The revolutionary development of machine learning(ML),data science,and analytics,coupled with its application in material science,stands as a significant milestone of the scientific community over the last decade.Inve...The revolutionary development of machine learning(ML),data science,and analytics,coupled with its application in material science,stands as a significant milestone of the scientific community over the last decade.Investigating active,stable,and cost-efficient catalysts is crucial for oxygen evolution reaction owing to the significance in a range of electrochemical energy co nversion processes.In this work,we have demonstrated an efficient approach of high-throughput screening to find stable transition metal oxides under acid condition for high-performance oxygen evolution reaction(OER)catalysts through density functional theory(DFT)calculation and a machine learning algorithm.A methodology utilizing both the Materials Project database and DFT calculations was introduced to assess the acid stability under specific reaction conditions.Building upon this,OER catalytic activity of acid-stable materials was examined,highlighting potential OER catalysts that meet the required properties.We identified IrO_(2),Fe(SbO_(3))_(2),Co(SbO_(3))_(2),Ni(SbO_(3))_(2),FeSbO_(4),Fe(SbO_(3))4,MoWO_(6),TiSnO_(4),CoSbO_(4),and Ti(WO_(4))_(2)as promising catalysts,several of which have already been experimentally discovered for their robust OER performance,while others are novel for experimental exploration,thereby broadening the chemical scope for efficient OER electrocatalysts.Descriptors of the bond length of TM-O and the first ionization energy were used to unveil the OER activity origin.From the calculated results,guidance has been derived to effectively execute advanced high-throughput screenings for the discovery of catalysts with favorable properties.Furthermore,the intrinsic correlation between catalytic performance and various atomic and structural factors was elucidated using the ML algorithm.Through these approaches,we not only streamline the choice of the promising electrocatalysts but also offer insights for the design of varied catalyst models and the discovery of superior catalysts.展开更多
Single-atom catalysts(SACs)have attracted considerable attention for electrochemical reactions due to their high atomic efficiency and tunable catalytic properties.Here,we systematically investigate the hydrogen evolu...Single-atom catalysts(SACs)have attracted considerable attention for electrochemical reactions due to their high atomic efficiency and tunable catalytic properties.Here,we systematically investigate the hydrogen evolution reaction(HER)activity of transition metal(TM)atoms embedded in hexagonal boron nitride(h-BN)with engineered vacancy defects,leveraging density functional theory(DFT)to examine 28 different TM@BN configurations at both single-and double-vacancy sites.Our calculations show that the TM atoms are strongly bound to the defect sites,ensuring robust structural integrity and resistance to aggregation and leaching under electrochemical conditions.The hydrogen adsorption free energies(ΔG_(H))indicate that Ir@SV-BN,Mo@SV-BN,and Pt@SV-BN exhibit near-optimal adsorption strengths,which was further supported by climbing-image nudged elastic band(CI-NEB)analyses revealing low activation barriers dominated by the Volmer-Heyrovsky mechanism.To expedite the discovery of high-performance catalysts,we employed machine learning(ML)models trained on the high-throughput DFT database,achieving an accuracy of R^(2)=0.96 in predicting overpotentials and identifying key structural and electronic descriptors governing HER activity.Ab initio molecular dynamics(AIMD)simulations confirm the thermal and electrochemical stability of selected TM@BN systems under realistic operational conditions.Taken together,these findings highlight the potential of BN-supported SACs as next-generation electrocatalysts for sustainable hydrogen production and underscore the effectiveness of integrating computational screening,ML-driven optimization,and mechanistic insight to guide the rational design of acid-resistant,high-performance HER catalysts.展开更多
Micro-light-emitting diodes(μLEDs)have gained significant interest as an activation source for gas sensors owing to their advantages,including room temperature operation and low power consumption.However,despite thes...Micro-light-emitting diodes(μLEDs)have gained significant interest as an activation source for gas sensors owing to their advantages,including room temperature operation and low power consumption.However,despite these benefits,challenges still exist such as a limited range of detectable gases and slow response.In this study,we present a blueμLED-integrated light-activated gas sensor array based on SnO_(2)nanoparticles(NPs)that exhibit excellent sensitivity,tunable selectivity,and rapid detection with micro-watt level power consumption.The optimal power forμLED is observed at the highest gas response,supported by finite-difference time-domain simulation.Additionally,we first report the visible light-activated selective detection of reducing gases using noble metal-decorated SnO_(2)NPs.The noble metals induce catalytic interaction with reducing gases,clearly distinguishing NH3,H2,and C2H5OH.Real-time gas monitoring based on a fully hardwareimplemented light-activated sensing array was demonstrated,opening up new avenues for advancements in light-activated electronic nose technologies.展开更多
基金supported by the Soonchunhyang University Research Fundsupported by the Supercomputing Center/Korea Institute of Science and Technology Information with supercomputing resources(KSC-2022-CRE-0354)+5 种基金supported by the “Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-004)a study on the“Leaders in INdustry-university Cooperation 3.0”Project,supported by the Ministry of Education and National Research Foundation of Koreafunded by BK 21 FOUR(Fostering Outstanding Universities for Research)(5199991614564)supported by the National Research Council of Science&Technology(NST)grant by the Korea government(MSIT)(CRC-20-01-NFRI)supported by the research fund of Hanyang University(HY-2022-3095)supported by the Technology Innovation Program(20023140,Development of an integrated low-power,highperformance,cryogenic high-vacuum exhaust system for analyzing impurity concentrations in the process in real time)funded By the Ministry of Trade,Industry&Energy(MOTIE,Korea)。
文摘The revolutionary development of machine learning(ML),data science,and analytics,coupled with its application in material science,stands as a significant milestone of the scientific community over the last decade.Investigating active,stable,and cost-efficient catalysts is crucial for oxygen evolution reaction owing to the significance in a range of electrochemical energy co nversion processes.In this work,we have demonstrated an efficient approach of high-throughput screening to find stable transition metal oxides under acid condition for high-performance oxygen evolution reaction(OER)catalysts through density functional theory(DFT)calculation and a machine learning algorithm.A methodology utilizing both the Materials Project database and DFT calculations was introduced to assess the acid stability under specific reaction conditions.Building upon this,OER catalytic activity of acid-stable materials was examined,highlighting potential OER catalysts that meet the required properties.We identified IrO_(2),Fe(SbO_(3))_(2),Co(SbO_(3))_(2),Ni(SbO_(3))_(2),FeSbO_(4),Fe(SbO_(3))4,MoWO_(6),TiSnO_(4),CoSbO_(4),and Ti(WO_(4))_(2)as promising catalysts,several of which have already been experimentally discovered for their robust OER performance,while others are novel for experimental exploration,thereby broadening the chemical scope for efficient OER electrocatalysts.Descriptors of the bond length of TM-O and the first ionization energy were used to unveil the OER activity origin.From the calculated results,guidance has been derived to effectively execute advanced high-throughput screenings for the discovery of catalysts with favorable properties.Furthermore,the intrinsic correlation between catalytic performance and various atomic and structural factors was elucidated using the ML algorithm.Through these approaches,we not only streamline the choice of the promising electrocatalysts but also offer insights for the design of varied catalyst models and the discovery of superior catalysts.
基金supported by the Soonchunhyang University Research Fundthe Supercomputing Center/Korea Institute of Science and Technology Information with supercomputing resources(TS-2024-RE-0041)+3 种基金Following are the results of a study on the“Leaders in INdustry-university Cooperation 3.0”Project,supported by the Ministry of Education and National Research Foundation of Koreasupported by the Technology Innovation Program(20023140,Development of an integrated low-power,high-performance,cryogenic high-vacuum exhaust system for analyzing impurity concentrations in the process in real time)funded by the Ministry of Trade,Industry&Energy(MOTIE,Korea)supported by the Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(RS-2024-00409639,HRD Program for Industrial Innovation)supported by the BK21 Four Program funded by the Ministry of Education(MOE,Korea)and the National Research Foundation of Korea(NRF),and in part by INHA university research grant.
文摘Single-atom catalysts(SACs)have attracted considerable attention for electrochemical reactions due to their high atomic efficiency and tunable catalytic properties.Here,we systematically investigate the hydrogen evolution reaction(HER)activity of transition metal(TM)atoms embedded in hexagonal boron nitride(h-BN)with engineered vacancy defects,leveraging density functional theory(DFT)to examine 28 different TM@BN configurations at both single-and double-vacancy sites.Our calculations show that the TM atoms are strongly bound to the defect sites,ensuring robust structural integrity and resistance to aggregation and leaching under electrochemical conditions.The hydrogen adsorption free energies(ΔG_(H))indicate that Ir@SV-BN,Mo@SV-BN,and Pt@SV-BN exhibit near-optimal adsorption strengths,which was further supported by climbing-image nudged elastic band(CI-NEB)analyses revealing low activation barriers dominated by the Volmer-Heyrovsky mechanism.To expedite the discovery of high-performance catalysts,we employed machine learning(ML)models trained on the high-throughput DFT database,achieving an accuracy of R^(2)=0.96 in predicting overpotentials and identifying key structural and electronic descriptors governing HER activity.Ab initio molecular dynamics(AIMD)simulations confirm the thermal and electrochemical stability of selected TM@BN systems under realistic operational conditions.Taken together,these findings highlight the potential of BN-supported SACs as next-generation electrocatalysts for sustainable hydrogen production and underscore the effectiveness of integrating computational screening,ML-driven optimization,and mechanistic insight to guide the rational design of acid-resistant,high-performance HER catalysts.
基金supported by the Nano&Material Technology Development Program through the National Research Foundation of Korea(NRF)funded by Ministry of Science and ICT(RS-2024-00405016)supported by“Cooperative Research Program for Agriculture Science and Technology Development(Project No.PJ01706703)”Rural Development Administration,Republic of Korea.The Inter-University Semiconductor Research Center and Institute of Engineering Research at Seoul National University provided research facilities for this work.
文摘Micro-light-emitting diodes(μLEDs)have gained significant interest as an activation source for gas sensors owing to their advantages,including room temperature operation and low power consumption.However,despite these benefits,challenges still exist such as a limited range of detectable gases and slow response.In this study,we present a blueμLED-integrated light-activated gas sensor array based on SnO_(2)nanoparticles(NPs)that exhibit excellent sensitivity,tunable selectivity,and rapid detection with micro-watt level power consumption.The optimal power forμLED is observed at the highest gas response,supported by finite-difference time-domain simulation.Additionally,we first report the visible light-activated selective detection of reducing gases using noble metal-decorated SnO_(2)NPs.The noble metals induce catalytic interaction with reducing gases,clearly distinguishing NH3,H2,and C2H5OH.Real-time gas monitoring based on a fully hardwareimplemented light-activated sensing array was demonstrated,opening up new avenues for advancements in light-activated electronic nose technologies.
