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
The conversion of methane to olefins,aromatics,and hydrogen(MTOAH)can be used to stably obtain hydrocarbons when the effect of the catalytic surface is optimized from the reaction engineering perspective.In this study...The conversion of methane to olefins,aromatics,and hydrogen(MTOAH)can be used to stably obtain hydrocarbons when the effect of the catalytic surface is optimized from the reaction engineering perspective.In this study,Fe/Si C catalysts were packed into a quartz tube reactor.The catalytic surfaces of Si C and the impregnated Fe species decreased the apparent activation energies(E_a)of methane consumption in the blank reactor between 965 and 1020℃.Consequently,the hydrocarbon yield increased by 2.4times at 1020℃.Based on the model reactions of ethane,ethylene,and acetylene mixed with hydrogen in the range of 500-1020℃,an excess amount of Fe in the reactor favored the C-C coupling reaction over the selective hydrogenation of acetylene;consequently,coke formation was favored over the hydrogenation reaction.The gas-phase reactions and catalyst properties were optimized to increase hydrocarbon yields while reducing coke selectivity.The 0.2Fe catalyst-packed reactor(0.26 wt%Fe)resulted in a hydrocarbon yield of 7.1%and a coke selectivity of<2%when the ratio of the void space of the postcatalyst zone to the catalyst space was adjusted to be≥2.Based on these findings,the facile approach of decoupling the reaction zone between the catalyst surface and the gas-phase reaction can provide insights into catalytic reactor design,thereby facilitating the scale-up from the laboratory to the commercial scale.展开更多
The vast compositional and configurational spaces of multi-elementmetal halide perovskites(MHPs)result in significant challenges when designing MHPs with promising stability and optoelectronic properties.In this paper...The vast compositional and configurational spaces of multi-elementmetal halide perovskites(MHPs)result in significant challenges when designing MHPs with promising stability and optoelectronic properties.In this paper,we propose a framework for the design of B-site-alloyed ABX_(3) MHPs by combining density functional theory(DFT)and machine learning(ML).We performed generalized gradient approximation with Perdew–Burke–Ernzerhof functional for solids(PBEsol)on 3,159 B-sitealloyed perovskite structures using a compositional step of 1/4.展开更多
Aiming toward a sustainable energy era,the design of efficient photocatalysts for water splitting by engineering their band properties has been actively studied.One promising avenue for the band engineering of active ...Aiming toward a sustainable energy era,the design of efficient photocatalysts for water splitting by engineering their band properties has been actively studied.One promising avenue for the band engineering of active photocatalysts is the use of solid-solution alloying.However,the enormous possible configurations of multicomponent alloys hinders the experimental screening of this multidimensional material space,providing an opportunity for machine learning(ML)approaches to help accelerate the discovery of new multicomponent alloy materials.A conventional prerequisite for ML approaches is a large database of accurate material properties,which may require exhaustive computational and/or experimental resources.This study demonstrates that the screening of solidsolution alloys(up to hexanary systems)can be performed using a small database to minimize(and optimize)the number of high-level computational calculations.Specifically,we use ZnTe-based alloys as a prototypical example and employ a secure independent screening and sparsifing operator with the recently developed agreement method(α-method).Furthermore,we discuss and propose design routes to determine the optimal solid-solution ZnTe-based alloys for photoassisted water-splitting reactions.展开更多
Emerging freestanding membrane technologies,especially using inorganic thermoelectric materials,demonstrate the potential for advanced thermoelectric platforms.However,using rare and toxic elements during material pro...Emerging freestanding membrane technologies,especially using inorganic thermoelectric materials,demonstrate the potential for advanced thermoelectric platforms.However,using rare and toxic elements during material processing must be circumvented.Herein,we present a scalable method for synthesizing highly crystalline CuS membranes for thermoelectric applications.By sulfurizing crystalline Cu,we produce a highly percolated and easily transferable network of submicron CuS rods.The CuS membrane effectively separates thermal and electrical properties to achieve a power factor of 0.50 mW m^(-1) K^(-2) and thermal conductivity of 0.37 W m^(-1) K^(-1) at 650 K(estimated value).This yields a record-high dimensionless figure-of-merit of 0.91 at 650 K(estimated value)for covellite.Moreover,integrating 12 CuS devices into a module resulted in a power generation of4μW atΔT of 40 K despite using a straightforward configuration with only p-type CuS.Furthermore,based on the temperature-dependent electrical characteristics of CuS,we develop a wearable temperature sensor with antibacterial properties.展开更多
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.
基金supported by the C1 Gas Refinery Program through the National Research Foundation of Korea (NRF)funded by the Ministry of Science,ICT&Future Planning (NRF2017M3D3A1A01037001)supported by the Ministry of Trade,Industry and Energy (MOTIE),Korea Institute for Advancement of Technology (KIAT)through the Virtual Engineering Platform Program (P0022334)。
文摘The conversion of methane to olefins,aromatics,and hydrogen(MTOAH)can be used to stably obtain hydrocarbons when the effect of the catalytic surface is optimized from the reaction engineering perspective.In this study,Fe/Si C catalysts were packed into a quartz tube reactor.The catalytic surfaces of Si C and the impregnated Fe species decreased the apparent activation energies(E_a)of methane consumption in the blank reactor between 965 and 1020℃.Consequently,the hydrocarbon yield increased by 2.4times at 1020℃.Based on the model reactions of ethane,ethylene,and acetylene mixed with hydrogen in the range of 500-1020℃,an excess amount of Fe in the reactor favored the C-C coupling reaction over the selective hydrogenation of acetylene;consequently,coke formation was favored over the hydrogenation reaction.The gas-phase reactions and catalyst properties were optimized to increase hydrocarbon yields while reducing coke selectivity.The 0.2Fe catalyst-packed reactor(0.26 wt%Fe)resulted in a hydrocarbon yield of 7.1%and a coke selectivity of<2%when the ratio of the void space of the postcatalyst zone to the catalyst space was adjusted to be≥2.Based on these findings,the facile approach of decoupling the reaction zone between the catalyst surface and the gas-phase reaction can provide insights into catalytic reactor design,thereby facilitating the scale-up from the laboratory to the commercial scale.
基金supported by the National Research Foundation(NRF)grant funded by the Korean government(MSIT)(RS-2023-00283597)the National Supercomputing Center with supercomputing resources and technical support(KSC-2019-CRE-0128).
文摘The vast compositional and configurational spaces of multi-elementmetal halide perovskites(MHPs)result in significant challenges when designing MHPs with promising stability and optoelectronic properties.In this paper,we propose a framework for the design of B-site-alloyed ABX_(3) MHPs by combining density functional theory(DFT)and machine learning(ML).We performed generalized gradient approximation with Perdew–Burke–Ernzerhof functional for solids(PBEsol)on 3,159 B-sitealloyed perovskite structures using a compositional step of 1/4.
基金funding from the European Union’s Horizon 2020 Research and Innovation Programme under Marie Sklodowska-Curie grant agreement No.101034297W.Jang acknowledges the support of the National Research Foundation of Korea(NRF)grant funded by the Korean Government(MSIT)(2022R1C1C200856712)Computational resources were provided by the KISTI Supercomputing Center(KSC-2022-CRE-0206).
文摘Aiming toward a sustainable energy era,the design of efficient photocatalysts for water splitting by engineering their band properties has been actively studied.One promising avenue for the band engineering of active photocatalysts is the use of solid-solution alloying.However,the enormous possible configurations of multicomponent alloys hinders the experimental screening of this multidimensional material space,providing an opportunity for machine learning(ML)approaches to help accelerate the discovery of new multicomponent alloy materials.A conventional prerequisite for ML approaches is a large database of accurate material properties,which may require exhaustive computational and/or experimental resources.This study demonstrates that the screening of solidsolution alloys(up to hexanary systems)can be performed using a small database to minimize(and optimize)the number of high-level computational calculations.Specifically,we use ZnTe-based alloys as a prototypical example and employ a secure independent screening and sparsifing operator with the recently developed agreement method(α-method).Furthermore,we discuss and propose design routes to determine the optimal solid-solution ZnTe-based alloys for photoassisted water-splitting reactions.
基金supported by the Korea Research Institute of Chemical Technology(KRICT)of the Republic of Korea(KS2321-10,BSK23-440,KK2351-10)supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(Ministry of Science and ICT)(RS-2024-00421857)supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Ministry of Trade,Industry and Energy(MOTIE)(2021202080023D).
文摘Emerging freestanding membrane technologies,especially using inorganic thermoelectric materials,demonstrate the potential for advanced thermoelectric platforms.However,using rare and toxic elements during material processing must be circumvented.Herein,we present a scalable method for synthesizing highly crystalline CuS membranes for thermoelectric applications.By sulfurizing crystalline Cu,we produce a highly percolated and easily transferable network of submicron CuS rods.The CuS membrane effectively separates thermal and electrical properties to achieve a power factor of 0.50 mW m^(-1) K^(-2) and thermal conductivity of 0.37 W m^(-1) K^(-1) at 650 K(estimated value).This yields a record-high dimensionless figure-of-merit of 0.91 at 650 K(estimated value)for covellite.Moreover,integrating 12 CuS devices into a module resulted in a power generation of4μW atΔT of 40 K despite using a straightforward configuration with only p-type CuS.Furthermore,based on the temperature-dependent electrical characteristics of CuS,we develop a wearable temperature sensor with antibacterial properties.
