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Computational design of bimetallic Tm_(2)@g-C_(9)N_(4)electrocatalysts for enhanced CO reduction toward C_(2)products

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Chinese Chemical Letters 2024年第7期35卷 484-490页

作者： Jiajun Wang Guolin Yi Shengling Guo Jianing Wang Shujuan Li Ke Xu Weiyi Wang Shulai Lei Tianjin Key Laboratory of Structure and Performance for Functional Molecules College of ChemistryTianjin Normal UniversityTianjin 300387China Department of Chemical Physics&Hefei National Laboratory for Physical Sciences at Microscale University of Science and Technology of ChinaHefei 230026China Hubei Key Laboratory of Low Dimensional Optoelectronic Materials and Devices Hubei University of Arts and ScienceXiangyang 441053China Hubei Longzhong Laboratory Xiangyang 441053China

Electrochemical CO reduction(ECOR)as a potential strategy for producing valuable chemicals and fuels has captured substantial ***,the currently available electrocatalysts suffer from poor selectivity and low Faradaic efficiency,limiting their industrial ***,we systematically investigate the potential of homonuclear bimetallic electrocatalysts,Tm_(2)@C_(9)N_(4)(TM=Fe,Co,Ni,and Cu),for the ECOR through extensive density functional theory *** findings suggest that all four proposed monolayers exhibit exceptional stability,making them highly suitable for experimental synthesis and practical ***,these transition-metal dual atoms anchored on C_(9)N_(4)monolayers show great potential in facilitating the production of high-value C_(2)products,such as C_(2)H_(5)OH and C_(2)H_(4),due to the significantly low limiting potentials(-0.06~-0.46 V)and small kinetic energy barriers(0.54–1.08 eV)for the CO coupling ***,with the exception of Ni_(2)@C_(9)N_(4),these bimetallic catalysts demonstrate the impressive suppression of the competitive hydrogen evolution reaction(HER),leading to a high selectivity for C_(2)products in *** predictions would accelerate the development of high-performance C_(9)N_(4)-based dual-atom catalysts for the ECOR.

关键词： C_(9)N_(4)monolayer double-atom catalysts CO electroreduction Multi-carbon products Density functional theory

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Computational prediction of Mo_(2)@g-C_(6)N_(6) monolayer as an efficient electrocatalyst for N_(2) reduction

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Chinese Chemical Letters 2022年第10期33卷 4623-4627页

作者： Jiajun Wang Mengyao Shi Guolin Yi Lu Wang Shulai Lei Ke Xu Shujuan Li Jianshuai Mu Tianjin Key Laboratory of Structure and Performance for Functional Molecules College of ChemistryTianjin Normal UniversityTianjin 300387China Hubei Key Laboratory of Low Dimensional Optoelectronic Materials and Devices Hubei University of Arts and ScienceXiangyang 441053China Institute of Mathematics Free University of BerlinBerlin D-14195Germany

Electrocatalytic nitrogen reduction reaction(NRR)is an environmentally friendly method for sustainable ammonia synthesis under ambient *** for efficient NRR electrocatalysts with high activity and selectivity is currently urgent but remains great ***,we systematically investigate the NRR catalytic activities of single and double transition metal atoms(TM=Fe,Co,Ni and Mo)anchored on g-C_(6)N_(6) monolayers by performing first-principles *** on the stability,activity,and selectivity analysis,Mo_(2)@g-C_(6)N_(6) monolayer is screened out as the most promising candidate for *** exploration of the reaction mechanism demonstrates that the Mo dimer anchored on g-C_(6)N_(6) can sufficiently activate and efficiently reduce the inert nitrogen molecule to ammonia through a preferred distal pathway with a particularly low limiting potential of -0.06 *** addition,we find that Mo_(2)@g-C_(6)N_(6) has excellent NRR selectivity over the competing hydrogen evolution reaction,with the Faradaic efficiency being 100%.Our work not only predicts a kind of ideal NRR electrocatalyst but also encouraging more experimental and theoretical efforts to develop novel double-atom catalysts(DACs)for NRR.

关键词： g-C_(6)N_(6)monolayer double-atom catalysts Nitrogen reduction reaction Hydrogen evolution reaction Limiting potential Density functional theory

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A density-functional-theory-based and machine-learning-accelerated hybrid method for intricate system catalysis

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Materials Reports(Energy) 2021年第3期1卷 81-90页

作者： Xuhao Wan Zhaofu Zhang Wei Yu Yuzheng Guo School of Electrical Engineering Wuhan UniversityWuhanHubei430072PR China Department of Engineering Cambridge UniversityCambridgeCB21PZUnited Kingdom

Being progressively applied in the design of highly active catalysts for energy devices,machine learning(ML)technology has shown attractive ability of dramatically reducing the computational cost of the traditional density functional theory(DFT)method,showing a particular advantage for the simulation of intricate system *** with a basic description of the whole workflow of the novel DFT-based and ML-accelerated(DFT-ML)scheme,and the common algorithms useable for machine learning,we presented in this paper our work on the development and performance test of a DFT-based ML method for catalysis program(DMCP)to implement the DFT-ML *** is an efficient and user-friendly program with the flexibility to accommodate the needs of performing ML calculations based on the data generated by DFT calculations or from materials *** also employed an example of transition metal phthalocyanine double-atom catalysts as electrocatalysts for carbon reduction reaction to exhibit the general workflow of the DFT-ML hybrid scheme and our DMCP program.

关键词： DMCP program Machine learning Density functional theory DFT-ML hybrid Scheme Catalysis double-atom catalysts

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