Hydrogenation catalysts frequently impose a compromise between activity and selectivity,where maximizing one property inevitably diminishes the other.Researchers from the Dalian Institute of Chemical Physics(DICP)of t...Hydrogenation catalysts frequently impose a compromise between activity and selectivity,where maximizing one property inevitably diminishes the other.Researchers from the Dalian Institute of Chemical Physics(DICP)of the Chinese Academy of Sciences,in collaboration with scholars from University of Science and Technology of China and the Karlsruhe Institute of Technology in Germany,cracked this dilemma by engineering bimetallic catalysts with atomic precision-a breakthrough that boosts hydrogenation efficiency by 35-fold while maintaining pinpoint accuracy,resolving the stubborn activity-selectivity paradox.展开更多
Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to chemicals.The multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile indust...Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to chemicals.The multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile industrial applications.However,selectively reducing CO_(2)to ethylene is still challenging as the additional energy required for the C–C coupling step results in large overpotential and many competing products.Nonetheless,mechanistic understanding of the key steps and preferred reaction pathways/conditions,as well as rational design of novel catalysts for ethylene production have been regarded as promising approaches to achieving the highly efficient and selective CO_(2)RR.In this review,we first illustrate the key steps for CO_(2)RR to ethylene(e.g.,CO_(2)adsorption/activation,formation of~*CO intermediate,C–C coupling step),offering mechanistic understanding of CO_(2)RR conversion to ethylene.Then the alternative reaction pathways and conditions for the formation of ethylene and competitive products(C_1 and other C_(2+)products)are investigated,guiding the further design and development of preferred conditions for ethylene generation.Engineering strategies of Cu-based catalysts for CO_(2)RR-ethylene are further summarized,and the correlations of reaction mechanism/pathways,engineering strategies and selectivity are elaborated.Finally,major challenges and perspectives in the research area of CO_(2)RR are proposed for future development and practical applications.展开更多
The high energy density of green synthetic liquid chemicals and fuels makes them ideal for sustainable energy storage and transportation applications.Electroreduction of carbon dioxide(CO_(2))directly into such high v...The high energy density of green synthetic liquid chemicals and fuels makes them ideal for sustainable energy storage and transportation applications.Electroreduction of carbon dioxide(CO_(2))directly into such high value-added chemicals can help us achieve a renewable C cycle.Such electrochemical reduction typically suffers from low faradaic efficiencies(FEs)and generates a mixture of products due to the complexity of controlling the reaction selectivity.This perspective summarizes recent advances in the mechanistic understanding of CO_(2) reduction reaction pathways toward liquid products and the state-of-the-art catalytic materials for conversion of CO_(2) to liquid C1(e.g.,formic acid,methanol)and C2+products(e.g.,acetic acid,ethanol,n-propanol).Many liquid fuels are being produced with FEs between 80%and 100%.We discuss the use of structure-binding energy relationships,computational screening,and machine learning to identify promising candidates for experimental validation.Finally,we classify strategies for controlling catalyst selectivity and summarize breakthroughs,prospects,and challenges in electrocatalytic CO_(2) reduction to guide future developments.展开更多
Bifunctional oxide-zeolite-based composites(OXZEO)have emerged as promising materials for the direct conversion of syngas to olefins.However,experimental screening and optimization of reaction parameters remain resour...Bifunctional oxide-zeolite-based composites(OXZEO)have emerged as promising materials for the direct conversion of syngas to olefins.However,experimental screening and optimization of reaction parameters remain resource-intensive.To address this challenge,we implemented a three-stage framework integrating machine learning,Bayesian optimization,and experimental validation,utilizing a carefully curated dataset from the literature.Our ensemble-tree model(R^(2)>0.87)identified Zn-Zr and Cu-Mg binary mixed oxides as the most effective OXZEO systems,with their light olefin space-time yields confirmed by physically mixing with HSAPO-34 through experimental validation.Density functional theory calculations further elucidated the activity trends between Zn-Zr and Cu-Mg mixed oxides.Among 16 catalyst and reaction condition descriptors,the oxide/zeolite ratio,reaction temperature,and pressure emerged as the most significant factors.This interpretable,data-driven framework offers a versatile approach that can be applied to other catalytic processes,providing a powerful tool for experiment design and optimization in catalysis.展开更多
文摘Hydrogenation catalysts frequently impose a compromise between activity and selectivity,where maximizing one property inevitably diminishes the other.Researchers from the Dalian Institute of Chemical Physics(DICP)of the Chinese Academy of Sciences,in collaboration with scholars from University of Science and Technology of China and the Karlsruhe Institute of Technology in Germany,cracked this dilemma by engineering bimetallic catalysts with atomic precision-a breakthrough that boosts hydrogenation efficiency by 35-fold while maintaining pinpoint accuracy,resolving the stubborn activity-selectivity paradox.
基金financially supported via Australian Research Council(FT180100705)the support by the National Natural Science Foundation of China(22209103)+3 种基金the support from UTS Chancellor's Research Fellowshipsthe support from Open Project of State Key Laboratory of Advanced Special Steel,the Shanghai Key Laboratory of Advanced Ferrometallurgy,Shanghai University(SKLASS 2021-**)Joint International Laboratory on Environmental and Energy Frontier MaterialsInnovation Research Team of High-Level Local Universities in Shanghai。
文摘Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to chemicals.The multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile industrial applications.However,selectively reducing CO_(2)to ethylene is still challenging as the additional energy required for the C–C coupling step results in large overpotential and many competing products.Nonetheless,mechanistic understanding of the key steps and preferred reaction pathways/conditions,as well as rational design of novel catalysts for ethylene production have been regarded as promising approaches to achieving the highly efficient and selective CO_(2)RR.In this review,we first illustrate the key steps for CO_(2)RR to ethylene(e.g.,CO_(2)adsorption/activation,formation of~*CO intermediate,C–C coupling step),offering mechanistic understanding of CO_(2)RR conversion to ethylene.Then the alternative reaction pathways and conditions for the formation of ethylene and competitive products(C_1 and other C_(2+)products)are investigated,guiding the further design and development of preferred conditions for ethylene generation.Engineering strategies of Cu-based catalysts for CO_(2)RR-ethylene are further summarized,and the correlations of reaction mechanism/pathways,engineering strategies and selectivity are elaborated.Finally,major challenges and perspectives in the research area of CO_(2)RR are proposed for future development and practical applications.
基金supported by the Joint Funds of the National Natural Science Foundation of China(U24B20201)National Natural Science Foundation of China(22372007 and 21972010)+1 种基金the Fundamental Research Funds for the Central Universities(JD2427)the SRC Center for Electron Transfer(2021R1A5A1030054)funded by NRF Korea and AI Graduate School Program(RS-2021-II211343).
文摘The high energy density of green synthetic liquid chemicals and fuels makes them ideal for sustainable energy storage and transportation applications.Electroreduction of carbon dioxide(CO_(2))directly into such high value-added chemicals can help us achieve a renewable C cycle.Such electrochemical reduction typically suffers from low faradaic efficiencies(FEs)and generates a mixture of products due to the complexity of controlling the reaction selectivity.This perspective summarizes recent advances in the mechanistic understanding of CO_(2) reduction reaction pathways toward liquid products and the state-of-the-art catalytic materials for conversion of CO_(2) to liquid C1(e.g.,formic acid,methanol)and C2+products(e.g.,acetic acid,ethanol,n-propanol).Many liquid fuels are being produced with FEs between 80%and 100%.We discuss the use of structure-binding energy relationships,computational screening,and machine learning to identify promising candidates for experimental validation.Finally,we classify strategies for controlling catalyst selectivity and summarize breakthroughs,prospects,and challenges in electrocatalytic CO_(2) reduction to guide future developments.
基金funded by the KRICT Project (KK2512-10) of the Korea Research Institute of Chemical Technology and the Ministry of Trade, Industry and Energy (MOTIE)the Korea Institute for Advancement of Technology (KIAT) through the Virtual Engineering Platform Program (P0022334)+1 种基金supported by the Carbon Neutral Industrial Strategic Technology Development Program (RS-202300261088) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea)Further support was provided by research fund of Chungnam National University。
文摘Bifunctional oxide-zeolite-based composites(OXZEO)have emerged as promising materials for the direct conversion of syngas to olefins.However,experimental screening and optimization of reaction parameters remain resource-intensive.To address this challenge,we implemented a three-stage framework integrating machine learning,Bayesian optimization,and experimental validation,utilizing a carefully curated dataset from the literature.Our ensemble-tree model(R^(2)>0.87)identified Zn-Zr and Cu-Mg binary mixed oxides as the most effective OXZEO systems,with their light olefin space-time yields confirmed by physically mixing with HSAPO-34 through experimental validation.Density functional theory calculations further elucidated the activity trends between Zn-Zr and Cu-Mg mixed oxides.Among 16 catalyst and reaction condition descriptors,the oxide/zeolite ratio,reaction temperature,and pressure emerged as the most significant factors.This interpretable,data-driven framework offers a versatile approach that can be applied to other catalytic processes,providing a powerful tool for experiment design and optimization in catalysis.
