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2022 A/L Chemistry Past Paper | Sinhala Medium

Gce advanced level 2022 chemistry sinhala medium paper quick download.

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GCE Advanced Level Chemistry Past Paper 2022 in Sinhala Medium

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2022 A/L Chemistry Past Paper in Sinhala Medium. 2022(2023) G.C.E. Advanced Level Exam Chemistry Past Papers PDF Free Download. 2022 A/L Rasayan Vidyawa Paper Part 1 MCQ and Part 2 Sinhala medium. අ.පො.ස උසස් පෙළ 2022 රසායන විද්‍යාව ප්‍රශ්න පත්‍ර. A.Po.Sa Usas Pela Prashna Pathra. General Certificate of Education Advanced Level (GCE A/L) Examination 2022 Past Paper in Sinhala Medium.

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Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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• Published: 08 May 2024

Accurate structure prediction of biomolecular interactions with AlphaFold 3

• Josh Abramson   ORCID: orcid.org/0009-0000-3496-6952 1   na1 ,
• Jonas Adler   ORCID: orcid.org/0000-0001-9928-3407 1   na1 ,
• Jack Dunger 1   na1 ,
• Richard Evans   ORCID: orcid.org/0000-0003-4675-8469 1   na1 ,
• Tim Green   ORCID: orcid.org/0000-0002-3227-1505 1   na1 ,
• Alexander Pritzel   ORCID: orcid.org/0000-0002-4233-9040 1   na1 ,
• Olaf Ronneberger   ORCID: orcid.org/0000-0002-4266-1515 1   na1 ,
• Lindsay Willmore   ORCID: orcid.org/0000-0003-4314-0778 1   na1 ,
• Andrew J. Ballard   ORCID: orcid.org/0000-0003-4956-5304 1 ,
• Joshua Bambrick   ORCID: orcid.org/0009-0003-3908-0722 2 ,
• Sebastian W. Bodenstein 1 ,
• David A. Evans 1 ,
• Chia-Chun Hung   ORCID: orcid.org/0000-0002-5264-9165 2 ,
• Michael O’Neill 1 ,
• David Reiman   ORCID: orcid.org/0000-0002-1605-7197 1 ,
• Kathryn Tunyasuvunakool   ORCID: orcid.org/0000-0002-8594-1074 1 ,
• Zachary Wu   ORCID: orcid.org/0000-0003-2429-9812 1 ,
• Akvilė Žemgulytė 1 ,
• Eirini Arvaniti 3 ,
• Charles Beattie   ORCID: orcid.org/0000-0003-1840-054X 3 ,
• Ottavia Bertolli   ORCID: orcid.org/0000-0001-8578-3216 3 ,
• Alex Bridgland 3 ,
• Alexey Cherepanov   ORCID: orcid.org/0000-0002-5227-0622 4 ,
• Miles Congreve 4 ,
• Alexander I. Cowen-Rivers 3 ,
• Andrew Cowie   ORCID: orcid.org/0000-0002-4491-1434 3 ,
• Michael Figurnov   ORCID: orcid.org/0000-0003-1386-8741 3 ,
• Fabian B. Fuchs 3 ,
• Hannah Gladman 3 ,
• Rishub Jain 3 ,
• Yousuf A. Khan   ORCID: orcid.org/0000-0003-0201-2796 3 ,
• Caroline M. R. Low 4 ,
• Kuba Perlin 3 ,
• Anna Potapenko 3 ,
• Pascal Savy 4 ,
• Sukhdeep Singh 3 ,
• Adrian Stecula   ORCID: orcid.org/0000-0001-6914-6743 4 ,
• Ashok Thillaisundaram 3 ,
• Catherine Tong   ORCID: orcid.org/0000-0001-7570-4801 4 ,
• Sergei Yakneen   ORCID: orcid.org/0000-0001-7827-9839 4 ,
• Ellen D. Zhong   ORCID: orcid.org/0000-0001-6345-1907 3 ,
• Michal Zielinski 3 ,
• Augustin Žídek   ORCID: orcid.org/0000-0002-0748-9684 3 ,
• Victor Bapst 1   na2 ,
• Pushmeet Kohli   ORCID: orcid.org/0000-0002-7466-7997 1   na2 ,
• Max Jaderberg   ORCID: orcid.org/0000-0002-9033-2695 2   na2 ,
• Demis Hassabis   ORCID: orcid.org/0000-0003-2812-9917 1 , 2   na2 &
• John M. Jumper   ORCID: orcid.org/0000-0001-6169-6580 1   na2  

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• Drug discovery
• Machine learning
• Protein structure predictions
• Structural biology

The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design 2–6 . In this paper, we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture, which is capable of joint structure prediction of complexes including proteins, nucleic acids, small molecules, ions, and modified residues. The new AlphaFold model demonstrates significantly improved accuracy over many previous specialised tools: far greater accuracy on protein-ligand interactions than state of the art docking tools, much higher accuracy on protein-nucleic acid interactions than nucleic-acid-specific predictors, and significantly higher antibody-antigen prediction accuracy than AlphaFold-Multimer v2.3 7,8 . Together these results show that high accuracy modelling across biomolecular space is possible within a single unified deep learning framework.

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Author information.

These authors contributed equally: Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore

These authors jointly supervised this work: Victor Bapst, Pushmeet Kohli, Max Jaderberg, Demis Hassabis, John M. Jumper

Authors and Affiliations

Core Contributor, Google DeepMind, London, UK

Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J. Ballard, Sebastian W. Bodenstein, David A. Evans, Michael O’Neill, David Reiman, Kathryn Tunyasuvunakool, Zachary Wu, Akvilė Žemgulytė, Victor Bapst, Pushmeet Kohli, Demis Hassabis & John M. Jumper

Core Contributor, Isomorphic Labs, London, UK

Joshua Bambrick, Chia-Chun Hung, Max Jaderberg & Demis Hassabis

Google DeepMind, London, UK

Eirini Arvaniti, Charles Beattie, Ottavia Bertolli, Alex Bridgland, Alexander I. Cowen-Rivers, Andrew Cowie, Michael Figurnov, Fabian B. Fuchs, Hannah Gladman, Rishub Jain, Yousuf A. Khan, Kuba Perlin, Anna Potapenko, Sukhdeep Singh, Ashok Thillaisundaram, Ellen D. Zhong, Michal Zielinski & Augustin Žídek

Isomorphic Labs, London, UK

Alexey Cherepanov, Miles Congreve, Caroline M. R. Low, Pascal Savy, Adrian Stecula, Catherine Tong & Sergei Yakneen

You can also search for this author in PubMed   Google Scholar

Corresponding authors

Correspondence to Max Jaderberg , Demis Hassabis or John M. Jumper .

Supplementary information

Supplementary information.

This Supplementary Information file contains the following 9 sections: (1) Notation; (2) Data pipeline; (3) Model architecture; (4) Auxiliary heads; (5) Training and inference; (6) Evaluation; (7) Differences to AlphaFold2 and AlphaFold-Multimer; (8) Supplemental Results; and (9) Appendix: CCD Code and PDB ID tables.

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Abramson, J., Adler, J., Dunger, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature (2024). https://doi.org/10.1038/s41586-024-07487-w

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Received : 19 December 2023

Accepted : 29 April 2024

Published : 08 May 2024

DOI : https://doi.org/10.1038/s41586-024-07487-w

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Journal of Materials Chemistry A

Synergistic pinning effect in layer-structured oxide cathode for enhancing stability towards potassium-ion batteries.

Manganese based layer-structured transition-metal oxides are highly regarded as excellent cathode materials for potassium-ion batteries (KIBs) for their excellent industrial processing feasibility and high voltage platform. However, the energy density and cycling lifetime cannot simultaneously satisfy the basic requirements of the market for energy storage systems. One of the primary causes results from the complex structural transformation and transition metal migration during the potassium intercalation/deintercalation process. Constructing a low-strain host with minimal lattice volume change, maintaining the Mn 3+/ Mn 4+ valence state balance and suppressing the Jahn-Teller effect has emerged as an excellent approach to improving structural and electrochemical stability. A P3-K 0.5 Mn 0.87 Co 0.05 Fe 0.05 Li 0.03 O 2 was synthesized and employed as cathode material. It effectively preserves the orbital and electronic integrity of octahedral center metal elements, mitigating the Jahn-Teller distortion caused by Mn 3+ and stabilizing the lamellar structure by suppressing harmful phase transitions. Excellent rate performance over a wide voltage range of 1.5 to 4.0 V was exhibited. It has initial specific capacities of 107.1 and 100.87 mAh g -1 at 20 and 50 mA g -1 , with capacity retentions of 82.3% and 99.2% after 100 cycles, respectively. Even at the high current densities of 200 mA g -1 and 500 mA g -1 , the initial capacity retention reached 88.5% and 74.56% after 300 and 900 cycles, respectively.

• This article is part of the themed collection: Journal of Materials Chemistry A HOT Papers

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X. Gao, S. Wang, Q. Li, R. Yang, Z. Liu and W. Luo, J. Mater. Chem. A , 2024, Accepted Manuscript , DOI: 10.1039/D4TA02504K

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