Machine learning-assisted screening and prediction of bifunctional OER/ORR single-atom catalysts for non-metal-doped C2N
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Electrocatalysts for Energy Conversion · Machine Learning in Materials Science · Ammonia Synthesis and Nitrogen Reduction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (51)
Show source-provided references
1. Arico · Nanostructured Materials for Advanced Energy Conversion and Storage Devices · 2005 · 4 · 366
2. Chu · Opportunities and Challenges for a Sustainable Energy Future · 2012 · 488 · 294
3. Gupta · Bifunctional Perovskite Oxide Catalysts for Oxygen Reduction and Evolution in Alkaline media · 2016 · 11 · 10
4. Li · Single Cobalt Atoms Anchored on Porous N-Doped Graphene with Dual Reaction Sites for Efficient Fenton-like Catalysis · 2018 · 140 · 12469
5. Fei · General Synthesis and Definitive Structural Identification of MN4C4 Single-Atom Catalysts with Tunable Electrocatalytic Activities · 2018 · 1 · 63
6. Liu · High Performance Platinum Single Atom Electrocatalyst for Oxygen Reduction Reaction · 2017 · 8 · 15938
7. 2D materials and heterostructures for photocatalytic water-splitting
8. Zhang · Sun, Mo 2 B 2 MBene-Supported Single-Atom Catalysts as Bifunctional HER/OER and OER/ORR Electrocatalysts · 2021 · 9 · 433
9. Active sites engineering leads to exceptional ORR and OER bifunctionality in P
10. Ying · Unravelling the Origin of Bifunctional OER/ORR Activity for Single-Atom Catalysts Supported on C 2 N by DFT and Machine Learning · 2021 · 9 · 16860
11. Mahmood · Nitrogenated Holey Two-Dimensional Structures · 2015 · 6 · 6486
12. Guan · Tunable Structural, Electronic, and Optical Properties of Layered Two-Dimensional C2N and MoS2 Van Der Waals Heterostructure as Photovoltaic Material · 2017 · 121 · 3654
13. A. Bafekry, C. Stampfl, M. Ghergherehchi, S.F.J.C. Shayesteh, A first-principles study of the effects of atom impurities, defects, strain, electric field and layer thickness on the electronic and magnetic properties of the C2N nanosheet, 157 (2020) 371-384.10.1016/j.carbon.2019.10.038 ↗
14. O’Connor · Interaction Trends between Single Metal Atoms and Oxide Supports Identified with Density Functional Theory and Statistical Learning · 2018 · 1 · 531
15. Li · PtRu Alloy Nanoparticles Embedded on C2N Nanosheets for Efficient Hydrogen Evolution Reaction in both Acidic and Alkaline Solutions · 2022 · 428
16. Du · Modulation of the Band Structures and Optical Properties of Holey C 2 N Nanosheets by Alloying with Group IV and V Elements · 2016 · 4 · 9294
17. Wu · Data-Driven High-Throughput Rational Design of Double-Atom Catalysts for Oxygen Evolution and Reduction · 2022 · 32
18. Feng · X.j.j.o.m.c.a. Zhang, Data-Driven Design of Dual-Metal-Site Catalysts for the Electrochemical Carbon Dioxide Reduction Reaction · 2022 · 10 · 18803
19. Chen · Applying Machine Learning to Rechargeable Batteries: from the Microscale to the Macroscale · 2021 · 133 · 24558
20. Gao · Data-Driven Insight into the Reductive Stability of Ion–solvent Complexes in Lithium Battery Electrolytes · 2023 · 145 · 23764
21. Wan · Machine-Learning-Accelerated Catalytic Activity Predictions of Transition Metal Phthalocyanine Dual-Metal-Site Catalysts for CO2 Reduction · 2021 · 12 · 6111
22. Chen · Machine Learning: Accelerating Materials Development for Energy Storage and Conversion · 2020 · 2 · 553
23. Tran · Active Learning across Intermetallics to Guide Discovery of Electrocatalysts for CO2 Reduction and H2 Evolution · 2018 · 1 · 696
24. Zhu · Wu, Accelerating the Design of Catalysts for CO2 Electroreduction to HCOOH: A Data-Driven DFT-ML Screening of Dual Atom Catalysts · 2024 · 99 · 627
25. Kresse · Efficient Iterative Schemes for Ab Initio Total-Energy Calculations Using a Plane-Wave Basis Set · 1996 · 54 · 11169
26. Kresse · Efficiency of Ab-Initio Total Energy Calculations for Metals and Semiconductors Using a Plane-Wave Basis Set · 1996 · 6 · 15
27. J.P. Perdew, K. Burke, M.J.P.R.L. Ernzerhof, Perdew, burke, and ernzerhof reply, 80 (1998) 891.10.1103/PhysRevLett.80.891 ↗
28. S. Grimme, J. Antony, S. Ehrlich, H.J.T.J.o.c.p. Krieg, A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu, 132 (2010) 154104.10.1063/1.3382344 ↗
29. Martyna · Nosé-Hoover Chains: the Canonical Ensemble via Continuous Dynamics · 1992 · 97 · 2635
30. Y. Li, P. Ren, X. Lu, J. Zhang, P. Yang, X. Yang, G. Wang, A. Liu, G. Wu, M.J.S. An, Elucidating the role of P on Mn‐and N‐doped graphene catalysts in promoting oxygen reduction: Density functional theory studies, 3 (2023) 390-401.10.1002/sus2.127 ↗
31. Ikeda · Carbon Alloy Catalysts: Active Sites for Oxygen Reduction Reaction · 2008 · 112 · 14706
32. Active sites and mechanisms for oxygen reduction reaction on nitrogen-doped carbon alloy catalysts
33. Holby · Linking Structure to Function: the Search for Active Sites in Non-Platinum Group Metal Oxygen Reduction Reaction Catalysts · 2016 · 29 · 54
34. Nørskov · Origin of the Overpotential for Oxygen Reduction at a Fuel-Cell Cathode · 2004 · 108 · 17886
35. Man · Universality in Oxygen Evolution Electrocatalysis on Oxide Surfaces · 2011 · 3 · 1159
36. Ma · Y. Zhu, A Strategy of Enhancing the Photoactivity of g-C3N4 via Doping of Nonmetal Elements: a First-Principles Study · 2012 · 116 · 23485
37. Wei · Nonmetal-Doped C2N Nanosheets for Removal of Methoxyphenols: a First-Principles Study · 2020 · 4 · 478
38. Kulkarni · Nørskov, Understanding Catalytic Activity Trends in the Oxygen Reduction Reaction · 2018 · 118 · 2302
39. Guo · Simultaneously Achieving High Activity and Selectivity toward Two-Electron O2 Electroreduction: the Power of Single-Atom Catalysts · 2019 · 9 · 11042
40. Rossmeisl · Electrolysis of Water on Oxide Surfaces · 2007 · 607 · 83
41. Liu · Enhancing the D/p-Band Center Proximity with Amorphous-Crystalline Interface Coupling for Boosted pH-Robust Water Electrolysis · 2023 · 13
42. Qiu · Tuning p-Band Center of Carbon Nitride Homojunction Photocatalysts through Strain Effect for Solar-Driven H2 Production · 2023 · 8 · 4173
43. Wang · Tuning p-Band Centers and Interfacial Built-in Electric Field of Heterostructure Catalysts to Expedite Bidirectional Sulfur Redox for High-Performance Li–s Batteries · 2024 · 34
44. Wiedemaier · Influence of Charge Delocalization on Manganese Catalyzed Transfer Hydrogenation · 2024 · 16
45. B. Yu, Z. Yao, Z. Cheng, Y. Jiang, H. Yang, Z. Wang, G. Jia, K. Wang, M. Song, Y.J.J.o.t.A.C.S. Li, Confined Assembly of Well-Defined Single-Unit Chain with Charge Delocalization for Boosting Catalysis, (2025) 34647-34658.10.1021/jacs.5c09743 ↗
46. Lin · Directly Predicting Limiting Potentials from Easily Obtainable Physical Properties of Graphene-Supported Single-Atom Electrocatalysts by Machine Learning · 2020 · 8 · 5663
47. Maulud · t. Trends, A Review on Linear Regression Comprehensive in Machine Learning · 2020 · 1 · 140
48. Biau · A Random Forest Guided Tour · 2016 · 25 · 197
49. Verrelst · Machine Learning Regression Algorithms for Biophysical Parameter Retrieval: Opportunities for Sentinel-2 and-3 · 2012 · 118 · 127
50. Varoquaux · Communications, Scikit-Learn: Machine Learning without Learning the Machinery · 2015 · 19 · 29
51. Pedregosa · Scikit-Learn: Machine Learning in Python · 2011 · 12 · 2825