The exploration of sustainable energy utilization requires the imple-mentation of advanced electrochemical devices for efficient energy conversion and storage,which are enabled by the usage of cost-effective,high-perf...The exploration of sustainable energy utilization requires the imple-mentation of advanced electrochemical devices for efficient energy conversion and storage,which are enabled by the usage of cost-effective,high-performance electro-catalysts.Currently,heterogeneous atomically dispersed catalysts are considered as potential candidates for a wide range of applications.Compared to conventional cata-lysts,atomically dispersed metal atoms in carbon-based catalysts have more unsatu-rated coordination sites,quantum size effect,and strong metal-support interactions,resulting in exceptional catalytic activity.Of these,dual-atomic catalysts(DACs)have attracted extensive attention due to the additional synergistic effect between two adja-cent metal atoms.DACs have the advantages of full active site exposure,high selectiv-ity,theoretical 100%atom utilization,and the ability to break the scaling relationship of adsorption free energy on active sites.In this review,we summarize recent research advancement of DACs,which includes(1)the comprehensive understanding of the synergy between atomic pairs;(2)the synthesis of DACs;(3)characterization meth-ods,especially aberration-corrected scanning transmission electron microscopy and synchrotron spectroscopy;and(4)electrochemical energy-related applications.The last part focuses on great potential for the electrochemical catalysis of energy-related small molecules,such as oxygen reduction reaction,CO_(2) reduction reaction,hydrogen evolution reaction,and N_(2) reduction reaction.The future research challenges and opportunities are also raised in prospective section.展开更多
Single atomic catalysts(SACs),especially metal-nitrogen doped carbon(M-NC)catalysts,have been extensively explored for the electrochemical oxygen reduction reaction(ORR),owing to their high activity and atomic utiliza...Single atomic catalysts(SACs),especially metal-nitrogen doped carbon(M-NC)catalysts,have been extensively explored for the electrochemical oxygen reduction reaction(ORR),owing to their high activity and atomic utilization efficiency.However,there is still a lack of systematic screening and optimization of local structures surrounding active centers of SACs for ORR as the local coordination has an essential impact on their electronic structures and catalytic performance.Herein,we systematic study the ORR catalytic performance of M-NC SACs with different central metals and environmental atoms in the first and second coordination sphere by using density functional theory(DFT)calculation and machine learning(ML).The geometric and electronic informed overpotential model(GEIOM)based on random forest algorithm showed the highest accuracy,and its R^(2) and root mean square errors(RMSE)were 0.96 and 0.21,respectively.30 potential high-performance catalysts were screened out by GEIOM,and the RMSE of the predicted result was only 0.12 V.This work not only helps us fast screen high-performance catalysts,but also provides a low-cost way to improve the accuracy of ML models.展开更多
基金This work was financially supported by the National Key Research and Development Program of China(2018YFA0702002)the Beijing Natural Science Foundation(Z210016)+1 种基金the National Natural Science Foundation of China(51967020,21935001)Shanxi Energy Internet Research Institute(SXEI 2023A004).
文摘The exploration of sustainable energy utilization requires the imple-mentation of advanced electrochemical devices for efficient energy conversion and storage,which are enabled by the usage of cost-effective,high-performance electro-catalysts.Currently,heterogeneous atomically dispersed catalysts are considered as potential candidates for a wide range of applications.Compared to conventional cata-lysts,atomically dispersed metal atoms in carbon-based catalysts have more unsatu-rated coordination sites,quantum size effect,and strong metal-support interactions,resulting in exceptional catalytic activity.Of these,dual-atomic catalysts(DACs)have attracted extensive attention due to the additional synergistic effect between two adja-cent metal atoms.DACs have the advantages of full active site exposure,high selectiv-ity,theoretical 100%atom utilization,and the ability to break the scaling relationship of adsorption free energy on active sites.In this review,we summarize recent research advancement of DACs,which includes(1)the comprehensive understanding of the synergy between atomic pairs;(2)the synthesis of DACs;(3)characterization meth-ods,especially aberration-corrected scanning transmission electron microscopy and synchrotron spectroscopy;and(4)electrochemical energy-related applications.The last part focuses on great potential for the electrochemical catalysis of energy-related small molecules,such as oxygen reduction reaction,CO_(2) reduction reaction,hydrogen evolution reaction,and N_(2) reduction reaction.The future research challenges and opportunities are also raised in prospective section.
基金financially supported by the National Key Research and Development Program of China (2018YFA0702002)the Beijing Natural Science Foundation (Z210016)the National Natural Science Foundation of China (21935001)。
文摘Single atomic catalysts(SACs),especially metal-nitrogen doped carbon(M-NC)catalysts,have been extensively explored for the electrochemical oxygen reduction reaction(ORR),owing to their high activity and atomic utilization efficiency.However,there is still a lack of systematic screening and optimization of local structures surrounding active centers of SACs for ORR as the local coordination has an essential impact on their electronic structures and catalytic performance.Herein,we systematic study the ORR catalytic performance of M-NC SACs with different central metals and environmental atoms in the first and second coordination sphere by using density functional theory(DFT)calculation and machine learning(ML).The geometric and electronic informed overpotential model(GEIOM)based on random forest algorithm showed the highest accuracy,and its R^(2) and root mean square errors(RMSE)were 0.96 and 0.21,respectively.30 potential high-performance catalysts were screened out by GEIOM,and the RMSE of the predicted result was only 0.12 V.This work not only helps us fast screen high-performance catalysts,but also provides a low-cost way to improve the accuracy of ML models.