and standards for archival and dissemination of thermodynamic data, especially critically evaluated data
• develops electronic database products
• maintains a web-repository of published data in ThermoML — an XML format developed by TRC for the representation of thermodynamic data

Critically Evaluated Data

An important and useful aspect of our work here at TRC is to provide critically evaluated data . Critical evaluation is a process of analyzing all available experimental data for a given property to arrive at recommended values together with estimates of uncertainty, providing a highly useful form of thermodynamic data for our customers. The analysis is based on intercomparisons, interpolation, extrapolation,and correlation of the original experimental data collected at TRC. Data are evaluated for thermodynamic consistency using fundamental thermodynamic principles, including consistency checks between data and correlations for related properties. While automated as much as possible, this process is overseen by experts with a great deal of experience in the field of thermodynamic data. Professional staff are responsible for the evaluation of each set of data that is committed to the archive.

For many years TRC delivered a variety of periodicals containing evaluated data for various types of chemical systems. In the last 15 years we have produced a new generation of software products implementing the concept of Dynamic Data Evaluation developed at NIST TRC, such as the NIST ThermoData Engine , Web Thermo Tables , ThermoPlan , and ThermoLit . These products are broadly used in industry, academia, and government, supporting engineering applications such as chemical process and product design, model development, and experimental planning for thermophysical property measurements.

A critical resource is our extensive in-house collection of published thermodynamic and thermophysical properties, the SOURCE data system. All of the data catalogued for critical evaluation by TRC are stored in this data system. An important aspect of TRC's mission is to continue to expand this collection to make it as complete a repository as possible for all published thermodynamic and thermophysical property data.

• A Brief History of TRC
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ThermoML: XML-Based Standard for Data Storage and Exchange

Efficient and reliable data dissemination is also at the core of our work and is the driver for our development of ThermoML as a standard format for data storage and exchange. We are currently partnering with five major journals to encourage authors to submit their data for validation as part of the publication process. To further this cooperation we have created automated tools as part of the software infrastructure supporting the ThermoML standard: Guided Data Capture (GDC) provides guidance for data processing, ThermoML Opener allows direct viewing of the data and metadata contained in a ThermoML file, and ThermoML Updater automatically converts files originally formatted in accordance to the ThermoML Recommendations 2006 to formats correspoding to the ThermoML Recommendations 2011 . All data submitted to NIST TRC through participating journals are publically available through our ThermoML Archive .

ThermoML is a new standard for thermodynamic data communication that was initially developed within IUPAC Project 2002-055-3-024 : XML-based IUPAC Standard for Experimentally and Critically Evaluated Thermodynamic Property Data Storage and Capture , and later extended under the IUPAC project 2007-039-1-024 : Extension of ThermoML - the IUPAC Standard for Thermodynamic Data Communications. The namespace , ThermoML, has been reserved by IUPAC.

TDE: ThermoData Engine

NIST TRC created the ThermoData Engine (NIST TDE), the first software product implementing the concept of dynamic data evaluation . This concept was itself formulated at NIST TRC. TDE is available in two editions: Standard Reference Database 103a - T hermo D ata E ngine - Pure Compounds only , and Standard Reference Database 103b - T hermo D ata E ngine - Pure Compounds, Binary Mixtures, Ternary Mixtures, and Chemical Reactions . The T hermo D ata E ngine software provides critically evaluated thermodynamic and transport property data on demand using an expert system to extract data from the TRC Source data archive.

WTT: Web Thermo Tables

This product provides the most up-to-date source for critically evaluated thermodynamic data. It is available in two versions: a Professional Edition and a Lite Edition . Both are available by subscription through the NIST Standard Reference Data program (SRD) . WTT - Professional Edition represents a complete collection of critically evaluated thermodynamic property data primarily for pure organic compounds. As of November, 2011, WTT contains information on 23,399 compounds. WTT - Lite Edition represents a collection of critically evaluated thermodynamic property data for 150 commonly-used (primarily organic) pure compounds.

The ThermoPlan web application provides support for experimental planning. It provides free and open access for the broader research community to the experimental planning utilities that are incorporated into ThermoData Engine (TDE). TDE provides recommendations for the relative merit of a proposed measurement via assessment of the existing body of knowledge, including availability of experimental thermophysical property data, variable ranges studied, associated uncertainties, state of prediction methods, and parameters for deployment of prediction methods. The web applications provides utilities for the assessment of specific property measurements for pure and binary chemical systems, the broader data needs of pure systems, and recommendations for binary mixture measurements that could extend the current UNIFAC model.

The primary focus of this recommendation service is molecular organic compounds. Some common inorganic and organometallic compounds are included, but, in general, polymers, radicals, ions, salt and acid solutions, metals, metal oxides, and inter-metallics are not considered.

The ThermoLit web application provides free and open access to literature information contained in the NIST SOURCE Data Archive [Int. J. Thermophys., 2012, Vol. 33, No. 1, p. 22-33.] and provides an easy-to-use tool for generation of a NIST Literature Report in PDF format. The tool is intended to aid researchers and reviewers in determining relevant literature sources for a given thermophysical property measurement; however, it is not intended to replace the comprehensive literature review required by all journals, and no guarantee is made regarding completeness of the information provided. The primary focus of this service is molecular organic compounds. Some common inorganic and organometallic compounds are included, but, in general, polymers, radicals, ions, salt and acid solutions, metals, metal oxides, and inter-metallics are not considered.

Please send comments and suggestions to Chris Muzny, TRC Director <[email protected]> .

Privacy Policy / Security Notice / Accessibility Statement / Disclaimer / Freedom of Information Act (FOIA) NIST is an agency of the U.S. Department of Commerce

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Finding Information for General Chemistry Laboratory Work: Popular Sources of Physical and Chemical Properties

• Evaluating Information
• Popular Sources of Physical and Chemical Properties

The following tools may be useful in locating physical and chemical properties of the substances that you are using in your general chemistry laboratory experiments.  Please be sure to reference the source from which you retrieved the value that you cite in your laboratory report or notebook.

CRC Handbook of Chemistry and Physics

The CRC Handbook of Chemistry and Physics is a classic resource containing critically evaluated data in all areas of chemistry and physics.  Penn has access to this work in print at several libraries, as well as online.  To locate the most recent print editions, please visit https://franklin.library.upenn.edu/catalog/FRANKLIN_99999753503681.

• CRC Handbook of Chemistry and Physics Online version of the 91st edition of the CRC Handbook of Chemistry and Physics containing frequently used data in science including the periodic table of elements, basic constants and units, thermodynamic and spectroscopic data; electric, magnetic, thermal and structural properties of solids, key data from nuclear science, astronomy and geophysics; and up-to-date health and safety information.
• Combined Chemical Dictionary

The Combined Chemical Dictionary (CCD), as its name implies, combines several smaller dictionaries of substances and their properties, including the Dictionary of Organic Compounds , the Dictionary of Inorganic and Organometallic Compounds , the Dictionary of Natural Products , the Dictionary of Commonly Cited Compounds , and the Dictionary of Drugs , among others.  It is searchable by name, formula, CAS Registry Number, and structure, as well as being useful for profiling substances by their properties.  Entries are edited by experts in the field, with references to the primary literature from which the values come.  It also includes safety and hazards information for many substances.

NIST Chemistry Web Book

The NIST Chemistry Webbook ( https://webbook.nist.gov/chemistry/ ) is produced by the National Institute of Standards and Technology, under the Standard Reference Data Act .  The NIST Webbook contains data on around 40,000 organic and small inorganic substances, including thermochemical data, reaction thermochemistry data, IR, MS, UV/Vis, GC, electronic and vibrational spectra, ion energetics data, constants of diatomics, and thermophysical properties of fluids.  Note that not every substance has every type of data available.

NIST does a very good job of indicating the source of all data points, and, when a value presented represents the average value observed for that property, it links to the individual data points from which the value was computed.

The NIST Chemistry Webbook is free to use and represents your tax dollars at work!

• Merck Index

The Merck Index has been produced for over 120 years, most recently by the Royal Society of Chemistry.  It contains information about over 11,500 substances that fall into the categories of drugs, agricultural agents, natural products, organic substances, and other substances of biological or agricultural interest, and its records are edited by experts in the field.

Citing Information Sources

The following recommendations are taken or derived from information provided in Chapter 14 of the ACS Style Guide .

Journal Articles:

LastName, Initials; LastName, Initials.  Journal Title .  Year , Volume , Pages.

Example: Scientist, V. I.; Student, U. G. Journal of Very Important Science. 2018 , 26 , 123-125.

LastName, Initials; LastName, Initials. Chapter Title. In Book Title , Edition Number (if any); Publisher: Place of Publication, Year; Volume Number (if any), Pagination.

Note: if the book is an edited book, the abbreviation Ed. or Eds. should come after the editor’s name.

Example: Scientist, V. I., Ed.  Properties of Obscure Compounds.  In The Best Physical Properties Ever Recorded , 6th Ed.; University of Pennsylvania Libraries Press: Philadelphia, PA, 2017, 127.

Free Databases:

Database Name.  URL (accessed Month DD, YEAR).  DATABASE Registry Number NUMBER.

Example: NIST Chemistry Webbook.  http://webbook.nist.gov/chemistry (accessed October 1, 2018).  NIST Chemistry Webbook Permanent Link https://webbook.nist.gov/cgi/inchi/InChI%3D1S/ClH.Na/h1H%3B/q%3B%2B1/p-1 .

Fee-based Databases:

Database Name , Version; Publisher: Place of publication, year.  DATABASE Registry Number NUMBER (accessed Month, DD, YEAR).

Example:   CRC Handbook of Chemistry and Physics ; CRC Press Taylor & Francis Group.  CRC Number HBCP 102543 (accessed October 1, 2018).

When Do I Need to Read Further?

All of the resources on this page have assembled property values that were originally published elsewhere and have presented them in one easy-to-use location.  They also reference the primary literature in which the values were found.  This naturally raises the question of what one should cite: the handbook/dictionary in which you found the value or the original journal article from which they obtained it.  Here are some things to take into consideration.

• You are only allowed to cite something that you have actually read .  Therefore, if you haven't read the original article, you aren't allowed to cite the original article.  Period.  End of story.
• You should read and cite the original article any time you have a question about the conditions under which the property values were observed.
• You should read and cite the original article any time the value that you are seeing in a secondary source does not agree with that found in other secondary sources.
• You should read and cite the handbook/dictionary/database any time you choose not to read the original article.
• You should ask your professor/TA if you are unsure when to read the original article.
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• Last Updated: Nov 16, 2022 11:25 AM
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Physical vs Chemical Properties

Core Concepts

In this tutorial on physical versus chemical properties, you will learn about the differences between physical, chemical, intensive and extensive properties. We will also cover physical versus chemical changes.

Topics Covered in Other Articles

• How to Find and Calculate Molar Mass
• Whats is Matter?
• Law of Conservation of Matter
• Phase Changes

Each property of matter can be classified as either extensive or intensive, and either a physical or a chemical property. Quantities such as mass and volume, that depend on the amount of matter present, are extensive properties. Other properties that that don’t rely on the amount of matter present, like color, are intensive properties. Extensive and intensive properties can be classified as a physical property because they can be measured without changing the substance’s unique chemical identity.

As an example, the freezing point of something is still considered a physical property. Picture water freezing or melting, when water changes temperature it is still water, just in a different state of matter. A chemical property is determined by a substances properties that become apparent during a chemical reaction.

Physical Properties

Physical properties can be measured or observed without changing the composition (chemical nature) of matter . Moreover, they can be further classified into intensive and extensive properties.

Some examples of a physical property include:

• color (intensive)
• density (intensive)
• volume (extensive)
• mass (extensive)
• boiling point (intensive): the temperature at which a substance boils
• melting point (intensive): the temperature at which a substance melts

Inside Physical vs Chemical Properties:

Intensive properties.

An intensive property is a property of matter that does not depend on the size or the amount of matter that is present. For example, melting point is an intensive physical property. No matter how much material you try and melt, the material must still reach the same melting temperature.

Extensive Properties

In contrast, an extensive property is a property of matter that does depend on the size or the amount of matter that is present. Therefore, it is considered additive. Mass is an extensive physical property. The mass of two objects together will be the sum of their individual masses.

Examples of physical properties, that are extensive properties:

Physical changes.

A physical change takes place without any changes in molecular composition. The same elemental composition is present throughout the change. For example, when water freezes into ice, the physical form of liquid water is changed; however, the constituent molecules stay the same. Things like cutting, tearing, grinding, and mixing are some more common types of a physical change. These processes change form, but not composition.

Chemical Properties

Chemical properties describe the ability of a substance to undergo chemical change or reaction to form new substances. 

Examples of chemical properties:

• When a compound undergoes complete combustion (burning) with oxygen, it releases energy known as the heat of combustion.
• Chemical stability refers to whether a compound will react with water or air. Hydrolysis and oxidation are reactions that are both chemical properties.
• Flammability is a determination of whether or not a compound will burn when exposed to flame. Again, burning is a chemical reaction.
• The preferred oxidation state is the lowest-energy oxidation state that a metal will try to adopt, through reactions with other elements capable of accepting or donating electrons.

Chemical Changes

To identify a chemical property, we first need to look for a chemical change, consequently we will have identified a chemical property. A chemical change results in new matter of an undeniably different composition from the original matter. The atoms and/or compounds rearrange their structure, bonds break, and new bonds are made to form new compounds. For instance, burned wood becomes ash, carbon dioxide, and water, which are entirely new chemical compounds that did not exist prior to burning.

Physical vs chemical properties: Practice Problems

State whether each of the following is a physical or chemical property, or a physical or chemical change.

• Iron reacts with sulfur to give heat and flames.
• The density of potassium carbonate is 2.43 g/cm 3 .
• Dissolution of a salt in water.
• Mixing baking soda and vinegar produces bubbles.
• The melting point of aluminum is 660.3°C.
• HCl is a strong acid.
• chemical change
• physical property
• chemical property

Further Reading about physical vs chemical properties

It is really interesting to see everything applied to real life, we recommend this book to understand why in In biotechnology, pharmaceuticals, and the food and beverage sectors, the physical and chemical characteristics of food items are crucial!

How to find molar mass

What is a racemic mixture?

Chemistry 303: Chemical Properties

• Chemical Properties
• Safety Data Sheets
• Tips for the assignment

Physical Properties

The Chem 303 lab assignment highlights two sources for locating chemical property information.

• MilliporeSigma Catalog (formerly the Sigma-Aldrich Catalog) MilliporeSigma sells organic compounds and general laboratory reagents. Their online catalog contains basic physical property and safety information.

⇒⇒ See the CRC Handbook Physical Constants of Organic Compounds overview for abbreviations.

Additional sources for chemical property data include:

• ChemSpider ChemSpider is "a free chemical structure database providing fast text and structure search access to over 34 million structures from hundreds of data sources." Maintained by the Royal Society of Chemistry.
• PubChem PubChem provides information on the chemical structures and biological activities of small molecules. Maintained by the National Center for Biotechnology Information (NCBI) of the National Library of Medicine.

Comprehensive source for chemical information on the upgraded SciFinder-n platform. Locate journal articles, dissertations, patents, synthesis plans, spectra, and more. Can search by structure or physical property. 

Account required . Note: See the SciFinder Guide or contact Holly Surbaugh for details. Coverage 1907-present.

Account required . Note: See the SciFinder Guide or contact Holly Surbaugh for details.

• ThermoDex Looking for more specialized chemical and physical data? ThermoDex indexes a wide variety of sources for finding physical property data, including both print and online sources. For help accessing resources at UNM, please contact your librarian.

Looking up Chemicals

When looking up chemicals in an alphabetic list, ignore any numbers or letters that determine location, such as 2,3-, o-, Z-, or iso-.  Only when you find the main compound and have to distinguish between isomers do you use these.

Then, take the pieces of the chemical name and run them altogether as one word, so 1-bromo-4-nitrobenzene becomes bromonitrobenzene.  When you get to the bromonitrobenzenes, then find the 1,4- among the other isomers.

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• Last Updated: Aug 8, 2024 12:06 PM
• URL: https://libguides.unm.edu/chem303

• Open access
• Published: 07 June 2022

Nanoparticle classification, physicochemical properties, characterization, and applications: a comprehensive review for biologists

• Nadeem Joudeh 1 &
• Dirk Linke 1  

Journal of Nanobiotechnology volume  20 , Article number:  262 ( 2022 ) Cite this article

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Interest in nanomaterials and especially nanoparticles has exploded in the past decades primarily due to their novel or enhanced physical and chemical properties compared to bulk material. These extraordinary properties have created a multitude of innovative applications in the fields of medicine and pharma, electronics, agriculture, chemical catalysis, food industry, and many others. More recently, nanoparticles are also being synthesized ‘biologically’ through the use of plant- or microorganism-mediated processes, as an environmentally friendly alternative to the expensive, energy-intensive, and potentially toxic physical and chemical synthesis methods. This transdisciplinary approach to nanoparticle synthesis requires that biologists and biotechnologists understand and learn to use the complex methodology needed to properly characterize these processes. This review targets a bio-oriented audience and summarizes the physico–chemical properties of nanoparticles, and methods used for their characterization. It highlights why nanomaterials are different compared to micro- or bulk materials. We try to provide a comprehensive overview of the different classes of nanoparticles and their novel or enhanced physicochemical properties including mechanical, thermal, magnetic, electronic, optical, and catalytic properties. A comprehensive list of the common methods and techniques used for the characterization and analysis of these properties is presented together with a large list of examples for biogenic nanoparticles that have been previously synthesized and characterized, including their application in the fields of medicine, electronics, agriculture, and food production. We hope that this makes the many different methods more accessible to the readers, and to help with identifying the proper methodology for any given nanoscience problem.

Nano etymology

The prefix nano is derived from the Greek word nanos, “a dwarf”. In 1947, at the 14th conference of the International Union of Pure and Applied Chemistry (IUPAC), the prefix nano was officially adopted to describe the one-billionth part (10 –9 ) of a unit Footnote 1 . In scientific literature, the prefix nano has been adopted as a popular label in many fields of modern science to describe small entities and processes. These terms include, but are not limited to nanoscience, nanotechnology, nanorobots, nanomagnets, nanoelectronics, nanoencapsulation, etc. [ 1 ]. In all of these cases, the prefix nano is used to describe “very small” entities or processes, most often at actual nanometer scale.

Definitions

Nanoscience is a branch of science that comprises the study of properties of matter at the nanoscale, and particularly focuses on the unique, size-dependent properties of solid-state materials [ 2 ]. Nanotechnology is the branch that comprises the synthesis, engineering, and utilization of materials whose size ranges from 1 to 100 nm, known as nanomaterials [ 3 ]. The birth of nanoscience and nanotechnology concepts is usually linked to the famous lecture of Nobel laureate Richard Feynman at the 1959 meeting of the American Physical Society, ‘‘There’s Plenty of Room at the Bottom’’ [ 4 ]. However, the use of nanotechnology and nanomaterials goes back in history long before that.

History of nanotechnology

Long before the era of nanotechnology, people were unknowingly coming across various nanosized objects and using nano-level processes. In ancient Egypt, dyeing hair in black was common and was for a long time believed to be based on plant products such as henna [ 5 ]. However, recent research on hair samples from ancient Egyptian burial sites showed that hair was dyed with paste from lime, lead oxide, and water [ 6 ]. In this dyeing process, galenite (lead sulfide, PbS) nanoparticles are formed. The ancient Egyptians were able to make the dyeing paste react with sulfur (part of hair keratin) and produce small PbS nanoparticles which provided even and steady dyeing.

Probably the most famous example for the ancient use of nanotechnology is the Lycurgus Cup (fourth century CE). This ancient roman cup possesses unusual optical properties; it changes its color based on the location of the light source. In natural light, the cup is green, but when it is illuminated from within (with a candle), it becomes red. The recent analysis of this cup showed that it contains 50–100 nm Au and Ag nanoparticles [ 7 ], which are responsible for the unusual coloring of the cup through the effects of plasmon excitation of electrons [ 8 ]. The ancient use of nanotechnology does not stop here, in fact, there is evidence for the early use of nanotechnology processes in Mesopotamia, Ancient India, and the Maya [ 9 , 10 ].

Why nanomaterials are different

Today, due to their unique properties, nanomaterials are used in a wide range of applications, such as catalysis, water treatment, energy storage, medicine, agriculture, etc . [ 11 , 12 , 13 ]. Two main factors cause nanomaterials to behave significantly differently than the same materials at larger dimensions: surface effects and quantum effects [ 14 ]. These factors make nanomaterials exhibit enhanced or novel mechanical, thermal, magnetic, electronic, optical, and catalytic properties [ 1 , 15 , 16 ].

Nanomaterials have different surface effects compared to micromaterials or bulk materials, mainly due to three reasons; (a) dispersed nanomaterials have a very large surface area and high particle number per mass unit, (b) the fraction of atoms at the surface in nanomaterials is increased, and (c) the atoms situated at the surface in nanomaterials have fewer direct neighbors [ 1 , 14 ]. As a consequence of each of these differences, the chemical and physical properties of nanomaterials change compared to their larger-dimension counterparts. For instance, having fewer direct neighbor atoms for the atoms situated at the surface results in lowering the binding energy per atom for nanomaterials. This change directly affects the melting temperature of nanomaterials following the Gibbs–Thomson equation, e.g., the melting point of 2.5 nm gold nanoparticles is 407 degrees lower than the melting point of bulk gold [ 14 ]. Larger surface areas and larger surface-to-volume ratios generally increases the reactivity of nanomaterials due to the larger reaction surface [ 1 ], as well as resulting in significant effects of surface properties on their structure [ 17 ]. The dispersity of nanomaterials is a key factor for the surface effects. The strong attractive interactions between particles can result in the agglomeration and aggregation of nanomaterials, which negatively affects their surface area and their nanoscale properties [ 18 ]. Agglomeration can be prevented by increasing the zeta potential of nanomaterials (increasing the repulsive force) [ 19 ], optimizing the degree of hydrophilicity/hydrophobicity of the nanomaterial, or by optimizing the pH and the ionic strength of the suspension medium [ 20 ].

Nanomaterials display distinct size-dependent properties in the 1–100 nm range where quantum phenomena are involved. When the material radius approaches the asymptotic exciton Bohr radius (the separation distance between the electron and hole), the influence of quantum confinement becomes apparent [ 17 ]. In other words, by shrinking the size of the material, quantum effects become more pronounced, and nanomaterials become quantal. Those quantum structures are physical structures where all the charge carriers (electrons and holes) are confined within the physical dimensions [ 21 ]. As a result of quantum confinement effects, for instance, some non-magnetic materials in bulk such as palladium, platinum, and gold become magnetic in the nanoscale [ 14 ]. Quantum confinement can also result in significant changes in electron affinity or the ability to accept or donate electrical charges, which is directly reflected on the catalytic properties of the material. For example, the catalytic activity of cationic platinum clusters in N 2 O decomposition is dictated by the number of atoms in the cluster. 6–9, 11, 12, 15, and 20 atom-containing clusters are very reactive, while clusters with 10, 13, 14, and 19 atoms have low reactivity [ 14 ].

Classification of nanomaterials

The key elements of nanotechnology are the nanomaterials. Nanomaterials are defined as materials where at least one of their dimensions is in the nanoscale, i.e. smaller than 100 nm [ 22 ]. Based on their dimensionalities, nanomaterials are placed into four different classes, summarized in Fig.  1 .

Zero-dimensional nanomaterials (0-D): the nanomaterials in this class have all their three dimensions in the nanoscale range. Examples are quantum dots, fullerenes, and nanoparticles.

One-dimensional nanomaterials (1-D): the nanomaterials in this class have one dimension outside the nanoscale. Examples are nanotubes, nanofibers, nanorods, nanowires, and nanohorns.

Two-dimensional nanomaterials (2-D): the nanomaterials in this class have two dimensions outside the nanoscale. Examples are nanosheets, nanofilms, and nanolayers.

Three-dimensional nanomaterials (3-D) or bulk nanomaterials: in this class the materials are not confined to the nanoscale in any dimension. This class contains bulk powders, dispersions of nanoparticles, arrays of nanowires and nanotubes, etc .

Nanomaterials classification based on dimensionality

Nanoparticles (NPs)

The International Organization for Standardization (ISO) defines nanoparticles as nano-objects with all external dimensions in the nanoscale, where the lengths of the longest and the shortest axes of the nano-object do not differ significantly. If the dimensions differ significantly (typically by more than three times), terms such as nanofibers or nanoplates maybe preferred to the term NPs Footnote 2 .

NPs can be of different shapes, sizes, and structures. They can be spherical, cylindrical, conical, tubular, hollow core, spiral, etc., or irregular [ 23 ]. The size of NPs can be anywhere from 1 to 100 nm. If the size of NPs gets lower than 1 nm, the term atom clusters is usually preferred. NPs can be crystalline with single or multi-crystal solids, or amorphous. NPs can be either loose or agglomerated [ 24 ].

NPs can be uniform, or can be composed of several layers. In the latter case, the layers often are: (a) The surface layer, which usually consists of a variety of small molecules, metal ions, surfactants, or polymers. (b) The shell layer, which is made of a chemically different material from the core layer. (c) The core layer, which is the central portion of the NP [ 25 ].

Classification of NPs

Based on their composition, NPs are generally placed into three classes: organic, carbon-based, and inorganic [ 23 ].

Organic NPs

This class comprises NPs that are made of proteins, carbohydrates, lipids, polymers, or any other organic compounds [ 26 ]. The most prominent examples of this class are dendrimers, liposomes, micelles, and protein complexes such as ferritin (shown in Fig.  2 ). These NPs are typically non-toxic, bio-degradable, and can in some cases, e.g., for liposomes, have a hollow core. Organic NPs are sensitive to thermal and electromagnetic radiation such as heat and light [ 23 ]. In addition, they are often formed by non-covalent intermolecular interactions, which makes them more labile in nature and offers a route for clearance from the body [ 27 ]. There are different parameters that determine the potential field of application of organic NPs, e.g., composition, surface morphology, stability, carrying capacity, etc . Today, organic NPs are mostly used in the biomedical field in targeted drug delivery [ 23 ] and cancer therapy [ 28 ].

Types of organic NPs. A Dendrimers; B liposomes; C micelles; and D ferritin

Carbon-based NPs

This class comprises NPs that are made solely from carbon atoms [ 23 ]. Famous examples of this class are fullerenes, carbon black NPs, and carbon quantum dots (shown in Fig.  3 ). Fullerenes are carbon molecules that are characterized by a symmetrical closed-cage structure. C 60 fullerenes consist of 60 carbon atoms arranged in the shape of a soccer ball [ 29 ], but also other types of fullerenes such as C 70 and C 540 fullerenes have been described [ 30 ]. Carbon black NPs are grape-like aggregates of highly fused spherical particles [ 31 ]. Carbon quantum dots consist of discrete, quasi-spherical carbon NPs with sizes below 10 nm [ 32 ]. Carbon-based NPs unite the distinctive properties of sp 2 -hybridized carbon bonds with the unusual physicochemical properties at the nanoscale. Due to their unique electrical conductivity, high strength, electron affinity, optical, thermal, and sorption properties [ 25 , 33 ], carbon-based NPs are used in a wide range of application such as drug delivery [ 34 ], energy storage [ 35 ], bioimaging [ 36 ], photovoltaic devices, and environmental sensing applications to monitor microbial ecology or to detect microbial pathogens [ 33 ]. Nanodiamonds and carbon nano onions are more complex, carbon-based NPs. Due to their characteristic low toxicity and biocompatibility, they are used in drug delivery and tissue engineering applications [ 37 , 38 ].

Different types of carbon-based NPs. A C 60 fullerene; B carbon black NPs; and C carbon quantum dots

Inorganic NPs

This class comprises NPs that not made of carbon or organic materials. The typical examples of this class are metal, ceramic, and semiconductor NPs. Metal NPs are purely made of metal precursors, they can be monometallic, bimetallic [ 39 ], or polymetallic [ 40 ]. Bimetallic NPs can be made from alloys or formed in different layers (core–shell) [ 39 ]. Due to the localized surface plasmon resonance characteristics, these NPs possess unique optical and electricals properties [ 25 ]. In addition, some metal NPs also possess unique thermal, magnetic, and biological properties [ 23 ]. This makes them increasingly important materials for the development of nanodevices that can be used in numerous physical, chemical, biological, biomedical, and pharmaceutical applications [ 41 , 42 ] (these applications are discussed in detail later in the applications section of the review). In present days, the size-, shape-, and facet-controlled synthesis of metal NPs is important for creating cutting-edge materials [ 43 ].

Semiconductor NPs are made of semiconductor materials, which possess properties between metals and non-metals. These NPs possess unique wide bandgaps and show significant alteration in their properties with bandgap tuning compared to bulk semiconductor materials [ 25 ]. As a result, these NPs are important materials in photocatalysis, optic, and electronic devices [ 44 , 45 ]. Ceramic NPs are inorganic solids made of carbonates, carbides, phosphates, and oxides of metals and metalloids, such as titanium and calcium [ 46 ]. They are usually synthesized via heat and successive cooling and they can be found in amorphous, polycrystalline, dense, porous or hollow forms [ 25 ]. They are mainly used in biomedical applications due to their high stability and high load capacity [ 47 ]. Nevertheless, they are also used in other applications such as catalysis, degradation of dyes, photonics and optoelectronics [ 46 , 48 ].

Physicochemical properties of NPs

As mentioned earlier, NPs can be used in a long list of applications due to their unique physical and chemical properties that do not exist in their larger-dimension counterparts of the same materials. The following sections summarize the most import physicochemical properties that are changing on the nanoscale.

Mechanical properties

Mechanical properties refer to the mechanical characteristics of a material under different conditions, environments, and various external forces. As for traditional materials, the mechanical properties of nanomaterials generally consist of ten parts: strength, brittleness, hardness, toughness, fatigue strength, plasticity, elasticity, ductility, rigidity, and yield stress [ 49 ]. Most inorganic, non-metallic materials are brittle materials and do not have significant toughness, plasticity, elasticity, or ductility properties. Organic materials on the other hand, are flexible materials and do not necessarily have brittleness and rigidity properties.

Due to surface and quantum effects, NPs display different mechanical properties compared to bulk materials [ 49 ]. For example, conventional FeAl powder which is composed of microparticles (larger than 4 µm), is brittle, while ultrafine FeAl alloy powder displays a good combination of strength and ductility as well as enhanced plasticity [ 50 ]. These new properties are believed to arise due to the diverse interaction forces between NPs or between them and a surface. The most important interaction forces involved are van der Waals forces, which consist of three parts, Keesom force, Debye force, and London force [ 51 , 52 , 53 ]. Other relevant interaction forces are electrostatic and electrical double layer forces, normal and lateral capillary forces, solvation, structural, and hydration forces [ 54 ].

There are different theories on how the interaction forces between NPs give them new mechanical properties, such as the DLVO (Derjaguin–Landau–Verwey–Overbeek) theory, JKR (Johnson–Kendall–Roberts) theory, and DMT (Derjaguin–Muller–Toporov) theory. The DLVO theory combines the effects of van der Waals attraction and electrostatic repulsion to describe the stability of colloidal dispersions [ 54 ]. This theory can explain many phenomena in colloidal science, such as the adsorption and the aggregation of NPs in aqueous solutions and the force between charged surfaces interacting through a liquid medium [ 55 , 56 ]. Nevertheless, the DLVO theory is inadequate for the colloidal properties in the aggregated state [ 54 ].

When the size of objects decreases to the nanoscale, the surface forces become a major player in their adhesion, contact, and deformation behaviors. The JRK theory is applicable to easily deformable, large bodies with high surface energies, where it describes the domination of surface interactions by strong, short-range adhesion forces. In contrast to this, the DMT theory is applicable to very small and hard bodies with low surface energies, where it describes the adhesion being caused by the presence of weak, long-range attractive forces. Although the DLVO, JKR and DMT theories have been widely used to describe and study the mechanical properties of NPs [ 57 , 58 ], it is still a matter of debate whether or not continuum mechanics can be used to describe a particle or collection of particles at the nanometer scale [ 54 ].

Thermal properties

Heat transfer in NPs primarily depends on energy conduction due to electrons as well as photons (lattice vibration) and the scattering effects that accompany both [ 59 ]. The major components of thermal properties of a material are thermal conductivity, thermoelectric power, heat capacity, and thermal stability [ 59 , 60 ].

NP size has a direct impact on electrical and thermal conductivity of NPs [ 60 ]. As the NP size decreases, the ratio of particle surface area respective to its volume increases hyperbolically [ 60 ]. Since the conduction of electrons is one of the two main ways in which heat is transferred, the higher surface-to-volume ratio in NPs provides higher number of electrons for heat transfer compared to bulk materials [ 61 ]. Moreover, thermal conductivity in NPs is also promoted by microconvection, which results from the Brownian motion of NPs [ 62 ]. Nevertheless, this phenomenon only happens when solid NPs are dispersed in a liquid (generating a Nanofluid) [ 63 ]. As an example, the addition of Cu NPs to ethylene glycol enhances the thermal conductivity of the fluid up to 40% [ 64 ].

The thermoelectric power of a material depends on its Seebeck coefficient and electrical conductivity ( \(P={S}^{2}\sigma \) , where P is thermoelectric power, S is the Seebeck coefficient, and \(\sigma \) is the electrical conductivity). The scattering of NPs in bulk materials (doping) is known to enhance the thermoelectric power factor [ 65 ]. This enhancement could come from the enhancement of the Seebeck coefficient or the enhancement of electrical conductivity. The embedding of size-controlled NPs in bulk thermoelectric materials helps to reduce the lattice thermal conductivity and enhances the Seebeck coefficient due to electron energy filtering [ 66 , 67 ]. Generally, the enhancement of electrical conductivity is accompanied by the reduction of the Seebeck coefficient and vice versa [ 65 ] However, the doping of InGaAlAs material with 2–3 nm Er NPs resulted in the significant increase of thermoelectric power of the material through the enhancement of the conductivity while keeping the Seebeck coefficient unchanged [ 65 ]. Depending on NP size, volume fraction, and band offset, a NP-doped sample can either enhance or suppress the electrical conductivity in comparison with undoped bulk sample.

Experimental studies have shown that the heat capacity of NPs exceeds the values of analogous bulk materials by up to 10% [ 68 ], e.g. in the case of Al 2 O 3 and SiO 2 NPs [ 69 , 70 ]. The major contribution to heat capacity at ambient temperatures is determined by the vibration degrees of freedom, i.e., the peculiarities of phonon spectra (vibrational energy that arises from oscillating atoms within a crystal) are responsible for the anomalous behavior of heat capacity of NPs [ 68 ]. NPs usually exhibit a significant decrease in melting temperature compared to their analogous bulk materials [ 71 ]. The main reason for this phenomenon is that the liquid/vapor interface energy is generally lower than the average solid/vapor interface energy [ 72 ]. When the particle size decreases, its surface-to-volume ratio increases, and the melting temperature decreases as a result of the improved free energy at the particle surface [ 73 ]. For instance, the melting temperature of 3 nm Au NPs is 300 degrees lower than the melting temperature of bulk gold [ 14 ]. In addition, NP composition plays an important role in thermal stability. For example, the thermal stability of Au in Au 0.8 Fe 0.2 is significantly higher than of pure Au or Au 0.2 Fe 0.8 [ 74 ]. Generally, bimetallic alloy NPs show higher thermal stabilities and melting temperatures than monometallic NPs due to the alloying effect [ 75 , 76 ].

Magnetic properties

All magnetic compounds include a ‘magnetic element’ in their formula, i.e., Fe, Co, or Ni (at ambient temperatures). There are only three known exceptions that are made from mixed diamagnetic elements, Sc 3 In, ZrZn 2 , and TiBe 2-x Cu x [ 77 , 78 , 79 , 80 ]. Otherwise, elements such as Pd, Au, or Ag are diamagnetic. This all changes in the nanoscale. Several materials become magnetic in the form of NPs as a result of uneven electronic distribution [ 25 ]. For instance, FeAl is not magnetic in bulk but in the form of NPs, it is becomes magnetic [ 50 ], other examples include Pd and Au [ 81 ]. In bulk materials, the key parameters for determining magnetic properties are composition, crystallographic structure, magnetic anisotropy, and vacancy defects [ 82 , 83 ]. However, on the nanoscale, two more important parameters are strongly involved, i.e., size and shape [ 84 ].

One of the interesting size-dependent phenomena of NPs is superparamagnetism [ 84 ]. As the size of the NPs decreases, the magnetic anisotropy energy per NP decreases. The magnetic anisotropy energy is the energy keeping the magnetic moment in a particular orientation. At a characteristic size for each type of NPs, the anisotropy energy becomes equal to the thermal energy, which allows the random flipping of the magnetic moment [ 85 ], in this case, the NP is defined as being superparamagnetic [ 86 ]. Superparamagnetic NPs display high magnetization only in the presence of a magnetic field, and once it is removed they do not retain any magnetization [ 87 ]. Superparamagnetism was long believed to form only in small ferromagnetic or ferrimagnetic NPs [ 88 ], but interestingly, other paramagnetic materials show magnetism in the nanoscale too [ 81 ].

NP size effects can also be observed in changes in magnetic coercivity, i.e., the resistance of a magnetic material to changes in magnetization (Fig.  4 ). In contrast to large particles or bulk materials, which possess multiple magnetic domain structures, small NPs possess single magnetic domain structures below a certain critical radius (r c ), where all magnetic spins in the NP align unidirectionally (blue arrows in Fig.  4 ). However, the NP radius has to be lower than the threshold radius for superparamagnetism (r sp ) in order to be superparamagnetic [ 89 ]. In the single-domain regime, between r sp and r c , the magnetic coercivity increases as the size of the NP increases until it reaches the maximum at r c [ 84 ]. In this size regime, due to the high magnetic coercivity, the NPs behave similarly as their larger dimension counterparts despite having a single domain structure, i.e., they become ferromagnetic for ferromagnetic materials or paramagnetic for paramagnetic materials etc . Above r c , the magnetic coercivity starts to decrease when multiple magnetic domains are formed in a single NP. The critical radius represents the size where it is energetically favored for the NP to exist without a domain wall [ 86 ]. The calculated critical radii for some common magnetic materials are 35 nm of Ni, 8 nm for Co, and 1 nm for Fe [ 90 ]. Above that point, multi-domain magnetism begins in which a smaller reversal magnetic field is required to make the net magnetization zero [ 84 ].

The change in magnetic coercivity of NPs as a function of particle radius. Figure adapted from Kalubowilage et al., 2019 [ 89 ]. rc critical radius, rsp threshold radius for superparamagnetism

The second key parameter for determining the magnetic properties of NPs is the shape of NPs. In comparison to the size parameter, there is significant less research on the effect of shape on the magnetic properties of NPs having the same volume [ 86 ]. However, large differences in coercivity were found between a set of cubic and spherical CoFe 2 O 4 NPs [ 91 ]. Unlike the curved topography in spherical CoFe 2 O 4 NPs, cubic CoFe 2 O 4 NPs have fewer missing oxygen atoms, and it was hypothesized that this led to less surface pinning and to lower coercivity for the cubic structures [ 86 ]. Other studies also found differences in magnetism between spherical and cubic Fe 3 O 4 NPs [ 92 , 93 ].

Similar to bulk materials, the composition also affects the magnetism of NPs. The magnetocrystalline phase of the NP is significant in determining its magnetic coercivity [ 94 ]. This effect can be observed in magnetic bimetallic core–shell or alloy NPs with anisotropic crystalline structures. For example, Co@Pt core–shell NPs composed of an isotropically structured face-centered cubic Co core and a non-magnetic Pt shell exhibit superparamagnetic behavior with zero coercivity at room temperature [ 95 ]. In general, the compositional modification of NPs by the adoption of magnetic dopants is known to significantly change the magnetism of NPs [ 96 ].

Electronic and optical properties

Metallic and semiconductor NPs possess interesting linear absorption, photoluminescence emission, and nonlinear optical properties due to the quantum confinement and localized surface plasmon resonance (LSPR) effect [ 97 , 98 ]. LSPR phenomena arise when the incident photon frequency is constant with the collective excitation of the conductive electrons [ 25 ].Due to this phenomenon, noble metal NPs exhibit a strong size-dependent UV–visible extinction band that is not present in the spectra of bulk metals. Generally, the optical properties of NPs depend on the size, shape, and the dielectric environment of the NPs [ 99 ].

The collective excitations of conductive electrons in metals are called plasmons [ 100 ]. Depending on the boundary conditions, bulk plasmons, surface-propagating plasmons, and surface-localized plasmons are distinguished (Fig.  5 A–C). Because of their longitudinal nature, the bulk plasmons cannot be excited by visible light. The surface-propagating plasmons propagate along metal surfaces in a waveguide-like fashion [ 98 ]. In the case of NPs, when they are irradiated by visible light, the oscillating electric field causes the conductive electrons to oscillate coherently. When the electron cloud is displaced relative to the nuclei, a restoring force rises from Coulomb attraction between electrons and nuclei that results in oscillation of the electron cloud relative to the nuclear framework [ 99 ]. This creates uncompensated charges at the NP surface (Fig.  5 D). As the main effect producing the restoring force is the polarization of the NP surface, these oscillations are called surface plasmons and have a well-defined resonance frequency [ 98 ].

Graphical illustration of the types of plasmons. A bulk; B surface propagating; and C surface localized plasmons (adapted from Khlebtsov et al., 2010 [ 98 ]). D graphical illustration of the localized surface plasmon resonance (LSPR) in NPs (adapted from Kelly et al., 2003 [ 99 ])

Experimental studies on Ag NPs showed significant differences in their optical properties based on the size of NPs. For Ag NPs with 30 nm radius, the main extinction peak was at 369 nm wavelength, while for Ag NPs with 60 nm radius, a totally different behavior was observed [ 99 ]. The same researchers found that the shape of the NPs also is critical for the optical properties, the plasmon resonance wavelength shifts to the red as the NPs become more oblate [ 99 ], demonstrating that plasmon resonance strongly depend on NPs shape. With respect to the dielectric environment of the NPs, both the surrounding solvent and the support (substrate) were found to be critical for the optical properties. For Ag NPs, both experimental and theorical studies on the effect of surrounding solvent show that plasmon wavelength linearly depends on the refractive index of the solvent [ 99 , 101 ]. At the same time, 10 nm Ag NPs supported on mica substrates displayed LSPR wavelength shifts to the red compared to unsupported NPs [ 102 ]. The biogenic synthesis of NPs can also improve the optical properties. Biologically produced CeO 2 NPs using Simarouba glauca leave extract were found to have different absorption bands and higher band gap energies compared to chemically produced CeO 2 NPs. These superior optical properties were attributed to the better crystallinity and small size of biogenic NPs compared to chemical NPs [ 103 ]. Biogenic NPs can also offer higher photocatalytic activities, e.g., ZnO NPs produced by Plectranthus amboinicus leaf extract had higher photocatalytic activity in the photodegradation of methyl red under UV illumination compared to chemical produced ZnO NPs [ 104 ].

Catalytic properties

Nano-catalysis, i.e., the use of NPs as catalysts, is a quickly evolving field within chemical catalysis. Significantly enhanced or novel catalytic properties such as reactivity and selectivity have been reported for NP catalysts compared to their bulk analogues. The catalytic properties of NPs depend on the size, shape, composition, interparticle spacing, the oxidation state, and the support of the NPs [ 76 ].

The dependency of catalytic activity on the size of NPs is well studied. The relation is an inverse one, i.e., the smaller the NPs the more catalytically active they are. This relationship was found e.g., in the electro-catalysis oxidation of CO by size-selected Au NPs (1.5, 4, and 6 nm) deposited on indium tin oxide. The researchers observed that the smallest NPs provided the highest normalized current densities [ 105 ]. The same relationship was also found in several other studies [ 106 , 107 , 108 , 109 , 110 ]. Goodman et al., 1998 [ 111 ] speculated originally that this behavior could be attributed to quantum-size effects generated by the confinement of electrons within a small volume. Later, size-dependent changes in the electronic structure of the clusters [ 112 ] and the resulting larger number of low-coordinated atoms available for interaction by the larger surface-to-volume ratios with smaller NPs were discussed [ 76 ].

The shape is also known to affect the reactivity and selectivity of the NPs. For the oxidation of CO by Au NPs, hemispherical NPs were found to be more active than spherical ones [ 113 ]. For the oxidation of styrene by Ag NPs, nanocubes were found to be fourteen times more efficient than nanoplates and four times more efficient than nanospheres [ 114 ]. The reason for these dramatical changes are attributed to the increase/decrease in the relative area of the catalytically active surface facets [ 76 ] or to the differences in stability for different NP shapes [ 115 ].

As for composition, several studies have shown that the use of alloys in NPs can enhance the catalytic activity as a result of the alloying effect causing changes in the electronic properties of the catalyst, decreasing poisoning effects, and providing distinct selectivities [ 76 ]. For example, the alloying of Pt with other metals such as Ru, Ni, and Co, was reported to enhance the hydrogenation and oxygen reduction activity of the NP catalyst material, as well as enhancing the resistance against CO poisoning [ 116 , 117 , 118 ]. However, the alloying of Pt with Fe, Ru, and Pd, resulted in reduced reactivity for methanol decomposition [ 119 ]. This reduction in reactivity was explained by the possible occupation of the surface with the addition metal atoms, since pure Fe, Ru, and Pd clusters are less reactive for methanol decomposition than similarly-sized pure Pt clusters. In general, the change in the composition of NPs changes the electronic structure of metal surfaces by the formation of bimetallic bonds as well as the modification of metal–metal bond lengths [ 76 ]. In addition, the charge-transfer phenomenon between different metals may favorably change the binding energy of adsorbents, lower the barriers for specific chemical reactions, and enhance resistance against poisoning [ 120 , 121 , 122 ].

The catalytic activity and stability of 2 nm Au NPs dispersed on polycrystalline TiC films displayed a strong dependence on interparticle spacing. In this study, Au NPs having two different interparticle spacing (30 and 80 nm) were analyzed by Thermal Desorption Spectroscopy. It was found that the sample with smaller interparticle spacing was poisoned and subsequently deactivated while the sample with longer interparticle spacing showed longer lifetime [ 123 ]. At the same time, the oxidation state of NPs was shown to affect the catalytic activities. Ru NPs under rich O 2 conditions and moderate temperatures oxidize and form RuO 2 , the reaction of CO oxidation was found to occur on the metal oxide surface not the metal surface [ 124 ]. A similar effect on CO oxidation was also observed with Pt NPs in which the reactivity of PtO 2 was found to be higher than Pt [ 125 ]. The reaction of CO oxidation was compared for several metal NPs (Ru, Pd, Ir, Os, and Pt) and their corresponding oxides, and the oxides were indeed more reactive than the metals [ 126 , 127 ]. The superior catalytic performance of RuO 2 over their metallic counterparts is generally agreed on, nevertheless, the same cannot be said for other catalytically active metals such as Pt [ 76 ]. In general, these differences in catalytic performance are attributed to the electron transfer processes at the metal/metal oxide interfaces. Consequently, the view that NP oxidation is an undesirable process that leads to the reduction of catalytic performance needs to be reconsidered [ 128 ].

An example for the effect of the support material is the role of the MgO support for Au NPs, where MgO was found to be important for CO oxidation and particularly, for controlling the rate of CO oxidation through oxygen vacancies [ 129 ]. Later, the process of electron charge transfer from oxygen vacancies at the metal-substrate interface of supported Au NPs was suggested to be an ideal environment for O 2 activation and oxidation reactions [ 130 ]. A similar behavior was also found in the decomposition of SO 2 and dissociation of water by Au NPs supported on CeO 2 , in which CeO 2 supports played a critical role [ 131 ]. The experiments showed that not only the chemical composition of the support affects the reactivity of the catalyst, but the crystal structure of the support, too [ 132 ]. Enhanced catalytic performance for CO oxidation and SO 2 dissociation have also been reported for Au NPs supported on metal carbides such as TiC [ 108 , 133 ]. In addition to enhanced catalytic reactivities, the support also plays an important role in NP stabilization [ 106 ], i.e., the stabilization of NPs against coarsening, the stabilization of metal oxides at the NP surface, and the stabilization of intermediate reactions species [ 76 ].

Characterization of NPs

The properties of NPs determine their potential applications. Hence, different methods and techniques are used for the analysis and characterization of the various physicochemical properties of NPs. Table 1 summarizes all characterization techniques mentioned in this review and shows what properties and features can be resolved by each technique.

Morphological and topographical characterization

The morphological and topographical features of NPs are of great interest since they influence most of the properties of NPs as described above. These features include the size, shape, dispersity, localization, agglomeration/aggregation, surface morphology, surface area, and porosity of the NPs. The following techniques are regularly used for the characterization of morphological and topographical features of NPs.

Electron microscopy (EM)

Scanning electron microscopy (SEM), scanning tunneling microscopy (STM), and transmission electron microscopy (TEM) are frequently employed for the analysis of NP size, shape, and surface. In SEM, an electron gun is used to produce a beam of electrons that is controlled by a set of lenses to follows a vertical path through the microscope until it hits the samples. Once the sample is hit by the beam, electrons and X-rays are ejected from the sample. Detectors are then used to collect the X-rays and scattered electrons in order to create a 3D image of the sample. SEM provides different information about the NPs such as size, shape, aggregation, and dispersion [ 134 ]. Similarly, TEM provides information about the size, shape, localization, dispersity, and aggregation of NPs in two-dimensional images [ 25 ]. TEM employs an electromagnetic lens that focuses a very fine beam of electrons into an ultrathin section of the sample. This beam passes through the specimen where the electrons either scatter or penetrate the sample and hit a fluorescent screen at the bottom of the microscope. The difference in electron densities is used for the contrast to create an image of the specimen. TEM can be also used for the characterization of NP crystal structure through the use of selected area electron diffraction (SAED), where the electron beam is focused on a selected area in the sample and the scattered electrons are used to obtain a diffraction pattern. STM is based on the phenomenon of quantum tunneling, where a metallic tip is brough very close to the sample surface and used to apply voltage. When voltage is applied, electrons from the sample surface are extracted creating an electrical current that is used to reconstruct an image of the surface with atomic resolution [ 135 ]. STM is mainly used to characterize the topography of NPs. For inorganic NPs, these techniques offer excellent approaches for the determination of morphological features of NPs. For organic NPs (or NPs coated with biological materials), these techniques require sophisticated sample preparations which constitute major restrictions to their use [ 136 ]. The sample preparation for these techniques might cause sample dehydration, which might lead e.g. to sample shrinking and aggregation [ 136 ].

Examples: TEM was used for the characterization of Ag NPs produced by Arbutus unedo leaf extract. In this example, the NPs have a spherical morphology with a uniform size of 30 nm. The NPs were found to agglomerate into small aggregates, each including 5–6 NPs. At the same time, the SAED approach was used to determine the crystal structure of the NPs. The majority of the NPs were found to be single crystalline cubic materials predominately oriented along their (111) direction [ 137 ]. For the characterization of Ag NPs produced by Diospyros kaki leaf extract, SEM helped to show that the NPs were also spherical and the size was 32 nm with some deviations [ 138 ]. STM is less frequently used for the characterization of biogenic NPs. The features of Ag NPs produced by lime, sweet-lime, and orange juices were compared using STM technique [ 139 ].

Dynamic light scattering (DLS)

This technique is a common approach for the analysis of NP size and size distribution. This approach involves the measurement of light interference based on the Brownian motion of NPs in suspension, and on the correlation of NP velocity (diffusion coefficient) with their size using Strokes-Einstein equation [ 140 ]. The size distribution range of NPs is shown as the polydispersity index, which is the output of an autocorrelation function [ 136 ]. The polydispersity index values lie between 0 and 1, where 0 represents a completely homogenous population and 1 represents a highly heterogeneous population. This technique also allows the analysis of non-spherical NPs through the use of multistage DLS [ 136 ]. This technique is also referred to as photon correlation spectroscopy (PCS) [ 141 ].

Examples: DLS was used to measure the size and the size distribution profile of a wide range of biogenic NPs. The average size of Ag NPs produced by Trichoderma koningii fungi was found to be around 25 nm and the size distribution profile was between 14 and 34 nm. The polydispersity index for those NPs was 0.681, which indicates that they are polydispersed [ 142 ]. While the average size of Ag NPs produced by potato ( Solanum tuberosum ) was found to be around 10–12 nm with a wider distribution profile between 3–65 nm [ 143 ]. In a different application, DLS was employed to study the size increase of biogenic MnO 2 NPs overtime, demonstrating that their size is 7.5 nm after 3 min of the initiation of the reaction, then their size grows overtime until it become 54 nm after 31 min [ 144 ].

Nanoparticle tracking analysis (NTA)

This method is used for the analysis of NP size in suspensions based on their Brownian motion. Like in DLS, the rate of NP movement is correlated with their size using Strokes-Einstein equation, allowing the measurement of size distribution profiles for NPs with 10–1000 nm diameter. Its advantage over DLS is that NP motion is analyzed by video. Individual positional changes of NPs are tracked in two dimensions, which are used to determine NP diffusion rates, and by knowing the diffusion coefficient, the hydrodynamic diameter of the particles can be calculated. In DLS, individual NPs are not visualized, but instead, the time-dependent intensity fluctuations caused by Brownian motion are used to calculate the polydispersity index [ 145 ]. NTA was found to be more precise for sizing monodisperse as well as polydisperse organic NPs compared to DLS [ 146 ].

Examples: NTA was used to measure the size and dispersity of Ag NPs produced by Camellia sinensis (green tea) powder, the NPs were found to be well dispersed in an aqueous medium with an average size of 45 ± 12 nm [ 147 ]. For Se NPs produced by lactic acid bacteria, NTA was employed to measure the size and the concentration of NPs. The average size was found to be 187 ± 56 nm with a concentration of (4.67 ± 0.30) × 10 9 Se NPs per ml [ 148 ].

Brunauer–Emmett–Teller (BET) method

This method is based on the adsorption and desorption principle developed by Stephen Brunauer, Paul Emmett, and Edward Teller, and it is considered one of the best methods for the analysis of NP surface area [ 25 ]. In BET analysis, a partial vacuum is created to produce adsorption between the sample and liquid N 2 (because the interaction between solid and gaseous phases is weak, the surface is cooled with liquid N 2 to obtain detectable amounts of adsorption). After the formation of adsorption monolayers, the sample is removed from the N 2 atmosphere and heated to cause the adsorbed N 2 to be released from the material (desorption) and quantified. The data collected is displayed in the form of isotherms (graphs representing the amount of N 2 adsorbed as a function of relative pressure at a constant temperature). The data is displayed in five isotherms where the information is used to determine the surface area of the sample [ 25 , 149 ]. Figure  6 graphically illustrates the principle of this method.

Principles of the BET and BJH methods. The BET method (steps 1–3) is based on the adsorption of nitrogen on the NP surface. After the formation of a monolayer, nitrogen is desorbed, and the surface area is calculated. The BJH method (steps 1, 2, 4, and 5) is based on the complete filling of NP pores with liquid nitrogen. When saturation is reached, nitrogen is desorbed, and pore size is calculated

Examples: The BET method was employed to measure the surface area of CeO 2 NPs produced by Eucalyptus globulus leaf extract. The surface area was found to be 40.96 m 2 /g of biogenic CeO 2 NPs, much higher than the commercial CeO 2 NPs (8.5 m 2 /g) [ 150 ]. BET was also used to measure the surface area of SiO 2 NPs produced by rice husk, CuO NPs produced by Leucaena leucocephala leaf extract, and Ag NPs produced by Acanthospermum hispidum leaf extract. In these examples, the surface area was 7.15 m 2 /g, 47.54 m 2 /g, and 9.91 m 2 /g, respectively [ 151 , 152 , 153 ].

Barrett–Joyner–Halenda (BJH) method

This method is based on the Barrett–Joyner–Halenda principle and is used for the determination of porosity (or pore size) of NPs. Similar to the BET method, this method also involves the use of N 2 gas to adsorb to the sample. In the BJH method, the process is extended so the gas condensates in the sample pores as pressure increases. The pressure is increased until a saturation point is achieved, at which all the pores of the sample are filled with liquid. Afterwards, the condensated gas is allowed to evaporate where the desorption data is calculated and correlated to the pore size using a modified Kelvin equation (Kelvin model of pore filling) [ 154 , 155 ]. Figure  6 graphically illustrates this method.

Examples: The BJH method was employed to study the pore size of a wide range of biogenic NPs, for instance, the pore size of CeO 2 NPs produced by Eucalyptus globulus leaf extract was found to be 7.8 nm [ 150 ], the pore size of CuO NPs produced by Leucaena leucocephala leaf extract was 2.13 nm [ 152 ], the pore size of SiO 2 NPs produced by rice husk and Ag NPs produced by Acanthospermum hispidum leaf extract were much larger, being 29.63 nm and 36.34 nm, respectively [ 151 , 153 ].

Structural and chemical characterization

The structural characterization of NPs and the study of their composition is of high interest due to the strong influence of these parameters on the physicochemical properties. The following techniques are commonly used for the analysis of NP composition, phase, crystallinity, functionalization, chemical state (oxidation), surface charge, polarity, bonding, and electrochemical properties.

X-ray diffraction analysis (XRD)

This technique is based on irradiating a material with incident X-rays and then measuring the intensities and scattering angles of the X-rays that leave the material [ 156 ]. This technique is widely used for the analysis of NP phase and crystallinity. However, the resolution and accuracy of XRD can be affected in cases where the samples have highly amorphous characteristics with varied interatomic distances or when the NPs are smaller than several hundreds of atoms [ 25 ].

Examples: For the characterization of biogenic Ag NPs, the XRD results of Ag NPs produced by Trichoderma koningii [ 142 ], Solanum tuberosum [ 143 ], and Acanthospermum hispidum leaf extract [ 153 ] displayed characteristic peaks occurring at roughly 2θ = 38 o , 44°, and 64 o corresponding to (111), (200), and (220) planes, respectively. These results are in good agreement with the reference to the face-centered cubic structure of crystalline silver. However, the XRD results of Ag NPs produced by Solanum tuberosum were not as clear as the other biogenic Ag NPs and had several impurities. The structural characterization of Pd NPs produced by Garcinia pedunculata Roxb leaf extract by XRD showed the distinct peaks of Pd, however, three other peaks were also observed at 2θ of 34.22˚, 55.72˚, and 86.38˚, indicating the presence of PdO phases along with Pd NPs [ 157 ].

Energy-dispersive X-ray spectroscopy (EDX)

This technique is based on the irradiation of the sample with an electron beam. Electrons of the electron beam when incident on the sample surface eject inner shell electrons, the transition of outer shell electrons to fill up the vacancy in the inner shell produces X-rays. Each element produces a characteristic X-ray emission pattern due to its unique atomic structure, and therefore can be used to perform compositional analysis [ 158 ]. The shortfall of EDX is that the resulting spectra give only qualitative compositional information (it shows the chemical elements present in the sample without quantification). However, the peak intensities to some extent give an estimate of the relative abundance of an element in a sample [ 159 ]. This technique does not require sophisticated additional infrastructures, usually it is a small device that is connected to an existing SEM or TEM. This allows the use of SEM or TEM for the morphological characterization and EDX is used simultaneously for the analysis of chemical composition [ 160 ].

Examples: The EDX technique is usually used for the confirmation of the presence of the element in question in biogenic NPs. For instance, EDX was used to confirm the presence of Au in Au NPs produced by Jasminum auriculatum leaf extract [ 161 ], the presence of Pd in Pd NPs produced by Pulicaria glutinosa extract [ 162 ], the presence of Te in Te NPs produced by Penicillium chrysogenum PTCC 5031 [ 163 ], and the presence of Ag in Ag NPs produced by Trichoderma viride [ 164 ].

High-angle annular dark-field imaging (HAADF)

This method is used for the elemental mapping of a sample using a scanning transmission electron microscope (STEM). The images are formed by the collection of incoherently scattering electrons with an annular dark-field detector [ 165 ]. This method offers high sensitivity to variations in the atomic number of elements of the sample, and it is used for elemental composition analysis usually when the NPs of interest consist of relatively heavy elements. The contrast of the images is strongly correlated with atomic number and specimen thickness [ 166 ].

Examples: The employment of HAADF-STEM in the characterization of biogenic Au–Ag–Cu alloy NPs confirmed the presence of the three elements in the same NP [ 167 ]. Similarly, this approach revealed that Ag NPs produced by Andrographis paniculata stem extract were coated with an organic polymer [ 168 ]. The employment of this approach in the characterization of Cu NPs produced by Shewanella oneidensis revealed that Cu NPs remained stable against oxidization under anaerobic conditions, but when they were exposed to air a thin shell of Cu 2 O develop around the NPs [ 169 ].

X-ray photoelectron spectroscopy (XPS)

This technique is considered the most sensitive approach for the determination of NP exact elemental ratios, chemical state, and exact bonding nature of NP materials [ 25 ]. XPS is based on the photoelectric effect that can identify the elements within a material, or covering a material, as well as their chemical state with high precision [ 170 ]. XPS can also be used to provide in-depth information on electron transfer, e.g., for Pt NPs supported on CeO 2 , it was found that per ten Pt atoms only one electron is transferred to the support [ 171 ].

Examples: The XPS technique can employed for different purposes. For instance, it was used for measuring the purity of Au NPs produced by cumin seed powder [ 172 ]. XPS was used for the determination of the oxidation states of Pt NPs produced by Nigella sativa seeds and Ag NPs produced by Rosa canina . XPS results of Pt NPs showed the presence of three oxidation states for Pt (Pt (0), Pt (II), and Pt (IV)) and two oxidation states for Ag NPs (Ag (0) and Ag (I)). In both cases, the zero-oxidation state was the abundant one, the presence of a small amount of the other oxidation states suggests that some of the NPs were oxidized or had unreduced species [ 173 , 174 ]. XPS was used for the determination of the exact elemental ratios and the bonding nature of FeS NPs produced by Shewanella putrefaciens CN32. For the exact elemental ratios, the researchers compared biogenic and abiotic FeS NPs and found that biogenic FeS NPs had a 2.3:1 Fe:S ratio while the abiotic NPs had a 1.3:1 Fe:S ratio. For the bonding nature, it was determined that the surface of NPs had Fe(II)-S, Fe(III)-S, Fe(II)-O, and Fe(III)-O bonds [ 175 ].

Fourier-transform infrared spectroscopy (FTIR)

This technique is based on irradiating a material with infrared light, where the absorbed or transmitted radiation is recorded. The resulting spectrum represents a unique fingerprint of samples, where information about the nature of the sample can be obtained such as the bonds involved, polarity, and oxidation state of the sample [ 176 , 177 ]. This technique is mainly used for the characterization of organic materials such as the surface chemical composition or functionalization of NPs. It is also used for the identification of contaminants when high purity is sought [ 178 ].

Examples: For biogenic NPs, FTIR is usually used for the identification of probable functional groups present on the surface of NPs that are responsible for the reduction and stabilization of the NPs. For plant-mediated NP synthesis, for instance for Ag NPs produced by Camellia sinensis , the FTIR results indicate the presence of Camellia sinensis phytocompounds, such as caffeine and catechin, on the surface of Ag NPs that could be responsible for the reduction of Ag or act as stabilizing agents [ 147 ]. For Ag NPs produced by Solanum tuberosum , the NPs were found to be capped by amide and amine groups [ 143 ]. For CeO 2 NPs produced by Eucalyptus globulus , the polyphenol groups present in Eucalyptus globulus extract were found on the surface of NPs suggesting their involvement in the reduction/stabilization process [ 150 ]. For microbe-mediated NP synthesis, FTIR results show the presence of protein residues on the surface of NPs confirming the involvement of different proteins in the reduction/stabilization process, such as in Ag NPs produced by Streptomyces sp. NH28 [ 179 ], in Te NPs produced by Penicillium chrysogenum PTCC 5031 [ 163 ], and in Se NPs produced by Azospirillum thiophilum [ 180 ].

Zeta potential analysis

Zeta potential measurements are used for the determination of NP surface charge in colloidal solutions. The surface charge of NPs attracts counter-ions that form a thin layer on the surface of the NPs (called Stern layer). This layer travels with the NPs as they diffuse thought the solution. The electric potential at the boundary of this layer is known as NP zeta potential [ 136 ]. The instruments used to measure this potential are called zeta potential analyzers [ 181 ]. Zeta potential values are indicative for NP stability, where higher absolute value of zeta potential indicate more stable NPs [ 136 ].

Examples: The zeta potential is a good indicator for the stability of NPs, where NPs with zeta potentials of more than + 30 mV or less than − 30 mV are considered stable. Zeta potentials have been measured for a wide range of biogenic NPs. The zeta potential for Ag NPs produced by Ziziphus jujuba leaf extract of − 26.4 mV [ 182 ]. Ag NPs produced by other organisms have different zeta potential values, for example, Ag NPs produced by Punica granatum peel extract have a zeta potential of − 40.6 mV indicating their higher stability [ 183 ], while Ag NPs produced by Aspergillus tubingensis have a zeta potential of + 8.48 indicating their relative instability [ 184 ]. The pH of the sample is another important parameter for zeta potential values, the higher pH the lower the zeta potential value [ 185 ]. Having different zeta potential values for the same type of NPs depending on the organism used for their synthesis is not unique to silver, Se NPs also show different potential values depending on the organism used for their synthesis [ 186 ].

Cyclic voltammetry (CV)

CV is an electrochemical technique for measuring the current response of redox-active solutions to a linearly cycled potential sweep between two or more set values. The CV technique involves the use of three electrodes: a working electrode, reference electrode, and counter electrode. These electrodes are introduced to an electrochemical cell filled with an electrolyte solution and where voltage is in excess, the potential of the working electrode is cycled and the resulting current is measured. This technique is used for determining information about the reduction potential of materials, the kinetics of electron transfer reactions, and the thermodynamics of redox processes [ 187 , 188 , 189 ].

Examples: The CV technique can be employed for two different purposes in the context of biogenic NP characterization. Firstly, it can be used for measuring the stability of NPs in electrocatalysis. For this purpose, the biogenic NPs are assembled on an electrode of the electrolysis cell and are tested for their electrocatalytic behavior against a redox reaction over different cycles. As an example, Ag NPs produced by Citrus sinensis were found to be stable in phenolic compounds redox reactions over multiple cycles [ 190 ]. Secondly, CV can be used for monitoring the progress of reduction of metallic NPs or for the determination of the reducing agent involved in the reduction. For example, for Ag NPs produced by Indian propolis, four cyclic voltammograms were recorded, one for a water extract of Indian propolis, another for an ethanol extract of Indian propolis, and two for the constituent flavonoids of Indian propolis (pinocembrin and galangin). The four cyclic voltammograms showed similar behaviors indicating the involvement of these flavonoids in the reduction of Ag and in forming Ag NPs [ 191 ].

Raman spectroscopy

This technique is based on irradiating a sample with monochromatic light emitted by a laser, in which the interactions between the laser light and molecular vibrations (photons and phonons) are recorded. The technique records the inelastically scattered photons, known as Raman scattering (named after the Indian physician C. V. Raman) [ 192 ]. The output of this technique is a unique fingerprint for each sample, which is used to characterize the chemical and intramolecular bonding of the sample. It can also be used to characterize the crystallographic orientation of the sample [ 193 ]. Surface-enhanced Raman spectroscopy (SERS) enhances Raman scattering of a sample and provides a more sensitive, specific, and selective technique for identifying molecular structures [ 194 ]. Both techniques are also used for the characterization of optical properties, where the recorded photons and phonons are used to understand the plasmonic resonance of NPs [ 25 ].

Examples: Raman spectroscopy was used to characterize Fe 3 O 4 NPs produced by Pisum sativum peel, the researchers found that the NPs were Fe 3 O 4 NPs with face centered cubic phase which was in agreement with their XRD measurements [ 195 ]. Other researchers used Raman spectroscopy for studying the trace deposits of carbohydrates on ferrihydrite NPs produced by Klebsiella oxytoca , the results showed that the pores of NPs had more deposits of carbohydrates that the surface of the NPs [ 196 ]. For Au NPs produced by Raphidocelis subcapitata (green algae), several biomolecules were suggested for their involvement in this process. SERS technique was used to study Au NPs surface-associated biomolecules in order to narrow down the list of biomolecules involved in the bioproduction process. The researchers found that several biomolecules such as, glutathione, β-carotene, chlorophyll a, hydroxyquinoline, and NAD were associated with Au NPs surface, thus, ruling out other molecules such as, glutaraldehyde fixing agent, saccharides, FAD, lipids, and DNA from the list [ 197 ].

Characterization of optical, electronic, and electrical properties

In addition to Raman spectroscopy and SERS, also other techniques can be employed to study and characterize the optical properties of NPs. These techniques give information about the absorption, reflectance, fluorescence, luminescence, electronic state, bandgap, photoactivity, and electrical conductance properties of NPs.

Ultraviolet–visible spectroscopy (UV–vis) and photoluminescence spectroscopy (PL)

In absorption spectroscopy such as UV–vis, the transition of electrons from the ground state to an excited state is measured, while in photoluminescence spectroscopy, the transition of electrons from the excited state to the ground state is measured [ 198 ]. UV–vis spectroscopy uses visible and UV light to measure the absorption or reflectance of a sample. In photoluminescence spectroscopy, usually UV light is used to excite the electron and then measure the luminescence or fluorescence properties of a sample [ 199 ].

Examples: UV–vis spectroscopy is a simple and common technique that is used for the characterization of the optical properties of NPs. For instance, for the characterization of the optical properties of Ag NPs produced by Trichoderma viride , the UV–vis spectrum showed that a Ag surface plasmon band occurs at 405 nm, which is a characteristic band for Ag NPs. The intensity of this band over the reaction time increased as a result of increasing Ag NP concentration in the solution. In the same study, the photoluminescence properties of these NPs were recorded, with an emission in the range between 320–520 nm, which falls in the blue-orange region [ 164 ]. For biogenic Cu NPs, the common absorption peaks are located between 530–590 nm. The difference in NP size and the bio-active molecules used for the reduction process are believed to be the reasons behind the differences in the absorption peaks [ 200 ]. For instance, 15 nm spherical Cu NPs produced by Calotropis procera have an absorption peak at 570 nm [ 201 ], while 76 nm spherical Cu NPs produced by Duranta erecta have an absorption peak at 588 nm [ 202 ]. The same applies to photoluminescence effects, where 27 nm spherical Cu NPs produced by Tilia extract emit light of 563 nm (dark brown) [ 203 ], while 19 nm spherical Cu NPs emit light of 430 nm (green) [ 204 ].

UV–vis diffuse reflectance spectroscopy (DRS)

This technique uses UV and visible light to measure the diffuse reflectance of a material (the reflection of light in many angles, as opposed to specular reflection). The resulting diffuse reflectance spectra are used to determine the electronic state of a sample, which is then used to calculate the bandgap [ 25 ]. Bandgap determination is crucial for determining conductance and photocatalytic properties especially for semiconductor NPs [ 205 ].

Examples: The DRS technique was used to calculate the bandgap for a wide range of biogenic NPs. For instance, TiO 2 NPs produced by Andrographis paniculata exhibit an optical energy bandgap of 3.27 eV [ 206 ]. Interestingly, biogenic ZnO NPs produced by different organism show different bandgaps, for example, ZnO NPs produced by Pseudomonas putida have a bandgap of 4 eV [ 207 ], while ZnO NPs produced by Calotropis procera leaf extract have a bandgap of 3.1 eV [ 208 ].

Spectroscopic ellipsometry

This technique is based on irradiating a sample with polarized light to measures changes in polarization. It is widely used to calculate the optical constants of a material (refractive index and extinction coefficient) [ 209 ]. This technique is also used to characterize the electrical conductivity and dielectric properties of materials [ 210 ].

Examples: Spectroscopic ellipsometry is not a common technique for the characterization of biogenic NPs. For chemically produced NPs, the optical properties for different-sized Au NPs partially embedded in glass substrate were measured by spectroscopic ellipsometry. In this example, a clear transition from LSPR to SPR mode was found as the thickness increases. Moreover, the partially-embedded Au NPs had much higher refractive index sensitivity compared to Au NPs fully immobilized in a glass substrate [ 211 ]. Spectroscopic ellipsometry was also used to measure the changes in the optical constants of a layer of 5 nm ZnO NPs induced by UV illumination. In this case, it was found that the UV illumination of ZnO NPs in inert atmospheres resulted in a clear blue shift in the absorption (Moss-Burstein shift). The UV illumination of ZnO NPs results in the desorption of O 2 from the NPs surface leading to the population of the lowest levels in conduction band with mobile electrons. This phenomenon is reversible, in which the exposure to O 2 from air results in the scavenging of these mobile electrons [ 212 ].

Characterization of magnetic properties

The magnetic properties of NPs are of high importance, as they potentially give NPs great advantages in catalysis, electronics, and medical applications. Several techniques were developed for the detection and quantification of small magnetic moments in NPs.

Magnetic force microscopy (MFM)

This technique is a variety of atomic force microscopy (AFM), in which a magnetic tip is used to scan the sample. The magnetic tip is approached very close to the sample, where the magnetic interactions between the tip and the sample are recorded [ 213 ]. At closer distances to the sample (0–20 nm), other forces such as van der Waals forces also interact with the tip. Therefore, MFM measurements are often operated with two-pass scanning method (also called lift height method) [ 214 ] (Fig.  7 ). In this method, the tip is firstly used to measure the topography of the sample including the molecular forces as van der Waals. Afterwards, the tip is lifted and a second scan is operated following the same topography outline. In the second scan, the short-ranged van der Waals forces disappear and the long-range magnetic forces are almost exclusively recorded. In an experimental study, researchers found that 22 nm was the optimal scanning height for the second scan, at which van der Waals forces are very weak while the distance is still small enough to measure the magnetic interactions for Pd-Fe bimetallic NPs [ 215 ].

Magnetic force microscopy lift height method. The first scan is done very close to the surface to obtain the topography of the sample. Then, the tip is lifted and a second scan is performed following the topography outline obtained in the first scan

Examples: MFM was heavily used for the characterization of magnetite NPs produced by magnetotactic bacteria. For instance, the size and orientation of the magnetic moment of magnetite NPs produced by Magnetospirillum gryphiswaldense strain MSR-1 were studied by MFM [ 216 ], in which the size of the magnetic moment was found to be 1.61 × 10 −17 Am 2 . In a different study, MFM was used to characterize the magnetic properties and to estimate the size of the magnetic kernel of the magnetosomes produced by the same strain, and it was determined that the NPs behaved like single mono-domain nanomagnets [ 217 ]. The magnetic properties of NPs made from materials such as Pd that only exhibit significant magnetism on the nanoscale can also be studied by MFM, however, the magnetic moment of these NPs is much lower than for ferromagnetic NPs. The magnetic decoration of Pd NP samples with Fe 2 O 3 NPs strongly enhances the weak magnetic signal of Pd NPs up to 15 times [ 218 ]. This approach could make the MFM technique useful for the characterization of weak magnetic NPs.

Vibrating-sample magnetometry (VSM)

This technique measures the magnetic properties of materials based on Faraday’s law of induction. In VSM, the sample is placed in a constant magnetic field in a special holder that vibrates vertically. As the holder starts vibrating, the magnetic moment of the sample creates a magnetic field that changes as function of time. The alternating magnetic field created in the sample induces an electric current that is recorded and used to calculate the magnetic properties of the sample [ 219 , 220 ].

Examples: For the characterization of Fe 2 O 3 NPs produced by Tridax leaf extract, VSM studies revealed that the NPs had a saturation magnetization of 7.78 emu/g, a remnant magnetization of 0.054 emu/g, and a coercivity of − 1.6 G [ 221 ]. In other studies, VSM was used to compare the magnetic properties of iron oxide NPs produced Moringa oleifera with the magnetic properties of the same NPs but coated with chitosan. The researchers found that saturation magnetisation, remnant magnetization, and coercivity have lower values when the NPs are coated with chitosan [ 222 ].

Superconducting quantum interference device (SQUID) magnetometry

This technique measures the magnetic properties of materials based on the Josephson effect. Niobium (Nb) or other metal alloys are used in the device which needs to be operated at temperatures very close to the absolute zero to main superconductivity, where liquid helium is used to maintain the cold environment [ 223 ]. However, other kinds of SQUID also exist where high-temperature superconductors are used [ 224 ]. After reaching superconducting environments, the Josephson junctions contained in the device help to create a supercurrent, which is recorded and used to calculate the magnetic properties of the sample [ 225 ].

Examples: For the characterization of iron oxide NPs produced by Cnidium monnieri seed extract, SQUID magnetometry revealed that the NPs had a saturation magnetization of 54.60 emu/g, a remnant magnetization of 1.15 emu/g, a coercivity of 11 Oe, and a magnetic susceptibility of + 1.69 × 10 –3 emu/ cm 3 ⋅ Oe at room temperatures, indicating the superparamagnetic behaviour of these NPs [ 226 ]. SQUID magnetometry was also used for the characterization of the magnetic properties of zinc incorporated magnetite NPs produced by Geobacter sulfurreducens , showing that the loading of only 5% zinc results in the enhancement of saturation magnetization of the NPs by more than 50% [ 227 ].

Electron spin resonance spectroscopy (ESR)

This technique measures the magnetic properties of materials by characterizing and quantifying the unpaired electrons in the sample. Electrons are charged particles that spin around their axis, which can align in two different orientations (+ ½ and − ½) when the sample is placed in strong magnetic field. These two alignments have different energies due to the Zeeman effect. Since unpaired electrons can change their spins by absorbing or emitting photons, in ESR the sample is irradiated with microwave pulses to excite electron spins until a resonance state is reached [ 228 ]. This technique is also referred to as electron paramagnetic resonance spectroscopy (EPR). It can be used to measure the ferromagnetic and antiferromagnetic properties of NPs [ 229 , 230 ].

Examples: ESR was used to characterize the magnetic properties of iron oxide NPs produced by Ficus carica . The trees naturally produce iron oxide NPs as a defence mechanism when are they are subjected to stress. The researchers found that the magnetic properties of iron oxide NPs produced by the same tree but grown in different environmental conditions have different magnetic properties. In addition, a magnetic anisotropy of the signal was visible as the magnetic properties of these NPs varied strongly at different temperatures [ 231 ]. ESR was also used to characterize the magnetic properties of Se nanomaterials produced by anaerobic granular sludge. The ESR results revealed the presence of Fe(III) atoms incorporated in the Se nanomaterial, which enhanced their overall magnetic properties, giving it ferromagnetic behaviour [ 232 ].

Characterization of thermal properties

Several techniques can be used for the characterization of the thermal properties of NPs, such as melting points, crystallization and structural-phase transition points, heat capacity, thermal conductivity, and thermal and oxidative stability.

Differential scanning calorimetry (DSC)

In this technique the analyte and a well-defined reference sample are put at the same temperature, then, the amount of heat required to increase the temperature of the sample and the reference in measured as a function of temperature. This technique is widely used to measure melting points [ 233 ], crystallization points, structural-phase transition points [ 234 ], latent heat capacity [ 235 ], heat of fusion [ 236 ], and oxidative stability [ 237 ].

Examples: For the characterization of Ag NPs produced by Rhodomyrtus tomentosa leaf extract, DSC showed three exothermic peaks at 44, 159, 243, and an endothermic peak at 441 °C. The first peak (at 44 °C) indicates that at this temperature the NPs face a gradual loss of water from their surface. The second peak (at 159 °C) shows that the thermal decomposition of the sample happens at this temperature. The last temperature (441 °C) indicates the melting temperature for those NPs [ 238 ]. For Ag NPs produced by Parthenium hysterophorus leaf extract, DSC showed that their melting temperature was at 750 °C. The researchers also found that these NPs had completely thermally decomposed and crystallized simultaneously [ 239 ].

Differential thermal analysis (DTA)

This technique is based on heating or cooling a sample and an inert reference under identical conditions, where any temperature difference between the sample and the reference is recorded. This technique is primarily used for the study of phase diagrams and transition temperatures [ 240 ]. However, it is also used to measure the melting points, thermal, and oxidative stability [ 241 , 242 ].

Thermogravimetric analysis (TGA)

This technique measures the change in the mass of a sample as a function of temperature and/or time in a controlled atmosphere [ 243 ]. This technique is mainly used to study the thermal stability of materials [ 244 ], in addition, it is also used to measure structural-phase transition points [ 245 ], thermal activation energies [ 246 ], and oxidative stability [ 247 ]. The resulting thermogram is unique for each compound and therefore can also be used for the determination of material composition [ 248 ]. TGA and DTA are usually combined in the same thermal analyzing instrument, called thermogravimetry/differential thermal analysis (TG/DTA) [ 244 ].

Examples: TG/DTA is a common technique for the characterization of thermal properties of biogenic NPs. For instance, the thermal properties of Ag NPs produced by Daphne mucronate leaf extract were studied in the range between 0–1000 °C where the sample was heated at a rate of 10 °C/min. The researchers found that between 400–500 °C the NPs faced a dominant weight loss, while the weight loss below 400 °C and above 500 °C was negligible. The DTA curve showed an intense exothermic peak in the range between 400–500 °C, this indicates that the crystallization of NPs happens in this temperature interval. Some minor weight loss events were seen below 400 °C, this may be caused by the evaporation of water or the degradation of the organic components [ 249 ]. In another study, the thermal properties of Ag NPs produced by two different plants ( Stereospermum binhchauensis and Jasminum subtriplinerve ) were compared. The researchers found that the major weight loss happens between 220–430 °C, which is attributed to the decomposition of biomolecules from the NP surface [ 250 ]. This shows that Ag NPs produced by these plants have much higher content of biomolecules on their surface than Ag NPs produced by Daphne mucronate. TG/DTA showed that Stereospermum binhchauensis Ag NPs crystallize at 315 °C and Jasminum subtriplinerve Ag NPs at 345 °C, around 100 °C less than Daphne mucronate Ag NPs [ 250 ].

Transient hot wire method (THW)

This method is used for the determination of thermal conductivity based on increasing the temperature of a material by a thin hot wire as a function of time, where the heating wire is located directly in the test sample. The advantage of this method over other thermal conductivity measurement methods is the very short measuring time, this gives high accuracy of thermal conductivity due to the negligible values of convection in such short times [ 251 ]. In this method, the NPs are added to a solution (usually water or ethylene glycol) forming a colloidal dispersion called a nanofluid. Then, the thermal conductivity of the nanofluid is measured and compared to the thermal conductivity of the base fluid, giving a thermal conductivity ratio which is used to evaluate the thermal conductivity of different NPs.

Examples: The thermal conductivity ratios of three different concentrations (0.12, 0.18, and 0.24%) of biogenic SnO 2 NPs produced by Punica granatum seed extract were measured in ethylene glycol at 303 K. The researchers found a linear relationship between NPs concentration and the thermal conductivity. The thermal conductivity enhancement of nanofluid to base fluid was between 6 and 24% [ 252 ]. In another study, the thermal conductivity of Fe 2 O 3 NPs produced by Psidium guajava leaf extract was measured in water and in ethylene glycol. The researchers found that the thermal conductivity enhancement in ethylene glycol was better than in water, the thermal conductivity enhancement for 0.025% Fe 2 O 3 NPs in water was 30% while in ethylene glycol was 34%. Moreover, the linear relationship between NPs concentration and thermal conductivity ratio was found for Fe 2 O 3 NPs in both water and ethylene glycol [ 253 ].

Characterization of mechanical properties

Several methods can be used for the characterization of mechanical properties of NPs, such as tensile and compressive strengths, elasticity, viscoelasticity, hardness, and stiffness.

Tensometery

The machine used for this method is called a universal testing machine (UTM) or a tensometer. It is used to measure the elasticity (elastic modulus), tensile and compressive strengths (Young’s modulus) of materials. In this machine, the sample is placed between grips and an extensometer, where changes in gauge length are recorded as a function of load [ 254 ]. However, other mechanical changes in addition to the change in gauge length are also recorded in this machine, such as the elasticity.

Examples: The mechanical properties of different biogenic NP-containing composites can be measured by this machine. For example, the mechanical properties of orthodontic elastic ligatures containing Ag NPs produced by Heterotheca inuloides were studied by comparing the maximum strength, tension, and displacement of the composite with and without the biogenic NPs. The researchers found that maximum strength, tension, and displacement have improved after the addition of Ag NPs [ 255 ]. Interestingly, the addition of biogenic Ag NPs produced by Diospyros lotus fruit extract to starch and polyvinyl alcohol hydrogel membranes resulted in an adverse effect. The tensile strength and modulus of the hydrogel membranes containing 50 and 100 ppm Ag NPs were much lower than of the neat hydrogel membrane. The researchers attributed this adverse effect to the possibility that the addition of Ag NPs could have resulted in blocking the crosslinking between starch and polyvinyl alcohol, or to the possibility of the formation of breakage points in the polymer matrix due to NPs agglomeration [ 256 ].

Instrumented indentation testing

This method is used to characterize the hardness features of materials by using a well-defined hard indenter tip typically made of diamond. The indenter tip is used to make an indentation in the sample by placing incremental loads on the tip, after which the area of indentation in the sample is measured and used to calculate the hardness features [ 257 ]. Light microscopy, SEM, or ATM technique are usually used to visualize the indentation in the sample. The method is also called micro- or nano-indentation testing.

Examples: This method was used to characterize the mechanical properties of calcite NPs produced by Ophiocoma wendtii brittlestar. The arm plates of this brittlestar are covered by hundreds of nanoscale calcite lenses that focus light onto photoreceptor nerve bundles positioned beneath the brittlestar. The researchers used the nanoindentation method to compare Young’s modulus, hardness and fracture toughness of biogenic calcite with geocalcite. The results showed that the biogenic calcite lenses have higher hardness and fracture toughness compared to geocalcite (more than twofold) [ 258 ]. Bamboo is well known for its high silica content in comparison to other wood species. It produces SiO 2 NPs and deposits it in its epidermis in the form of silica cells. The mechanical properties of silica cells compared to other types of cells of Moso bamboo ( Phyllostachys pubescens ) were studied by instrumented indentation testing. The researchers found that the cell wall of silica cells display higher hardness and elastic recovery compared to fibre and epidermal cells, which is attributed to the presence of biogenic SiO 2 NPs in the silica cells [ 259 ].

Dynamic mechanical analysis (DMA)

This method is used to study the mechanical properties of materials by measuring the strain of a material after applying a stress. This method helps to obtain three different values: storage modulus, loss modulus, and loss tangent. These values are important to give an overview about the stiffness and viscoelasticity behavior of materials [ 260 ].

Examples: The DMA method was used to characterize the mechanical properties of polymethyl methacrylate denture base polymer filled with Ag NPs produced by Boesenbergia rotunda . In this study frequency sweep test was used to determine the viscoelastic behavior of this nanocomposite where the temperature was constant at 37 °C and the frequency was increasing from 0.5 to 100 Hz in tension mode. The researchers found a frequency dependence for storage modulus, loss modulus, and loss tangent for the nanocomposite with various Ag NPs loading concentrations. The frequency dependence of storage modulus, loss modulus, and loss tangent indicates the viscoelastic response of this polymer. However, the results showed that the storage modulus for the nanocomposite is much higher than the loss modulus over the range of frequencies, indicating the elastic dominance of the nanocomposite. Moreover, the researchers found that storage and loss moduli increase with increasing Ag NPs loading concentrations, which is due to the interaction between polymethyl methacrylate and Ag NPs [ 261 ].

In a different study, DMA was used to determine the thermomechanical properties of pol(S-co-BuA) polymer filled with cellulose nanocrystals produced by Posidonia oceanica . In this case, the behaviour of storge modulus and loss tangent were studied as a function of temperature for different cellulose nanocrystals loading concentrations. The results showed that the unloaded polymer behaves like an amorphous polymer, the storage modulus remains constant until the temperature reaches 25 °C then it starts to sharply decrease due to glass–rubber transition. A relaxation process was also evident for the unloader polymer, where the loss tangent reaches its maximum at 35 °C then it starts to fall. The addition of cellulose nanocrystals to the polymer positively enhanced both effects. The dramatic drop of storage modulus at 25 °C was less for the nanocomposite, where the drop for the polymer loaded with 15% cellulose nanocrystals was almost cancelled. Similar positive enhancement was found for loss tangent. These enhancements could be attributed to the mechanical coupling effect, in which the NPs connect and form a stiff continuous network linked through hydrogen bonding [ 262 ].

Applications of NPs

NPs, due to their above-mentioned unique or enhanced physicochemical properties, are used in a wide range of applications in different fields. In addition, several potential applications are in research and development. Here we present some examples of these applications.

Applications in medicine and pharma

Metallic and semiconductor NPs have huge potential for cancer diagnosis and therapy based on their enhanced light scattering and absorption properties due to LSPR effect. For instance, Au NPs efficiently absorb light and convert it into localized heat, which can be exploited for selective photothermal therapy of cancer (cancer cell death by heat generated in tumor tissue) [ 263 , 264 ]. In addition, the unique optical properties of Au NPs make them a great candidate for the photodynamic therapy of cancer (the use of a drug that is activated by light to kill cancer cells) [ 265 ]. Gd based NPs have also shown great abilities in tumor growth inhibition [ 266 ], metastasis inhibition [ 267 ], and tumor-specific magnetic resonance contrast enhancement [ 268 ]. Targeted drug delivery is also an important potential application of NPs. ZnO and Fe 3 O 4 NPs were efficiently used for targeted drug delivery and selective destruction of tumor cells [ 269 , 270 , 271 ].

Moreover, NPs have been successfully used in different medical applications such as cellular imaging [ 272 ], or in biosensors for DNA, carbohydrates, proteins, and heavy metal ions [ 273 , 274 ], determination of blood glucose levels [ 275 ], and for medical diagnostics to detect bacteria [ 276 ] and viruses [ 277 ]. For instance, Au NPs were conjugated with SARS-CoV-2 antigens to rapidly detect the presence of SARS-CoV-2 IgM/IgA antibodies in blood samples within 10–15 min [ 278 ], At the same time, due to their antimicrobial and antibacterial activities, NPs such as TiO 2 , ZnO, CuO, and BiVO 4 are being increasing used in various medical products such as catheters [ 279 , 280 ].

Applications in electronics

NPs, due to their novel electronic and optical properties, have a wide range of potential applications in imaging techniques and electronics. For instance, Gd-based NPs can improve the imaging quality and the contrast agent administration dose of magnetic resonance imaging (MRI). The use of Gd 2 O 3 NPs as a contrasting agent was found to be more efficient than the commonly used agent (Gd-DOTA) at the same concentration [ 281 ]. At the same time, GdPO 4 NPs were successfully used for tumor detection using MRI in 1/10 of the dose typically used with Gd-DTPA agent [ 282 ]. Interestingly, NPs also offer the ability to image and track a single molecule, which can reveal important information about cellular processes such as membrane protein organization and interaction with other proteins. For example, Eu 3+ -doped oxide NPs were used to track a single toxin receptor with a localization precision of 30 nm [ 283 ].

Regarding applications in batteries, an important component in lithium-ion batteries is the separators. Their main function is to prevent the physical contact of anode and cathode, and to provide channels for the transport of ions. The commonly used commercial material in battery separators, a polyolefin microporous membrane, suffers from poor electrolyte uptake and poor thermal stability [ 284 ]. Due to the aerogel structure of some NPs (such as ZnO NPs), they are an ideal choice for separator plates in batteries [ 284 ]. This makes the batteries store a significantly higher amount of energy compared to traditional batteries. For lithium-air batteries, using Pt-Au bimetallic NPs strongly enhances oxygen reduction and oxygen evolution reactions [ 285 ]. Moreover, batteries made of nanocrystalline Ni and metal hydrides last longer and require less charging [ 23 ]. In addition to battery applications, several NPs such as CdS and ZnSe are also used in light-emitting diodes (LED) of modern displays to get higher brightness and bigger screens [ 23 , 286 ]. Other NPs such as CdTe NPs are also used in liquid crystal displays (LCDs) [ 287 ]. The addition of a NP layer to LED and LCD enables them to generate more light using the same amount of energy and enhances their lifetime.

Applications in agriculture

NPs have potential to benefit the agriculture field by providing new solutions to current agricultural and environmental problems [ 288 ]. NPs are mainly used in two forms in agriculture, as nanofertilizers and nanopesticides. Chemical fertilizers have poor efficiency due to leaching and volatilization. In these cases, the farmers usually react by using excessive amounts of fertilizers, which increases crops productivity but has an environmental cost [ 288 ]. In contrast, nanofertilizers are compounds that are applied in smaller amounts than regular chemical fertilizers but yet have better efficiencies [ 289 ]. The difference in efficiency comes from the fact that they are able to release the nutrients just when and where they are required by the plants. In that way, they limit the conversion of excess amounts of fertilizer to gaseous forms or from leaking into the ground water [ 290 ]. Several NPs have been employed in the development of fertilizers, including SiO 2 , ZnO, CuO, Fe, and Mg NPs [ 291 , 292 , 293 ]. These nanofertilizers provide the plants with increased nitrogen fixation, improved seed germination, amelioration to drought stress, increased seed weight, and increased photosynthesis ability [ 291 , 292 , 293 ]. The large surface area and small size of these NPs are the main reasons for the better efficiencies of nanofertilizers over conventional fertilizers [ 294 ].

Several NPs have proven antimicrobial, insecticidal, and nematicidal activities, which makes them a promising alternative to chemical pesticides and a potentially cheaper alternative to biopesticides [ 294 ]. For instance, the photocatalytic activity of TiO 2 NPs gives them a potent antimicrobial activity against Xanthomonas perforans , the causing agent of tomato spot disease [ 295 ]. CuO NPs show insecticidal activity against Spodoptera littoralis , known as African cotton leafworm [ 296 ]. Ag NPs show nematicidal activity against Meloidogyne spp. , root-knot nematodes [ 297 ].

Applications in the food industry

NPs, despite toxological concerns, have impactful applications in several food industry-related process such as food production, preservation, and packaging. TiO 2 NPs are a major promising player in this industry. Their photocatalytic antimicrobial activity makes them an interesting material for food packaging [ 298 ]. In addition, they are also used in sensors to detect volatile organic compounds [ 299 ]. Ag NPs are also promising in food packaging due to their antimicrobial activity. They play an important role in reducing the risk of pathogens and extending food shelf-life [ 294 ]. The efficiency of doping Ag and ZnO NPs to degradable and non-degradable packaging materials for meat, bread, fruit, and dairy products was tested against several yeast, molds, aerobic, and anaerobic bacteria [ 300 ]. For instance, polyvinyl chloride doped with Ag NPs was evaluated for packing minced meet at refrigerator temperature (4 °C); the results showed that Ag NPs significantly helped to slow down bacterial growth, increasing the shelf-life of minced meet from 2 to 7 days [ 301 ].

Effects of NPs on biological systems

Although the use of NPs is exponentially growing, their possible toxicological and hazardous impacts to human health and environment cannot be ignored. NPs may get released to the environment during production stages, usage, recycling, or disposal. These NPs may persist in air, soil, water, or biological systems [ 302 ]. NPs can enter the human or animal body though the skin, orally, or via the respiratory tract, and afterwards move to other parts of the body. The exposure to NPs was found to activate proinflammatory cytokines and chemokines with recruitment of inflammatory cells, which impacts the immune system homeostasis and can lead to autoimmune, allergic, or neoplastic diseases [ 302 ]. Moreover, the exposure to ultrafine particles can cause pulmonary, cardiac, and central nervous system diseases [ 303 , 304 , 305 ]. Similarly, NPs can enter plants cells and cause harmful effects [ 306 ]. For instance, the exposure of ZnO and Al NPs was found to cause root growth inhibition in plants [ 307 , 308 ].

Nanoscience and nanotechnology are inherently transdisciplinary fields of science. With new bio-based approaches, there is a need for biologists to understand not only the basic principles of nanoscience, but also the technologies and methods traditionally employed to characterize nanomaterials. We hope that this review can help to inspire new collaborations across different scientific disciplines, by helping biologists to identify the best technologies—and partners—to characterize their nanomaterials. At the same time, we recommend to take potential biological risks of these new materials into careful consideration already during the planning phase of such experiments.

Availability of data and materials

Not applicable.

https://www.etymonline.com/word/nano .

[SOURCE: ISO/TS 80,004‑2:2015, 4.4].

Abbreviations

Atomic force microscopy

Brunauer–Emmett–Teller

Barrett–Joyner–Halenda

Cyclic voltammetry

Dynamic light scattering

Derjaguin–Landau–Verwey–Overbeek

Dynamic mechanical analysis

Derjaguin–Muller–Toporov

UV–vis diffuse reflectance spectroscopy

Differential scanning calorimetry

Differential thermal analysis

Energy-dispersive X-ray spectroscopy

Electron microscopy

Electron paramagnetic resonance spectroscopy

Electron spin resonance spectroscopy

Fourier-transform infrared spectroscopy

High-angle annular dark-field imaging

International Organization for Standardization

Johnson–Kendall–Roberts

Liquid crystal display

Light-emitting diode

Localized surface plasmon resonance

Magnetic force microscopy

Magnetic resonance imaging

Nanoparticles

Nanoparticle tracking analysis

Photoluminescence spectroscopy

Critical radius

Threshold radius for superparamagnetism

Selected area electron diffraction

Scanning electron microscopy

Surface-enhanced Raman spectroscopy

Surface plasmon resonance

Superconducting quantum interference device

Scanning transmission electron microscopy

Scanning tunneling microscopy

Transmission electron microscopy

Thermogravimetry/differential thermal analysis

Thermogravimetric analysis

Transient hot wire

Universal testing machine

Ultraviolet

Ultraviolet–visible spectroscopy

Vibrating-sample magnetometry

X-ray photoelectron spectroscopy

X-ray diffraction analysis

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Acknowledgements

This work was supported by the Research Council of Norway, Grant 294605 (Center for Digital Life) to DL.

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Joudeh, N., Linke, D. Nanoparticle classification, physicochemical properties, characterization, and applications: a comprehensive review for biologists. J Nanobiotechnol 20 , 262 (2022). https://doi.org/10.1186/s12951-022-01477-8

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A review on nanoparticles: characteristics, synthesis, applications, and challenges

The significance of nanoparticles (NPs) in technological advancements is due to their adaptable characteristics and enhanced performance over their parent material. They are frequently synthesized by reducing metal ions into uncharged nanoparticles using hazardous reducing agents. However, there have been several initiatives in recent years to create green technology that uses natural resources instead of dangerous chemicals to produce nanoparticles. In green synthesis, biological methods are used for the synthesis of NPs because biological methods are eco-friendly, clean, safe, cost-effective, uncomplicated, and highly productive. Numerous biological organisms, such as bacteria, actinomycetes, fungi, algae, yeast, and plants, are used for the green synthesis of NPs. Additionally, this paper will discuss nanoparticles, including their types, traits, synthesis methods, applications, and prospects.

1. Introduction

Nanotechnology evolved as the achievement of science in the 21st century. The synthesis, management, and application of those materials with a size smaller than 100 nm fall under the interdisciplinary umbrella of this field. Nanoparticles have significant applications in different sectors such as the environment, agriculture, food, biotechnology, biomedical, medicines, etc. like; for treatment of waste water ( Zahra et al., 2020 ), environment monitoring ( Rassaei et al., 2011 ), as a functional food additives ( Chen et al., 2023 ), and as a antimicrobial agents ( Islam et al., 2022 ). Cutting-edge properties of NPs such as; nature, biocompatibility, anti-inflammatory and antibacterial activity, effective drug delivery, bioactivity, bioavailability, tumor targeting, and bio-absorption have led to a growth in the biotechnological, and applied microbiological applications of NPs.

A particle of matter with a diameter of one to one hundred nanometers (nm) is commonly referred to as a nanoparticle or ultrafine particle. Nanoparticles frequently exhibit distinctive size-dependent features, mostly due to their tiny size and colossal surface area. The periodic boundary conditions of the crystalline particle are destroyed when the size of a particle approaches the nano-scale with the characteristic length scale close to or smaller than the de Broglie wavelength or the wavelength of light ( Guo et al., 2013 ). Because of this, many of the physical characteristics of nanoparticles differ significantly from those of bulk materials, leading to a wide range of their novel uses ( Hasan, 2015 ).

2. Emergence of nanotechnology

Nanotechnology emerged in the 1980s due to the convergence of experimental advances such as the invention of the scanning tunneling microscope in 1981 and the discovery of fullerenes in 1985 ( Bayda et al., 2019 ), with the elucidation. The popularization of a conceptual framework for nanotechnology goals began with the publication of the book Engines of Creation in 1986 ( Bayda et al., 2019 ).

2.1. Early stage of NPs

Carbon nanotubes have been discovered in pottery from Keeladi, India, dating from around 600–300 BC ( Bayda et al., 2019 ; Kokarneswaran et al., 2020 ). Cementite nanowires have been discovered in Damascus steel, a material that dates back to around 900 AD; nevertheless, its origin and creation method are unclear ( Kokarneswaran et al., 2020 ). However, it is unknown how they developed or whether the material containing them was used on purpose.

2.2. Discovery of C, Ag, Zn, Cu, and Au nanoparticles

Carbon NPs were found in 1991, and Iijima and Ichihashi announced the single-wall carbon nanotube synthesis with a diameter of 1 nanometer in 1993 ( Chen et al., 2021 ). Carbon nanotubes (CNTs), also known as Bucky tubes, are a kind of nanomaterial made up of a two-dimensional hexagonal lattice of carbon atoms. They are bent one way and joined to produce a hollow cylindrical cylinder. Carbon nanotubes are carbon allotropes that fall between Fullerene (0 dimensional) and Grapheme (2 dimensional) ( Chen et al., 2021 ).

In addition, M. C. Lea reported that the synthesis of citrate-stabilized silver colloid almost 120 years ago ( Nowack et al., 2011 ). This process produces particles with an average diameter of 7 to 9 nm. Nanoscale size and citrate stabilization are analogous to recent findings on nanosilver production employing silver nitrate and citrate ( Majeed Khan et al., 2011 ). The use of proteins to stabilize nanosilver has also been documented as early as 1902 ( Nowack et al., 2011 ; Beyene et al., 2017 ). Since 1897, a nanosilver known as “Collargol” has been made commercially and used for medicinal purposes ( Nowack et al., 2011 ). Collargol, a type of silver nanoparticle, has a particle size of about 10 nanometers (nm). This was determined as early as 1907, and it was found that the diameter of Collargol falls within the nanoscale range. In 1953, Moudry developed a different type of silver nanoparticle called gelatin-stabilized silver nanoparticles, with a diameter ranging from 2–20 nm. These nanoparticles were produced using another method than Collargol. The necessity of nanoscale silver was recognized by the creators of nanosilver formulations decades ago, as seen by the following remark from a patent: “for optimal efficiency, the silver must be disseminated as particles of colloidal size less than 25 nm in crystallite size”( Nowack et al., 2011 ).

Gold NPs (AuNPs) have a long history in chemistry, going back to the Roman era when they were used to decorate glassware by staining them. With the work of Michael Faraday, who may have been the first to notice that colloidal gold solutions have characteristics different from bulk gold, the contemporary age of AuNP synthesis began more than 170 years ago. Michael Faraday investigated the making and factors of colloidal suspensions of “Ruby” gold in 1857. They are among the magnetic nanoparticles due to their distinctive optical and electrical characteristics. Under specific illumination circumstances, Faraday showed how gold nanoparticles might create solutions of various colors ( Bayda et al., 2019 ; Giljohann et al., 2020 ).

3. Classification of NPs

Nanoparticles (NPs) are categorized into the following classes based on their shape, size, and chemical characteristics;

3.1. Carbon-based NPs

Fullerenes and carbon nanotubes (CNTs) are the two essential sub-categories of carbon-based NPs. NPs of globular hollow cages, like allotropic forms of carbon, are found in fullerenes. Due to their electrical conductivity, high strength, structure, electron affinity, and adaptability, they have sparked significant economic interest. These materials have organized pentagonal and hexagonal carbon units, each of which is sp2 hybridized. While CNTs are elongated and form 1–2 nm diameter tubular structures. These fundamentally resemble graphite sheets rolling on top of one another. Accordingly, they are referred to as single-walled (SWNTs), double-walled (DWNTs), or multi-walled carbon nanotubes (MWNTs) depending on how many walls are present in the rolled sheets ( Elliott et al., 2013 ; Astefanei et al., 2015 ).

3.2. Metal NPs

Metal NPs are purely made of metals. These NPs have distinctive electrical properties due to well-known localized surface Plasmon resonance (LSPR) features. Cu, Ag, and Au nanoparticles exhibit a broad absorption band in the visible region of the solar electromagnetic spectrum. Metal NPs are used in several scientific fields because of their enhanced features like facet, size, and shape-controlled synthesis of metal NPs ( Khan et al., 2019 ).

3.3. Ceramics NPs

Ceramic NPs are tiny particles made up of inorganic, non-metallic materials that are heat-treated and cooled in a specific way to give particular properties. They can come in various shapes, including amorphous, polycrystalline, dense, porous, and hollow, and they are known for heat resistance and durable properties. Ceramic NPs are used in various applications, including coating, catalysts, and batteries ( Sigmund et al., 2006 ).

3.4. Lipid-based NPs

These NPs are helpful in several biological applications because they include lipid moieties. Lipid NPs typically have a diameter of 10–1,000 nm and are spherical. Lipid NPs, i.e., polymeric NPs, have a solid lipid core and a matrix consisting of soluble lipophilic molecules ( Khan et al., 2019 ).

3.5. Semiconductor NPs

Semiconductor NPs have qualities similar to metals and non-metals. That is why Semiconductor NPs have unique physical and chemical properties that make them useful for various applications. For example, semiconductor NPs can absorb and emit light and can be used to make more efficient solar cells or brighter light-emitting diodes (LEDs). They can make smaller and faster electronic devices, such as transistors, and can be used in bio imaging and cancer therapy ( Biju et al., 2008 ).

3.6. Polymeric NPs

Polymeric NPs with a size between 1 and 1,000 nm can have active substances surface-adsorbed onto the polymeric core or entrapped inside the polymeric body. These NPs are often organic, and the term polymer nanoparticle (PNP) is commonly used in the literature to refer to them. They resemble Nano spheres or Nano capsules for the most part ( Khan et al., 2019 ; Zielińska et al., 2020 ).

4. Types of different metal-based NPs

Metal NPs are purely made of metal precursors. Due to well-known localized surface plasmon resonance (LSPR) characteristics, these NPs possess unique optoelectrical properties. NPs of the alkali and noble metals, i.e., Cu, Ag, and Au, have a broad absorption band in the visible zone of the solar electromagnetic spectrum. The facet, size, and shape-controlled synthesis of metal NPs are essential in present-day cutting-edge materials ( Dreaden et al., 2012 ; Khan et al., 2019 ).

4.1. Silver nanoparticles (AgNPs)

AgNPs are particles with a size range of 1–100 nanometers made of silver. They have unique physical and chemical properties due to their small size, high surface area-to-volume ratio, and ability to absorb and scatter light in the visible and near-infrared range. Because of their relatively small size and high surface-to-volume ratios, which cause chemical and physical differences in their properties compared to their bulk counterparts, silver nanoparticles may exhibit additional antimicrobial capabilities not exerted by ionic silver ( Shenashen et al., 2014 ).

Besides, AgNPs can be created in various sizes and forms depending on the manufacturing process, the most common of which is chemical reduction. The AgNPs were created by chemically reducing a 12 mM AgNO3 aqueous solution. The reaction was carried out in an argon environment using 70 mL of this solution containing PVP (keeping the molar ratio of the repeating unit of PVP and Ag equal to 34) and 21 mL of Aloe Vera. The mixture was agitated in ultrasonic for 45 min at ambient temperature, then heated 2°C/min to 80°C and left for 2 h to generate a transparent solution with tiny suspended particles that must be removed by simple filtering ( Shenashen et al., 2014 ; Gloria et al., 2017 ).

4.2. Zinc nanoparticles (ZnONPs)

Zinc nanoparticles (ZnONPs) are particles with a size range of 1–100 nm made of zinc. Zinc oxide (ZnO) NPs are a wide band gap semiconductor with a room temperature energy gap of 3.37 eV. Its catalytic, electrical, optoelectronic, and photochemical capabilities have made it widely worthwhile ( Kumar S.S. et al., 2013 ). ZnO nanostructures are ideal for catalytic reaction processes ( Chen and Tang, 2007 ). Laser ablation, hydrothermal methods, electrochemical depositions, sol-gel method, chemical vapor deposition, thermal decomposition, combustion methods, ultrasound, microwave-assisted combustion method, two-step mechanochemical-thermal synthesis, anodization, co-precipitation, electrophoretic deposition, and precipitation processes are some methods for producing ZnO nanoparticles ( Madathil et al., 2007 ; Moghaddam et al., 2009 ; Ghorbani et al., 2015 ).

4.3. Copper nanoparticles (CuNPs)

Copper nanoparticles (CuNPs) comprise a size range of 1–100 nm of copper-based particles ( Khan et al., 2019 ). Cu and Au metal fluorescence have long been known to exist. For excitation at 488 nm, a fluorescence peak centering on the metals’ interband absorption edge has been noted. Additionally, it was noted that the fluorescence peaked at the same energy at two distinct excitation wavelengths (457.9–514.5 and 300–400 nm), and the high-energy tail somewhat grows with increased photon energy pumping. A unique, physical, top-down EEW approach has been used to create Cu nanoparticles. The EEW method involves sending a current of *1,010 A/m2 (1,010 A/m2) across a thin Cu wire, which explodes on a Cu plate for a duration of 10–6 s ( Siwach and Sen, 2008 ).

4.4. Gold nanoparticles (AuNPs)

Gold nanoparticles(AuNPs) are nanometers made of gold. They have unique physical and chemical properties and can absorb and scatter light in the visible and near-infrared range ( Rad et al., 2011 ; Compostella et al., 2017 ).

Scientists around the turn of the 20th century discovered anisotropic AuNPs. Zsigmond ( Li et al., 2014 ) said that gold particles “are not always spherical when their size is 40 nm or lower” in his book, released in 1909. Additionally, he found anisotropic gold particles of various colors. Zsigmondy won the Nobel Prize in 1925 for “his demonstration of the heterogeneous character of colloidal solutions and the methods he utilized” and for developing the ultramicroscope, which allowed him to see the forms of Au particles. He noticed that gold frequently crystallized into a six-sided leaf shape ( Li et al., 2014 ).

AuNPs are the topic of extensive investigation due to their optical, electrical, and molecular-recognition capabilities, with numerous prospective or promised uses in a wide range of fields, including electron microscopy, electronics, nanotechnology, materials science, and biomedicine ( Rad et al., 2011 ).

4.5. Aluminum nanoparticles (AlNPs)

Aluminum nanoparticles (AlNPs) are nanoparticles made of aluminum. Aluminum nanoparticles’ strong reactivity makes them promising for application in high-energy compositions, hydrogen generation in water processes, and the synthesis of alumina 2D and 3D structures ( Lerner et al., 2016 ).

4.6. Iron nanoparticles (FeNPs)

Iron nanoparticles(FeNPs) are particles with a size range of 1−100 nanometers ( Khan et al., 2019 ) made of iron. FeNPs have several potential applications, including their use as catalysts, drug delivery systems, sensors, and energy storage and conversion. They have also been investigated for use in photovoltaic and solar cells and water purification and environmental remediation. FeNPs can also be used in magnetic resonance imaging (MRI) as contrast agents to improve the visibility of tissues and organs. They can also be used in magnetic recording media, such as hard disk drives ( Zhuang and Gentry, 2011 ; Jamkhande et al., 2019 ).

As with any NPs, there are potential health and safety concerns associated with using FeNPs, e.g., FeNPs are used to deliver drugs to specific locations within the body, such as cancer cells and used in MRI, and used to remove contaminants from water ( Farrell et al., 2003 ; Zhuang and Gentry, 2011 ). Tables 1 , ​ ,2 2 show the characteristics of metal-based nanoparticles and the techniques to study their characteristics, respectively.

Characteristics of metal based nanoparticles.

Different analytical techniques and their purposes in studying nanoparticles.

5. Approaches for the synthesis of metal NPs

There are mainly three types of approaches for the synthesis of NPs: the physical, chemical, and biological approaches. The physical approach is also called the top-down approach, while chemical and biological approaches are collectively called the bottom-up approach. The biological approach is also named green systems of NPs. All these approaches are further sub-categorized into various types based upon their method adopted. Figure 1 illustrates each approach’s reported methods for synthesizing NPs.

Approaches of NPs synthesis.

5.1. Top down/physical approach

Bulk materials are fragmented in top-down methods to create nano-structured materials ( Figure 2 ). They are additionally known as physical approaches ( Baig et al., 2021 ). The following techniques can achieve a top-down approach;

Difference between top-down and bottom-up approaches.

5.1.1. Mechanical milling

The mechanical milling process uses balls inside containers and may be carried out in various mills, typically planetary and shaker mills, which is an impact process with high energy ( Gorrasi and Sorrentino, 2015 ). Mechanical milling is a practical approach for creating materials at the nanoscale from bulk materials. Aluminum alloys that have been strengthened by oxide and carbide, spray coatings that are resistant to wear, nanoalloys based on aluminum, nickel, magnesium, and copper, and a variety of other nanocomposite materials may all be created mechanically. A unique class of nanoparticles known as ball-milled carbon nanomaterials has the potential to meet the needs for energy storage, energy conversion, and environmental remediation ( Yadav et al., 2012 ; Lyu et al., 2017 ).

5.1.2. Electrospinning

Typically, it is used to create nanofibers from various materials, most often polymers ( Ostermann et al., 2011 ). A technique for creating fibers called electrospinning draws charged threads from polymer melts or solutions up to fiber sizes of a few hundred nanometers ( Chronakis, 2010 ). Coaxial electrospinning was a significant advancement in the field of electrospinning. The spinneret in coaxial electrospinning is made up of two coaxial capillaries. Core-shell nanoarchitectures may be created in these capillaries using two viscous liquids, a viscous liquid as the shell and a non-viscous liquid as the core ( Du et al., 2012 ). Core-shell and hollow polymer, inorganic, organic, and hybrid materials have all been developed using this technique ( Kumar R. et al., 2013 ).

5.1.3. Laser ablation

A microfeature can be made by employing a laser beam to vaporize a single material ( Tran and Wen, 2014 ). Laser ablation synthesis produces nanoparticles by striking the target material with an intense laser beam. Due to the high intensity of the laser irradiation used in the laser ablation process, the source material or precursor vaporizes, causing the production of nanoparticles ( Amendola and Meneghetti, 2009 ). Laser ablation is an environmentally friendly for producing noble metal nanoparticles ( Baig et al., 2021 ). This method may be used to create a wide variety of nanomaterials, including metal nanoparticles, carbon nanomaterials, oxide composites, and ceramics ( Su and Chang, 2018 ; Baig et al., 2021 ).

5.1.4. Sputtering

Microparticles of a solid material are expelled off its surface during the phenomenon known as sputtering, which occurs when the solid substance is assaulted by intense plasma or gas particles ( Behrisch, 1981 ). According to the incident gaseous ion energy, energetic gaseous ions used in the sputtering deposition process physically expel tiny atom clusters off the target surface ( Muñoz-García et al., 2009 ). The sputtering method is intriguing because it is more affordable than electron-beam lithography, and the composition of the sputtered nanomaterials is similar to the target material with fewer contaminants ( Baig et al., 2021 ).

5.1.5. Electron explosion

In this technique, a thin metal wire is subjected to a high current pulse that causes an explosion, evaporation, and ionization. The metal becomes vaporized and ionized, expands, and cools by reacting with the nearby gas or liquid medium. The condensed vapor finally forms the nanoparticles ( Joh et al., 2013 ). Electron explosion method because it produces plasma from the electrical explosion of a metallic wire, which may produce nanoparticles from a Pt solution without using a reducing agent ( Joh et al., 2013 ).

5.1.6. Sonication

The most crucial step in the creation of nanofluids is sonication. After the mixture has been magnetically stirred in a magnetic stirrer, sonication is performed in an ultrasonication path, ultrasonic vibrator, and mechanical homogenizer. Sonicators have become the industry standard for Probe sonication and are noticeably more powerful and effective when compared to ultrasonic cleaner baths for nanoparticle applications. Probe sonication is highly effective for processing nanomaterials (carbon nanotubes, graphene, inks, metal oxides, etc.) ( Zheng et al., 2010 ).

5.1.7. Pulsed wire discharge method

This is the most used method for creating metal nanoparticles. A pulsating current causes a metal wire to evaporate, producing a vapor that is subsequently cooled by an ambient gas to form nanoparticles. This plan may quickly produce large amounts of energy ( Patil et al., 2021 ).

5.1.8. Arc discharge method

Two graphite rods are adjusted in a chamber with a constant helium pressure during the Arc Discharge procedure. It is crucial to fill the chamber with helium because oxygen or moisture prevents the synthesis of fullerenes. Arc discharge between the ends of the graphite rods drives the vaporization of carbon rods. Achieving new types of nanoparticles depends significantly on the circumstances in which arc discharge occurs. The creation of several nanostructured materials may be accomplished with this technique ( Berkmans et al., 2014 ). It is well-recognized for creating carbon-based materials such as fullerenes, carbon nanohorns (CNHs), carbon nanotubes ( Shi et al., 2000 ), few-layer graphene, and amorphous spherical carbon nanoparticles ( Kumar R. et al., 2013 ).

5.1.9. Lithography

Lithography typically uses a concentrated beam of light or electrons to create nanoparticles, a helpful technique ( Pimpin and Srituravanich, 2012 ). Masked and maskless lithography are the two primary categories of lithography. Without a mask, arbitrary nano-pattern printing is accomplished in maskless lithography. Additionally, it is affordable and easy to apply ( Brady et al., 2019 ).

5.2. Bottom-up approach

Tiny atoms and molecules are combined in bottom-up methods to create nano-structured particles ( Figure 2 ; Baig et al., 2021 ). These include chemical and biological approaches:

5.2.1. Chemical vapor deposition (CVD)

Through a chemical process involving vapor-phase precursors, a thin coating is created on the substrate surface during CVD ( Dikusar et al., 2009 ). Precursors are deemed appropriate for CVD if they exhibit sufficient volatility, high chemical purity, strong evaporation stability, cheap cost, a non-hazardous nature, and long shelf life. Additionally, its breakdown should not leave behind any contaminants. Vapor phase epitaxy, metal-organic CVD, atomic layer epitaxy, and plasma-enhanced CVD are only a few CVD variations. This method’s benefits include producing very pure nanoparticles that are stiff, homogeneous, and strong ( Ago, 2015 ). CVD is an excellent approach to creating high-quality nanomaterials ( Machac et al., 2020 ). It is also well-known for creating two-dimensional nanoparticles ( Baig et al., 2021 ).

5.2.2. Sol-gel process

A wet-chemical approach, called the sol-gel method, is widely utilized to create nanomaterials ( Das and Srivasatava, 2016 ; Baig et al., 2021 ). Metal alkoxides or metal precursors in solution are condensed, hydrolyzed, and thermally decomposed. The result is a stable solution or sol. The gel gains greater viscosity as a result of hydrolysis or condensation. The particle size may be seen by adjusting the precursor concentration, temperature, and pH levels. It may take a few days for the solvent to be removed, for Ostwald ripening to occur, and for the phase to change during the mature stage, which is necessary to enable the growth of solid mass. To create nanoparticles, the unstable chemical ingredients are separated. The generated material is environmentally friendly and has many additional benefits thanks to the sol-gel technique ( Patil et al., 2021 ). The uniform quality of the material generated, the low processing temperature, and the method’s ease in producing composites and complicated nanostructures are just a few of the sol-gel technique’s many advantages ( Parashar et al., 2020 ).

5.2.3. Co-precipitation

It is a solvent displacement technique and is a wet chemical procedure. Ethanol, acetone, hexane, and non-solvent polymers are examples of solvents. Polymer phases can be either synthetic or natural. By mixing the polymer solution, fast diffusion of the polymer-solvent into the non-solvent phase of the polymer results. Interfacial stress at two phases results in the formation of nanoparticles ( Das and Srivasatava, 2016 ). This method’s natural ability to produce high quantities of water-soluble nanoparticles through a straightforward process is one of its key benefits. This process is used to create many commercial iron oxide NP-based MRI contrast agents, including Feridex, Reservist, and Combidex ( Baig et al., 2021 ; Patil et al., 2021 ).

5.2.4. Inert gas condensation/molecular condensation

Metal NPs are produced using this method in large quantities. Making fine NPs using the inactive gas compression approach has been widespread, which creates NPs by causing a metallic source to disappear in an inert gas. At an attainable temperature, metals evaporate at a tolerable pace. Copper metal nanoparticles are created by vaporizing copper metal inside a container containing argon, helium, or neon. The atom quickly loses its energy by cooling the vaporized atom with an inert gas after it boils out. Liquid nitrogen is used to cool the gases, forming nanoparticles in the range of 2–100 nm ( Pérez-Tijerina et al., 2008 ; Patil et al., 2021 ).

5.2.5. Hydrothermal

In this method, for the production of nanoparticles, hydrothermal synthesis uses a wide temperature range from ambient temperature to extremely high temperatures. Comparing this strategy to physical and biological ones offers several benefits. At higher temperature ranges, the nanomaterials produced by hydrothermal synthesis could become unstable ( Banerjee et al., 2008 ; Patil et al., 2021 ).

5.2.6. Green/biological synthesis

The synthesis of diverse metal nanoparticles utilizing bioactive agents, including plant materials, microbes, and various biowastes like vegetable waste, fruit peel waste, eggshell, agricultural waste, algae, and so on, is known as “green” or “biological” nanoparticle synthesis ( Kumari et al., 2021 ). Developing dependable, sustainable green synthesis technologies is necessary to prevent the formation of undesirable or dangerous byproducts ( Figure 3 ). The green synthesis of nanoparticles also has several advantages, including being straightforward, affordable, producing NPs with high stability, requiring little time, producing non-toxic byproducts, and being readily scaled up for large-scale synthesis ( Malhotra and Alghuthaymi, 2022 ).

Schematic diagram for biosynthesis of NPs.

5.2.6.1. Biological synthesis using microorganisms

Microbes use metal capture, enzymatic reduction, and capping to create nanoparticles. Before being converted to nanoparticles by enzymes, metal ions are initially trapped on the surface or interior of microbial cells ( Ghosh et al., 2021 ). Use of microorganisms (especially marine microbes) for synthesis of metalic NPs is environmental friendly, fast and economical ( Patil and Kim, 2018 ). Several microorganisms are used in the synthesis of metal NPs, including:

Biosynthesis of NPs by bacteria: A possible biofactory for producing gold, silver, and cadmium sulfide nanoparticles is thought to be bacterial cells. It is known that bacteria may produce inorganic compounds either inside or outside of their cells ( Hulkoti and Taranath, 2014 ). Desulforibrio caledoiensis ( Qi et al., 2013 ), Enterococcu s sp. ( Rajeshkumar et al., 2014 ), Escherichia coli VM1 ( Maharani et al., 2016 ), and Ochrobactrum anhtropi ( Thomas et al., 2014 ) based metal NPs are reported previously for their potential photocatalytic properties ( Qi et al., 2013 ), antimicrobial activity ( Rajeshkumar et al., 2014 ), and anticancer activity ( Maharani et al., 2016 ).

Extracellular synthesis of NPs by bacteria: The microorganisms’ extracellular reductase enzymes shrink the silver ions to the nanoscale range. According to protein analysis of microorganisms, the NADH-dependent reductase enzyme carries out the bio-reduction of silver ions to AgNPs. The electrons for the reductase enzyme come from NADH, which is subsequently converted to NAD+. The enzyme is also oxidized simultaneously when silver ions are reduced to nanosilver. It has been noted that bio-reduction can occasionally be caused by nitrate-dependent reductase. The decline occurs within a few minutes in the quick extracellular creation of nanoparticles ( Mathew et al., 2010 ). At pH 7, the bacterium R. capsulata produced gold nanoparticles with sizes ranging from 10−20 nm. Numerous nanoplates and spherical gold nanoparticles were produced when the pH was changed to four ( Sriram et al., 2012 ). By adjusting the pH, the gold nanoparticles’ form may be changed. Gold nanoparticle shape was controlled by regulating the proton content at various pH levels. The bacteria R. capsulata ’s release cofactor NADH and NADH-dependent enzymes may cause the bioreduction of Au (3+) to Au (0) and the generation of gold nanoparticles. By using NADH-dependent reductase as an electron carrier, it is possible to start the reduction of gold ions ( Sriram et al., 2012 ).

Intracellular synthesis of NPs by bacteria: Three processes are involved in the intracellular creation of NPs: trapping, bioreduction, and capping. The cell walls of microorganisms and ions charge contribute significantly to creating NPs in the intracellular route. This entails specific ion transit in the presence of enzymes, coenzymes, and other molecules in the microbial cell. Microbes have a range of polysaccharides and proteins in their cell walls, which function as active sites for the binding of metal ions ( Slavin et al., 2017 ). Not all bacteria can produce metal and metal oxide nanoparticles. The only ions that pose a significant hazard to microorganisms are heavy metal ions, which, in response to a threat, cause the germs to react by grabbing or trapping the ions on the cell wall via electrostatic interactions. This occurs because a metal ion is drawn to the cell wall’s carboxylate groups, including cysteine and polypeptides, and certain enzymes with a negative charge ( Zhang et al., 2011 ).

Additionally, the electron transfers from NADH via NADH-dependent educates, which serves as an electron carrier and is located inside the plasma membrane, causing the trapped ions to be reduced into the elemental atom. The nuclei eventually develop into NPs and build up in the cytoplasm or the pre-plasmic space. On the other hand, the stability of NPs is provided by proteins, peptides, and amino acids found inside cells, including cysteine, tyrosine, and tryptophan ( Mohd Yusof et al., 2019 ).

Biosynthesis of NPs by fungi: Because monodisperse nanoparticles with distinct dimensions, various chemical compositions, and sizes may be produced, the biosynthesis of nanoparticles utilizing fungus is frequently employed. Due to the existence of several enzymes in their cells and the ease of handling, fungi are thought to be great candidates for producing metal and metal sulfide nanoparticles ( Mohanpuria et al., 2008 ).

The nanoparticles were created on the surface of the mycelia. After analyzing the results and noting the solution, it was determined that the Ag + ions are initially trapped on the surface of the fungal cells by an electrostatic interaction between gold ions and negatively charged carboxylate groups, which is facilitated by enzymes that are present in the mycelia’s cell wall. Later, the enzymes in the cell wall reduce the silver ions, causing the development of silver nuclei. These nuclei then increase as more Ag ions are reduced and accumulate on them.

The TEM data demonstrate the presence of some silver nanoparticles both on and inside the cytoplasmic membrane. The findings concluded that the Ag ions that permeate through the cell wall were decreased by enzymes found inside the cytoplasm and on the cytoplasmic membrane. Also possible is the diffusion of some silver nanoparticles over the cell wall and eventual cytoplasmic entrapment ( Mukherjee et al., 2001 ; Hulkoti and Taranath, 2014 ).

It was observed that the culture’s age does not affect the shape of the synthesized gold nanoparticles. However, the number of particles decreased when older cells were used. The different pH levels produce a variety of shapes of gold nanoparticles, indicating that pH plays a vital role in determining the shape. The incubation temperature also played an essential role in the accumulation of the gold nanoparticles. It was observed that the particle growth rate was faster at increased temperature levels ( Mukherjee et al., 2001 ; Ahmad et al., 2003 ). The form of the produced gold nanoparticles was shown to be unaffected by the age of the culture. However, when older cells were utilized, the particle count fell. The fact that gold nanoparticles take on various forms at different pH levels suggests that the pH is crucial in determining the shape. The incubation temperature significantly influenced the accumulation of the gold nanoparticles. It was found that higher temperatures caused the particle development rate to accelerate ( Mukherjee et al., 2001 ; Ahmad et al., 2003 ). Verticillium luteoalbum is reported to synthesize gold nanoparticles of 20–40 nm in size ( Erasmus et al., 2014 ). Aspergillus terreus and Penicillium brevicompactum KCCM 60390 based metal NPs are reported for their antimicrobial ( Li G. et al., 2011 ) and cytotoxic activities ( Mishra et al., 2011 ), respectively.

Biosynthesis of NPs using actinomycetes: Actinomycetes have been categorized as prokaryotes since they share significant traits with fungi. They are sometimes referred to as ray fungi ( Mathew et al., 2010 ). Making NPs from actinomycetes is the same as that of fungi ( Sowani et al., 2016 ). Thermomonospora sp., a new species of extremophilic actinomycete, was discovered to produce extracellular, monodispersed, spherical gold nanoparticles with an average size of 8 nm ( Narayanan and Sakthivel, 2010 ). Metal NPs synthesized by Rhodococcus sp. ( Ahmad et al., 2003 ) and Streptomyces sp. Al-Dhabi-87 ( Al-Dhabi et al., 2018 ) are reported for their antimicrobial activities.

Biosynthesis of NPs using algae: Algae have a high concentration of polymeric molecules, and by reducing them, they may hyper-accumulate heavy metal ions and transform them into malleable forms. Algal extracts typically contain pigments, carbohydrates, proteins, minerals, polyunsaturated fatty acids, and other bioactive compounds like antioxidants that are used as stabilizing/capping and reducing agents ( Khanna et al., 2019 ). NPs also have a faster rate of photosynthesis than their biosynthetic counterparts. Live or dead algae are used as model organisms for the environmentally friendly manufacturing process of bio-nanomaterials, such as metallic NPs ( Hasan, 2015 ). Ag and Au are the most extensively researched noble metals to synthesized NPs by algae either intracellularly or extracellularly ( Dahoumane et al., 2017 ). Chlorella vulgaris ( Luangpipat et al., 2011 ), Chlorella pyrenoidosa ( Eroglu et al., 2013 ), Nanochloropsis oculata ( Xia et al., 2013 ), Scenedesmus sp. IMMTCC-25 ( Jena et al., 2014 ) based metal NPs are reported for their potential catalytic ( Luangpipat et al., 2011 ; Eroglu et al., 2013 ) and, antimicrobial ( Eroglu et al., 2013 ; Jena et al., 2014 ) activities along with their use in Li-Ion batteries ( Xia et al., 2013 ).

Intracellular synthesis of NPs using algae: In order to create intracellular NPs, algal biomass must first be gathered and thoroughly cleaned with distilled water. After that, the biomass (living algae) is treated with metallic solutions like AgNO3. The combination is then incubated at a specified pH and a specific temperature for a predetermined time. Finally, it is centrifuged and sonicated to produce the extracted stable NPs ( Uzair et al., 2020 ).

Extracellular synthesis of NPs using algae: Algal biomass is first collected and cleaned with distilled water before being used to synthesize NPs extracellularly ( Uzair et al., 2020 ). The following three techniques are frequently utilized for the subsequent procedure:

(i) A particular amount of time is spent drying the algal biomass (dead algae), after which the dried powder is treated with distilled water and filtered.

(ii) The algal biomass is sonicated with distilled water to get a cell-free extract.

(iii) The resultant product is filtered after the algal biomass has been rinsed with distilled water and incubated for a few hours (8–16 h).

5.2.6.2. Biological synthesis using plant extracts

The substance or active ingredient of the desired quality extracted from plant tissue by treatment for a particular purpose is a plant extract ( Jadoun et al., 2021 ). Plant extracts are combined with a metal salt solution at room temperature to create nanoparticles. Within minutes, the response is finished. This method has been used to create nanoparticles of silver, gold, and many other metals ( Li X. et al., 2011 ). Nanoparticles are biosynthesized using a variety of plants. It is known that the kind of plant extract, its concentration, the concentration of the metal salt, the pH, temperature, and the length of contact time all have an impact on how quickly nanoparticles are produced as well as their number and other properties ( Mittal and Chisti, 2013 ). A leaf extract from Polyalthia longifolia was used to create silver nanoparticles, the average particle size was around 58 nm ( Kumar and Yadav, 2009 ; Kumar et al., 2016 ).

Acacia auriculiformis ( Saini et al., 2016 ), Anisomeles indica ( Govindarajan et al., 2016 ), Azadirachta indica ( Velusamy et al., 2015 ), Bergenia ciliate ( Phull et al., 2016 ), Clitoria ternatea , Solanum nigrum ( Krithiga et al., 2013 ), Coffea arabica ( Dhand et al., 2016 ), Coleus forskohlii ( Naraginti et al., 2016 ), Curculigo orchioides ( Kayalvizhi et al., 2016 ), Digitaria radicosa ( Kalaiyarasu et al., 2016 ), Dioscorea alata ( Pugazhendhi et al., 2016 ), Diospyros paniculata ( Rao et al., 2016 ), Elephantopus scaber ( Kharat and Mendhulkar, 2016 ), Emblica officinalis ( Ramesh et al., 2015 ), Euphorbia antiquorum L. ( Rajkuberan et al., 2017 ), Ficus benghalensis ( Nayak et al., 2016 ), Lantana camara ( Ajitha et al., 2015 ), Cinnamomum zeylanicum ( Soni and Sonam, 2014 ), and Parkia roxburghii ( Paul et al., 2016 ) are the few examples of plants which are reported for the green synthesis of metal NPs (i.e., AgNPs). These were evaluated for their antifilaria activity ( Saini et al., 2016 ), mosquitocidal activity ( Govindarajan et al., 2016 ), antibacterial activity ( Velusamy et al., 2015 ), catalytic activity ( Edison et al., 2016 ), antioxidant activity ( Phull et al., 2016 ), and Cytotoxicity ( Patil et al., 2017 ).

5.2.6.3. Biological synthesis using biomimetic

“Biomimetic synthesis” typically refers to chemical processes that resemble biological synthesis carried out by living things ( Dahoumane et al., 2017 ). In the biomimetic approach, proteins, enzymes, cells, viruses, pollen, and waste biomass are used to synthesize NPs. Two categories are used to classify biomimetic synthesis:

Functional biomimetic synthesis uses various materials and approaches to emulate particular characteristics of natural materials, structures, and systems ( Zan and Wu, 2016 ).

Process biomimetic synthesis is a technique that aims to create different desirable nanomaterials/structures by imitating the synthesis pathways, processes, or procedures of natural chemicals and materials/structures. For instance, several distinctive nano-superstructures (such as satellite structures, dendrimer-like structures, pyramids, cubes, 2D nanoparticle arrays, 3D AuNP tubes, etc.) have been put together in vitro by simulating the protein manufacturing process ( Zan and Wu, 2016 ).

6. Applications of NPs

6.1. applications of nps in environment industry.

Due to their tiny size and distinctive physical and chemical characteristics, NPs appeal to various environmental applications. The properties of nanoparticals and their advantages are illustrated in Figure 4 . The following are some possible NP uses in the environment.

Properties of nanoparticals and their advantages.

6.1.1. Bioremediation

Nanoparticles (NPs) can remove environmental pollutants, such as heavy metals from water or organic contaminants from soil ( Zhuang and Gentry, 2011 ). For example, silver nanoparticles (AgNPs) effectively degrade certain pollutants, such as organic dyes and compounds found in wastewater. Several nanomaterials have been considered for remediation purposes, such as nanoscale zeolites, metal oxides, and carbon nanotubes and fibers ( Zhuang and Gentry, 2011 ). Nanoscale particles used in remediation can access areas that larger particles cannot. They can be coated to facilitate transport and prevent reaction with surrounding soil matrices before reacting with contaminants. One widely used nanomaterial for remediation is Nanoscale zerovalent iron (nZVI). It has been used at several hazardous waste sites to clean up chlorinated solvents that have contaminated groundwater ( Elliott et al., 2013 ). Removing heavy metals such as mercury, lead, thallium, cadmium, and arsenic from natural water has attracted considerable attention because of their adverse effects on environmental and human health. Superparamagnetic iron oxide NPs are an effective sorbent material for this toxic soft material. So, no measurements of engineered NPs in the environment have been available due to the absence of analytical methods able to quantify the trace concentration of NPs ( Elliott et al., 2013 ).

6.1.2. Sensors in environment

Nanotechnology/NPs are already being used to improve water quality and assist in environmental clean-up activities ( Pradeep, 2009 ). Their potential use as environmental sensors to monitor pollutants is also becoming viable NPs can be used as sensors to detect the presence of certain compounds in the environment, such as heavy metals or pollutants. The nano-sensors small size and wide detection range provide great flexibility in practical applications. It has been reported that nanoscale sensors can be used to detect microbial pathogens and biological compounds, such as toxins, in aqueous environments ( Yadav et al., 2010 ). NPS can be designed to selectively bind to specific types of pollutants, allowing them to be detected at low concentrations. For example, gold nanoparticles (AuNPs) have been used as sensors for the detection of mercury in water ( Theron et al., 2010 ).

6.1.3. Catalysts in environment

Nanoparticles (NPs) are used as catalysts in chemical reactions, such as in the production of biofuels or environmental remediation processes, and to catalyze biomass conversion into fuels, such as ethanol or biodiesel. For example, platinum nanoparticles (PtNPs) have been explored for use in the production of biofuels due to their ability to catalyze the conversion of biomass into fuels ( Lam and Luong, 2014 ). PtNPs also showed promising sensing properties; for example, Using Pt NPs, the Hg ions were quantified in the range of 50–500 nM in MilliQ, tap, and groundwater samples, and the limit of quantifications for Hg ions were 16.9, 26, and 47.3 nM. The biogenic PtNPs-based probe proved to be applicable for detecting and quantifying Hg ions ( Kora and Rastogi, 2018 ).

Overall, NPs have significant potential for use in the environment and are being actively researched for a variety of applications.

6.2. Applications of NPs in medicine industry

Nanoparticles (NPs) have unique physical and chemical properties due to their small size, making them attractive for use in various applications, including the medicine industry. Some potential applications of NPs in medicine include:

6.2.1. Drug delivery

Technological interest has been given to AuNPs due to their unique optical properties, ease of synthesis, and chemical stability. The particles can be used in biomedical applications such as cancer treatment ( Sun et al., 2014 ), biological imaging ( Abdulle and Chow, 2019 ), chemical sensing, and drug delivery. Sun et al. (2014) mentioned in detail about two different methods of controlled release of drugs associated with NPs, which were (1) sustained (i.e., diffusion-controlled and erosion-controlled) and (2) stimuli-responsive (i.e., pH-sensitive, enzyme-sensitive, thermoresponsive, and photosensitive). Figure 5 illustrates that how NPs acts as targeted delivery of medicines to treat cancer cells ( Figure 5A ) and therapeutic gene delivery to synthesis proteins of interests in targeted cells ( Figure 5B ). NPs can deliver drugs to specific body areas, allowing for more targeted and effective treatment ( Siddique and Chow, 2020 ). For example AgNPs have been explored for use in drug delivery due to their stability and ability to accumulate in certain types of cancerous tumors ( Siddique and Chow, 2020 ). ZnONPs have also been explored for drug delivery due to their ability to selectively target cancer cells ( Anjum et al., 2021 ). CuNPs have been shown to have antimicrobial properties and are being explored for drug delivery to treat bacterial infections ( Yuan et al., 2018 ). AuNPs have unique optical, electrical, and catalytic properties and are being explored for drug delivery due to their ability to accumulate in certain cancerous tumors. Silver NPs (AgNPs) have been incorporated into wound dressings, bone cement, and implants ( Schröfel et al., 2014 ).

Application of nanoparticles as; targated drug delivery (A) , and therapeutic protein generation in targated cells (B) .

6.2.2. Diagnostics

Nanoparticles (NPs) can be used as imaging agents to help visualize specific body areas. For example, iron oxide nanoparticles (Fe 3 O 4 NPs) have been used as magnetic resonance imaging (MRI) contrast agents to help visualize tissues and organs ( Nguyen et al., 2013 ). AuNPs have unique optical, electrical, and catalytic properties and are being explored for diagnostics due to their ability to accumulate in certain cancerous tumors ( Siddique and Chow, 2020 ).

6.2.3. Tissue engineering

Nanoparticles (NPs) can help stimulate the growth and repair of tissues and organs. For example, titanium dioxide nanoparticles (TiO2 NPs) have been explored for tissue engineering due to their ability to stimulate the growth of bone cells ( Kim et al., 2014 ).

6.2.4. Antimicrobials

Some NPs, such as silver nanoparticles (AgNPs) and copper nanoparticles (CuNPs), have strong antimicrobial properties and are being explored for use in a variety of medical products, such as wound dressings and medical devices ( Hoseinzadeh et al., 2017 ).

Overall, NPs have significant potential for use in the medical industry and are being actively researched for various applications. However, it is essential to carefully consider the potential risks and benefits of using NPs in medicine and ensure their safe and responsible use.

6.3. Applications of NPs in agriculture industry

There are several ways in which nanoparticles (NPs) have the potential to alter the agricultural sector. NPs may be used in agriculture for a variety of reasons, including:

6.3.1. Pesticides and herbicides

Nanoparticles (NPs) can be used to deliver pesticides and herbicides in a targeted manner, reducing the number of chemicals needed and minimizing the potential for environmental contamination ( Khan et al., 2019 ). AgNPs and CuNPs have antimicrobial properties, making them potentially useful for controlling pests and diseases in crops. They can also be used as delivery systems for active ingredients, allowing for more targeted application and reducing the potential for environmental contamination ( Hoseinzadeh et al., 2017 ; Dangi and Verma, 2021 ).

It is important to note that using metal NPs in pesticides and herbicides is still in the early stages of development. More research is needed to understand their potential impacts on human health and the environment ( Dangi and Verma, 2021 ).

6.3.2. Fertilizers and plant growth

Nano fertilizers offer an opportunity for efficiently improving plant mineral nutrition. Some studies have shown that nanomaterials can be more effective than conventional fertilizers, with a controlled release of nutrients increasing the efficiency of plant uptake and potentially reducing adverse environmental outcomes associated with the loss of nutrients in the broader environment. However, other studies have found that nanomaterial has the same or even less effective effectiveness than conventional fertilizers. NPs used to deliver fertilizers to plants more efficiently, reducing the amount of fertilizer needed, and reducing the risk of nutrient runoff ( Kopittke et al., 2019 ).

Ag ( Jaskulski et al., 2022 ), Zn ( Song and Kim, 2020 ), Cu, Au, Al, and Fe ( Kopittke et al., 2019 ) based NPs have been shown to have fertilizing properties and plant growth-promoting properties, and may help provide essential nutrients to plants and improve plant growth and yield. It is important to note that the use of NPs in fertilizers is still in the early stages of development. More research is needed to understand their potential impacts on human health and the environment.

6.3.3. Food safety

Nanoparticles (NPs) can detect and eliminate pathogens in food products, improving food safety, and reducing the risk of foodborne illness ( Zhuang and Gentry, 2011 ).

6.3.4. Water purification

Nanoparticles (NPs) can purify irrigation water, reducing the risk of crop contamination and improving crop yield ( Zhuang and Gentry, 2011 ). Using NPs in agriculture can improve crop yields, reduce agriculture’s environmental impact, and improve food products’ safety and quality.

6.4. Applications of NPs in food industry

Numerous applications for nanoparticles (NPs) in the food sector are possible, including:

6.4.1. Food processing and food preservation/food packaging

Nanoparticles (NPs) can be used to improve the efficiency and performance of food processing operations, such as grinding, mixing, and drying, e.g., AgNPs have been used as a natural antimicrobial agent in food processing operations, helping to prevent the growth of bacteria and other microorganisms ( Dangi and Verma, 2021 ) and also NPs are used to enhance the performance of materials used in food packaging, making them more resistant to pollutants like moisture and gases.

6.4.2. Food fortification

Nanoparticles (NPs) can deliver essential nutrients to food products, such as vitamins and minerals, more efficiently and effectively. e.g., Fe 2 O 3 , and CuNPs have been used to fortify food products with iron, and Cu is an essential nutrient necessary for the metabolism of iron and other nutrients. Iron is an essential nutrient often lacking in many people’s diets, particularly in developing countries ( Kopittke et al., 2019 ).

6.4.3. Sensors

Nanoparticles (NPs) used to improve the sensitivity and specificity of food sensors, allowing them to detect a broader range of substances or signals ( Yadav et al., 2010 ).

Overall, using NPs in the food industry can improve the performance, safety, and nutritional value of a wide range of food products and processes.

6.5. Applications of NPs in electronics industry and automotive industry

In many aspects, nanoparticles (NPs) can transform the electronics sector. NPs may be used in a variety of electrical applications, such as:

6.5.1. Display technologies/storage devices

Nanoparticles (NPs) can be used to improve the performance of displays ( Park and Choi, 2019 ; Bahadur et al., 2021 ; Triana et al., 2022 ), such as LCD and OLED displays, by enhancing the brightness, color, and contrast of the image, such as silver NPs and gold NPs, have been explored for use in LCD and OLED displays as a means of improving the conductivity of the display ( Gwynne, 2020 ). NPs improve the performance and durability of energy storage devices, such as batteries and supercapacitors, by increasing energy density and charging speed. Zinc oxide nanoparticles (ZnO NPs) have the potential to be used in energy storage devices, such as batteries and supercapacitors, due to their ability to store and release energy ( Singh et al., 2011 ).

6.5.2. Data storage

Nanoparticles (NPs) can improve the capacity and speed of data storage devices, such as hard drives and flash drives. Magnetic NPs, such as iron oxide NPs, have been explored for use in data storage devices, such as hard drives, due to their ability to store, and retrieve data using magnetism. These NPs are often composed of a magnetic metal, such as iron, cobalt, or nickel. They can be magnetized and demagnetized, allowing them to store and retrieve data ( Ahmad et al., 2021 ).

Overall, the use of NPs in electronics has the potential to improve the performance and efficiency of a wide range of electronic devices and systems.

Applications of NPs in chemical industry: The chemical industry might be entirely transformed by nanoparticles (NPs) in various ways. The following are potential uses for NPs in the chemical industry ( Salem and Fouda, 2021 ).

6.5.3. Chemical processing/catalysis

Nanoparticles (NPs) can be used as catalysts in chemical reactions, allowing them to be carried out more efficiently and at lower temperatures. Some examples of metal NPs that have been used as catalysts in the chemical industry include: PtNPs have been used as catalysts in a variety of chemical reactions, including fuel cell reactions ( Bhavani et al., 2021 ), hydrogenation reactions, and oxidation reactions ( Lara and Philippot, 2014 ), PdNPs have been used as catalysts in a variety of chemical reactions, including hydrogenation reactions and cross-coupling reactions ( Pérez-Lorenzo, 2012 ), FeNPs have been used as catalysts in a variety of chemical reactions, including hydrolysis reactions ( Jiang and Xu, 2011 ), and oxygen reduction reactions, NiNPs have been used as catalysts in a variety of chemical reactions, including hydrogenation reactions, and hydrolysis reactions ( Salem and Fouda, 2021 ).

6.5.4. Separation and purification

NPs are used to separate and purify chemicals and other substances, such as gases and liquids, by exploiting their size-based properties ( Hollamby et al., 2010 ). Several types of metal nanoparticles (NPs) have been explored for use in separation and purification processes in the chemical industry, including Fe 2 O 3 NPs have been used to separate and purify gases, liquids, and chemicals. They have also been used to remove contaminants from water ( Pradeep, 2009 ; Siddique and Chow, 2020 ). AgNPs have been used to purify water and remove contaminants ( Pradeep, 2009 ), such as bacteria and viruses. They have also been used to remove heavy metals from water and other substances ( Zhuang and Gentry, 2011 ). AuNPs have been used to purify water and remove contaminants, such as bacteria and viruses ( Siddique and Chow, 2020 ). They have also been used to separate and purify gases and liquids ( Zhuang and Gentry, 2011 ). AlNPs have been used to remove contaminants from water and other substances, such as oils and fuels. They have also been used to purify gases ( Zhuang and Gentry, 2011 ).

6.6. Applications of NPs in defense industry

Nanoparticles (NPs) can be used to improve the efficiency and performance of chemical processing operations, such as refining and synthesizing chemicals ( Schröfel et al., 2014 ). Nanoparticles (NPs) have the potential to be used in the defense industry in several ways, including:

6.6.1. Sensors

Nanoparticles (NPs) can improve the sensitivity and specificity of sensors used in defense systems, such as sensors for detecting chemical, biological, or radiological threats ( Zheng et al., 2010 ).

6.6.2. Protective coatings

Nanoparticles (NPs) can improve the performance and durability of protective coatings applied to defense equipment, such as coatings resistant to chemical or biological agents. For example, metal NPs can improve the mechanical properties and durability of the coating, making it more resistant to wear and corrosion. For example, adding Al or Zn based NPs to a polymer coating can improve its corrosion resistance. In contrast, adding Ni or Cr-based NPs can improve their wear resistance ( Rangel-Olivares et al., 2021 ).

6.6.3. Weapons

Nanoparticles (NPs) are used as weapons against viruses, bacteria, etc, ( Ye et al., 2020 ) and as well as in the development of armor and protective materials. There have been some reports of the potential use of NPs in military and defense applications, such as in the development of armor and protective materials. For example, adding nanoparticles, such as ceramic or metal NPs, to polymers or other materials can improve their mechanical properties and make them more resistant to damage. In addition, there have been reports of the use of NPs in developing sensors and detection systems for defense purposes.

6.6.4. Manufacturing

Nanoparticles (NPs) can improve the performance and durability of materials used in defense equipment, such as armor or structural materials. Metal NPs can be used in materials by adding them as a filler or reinforcement in polymers. For example, the addition of metal NPs such as aluminum (Al), copper (Cu), or nickel (Ni) to polymers can improve the mechanical properties, thermal stability, and electrical conductivity of the resulting composite material ( Khan et al., 2019 ).

Metal NPs can also make functional materials, such as catalysts and sensors. For example, metal NPs, such as gold (Au), and platinum (Pt), can be used as catalysts in various chemical reactions due to their high surface area and ability to adsorb reactants ( Zheng et al., 2010 ).

6.6.5. Energy storage

Nanoparticles (NPs) can improve the performance and efficiency of energy storage systems used in defense systems, such as batteries or fuel cells ( Morsi et al., 2022 ). In batteries, nanoparticles can be used as a cathode material to increase the battery’s energy density, rate capability, and cycling stability. For example, lithium cobalt oxide (LiCoO 2 ) nanoparticles have been used as cathode materials in lithium-ion batteries due to their high capacity and good rate performance. In addition, nanoparticles of transition metal oxides, such as iron oxide (Fe 2 O 3 ), and manganese oxide (MnO 2 ), have been used as cathode materials in rechargeable lithium batteries due to their high capacity and good rate performance. In supercapacitors, nanoparticles can be used as the active material in the electrodes to increase the specific surface area, leading to an increase in the device’s capacitance ( Morsi et al., 2022 ). Using NPs in the defense industry can improve defense systems’ performance, efficiency, and safety.

7. Future perspectives

Metal nanoparticles (NPs) have many potential applications in various fields, including electronics, energy storage, catalysis, and medicine. However, there are also several challenges and potential future directions for developing and using metal NPs.

One major challenge is synthesizing and processing metal NPs with precise size and shape control. Many methods for synthesizing metal NPs involve high temperatures and harsh chemical conditions, which can be challenging to scale up for large-scale production. In addition, the size and shape of metal NPs can significantly impact their properties and potential applications, so it is essential to synthesize NPs with precise size and shape control.

Another challenge is the environmental impact of metal NPs. Some metal NPs, such as silver NPs, can be toxic to aquatic life and may have other environmental impacts. There is a need for more research on the environmental effects of metal NPs and the development of more environmentally friendly (Green) synthesis and processing methods.

In terms of future directions, one promising area is the use of metal NPs for energy storage, conversion, and protection of the environment. For example, metal NPs could be used to improve batteries’ performance or develop more efficient solar cells. In addition, metal NPs could be used in catalysis to improve the efficiency of chemical reactions. There is also ongoing research on metal NPs in medicine, including drug delivery and cancer therapy.

Author contributions

KAA: conceptualization, methodology, validation, formal analysis, investigation, writing – original draft, writing – review and editing, and visualization.

Acknowledgments

The author thanks Prof. Dr. Mona M. Sobhy, Department of Reproductive Diseases, Animal Reproduction Research Institute, ARC, Giza, Egypt, and Dr. Omar Hewedy, University of Guelph, Canada, for the critical reading of the manuscript.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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• Published: 13 August 2024

Untangling the chemical complexity of plastics to improve life cycle outcomes

• Kara Lavender Law   ORCID: orcid.org/0000-0002-5298-6751 1 ,
• Margaret J. Sobkowicz 2 ,
• Michael P. Shaver   ORCID: orcid.org/0000-0002-7152-6750 3 &
• Mark E. Hahn   ORCID: orcid.org/0000-0003-4358-2082 4 , 5  

Nature Reviews Materials ( 2024 ) Cite this article

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A diversity of chemicals are intentionally added to plastics to enhance their properties and aid in manufacture. Yet the accumulated chemical composition of these materials is essentially unknown even to those within the supply chain, let alone to consumers or recyclers. Recent legislated and voluntary commitments to increase recycled content in plastic products highlight the practical challenges wrought by these chemical mixtures, amid growing public concern about the impacts of plastic-associated chemicals on environmental and human health. In this Perspective, we offer guidance for plastics manufacturers to collaborate across sectors and critically assess their use of added chemicals. The ultimate goal is to use fewer and better additives to promote a circular plastics economy with minimal risk to humans and the environment.

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The authors thank S. Trenor and K. Kortsen for feedback on earlier drafts of the manuscript. K.L.L. was supported by March Marine Initiative (a programme of March Limited, Bermuda). M.J.S. acknowledges support from the Donahue Center for Business Ethics and Social Responsibility at the University of Massachusetts Lowell and funding from NSF CAS-MNP grant no. 2304991. M.P.S. thanks the Industrial Strategy Challenge Fund in Smart Sustainable Plastic Packaging (grant NE/V01045X/1). M.E.H. was supported by March Marine Initiative and by the Woods Hole Center for Oceans and Human Health (funding from NIH/NIEHS grant P01ES028938 and NSF grant OCE-1840381).

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Geometric deep learning for molecular property predictions with chemical accuracy across chemical space

• Maarten R. Dobbelaere 1 ,
• István Lengyel 1 , 2 ,
• Christian V. Stevens 3 &
• Kevin M. Van Geem 1  

Journal of Cheminformatics volume  16 , Article number:  99 ( 2024 ) Cite this article

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Chemical engineers heavily rely on precise knowledge of physicochemical properties to model chemical processes. Despite the growing popularity of deep learning, it is only rarely applied for property prediction due to data scarcity and limited accuracy for compounds in industrially-relevant areas of the chemical space. Herein, we present a geometric deep learning framework for predicting gas- and liquid-phase properties based on novel quantum chemical datasets comprising 124,000 molecules. Our findings reveal that the necessity for quantum-chemical information in deep learning models varies significantly depending on the modeled physicochemical property. Specifically, our top-performing geometric model meets the most stringent criteria for “chemically accurate” thermochemistry predictions. We also show that by carefully selecting the appropriate model featurization and evaluating prediction uncertainties, the reliability of the predictions can be strongly enhanced. These insights represent a crucial step towards establishing deep learning as the standard property prediction workflow in both industry and academia.

Scientific contribution

We propose a flexible property prediction tool that can handle two-dimensional and three-dimensional molecular information. A thermochemistry prediction methodology that achieves high-level quantum chemistry accuracy for a broad application range is presented. Trained deep learning models and large novel molecular databases of real-world molecules are provided to offer a directly usable and fast property prediction solution to practitioners.

Introduction

Chemical engineering hinges on accurate understanding of physicochemical properties to effectively model processes, design products, and assess environmental impacts [ 1 , 2 , 3 ]. Since experimental determination of all properties of every chemical compound is practically infeasible, they are typically estimated computationally [ 4 ]. The classical property prediction toolkit comprises, next to quantum chemical calculations, empirical methods such as group contributions. Although various machine learning (ML) approaches have shown higher accuracies and wider application ranges than empirical methods [ 5 , 6 ], they are not yet a standard tool for many researchers.

Industrial experts emphasize the pressing need for faster and more accurate property prediction methods. These methods should consistently predict properties with high accuracy, particularly of chemical structures that are found in various industrial processes, thereby enabling faster decision-making [ 7 , 8 ]. The target accuracy for a computational method is only well-defined for thermochemistry, where “chemical accuracy” (approximately 1 kcal mol −1 ) is demanded to construct thermodynamically consistent kinetic models [ 9 , 10 ]. By extending this definition, it is reasoned that “chemically accurate” octanol–water partitioning coefficients (logK OW ) correspond to errors below 0.7 log units [ 11 ]. On the other hand, relative accuracies are suggested for other thermodynamic properties in analogy to process modeling assessment [ 12 ]. The main limitation of molecular machine learning models is the lack of high-quality data, which hampers their reliability and application range. To overcome challenges related to the low data regime, methods such as transfer learning and \(\Delta \) -ML have increasingly been adopted. In transfer learning, a model is trained on a large database with low-accuracy data and a vast application domain to learn a molecular representation [ 13 , 14 , 15 ]. The knowledge from that model is then “transferred”; that is, the model is trained for a few epochs on a small dataset with highly accurate data to finally obtain a model that can predict with the accuracy of the small dataset for the application range of the large dataset. \(\Delta \) -ML consists of training a model on the residual between high-quality and low-quality data [ 16 , 17 ]. This method is especially effective for quantum chemical data, where a consistent difference exists between high level-of-theory and low level-of-theory data.

Graph neural networks, particularly message-passing neural networks (MPNN), have emerged as the primary model type for property prediction. The neural message-passing framework was introduced in 2017 by Gilmer et al . [ 18 ]. Initially, these models only considered two-dimensional (2D) information with a string-based identifier as the sole input. A molecule is mathematically represented as a graph, with the nodes representing the atoms and the edges representing the bonds. The molecular graph is then converted with a graph-traversing algorithm into a numerical representation, which is the input for a nonlinear regression model, typically a neural network. The algorithm’s core comprises the message-passing phase, where atom representations are iteratively updated using “messages” from neighboring atoms. Yang et al . [ 19 ] adapted this framework to directed MPNNs (D-MPNN) in which messages are related to directed edges rather than nodes. The inclusion of directed edges was motivated to prevent noise in the model training by avoiding unnecessary loops during the message-passing stage. The inclusion of 3D molecular information in a D-MPNN necessitates the handling of DFT-optimized 3D molecular coordinates. Such 3D models fall under the umbrella term geometric deep learning and are reviewed in detail by Atz et al . [ 20 ] and Duval et al . [ 21 ]. There are various approaches to combine 3D information and MPNNs. Biswas et al . [ 22 ] incorporated quantum chemically calculated descriptors in the featurization of nodes and edges of a 2D D-MPNN. Axelrod et al . [ 23 ], on the other hand, utilized a 3D graph with node and edge featurization. Powerful graph neural network interatomic potentials use invariant geometric information, such as radial distances or angles, to learn representations [ 24 , 25 , 26 , 27 , 28 , 29 ]. Individual studies have reported that 3D MPNNs outperform their 2D counterparts on quantum chemical data and in virtual screening tasks [ 23 , 30 ]. However, it remains unclear whether using geometric information in D-MPNNs is a prerequisite to achieving the desired accuracy for compounds and physicochemical properties that are relevant in an industrial setting.

This study introduces a novel tool designed for rapid prediction of physicochemical properties crucial to a wide array of industrial applications. By constructing four new quantum chemical databases comprising over 124,000 molecules relevant to the chemical and pharmaceutical sectors, we ensure applicability across diverse chemical systems. Additionally, we compile 26,000 experimental data points from public databases, covering six key physicochemical properties. Our model, built on the D-MPNN architecture, is capable of handling both 2D and 3D graph representations. We investigate whether incorporating 3D chemical information enhances prediction accuracy significantly. To achieve chemical accuracy across all properties, we employ Δ-ML for thermochemical properties and transfer learning for liquid-phase thermodynamic properties. Extrapolative tests using various data splits and analysis of learning curves are conducted to assess model robustness. Open-source access to the source code, datasets, and optimized models is provided on https://github.com/mrodobbe/chemperium/ for transparency and reproducibility.

Results and discussion

Chemical datasets.

Existing large training and pretraining datasets utilized for physicochemical property prediction serve as benchmarks for algorithm evaluation but lack specific alignment with industrial demands. The molecules targeted for reliable prediction tools vary significantly depending on industry sectors (e.g., base chemicals, pharmaceuticals) and applications (e.g., kinetic modeling, solvent selection). To address this diversity of needs, we have taken into account several criteria in the creation of quantum chemical databases, including molecule size, presence of heteroatoms, and the constituent elements of the molecule. A descriptive evaluation of the composition of the four databases ThermoG3, ThermoCBS, ReagLib20, and DrugLib36 is provided in Fig.  1 .

Overview of the new quantum chemical databases ThermoG3 (yellow), ThermoCBS (blue), ReagLib20 (orange), and DrugLib36 (green). a Heavy atom distribution. b Relationship between logK ow as function of molecular weight for liquid-phase databases. c \(\Delta {\text{H}}_{\text{f},298\text{K}}^{^\circ }\) as function of molecular weight for ThermoG3 and ThermoCBS. d Atom types distribution. e Overlap between the ReagLib20, ThermoG3, and QM9 database. DrugLib36 does not overlap with any of the datasets. f Number of molecules per type, classified by constituent elements

ThermoG3 is a database with quantum chemical properties of 53,550 structures, including radicals, calculated at the B3LYP/6-31G* and the G3MP2B3 levels. ThermoCBS is similar to ThermoG3 but contains 52,837 compounds with properties calculated at the CBS-QB3 level. Compared to the principal quantum chemical benchmark, QM9 [ 31 ], ThermoG3 and ThermoCBS have a greater diversity of chemical species, including radical species, different conformers for several compounds, and molecules with up to 23 heavy atoms (Fig.  1 a and e). These species are representative of detailed kinetic modeling tasks involving renewable feedstocks. In contrast, QM9 comprises molecules up to nine heavy atoms, and 98% of its molecules belong to four classes (HCON, HCO, HCN, and HC), while only 63% of ThermoG3’s and 52% of ThermoCBS’s molecules belong to these classes (Fig.  1 f). As shown in Fig.  1 e, only 3,898 compounds from ThermoG3 and ThermoCBS are found in QM9, making it unique benchmarks for thermochemical property prediction.

ReagLib20 and DrugLib36 are two quantum chemical solvation datasets containing 48 physicochemical properties, constructed using COSMO-RS [ 32 , 33 ] as pretraining sets in the transfer learning tasks. ReagLib20 contains 45,478 organic molecules of biological and industrial relevance, selected from internal databases, and DrugLib36 counts 40,080 organic molecules selected from Enamine’s DDS-50 [ 34 ]. It is illustrated in Fig.  1 a and b that the databases are complementary in terms of molecular size. ReagLib20 is focused on smaller molecules than DrugLib36, with a much greater diversity in heteroatoms (Fig.  1 d and f) in terms of heteroatoms. Hence, ReagLib20 is considered to represent the chemical space of reagent-like molecules, while DrugLib36 covers drug-like molecules.

Experimental data points for six properties (T b , T c , P c , V c , logK OW , logS aq ) of 17,156 chemical compounds are collected from various public sources [ 5 , 22 , 35 ]. All chemical compounds in the experimental database have at least one property listed with an experimental value. There is an imbalance in the distribution of compounds since T b data is mainly available for compounds with up to 12 heavy atoms, while most experimental data points for logK OW and logS aq are for compounds with 12 to 36 heavy atoms. An overview of the data statistics is given in Table S1.

Figure  2 shows the accuracy for six COSMO-RS-calculated properties. For each of the properties, experimental data is available for only a small subset of the molecules. Experimental data for the boiling point and the critical parameters does not overlap with the DrugLib36 dataset, as larger, drug-like compounds will likely decompose before reaching their critical state or even boiling point. The calculated data is especially, but not surprisingly, accurate for the critical volume, and the boiling point, octanol–water partition coefficient, and critical temperature are also in good agreement with experiments. The lower accuracy of logS aq is related to the accuracy of Abraham’s linear free energy relationship [ 36 , 37 ], of which the descriptors are calculated from COSMO-RS \(\sigma \) -moments. T c has the lowest accuracy against experimental data, which might be explained by large experimental uncertainties [ 38 ].

Parity plots showing the agreement between experimental and COSMO-RS calculated data for six properties. The bar plots show the difference between calculated and experimental data

Geometric directed message-passing neural networks

We used directed message-passing neural networks (D-MPNN) to learn the relationship between the molecular structure and a physicochemical property. We based the architecture of the 2D D-MPNN on the methodology described by Yang et al . [ 19 ]. To include the third dimension of molecular information, two different geometric D-MPNNs are created which differ from each other by the initial featurization of nodes and edges. We considered in this work geometric D-MPNNs using 3D graphs that differ from 2D graphs by the incorporation of the xyz-coordinates of the atoms. The first geometric D-MPNN uses the same initial atomic featurization as the 2D D-MPNN, which is a well-documented approach [ 23 , 24 , 30 ]. In the second model, we introduce the atomic radial distribution function (RDF) [ 39 ] as a novel atom featurization for geometric D-MPNNs. RDFs were chosen as an atom descriptor in accordance with the findings from Wojtuch et al . [ 40 ] that information about the atomic neighborhood boosts the predictive performance. In both 3D models, the edges correspond to all atom pairs that are separated from each other by a distance shorter than a cutoff radius \({r}_{C}\) . An illustration of the RDF-featurized geometric D-MPNN is shown in Fig.  3 .

Working principle of the geometric D-MPNN. a Initial atomic featurization by atomic RDFs with maximal radius \({r}_{C}\) . b Directional edges are updated for \(T\) steps using messages from incoming edges within a sphere with radius \({r}_{C}\) . c Atomic representations are created by averaging the incoming edges from covalently bonded atoms. The molecular representation is made by averaging the atomic representations. d A feedforward neural network is used for making a regression between the learned molecular representation and the physicochemical properties

We have trained the two geometric D-MPNNs with \({r}_{C}\) values ranging from 1.5 Å to 3.0 Å on the ThermoG3 dataset. The ground truth data is composed of the residual between the standard enthalpy of formation at 298 K ( \(\Delta {\text{H}}_{\text{f},298\text{ K}}^{^\circ }\) ) values calculated at G3MP2B3 and B3LYP level-of-theory. \(\Delta {\text{H}}_{\text{f},298\text{ K}}^{^\circ }\) is, as a physicochemical property of a molecular conformation, an appropriate property to evaluate the effect of atomic featurization of geometric D-MPNNs on. Since DFT-optimized molecular geometries are used as input, the \(\Delta {\text{H}}_{\text{f},298\text{K}}^{^\circ }\) with DFT quality is calculated without extra cost and can therefore be used as input, validating the choice for \(\Delta \) -learning. A summary of the results is given in Fig.  4 a. The baseline model is a 3D D-MPNN with simple atomic features, in which messages are sent only through edges corresponding to covalent bonds. This baseline model is outperformed by models with the same atomic featurization that use spherical message-passing. Remarkably, the value of the cutoff radius only affects RDF-based models, where large errors are found for \({r}_{C}\) smaller than 1.9 Å. This is explained by the presence of bonds in the molecule with larger bond lengths, such as Si-Cl bonds with a length of 2.08 Å. Indeed, the combination of RDFs with a small cutoff radius and limited spherical message-passing lead to disconnected parts in the molecule. In such case, the complete graph lacks information about that bond and the test set error increases exponentially with the number of unmodeled bonds (see Figure S1). This scenario is not observed with simple atomic features, as these contain information about the atomic neighborhood regardless of the \({r}_{C}\) value. Even at the lowest evaluated \({r}_{C}\) value of 1.5 Å, which is shorter than most covalent bonds, the feature-based model is able to accurately learn a representation of the molecule. Since message-passing is then only performed for the shortest bonds ( e.g., C–H, C≡C, …), the molecular representation will then mainly consist of learned atomic contributions, which appears to be sufficient when using a large training set. It is assumed that the low variance in prediction errors over the \({r}_{C}\) values is due to using averaged predictions with ensemble learning.

Predictive performance for thermochemistry predictions on a random split of test set molecules. a Effect of cutoff radius on the mean absolute error (MAE) and root-mean-squared error (RMSE) using tenfold ensemble models for ThermoG3 \(\Delta {\text{H}}_{\text{f},298\text{ K}}^{^\circ }\) predictions. 3D D-MPNNs are used of which the atoms are featurized with RDFs and simple atomic features. Error bars have been omitted for visual clarity and are given in Figure S2 and S3. b Parity plot and error distribution of B3LYP-calculated (blue) and ML-predicted (yellow) \(\Delta {\text{H}}_{\text{f},298\text{ K}}^{^\circ }\) values against CBS-QB3 calculated data. An optimized tenfold 3D MPNN with RDF featurization and molecular feature descriptor is used for ML predictions. c 95% confidence interval for ensemble when predictions with an uncertainty above a threshold are excluded. d Error distribution of all data (yellow), multi-substituted aromatics (orange), and polyhalogenated compounds (purple) for predictions with an RDF-featurized 3D D-MPNN

An ideal \({r}_{C}\) is found around 2.1 Å and corresponds to a graph with mainly covalent bonds. More details about the uncertainties as function of the cutoff radius are provided in Figure S2 and S3. Increasing the cutoff radius does not lead to a better predictive performance but to a higher computational effort. Therefore, it is recommended to keep \({r}_{C}\) as small as reasonably possible. A similar finding is given in the work by Isert et al . [ 30 ]. Radii above 3.0 Å are not evaluated to ensure sufficient memory during training.

Predictive performance for thermochemistry

To probe what the impact of geometric information inclusion is, we compared the performance of 2D and 3D D-MPNNs for predicting \(\Delta {\text{H}}_{\text{f},298\text{ K}}^{^\circ }\) and \(\Delta {\text{H}}_{\text{f},1000\text{K}}\) directly and via \(\Delta \) -ML. The results are summarized in Table  1 by means of the mean absolute error (MAE) and the root-mean-squared error (RMSE) on a random split test set. There is a difference between the order of magnitude of the errors in the direct prediction models and in the \(\Delta \) -ML models. This discrepancy can be attributed to the output range, which spans over 3,000 kJ/mol for the direct predictions and around 300 kJ/mol for the residual prediction. None of the tested D-MPNNs is capable of reaching “chemical accuracy” in the direct prediction test. Hereby, it is tacitly assumed that the 2D model predicts the value of the lowest-energy conformer. Despite being incapable of distinguishing various conformations, which are present in ThermoG3 and ThermoCBS for thousands of molecules, the 2D models reach a comparable performance with the best 3D models. Additionally, where previous work failed to accurately account for radical species using 2D D-MPNNs [ 41 ], the 2D model in this work is able to do so since hydrogens are explicitly added to the graph.

Indeed, with a 2D model, an RMSE as low as 1.84 kJ/mol is reached on the ThermoCBS dataset. However, the use of \(\Delta \) -ML requires a DFT calculation per se so that the optimized molecular geometry is given without a cost. Since a 2D model is not able to distinguish between various conformations of the same compound, it is not possible to use it for tasks such as conformer search when a workflow is created with a conformer ensemble generation software [ 42 , 43 , 44 ]. In that case, a 2D D-MPNN will predict the same output value for every conformation, while the 3D D-MPNN can differentiate. Moreover, conformer ordering is dependent on the accuracy of the computational method and lower-level-of-theory optimized conformers do not guarantee that the high-level-of-theory minimum energy conformer is found [ 17 ]. The necessity for predictions with high-level-of-theory accuracy motivates the use of geometric \(\Delta \) -ML for thermochemistry tasks.

The \(\Delta {\text{H}}_{\text{f}}\) at temperatures above 298.15 K are calculated using predicted heat capacity values. Only a slight increase in the prediction error is observed at 1000 K as the prediction error is mainly determined by the error on \(\Delta {\text{H}}_{\text{f},298\text{ K}}^{^\circ }\) . The heat capacity values at 45 different temperatures between 298.15 and 1500 K are predicted in a multitask model that also includes \({\text{S}}_{298}\) . NASA polynomials are fitted from the predicted values. These polynomial fits allow to calculate \(\Delta {\text{H}}_{\text{f}}\) , \({\text{S}}_{\text{f}}\) , \({c}_{p}\) , and \(\Delta {\text{G}}_{\text{f}}\) at any temperature between 298.15 and 1500 K. Furthermore, the NASA coefficients allow thermochemistry prediction in CHEMKIN ® input format, and direct integration into reaction network generation and reactor simulation packages [ 45 ]. A complete overview of the prediction accuracies for each model is given in Table S2 to S9.

Figure  4 b depicts the tenfold ensemble performance of the best performing 3D D-MPNN with RDF featurization and molecular feature descriptor using \(\Delta \) -learning, trained on ThermoCBS \(\Delta {\text{H}}_{\text{f},298\text{ K}}^{^\circ }\) . A systematic deviation is noticeable for the low-level-of-theory data, which is larger at the lower end of the value range and smaller for positive \(\Delta {\text{H}}_{\text{f},298\text{ K}}^{^\circ }\) values. This deviation arises from approximations that are made in the lower-level-of-theory quantum chemistry calculations, in this case with B3LYP/6-31G*. Nevertheless, lower-level-of-theory methods are sufficiently accurate to locate the minima on the potential energy surface. That is why higher-level-of-theory quantum chemistry methods ( e.g. G3MP2B3 or CBS-QB3), which take electron correlations into account in more detail, use DFT-optimized molecular geometries. Therefore, the difference in energies is related to local structural features around the atom. The ML models can learn this residual with high accuracy and the data points coincide with the parity line. As such, the \(\Delta \) -ML approach reduces the computational effort to obtain the thermochemistry to the time required for the DFT optimization of the molecular geometry. The DFT optimization and subsequent vibrational frequency calculations of a molecule with more than 15 non-hydrogen atoms require a computational time in the order of 10 3  s on a workstation with 8 central processing units (CPU). The most time-consuming part consists of the sequence of single-point calculations with a high-level-of-theory quantum chemistry method, taking approximately 10 6  s, and is sped up to less than a second using the trained ML models.

Ensemble learning allows for determining uncertainty in the prediction by calculating the standard deviation over the individual model predictions. By selecting a threshold standard deviation, the 95% confidence intervals ( \({\text{u}}_{95}\) ) can be tightened to meet even the most stringent chemical accuracy definition. A detailed overview for the ThermoG3 predictions is given in Fig.  4 c. Selecting an uncertainty threshold of 2 kJ/mol, can lower the \({\text{u}}_{95}\) to 3 kJ/mol, so that only 1% of the remaining values have a test set error above 4.184 kJ/mol. The model confidence can be further increased by lowering the threshold value. The RDF-featurized model appears to be the most reliable one, as it can most effectively remove poor predictions and tighten \({\text{u}}_{95}\) .

Analogies can be drawn between MPNNs and group contribution methods. Essentially, an MPNN implicitly learns to incorporate higher-order group neighborhoods in the message-passing phase and, as such, outperforms traditional second-order group contribution methods, which solely use the additive character in 2D graph information [ 46 ]. Some compound classes that are highly relevant in industrial projects, such as multi-substituted aromatics and polyhalogenated hydrocarbons, are deemed problematic to estimate accurately with group contributions [ 47 , 48 ]. The limitations of the group additivity are then tried to overcome by introducing non-nearest neighbor interactions [ 49 ]. This is a futile job given the immense diversity of aromatic systems [ 50 ]. Figure  4 d shows that 3D D-MPNNs exhibit a comparable performance for multi-substituted aromatics as for the complete test set. Polyhalogenated compounds, which are about 5% of the complete database, have a wider error distribution than the average molecule in the database.

Prediction of solvation and phase transition properties

Thermochemical property prediction benefits of using a gas-phase optimized molecular geometry because of the existing relationship between that structure and the property. For many other physical and chemical properties of a molecule, additional effects on the geometry must also be taken into account, which further increases the computation time. In this work, we have evaluated the predictive performance for six molecular properties using a gas-phase single-conformer geometry. The results of this evaluation are given in Fig.  5 , in which 2D and 3D D-MPNN architectures are evaluated, as well as two learning strategies: direct property prediction and transfer learning by pretraining on the ReagLib20 and DrugLib36 databases. Pretraining is performed for T b and the critical properties by training models on the respective properties in the ReagLib20 database, since these properties are of interest for smaller compounds. Then, these models are fine-tuned on the experimental dataset. The transfer learning procedure for logK OW and logS aq is analogous with the difference that the models are initially trained on the combination of the ReagLib20 and DrugLib36 databases.

RMSE for various D-MPNNs with and without transfer learning tested on experimental T b ( a ), logK OW ( b ), logS aq ( c ), T c ( d ), P c ( e ), and V c data ( f )

The 2D model is superior over 3D models in direct property prediction. This is especially the case for the critical properties of which the experimental data is scarce. However, the improvement with pretraining is statistically insignificant for 2D models as opposed to the 3D models. This might indicate that 3D models need larger data sets to effectively learn structure–property relationships.

Generalizability and extrapolative performance

To understand the model’s reliability for unseen molecules, it is necessary to quantify the accuracy of extrapolative tests. This is done with scaffold-based test splits [ 51 ], where none of the molecules in the training set has the same Bemis-Murcko scaffold [ 52 ] as the molecules in the test set. Because the tested molecules are structurally different from the ones the model has seen, this method allows to assess the generalizability capacities. In Fig.  6 , we show learning curves for the \(\Delta {\text{H}}_{\text{f},298\text{K}}^{^\circ }\) residual and logK OW using the three D-MPNN architectures that have been evaluated throughout this article. In Fig.  6 a and c, the learning curves are determined for random (interpolative) test sets and in Fig.  6 b and d, a scaffold-based split is used. Common for all situations is that the RDF-based 3D D-MPNN has a much larger error than the models that use simple atomic features. This result is in line with the results in Fig.  5 , where the RDF-based model has a lower accuracy for the properties with the smallest training set sizes, namely the directly predicted critical properties.

Learning curves for models trained on the CBS-QB3-B3LYP \(\Delta {\text{H}}_{\text{f},298\text{ K}}\) correction ( a , b ) and experimental logK OW data ( c , d ). The plots show the test set mean absolute error (MAE) against the training set size for random and scaffold-based splits. In plots a and b , the value for RDF-based models trained on 500 data points is omitted for visual reasons

The errors from random splitting are lower than those from the scaffold-based splits, which is an expected result. However, the rate at which the error drops for RDF-based models is in all cases higher than for the models that use simple atomic features. Both the 2D and 3D model appear to learn at a similar rate, whereas the RDF-based model takes better advantage of increased training set sizes. In the logK OW results, the 2D D-MPNN outperforms the 3D D-MPNNs in the entire domain. This is in line with the results in Fig.  5 , where the 2D model outperformed the 3D models in the direct prediction of liquid-phase properties. The logK OW data is still in the limited data range, since the learning curves follow a continuous linear trend. This indicates that adding more data will further improve the model performance. Both in random and scaffold splits, an error below 0.7 log units is obtained for logK OW models trained on only 1,000 data points, which corresponds to the “chemical accuracy” in theoretical chemistry [ 11 ].

The benefit of performing DFT optimization of molecular geometries prior to predicting liquid-phase and critical properties was found to be negligible with the tested D-MPNN models. The utilization of a single gas-phase conformation might even induce an undesired bias since these properties are less sensitive to conformational differences. On the other hand, gas-phase thermochemistry, and in particular energy, strongly depends on the 3D arrangement of atoms. Especially in cases where larger amounts of data are available, an RDF-featurized 3D D-MPNN with ensemble uncertainty increases accuracy and reliability of the predictions. Therefore, the main advantage of geometric models is found in building a sequence of molecular geometry optimization and \(\Delta \) -ML to significantly accelerate gas-phase thermochemistry calculations while maintaining the accuracy of expensive single-point calculations.

Conclusions

In this work, we have focused on the importance of geometric information in directed message-passing neural networks (D-MPNN), and their potential to reach “chemically accurate” property predictions for molecules of industrial interest. To this extent, diverse quantum chemical datasets with more than 124,000 molecules, relevant to chemical and pharmaceutical processes, were developed for training or pretraining machine learning (ML) models. We have found that D-MPNNs are capable of meeting the strictest definition of “chemical accuracy” for \(\Delta {\text{H}}_{\text{f},298\text{K}}^{^\circ }\) predictions by setting up threshold values for the prediction uncertainty. It was shown that only a slight drop in accuracy is witnessed in temperature-dependent thermochemistry predictions up to 1500 K. There are two main arguments for optimizing molecular geometries with DFT before performing ML predictions. Firstly, this enables the use of \(\Delta \) -ML, in which a correction is learned for a low level-of-theory value, and is crucial for obtaining the desired accuracy. Secondly, 2D models cannot be used for conformational search because of their invariance. The use of a novel radial distribution function (RDF) based atomic featurization outperforms the other models on uncertainty quantification and learning rate tests, hence, increasing the predictive reliability in extrapolative tests. However, the benefits of using molecular geometries were not observed for the prediction of liquid-phase and critical properties. In fact, 2D models obtained similar or even better performance compared to 3D models on both inter- and extrapolative testing. One reason might be that a gas-phase geometry insufficiently relates to the desired properties, while the low number of available highly accurate data points might be another reason. In conclusion, we believe that D-MPNNs are ready for use in industrial chemical engineering applications if (1) the model architecture is carefully chosen depending on the application and available data, and (2) the reliability of the predictions is assessed by setting suitable uncertainty thresholds. The property prediction algorithm developed and used in this work is freely accessible at https://github.com/mrodobbe/chemperium/ .

Quantum chemical calculations

The enthalpy of formation of molecules in the ThermoG3 database is computed with the G3MP2B3 method, which is a composite method based on G3 theory [ 53 , 54 ]. The computation sequence starts with geometry optimizations at the B3LYP/6-31G* level. Then, vibrational frequency computations and a sequence of increasing accuracy single-point energy computations are performed. The enthalpy of formation is calculated from the primary data based on atomization energies.

The liquid-phase properties of molecules in the ReagLib20 and DrugLib36 databases are calculated using the commercial software COSMOtherm, which calculates data based on the COSMO-RS theory [ 33 ]. TurboMole [ 55 ] was used to perform geometry optimizations and single-point calculations at BP/TZVP level, followed by COSMO-RS/COSMOtherm calculations for solvent effects. Partition coefficients were calculated using the Abraham QSPR module in COSMOtherm [ 37 ].

NASA polynomials

Thermochemical properties at higher temperatures are calculated using the empirical equations developed by Gordon and McBride [ 56 ]. The equations contain dimensionless coefficients ( \({a}_{1}\) to \({a}_{7}\) ) which can be derived from fitting the heat capacity ( \({c}_{p}\) ) at various temperatures. Equation ( 1 ) is the empirical NASA polynomial for \({c}_{p}\) .

The temperature-dependent enthalpy of formation ( \(\Delta {\text{H}}_{\text{f}}\) ) is obtained via Eq. ( 2 ), so that the NASA polynomial for \(\Delta {\text{H}}_{\text{f}}\) is obtained in Eq. ( 3 ).

The temperature-dependent entropy of formation ( \({S}_{f}\) ) is calculated via Eq. ( 4 ).

Geometric message-passing neural networks

This section describes the proposed geometric MPNN framework, which contains four parts: the initial featurization of a 3D molecular graph, the spherical message-passing phase, the readout phase, and a feedforward neural network. The architecture is depicted in Fig.  3 .

Initial featurization

A molecule with \(n\) atoms is treated as a 3D molecular graph \(\mathcal{G}=(V, E,P)\) . \(V={\left\{{\mathbf{v}}_{i}\right\}}_{i=1:n}\) is the set of node (atom) features with \({\mathbf{v}}_{i}\in {\mathbb{R}}^{{d}_{v}}\) the feature vector for atom \(i\) . \(E={\left\{{\mathbf{e}}_{ij}\right\}}_{j=1:n,k=1:n,k\in \mathcal{N}(j)}\) is the set of edge (bond) features with \({\mathbf{e}}_{ij}\in {\mathbb{R}}^{{d}_{e}}\) the feature vector for the bond between atom \(i\) and atom \(j\) , where \(\mathcal{N}(i)\) denotes the nearest neighboring atoms of atom \(j\) . It holds that \({\mathbf{e}}_{ij}={\mathbf{e}}_{ji}\) . \(P={\left\{{\mathbf{r}}_{i}\right\}}_{i=1:n}\) is the set of three-dimensional coordinates with \({\mathbf{r}}_{i}\in {\mathbb{R}}^{3}\) denoting the x, y, and z-coordinate of atom \(i\) . We compare two different initial atom embeddings \({\mathbf{v}}_{i}\) : the atomic features as implemented in Chemprop [ 57 ] and an atomic radial distribution function. The atomic features consist of the atomic number, the aromaticity (0 or 1), and three one-hot vectors that denote the degree of the atom, the hybridization, and the chirality. The atomic radial distribution function \({g}_{i}(r)\) for atom \(i\) is a convolution of the intramolecular distances around atom \(i\) , and is given in Eq. ( 5 ).

The radial distribution function is defined by the following parameters: \(b\) and \(c\) are respectively the decay position and width, \({m}_{i}\) is the atomic mass of atom \(i\) , \(B\) is the smoothing parameter, \({d}_{ik}\) is the interatomic distance between atoms \(i\) and \(k\) . The values of the parameters are taken from the work of Plehiers et al . [ 17 ]. The distance \(r\) runs from 0.8 Å to \({r}_{C}\) , which is a cutoff distance. The length of the atomic radial distribution function is taken as 100. In this directed MPNN, the directed edge \({\mathbf{e}\mathbf{^{\prime}}}_{ij}\) represents an interatomic distance between two atoms \(i\) and \(j\) , which are not necessarily chemically bonded. A directed edge \({\mathbf{e}\mathbf{^{\prime}}}_{ij}\) is constructed if \({d}_{ij}<{r}_{C}\) , and is defined in Eq. ( 6 ) as the concatenation of the atomic feature vector \({\mathbf{v}}_{i}\) and the edge feature vector \({\mathbf{e}}_{ij}\) . In case atoms \(i\) and \(j\) are not chemically bonded, then \({\mathbf{e}}_{ij}\) is a vector of dimension \({d}_{e}\) consisting of all zeros. This approach can be considered to be a variant on spherical MPNNs, since edges are constructed for all \(j\in \mathcal{U}(i)\) , with \(\mathcal{U}(i)\) the spherical environment (dt: Umgebung ) of atom \(i\) with radius \({r}_{C}\) .

Before starting the message-passing step, the directed edge hidden states are initialized as given by Eq. ( 7 ).

Here, \(\tau \) is the rectified linear unit (ReLU) activation function and \({\mathbf{W}}_{0}\in {\mathbb{R}}^{{d}_{v}+{d}_{e}\times {d}_{h}}\) is a learned weight matrix with \({d}_{h}\) the size of the edge hidden state.

Directional message-passing

The message-passing phase is the first part of the MPNN and operates for \(T\) iterations on the directed 3D molecular graph. In the message-passing phase, information is transmitted through the molecule using message functions. The MPNN updates in iteration \(t\) the edge’s hidden states \({h}_{ij}^{t}\) and messages \({m}_{ij}^{t}\) using message function \({M}_{t}\) and update function \({U}_{t}\) . The updated hidden state \({h}_{ij}^{t+1}\) and message \({m}_{ij}^{t+1}\) are defined in Eqs. ( 8 ) and ( 9 ). \({n}_{i}\) is the number of atoms in the spherical atomic environment of atom \(i\) . \({\mathbf{W}}_{m}\in {\mathbb{R}}^{{d}_{h}\times {d}_{h}}\) is a learned weight matrix.

Readout phase

In the readout phase, a molecular representation is created from the edge hidden states. First, an atomic message \({m}_{i}\) is created by averaging the incoming hidden edges at iteration \(T\) [Eq. ( 10 )]. The atomic representation \({h}_{i}\) is calculated by concatenating the atomic feature vector \({\mathbf{v}}_{i}\) and the atomic message \({m}_{i}\) , multiplying this new vector with a weight matrix \({\mathbf{W}}_{h}\in {\mathbb{R}}^{{d}_{h}\times {d}_{o}}\) and sending it through a ReLU activation function \(\tau \) [Eq. ( 11 )].

A molecular representation \(h\) is obtained by averaging the atomic representations, as shown in Eq. ( 12 ). Generic MPNNs aggregate by summing edge hidden states and atomic representations, but in agreement to the findings of Isert et al . [ 30 ] an averaging operation is used to prevent exploding gradients. The learned molecular representation \(h\) is used as input for a feedforward neural network.

Hyperparameter optimization and training details

The model is written using the Python deep learning library Keras (version 2.15) [ 58 ], as implemented in TensorFlow (version 2.15) [ 59 ]. The training is performed on NVIDIA V100 GPUs. Hyperparameters were optimized using the Hyperband optimizer [ 60 ] in Keras-Tuner and a fixed set of hyperparameters is chosen that performs well for the various model configurations and datasets. The size of the edge hidden states \({d}_{h}\) is 512 and the size of the molecular representation \({d}_{o}\) is 256. The message-passing iteration depth \(T\) equals 6. A feedforward neural network with 5 layers and hidden layers size 500 was used. The layers have a bias and are connected with Leaky ReLU activation functions. The weights and biases are initialized using the Glorot initialization scheme [ 61 ]. To avoid memory problems, a batch size of 16 was used. The neural network learning is performed with an Adam optimizer using an exponentially decaying learning rate schedule [ 62 ].

All model comparisons are made on a single trained model. The optimized performances are given based on the performance of a tenfold model ensemble. Ensemble learning is a common technique in literature to improve model performance by training independent models and averaging their predictions. The averaged predictions of the ten models is used as the final prediction value and the standard deviation on the predictions is used as an uncertainty estimate.

Availability of data and materials

The four quantum chemical datasets generated in this work (ThermoG3, ThermoCBS, DrugLib36, ReagLib20) and the experimental dataset can be downloaded from Zenodo ( https://www.doi.org/ https://doi.org/10.5281/zenodo.11409710 ). The entire source code is provided as open-source software under MIT license in the following repository: https://www.github.com/mrodobbe/chemperium . All conclusions from the paper can be reproduced using the provided scripts. A demo notebook is available in the folder notebooks/demo.ipynb.

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Acknowledgements

Maarten Dobbelaere acknowledges financial support from the Research Foundation—Flanders (FWO) through doctoral fellowship Grant 1S45522N. The authors acknowledge funding from the European Research Council under the European Union's Horizon 2020 Research and Innovation Programme/ERC Grant agreement No 818607. This project has received funding from the European Union’s Horizon Europe Research and Innovation Programme under Grant agreement No 101057816. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation—Flanders (FWO) and the Flemish Government—department EWI.

Maarten Dobbelaere acknowledges financial support from the Research Foundation—Flanders (FWO) through doctoral fellowship Grant 1S45522N. The authors acknowledge funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme/ERC Grant agreement No 818607. This project has received funding from the European Union’s Horizon Europe Research and Innovation Programme under Grant agreement No 101057816. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation—Flanders (FWO) and the Flemish Government—department EWI.

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Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Faculty of Engineering and Architecture, Ghent University, Technologiepark 125, 9052, Ghent, Belgium

Maarten R. Dobbelaere, István Lengyel & Kevin M. Van Geem

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M.R.D. conceived the study, developed the software, trained the models, analyzed the results, and wrote the initial manuscript. I.L. compiled the databases and provided support in analyzing the results. C.V.S. and K.M.V.G. supervised the study. All authors contributed to writing and editing the manuscript.

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Dobbelaere, M.R., Lengyel, I., Stevens, C.V. et al. Geometric deep learning for molecular property predictions with chemical accuracy across chemical space. J Cheminform 16 , 99 (2024). https://doi.org/10.1186/s13321-024-00895-0

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“bottom-up” and “top-down” strategies toward strong cellulose-based materials.

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a Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, International Innovation Center for Forest Chemicals and Materials, College of Materials Science and Engineering, Nanjing Forestry University, Nanjing 210037, China E-mail: [email protected] , [email protected]

b State Key Laboratory of New Textile Materials and Advanced Processing Technologies, School of Textile Science and Engineering, Wuhan Textile University, Wuhan 430200, China E-mail: [email protected]

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Cellulose, as the most abundant natural polymer on Earth, has long captured researchers' attention due to its high strength and modulus. Nevertheless, transferring its exceptional mechanical properties to macroscopic 2D and 3D materials poses numerous challenges. This review provides an overview of the research progress in the development of strong cellulose-based materials using both the “bottom-up” and “top-down” approaches. In the “bottom-up” strategy, various forms of regenerated cellulose-based materials and nanocellulose-based high-strength materials assembled by different methods are discussed. Under the “top-down” approach, the focus is on the development of reinforced cellulose-based materials derived from wood, bamboo, rattan and straw. Furthermore, a brief overview of the potential applications fordifferent types of strong cellulose-based materials is given, followed by a concise discussion on future directions.

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Fabrication and Characterization of Citric Acid Crosslinked, Sea Buckthorn Leaves Extract Incorporated PVA-Based Films with Improved Antioxidative and UV-Shielding Properties for Food Packaging Applications

• Research Article
• Published: 13 August 2024

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• Akbar Ali   ORCID: orcid.org/0000-0003-2934-6286 1 ,
• Mubaraka Banoo 1 ,
• Hakima Banoo 1 &
• Gh. Ali 1  

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The present work demonstrates the fabrication and characterization of bioactive, citric acid (CA) crosslinked polyvinyl alcohol (PVA) film containing sea buckthorn leaves extract (SBLE) and evaluated for its various functional properties. Crosslinking produced highly stable and water-swellable films that were transparent, homogenous, and flexible, demonstrating the compatibility of CA with PVA. The incorporation of SBLE significantly enhanced various functional properties. The chemical structure and interaction among the different components of PVA films were analyzed by using ATR-FTIR spectroscopy. Total phenolic content in the SBLE was found to be 41.20 ± 0.54 mg GAE/g and the yield varies between 46 and 54% based on the dry mass of the dried leaves. SBLE-loaded films exhibit potent antioxidant activity due to the free radical scavenging ability of polyphenolic compounds in the extract, with a maximum value of 66.09% at 6% SBLE. These films also exhibit strong UV-blocking character with utmost transparency. The biodegradability is also enhanced with the addition of SBLE, with around 89% of the film degrading in 35 weeks. Thus, such films containing natural extract could be a potential material for food packaging applications.

• Citric acid crosslinked PVA-based water-insoluble films.

• Citric acid as nontoxic, greener crosslinker.

• Sea buckthorn leaves extract.

• Sea buckthorn leaves extract containing antioxidative films.

• Biodegradability of Sea buckthorn leaves extract containing films.

• PVA based films having UV-blocking property.

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Acknowledgements

All the authors acknowledged University of Ladakh for providing the necessary infrastructure facilities.

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Akbar Ali, Mubaraka Banoo, Hakima Banoo & Gh. Ali

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Akbar Ali: conceptualization, data curation, methodology, formal analysis, investigation, supervision, writing, and review of original draft. Mubaraka Banoo: methodology, formal analysis, investigation, writing, and review. Hakima Banoo: methodology, formal analysis, investigation, writing, and review. Gh. Ali: investigation, writing, and review.

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Ali, A., Banoo, M., Banoo, H. et al. Fabrication and Characterization of Citric Acid Crosslinked, Sea Buckthorn Leaves Extract Incorporated PVA-Based Films with Improved Antioxidative and UV-Shielding Properties for Food Packaging Applications. J Package Technol Res (2024). https://doi.org/10.1007/s41783-024-00174-1

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DOI : https://doi.org/10.1007/s41783-024-00174-1

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MINNEAPOLIS / ST. PAUL (8/13/2024) – Regents Professor Lawrence “Larry” Que Jr. retired from the Department of Chemistry on May 26th, 2024, after serving the University of Minnesota for more than four decades. Que’s tremendous impact in the field of bioinorganic chemistry earned him the title of Regents Professor in 2009 and election to the National Academy of Sciences in 2022. 

The early years

Born and raised in Manila, Philippines, Que’s chemistry career began in the undergraduate chemistry program at the Ateneo de Manila University in Quezon City, Philippines. He earned his bachelor’s degree 1969 before arriving at the University of Minnesota to continue his chemistry education in the PhD program, which he completed in 1973. During his PhD, Que was advised by Louis H. Pignolet. Throughout his doctoral studies, Que used NMR spectroscopy to research intramolecular rearrangement reactions of transition metal complexes. He went on to conduct postdoctoral research under Professor Richard H. Holm at the Massachusetts Institute of Technology (1973-74) and under Professor Eckard Münck at the University of Minnesota (1975-77) which set the stage for his lifelong career in bioinorganic chemistry.

With his affinity for and expertise in iron chemistry firmly in place by 1977, Que started his independent career as an Assistant Professor of Chemistry at Cornell University. While at Cornell, Que utilized Resonance Raman spectroscopy to study dioxygenases; these enzymes would ultimately form the bedrock of Que’s research for the next 40 years. 

Return to Minnesota

In 1983, Que returned to the University of Minnesota, this time as a member of the faculty.  “Returning to Minnesota was the best decision I ever made for my career,” Que says. “I fell in love with this department during graduate school. I was very happy to have the opportunity to return, it’s been an honor to contribute to building our program for the last four decades.”

Described in more than 550 publications, Que’s research spanned the subfields of stereochemistry, catalysis, and crystallography. He established himself as an expert and innovator in bioinorganic chemistry, playing a pioneering role in understanding the function that nonheme iron centers play in dioxygen activation in biology. His work produced the first synthetic models for high-valent iron-oxo intermediates, which are crucial for understanding the electronic structures, spectroscopic properties, and reactivities of these units. Additionally, Que led efforts to create functional models for various iron oxygenases, including catechol dioxygenases, α-ketoglutarate-dependent oxygenases, and cis-dihydroxylating arene dioxygenases. These chemical tools aim to perform two specific types of chemical reactions that enable stereospecific alkane hydroxylation and highly enantioselective olefin cis-dihydroxylation. These advancements could lead to more environmentally friendly and sustainable alternatives to current heavy-metal oxidation catalysts. Que’s key dioxygen discoveries were published in ACS  Chemical Reviews in 2004, in an article titled  “Dioxygen Activation at Mononuclear Nonheme Iron Active Sites:  Enzymes, Models, and Intermediates ;” this paper would turn out to be the most cited work of his career. His prolific research portfolio garnered invitations to present more than 400 lectures around the world. 

Over the years, Que’s research group focused on the topics of iron, oxygen, and biocatalysis in the area of bioinorganic chemistry, The group’s primary effort, involving a combination of biochemical, synthetic inorganic, and spectroscopic approaches, was aimed at elucidating the oxygen activation mechanisms of nonheme iron enzymes, designing functional models for such enzymes, trapping and characterizing reaction intermediates, and developing bio-inspired oxidation catalysts for green chemistry applications. Que advised 55 graduate students and 80 postdoctoral researchers over the course of his career. Over 50 Que Group alumni have tenure-track or tenured faculty positions in colleges or universities.

Que’s critical dioxygen research earned him the title of  Regents Professor in 2009. A quote from the citation for the award reads “Undoubtedly, Professor Que is currently the top bioinorganic chemist in the world. In his chosen field, oxygen activation of iron-containing enzymes and biomimetic compounds, his group, in my estimation, is at least three years ahead of his closest competitors. Almost single-handedly he has developed the major fraction of the synthetic chemistry of iron in high-oxidation states. This chemistry is vital to our understanding of many processes in biochemistry, to the development of new drugs, and most importantly, to developing a green chemistry that can alleviate the problems caused by pollutants and pathogens that afflict human health." The Regents Professorship is the highest honor the University of Minnesota bestows on its faculty. The title recognizes faculty who have made exceptional contributions to the University through teaching, research, scholarship, or creative work, and contributions to the public good.

Beyond his research success, Que demonstrated significant commitment to service to the University across his career. He is credited with establishing the University of Minnesota as a world-renowned center of excellence in bioinorganic chemistry. He organized the International Conference on Oxygen Intermediates in Nonheme Metallobiochemistry (1996) and the Ninth International Conference on Biological Inorganic Chemistry (1999). From 1999 to 2002 – and again from 2008 - 2012 – he served as the inaugural PI on the National Institutes of Health  Chemistry-Biology Interface Training Grant that brings faculty and students from various departments together. He also led the effort to establish the University of Minnesota Center for Metals in Biocatalysis, which allowed faculty and students from multiple units to collaborate in exploring the roles of metals in biology.

Que was the first editor-in-chief of the Springer  Journal of Biological Inorganic Chemistry (JBIC)  and served the journal for 20 years .  JBIC – the official journal of the Society of Biological Inorganic Chemistry since 1996 – is a peer-reviewed journal promoting the field of biological inorganic chemistry internationally. The publication aims to provide insight into systems of metals in biology at biochemical, molecular, and cellular levels.

For his research, service, and mentorship, Que has been honored with many awards over the course of his career. These honors include the 3M/Alumni Distinguished Professorship (1999), the National Institutes of Health MERIT Award (2000), the UMN Distinguished Teaching Professorship (2000), the Royal Society of Chemistry Inorganic Reaction Mechanisms Award (2011), and the American Chemical Society Award in Inorganic Chemistry (2017). He was also elected a fellow of the American Association for the Advancement of Science in 2001, a fellow of the Royal Society of Chemistry in 2008, and a fellow of the American Chemical Society in 2011. In 2022, Que was elected to the National Academy of Sciences. Membership in the NAS is one of the highest honors given to a scientist or engineer.

LQ Fest: 40 Years of Fun with Iron Chemistry at the University of Minnesota

In July 2023, the Department of Chemistry hosted LQ Fest: 40 Years of Fun with Iron Chemistry at the University of Minnesota in honor of Que. 19 of Que’s collaborators, mentees, and even his daughter, Emily Que – who is an Associate Professor of Chemistry at the University of Texas at Austin – presented lectures, stories, and memories related to Que’s research and career. When recalling the event he said “I’ve loved my job. I’ve loved the people that I’ve worked with. I spent the last 50 years of my life dedicated to chemistry, and I never looked back. The event was a wonderful opportunity to get together with many people that I’ve cared about to celebrate my career.” 

The next chapter

“Nothing changes, really. I’ve always thought about chemistry, and I’ll continue to think about chemistry all the time,” Que said. In this next chapter of life, Que is adopting a  come what may attitude. He says he is looking forward to spending more time with his grandchildren in Texas and embarking on new adventures with his wife. 

Memories and Notes from the Chemistry Faculty

"Larry is an extraordinary scientist who has made incredible discoveries in the field of bioinorganic chemistry that have changed the way we think about how important iron-containing enzymes work. His passion for research is unparalleled, and his infectious enthusiasm has made a difference in the lives of many students over his career. His leadership was critical in making UMN a respected centerpiece of high quality bioinorganic chemistry research and teaching, well-known across the globe. On a personal level, I am deeply grateful for his mentorship during my career at the University of Minnesota; his insights and advice made a major difference in my life! Thank you, Larry, and congratulations on your retirement!" – Professor Bill Tolman, Dean, College of Arts & Sciences, St. Thomas University

"The University of Minnesota Department of Chemistry has been lucky to count Prof. Larry Que among our faculty. His chemical creativity and passion have had a big impact on bioinorganic chemistry as a field, and his thoughtfulness as a colleague has had a major impact on our department community." – Professor Christy Haynes, Chemistry Department Head

"Larry has been a standout in the bioinorganic community, rising to the highest levels of academic achievement at the University of Minnesota as a Regent’s professor, and nationally, as a member of the National Academy of Sciences.  I have always appreciated his scholarly approach to studying catalysis at the fundamental level for connecting with biology.  When I joined the department in 2012, starting my lab’s research program in chemical biology, I always loved hearing from the outside community of what high regard they held for him as a giant in the field of iron-mediated (bio)catalysis.  I also benefited significantly from Larry’s effort for initiating our NIH T32 Chemistry and  Biology Interface training grant (CBITG), for which he served as the first director, and established a trajectory of continual funding for the next 25 years.  This grant has impacted the careers of well over 100 graduate students, and has been a true gem of the three departments of Chemistry, Biochemistry, Molecular Biology, and Biophysics (BMBB) and Medicinal Chemistry.  Beyond being a true iron man in his field, one of Larry’s main legacies is a long track record of highly successful trainees, which was on full display at last year’s retirement party, Larry Que Fest. He’s leaving our department having made an indelible mark and will be deeply missed. " – Professor William C.K. Pomerantz

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