Plasma catalysis technology is emerging as a promising approach for addressing energy and environmental challenges in sustainability.This review provides an overview of plasma technology and summarizes recent advances...Plasma catalysis technology is emerging as a promising approach for addressing energy and environmental challenges in sustainability.This review provides an overview of plasma technology and summarizes recent advances in plasma catalysis from both experimental and theoretical perspectives.Current laboratory-scale studies have demonstrated the versatility of plasma catalysis in various processes,including carbon conversion,hydrogen production,and the removal of volatile organic compounds.The inherently complex environment of plasma catalysis requires in situ characterization and theoretical modeling to elucidate the underlying reaction mechanisms,which in turn guide the rational design of efficient catalysts and optimized reactor configurations.These advances are vital for enhancing the economic feasibility and accelerating the commercialization of this technology.Nevertheless,the scale-up and practical deployment of plasma-catalytic systems from laboratory to industrial scales remain challenging.In this review,we critically examine the current state of plasma catalysis research and its applications across a wide range of reactions.Particular attention is given to in situ mechanistic studies,reactor design,catalyst development,process scale-up,and theoretical modeling.Finally,we provide a forward-looking perspective on the opportunities and future directions to address existing challenges and harness the potential of plasma catalysis toward sustainable development.展开更多
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces.To reach target properties efficiently,these platforms are increasingly paired with int...The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces.To reach target properties efficiently,these platforms are increasingly paired with intelligent experimental design.However,current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints.Here,we devise an Evolution-Guided Bayesian Optimization(EGBO)algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement(qNEHVI)optimizer;this not only solves for the Pareto Front(PF)efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space.展开更多
基金the Central Research Fund(2022),the SCIENCE AND ENGINEERING RESEARCH COUNCIL,the A*STAR(Agency for Science,Technology and Research)the financial support from the Central Research Fund(2024),the SCIENCE AND ENGINEERING RESEARCH COUNCIL,the A*STAR(Agency for Science,Technology and Research)+2 种基金the financial support by the A*STAR AME IAF-PP grant(Grant No.A19E9a0103)the National Research Foundation,Singapore,and the A*STAR(Agency for Science,Technology and Research)under its LCERFI program Award No U2102d2002the financial support by the project under the Australian Research Council(Grant No.FL230100023).
文摘Plasma catalysis technology is emerging as a promising approach for addressing energy and environmental challenges in sustainability.This review provides an overview of plasma technology and summarizes recent advances in plasma catalysis from both experimental and theoretical perspectives.Current laboratory-scale studies have demonstrated the versatility of plasma catalysis in various processes,including carbon conversion,hydrogen production,and the removal of volatile organic compounds.The inherently complex environment of plasma catalysis requires in situ characterization and theoretical modeling to elucidate the underlying reaction mechanisms,which in turn guide the rational design of efficient catalysts and optimized reactor configurations.These advances are vital for enhancing the economic feasibility and accelerating the commercialization of this technology.Nevertheless,the scale-up and practical deployment of plasma-catalytic systems from laboratory to industrial scales remain challenging.In this review,we critically examine the current state of plasma catalysis research and its applications across a wide range of reactions.Particular attention is given to in situ mechanistic studies,reactor design,catalyst development,process scale-up,and theoretical modeling.Finally,we provide a forward-looking perspective on the opportunities and future directions to address existing challenges and harness the potential of plasma catalysis toward sustainable development.
基金funding from AME Programmatic Funds by the Agency for Science,Technology and Research under Grant No.A1898b0043 and No.A20G9b0135KH also acknowledges funding from the National Research Foundation(NRF),Singapore under the NRF Fellowship(NRF-NRFF13-2021-0011)+2 种基金SAK and FMB also acknowledge funding from the 25th NRF CRP programme(NRF-CRP25-2020RS-0002)QL also acknowledges support from the NRF fellowship(project No.NRF-NRFF13-2021-0005)the Ministry of Education,Singapore,under its Research Centre of Excellence award to the Institute for Functional Intelligent Materials(I-FIM,project No.EDUNC-33-18-279-V12).
文摘The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces.To reach target properties efficiently,these platforms are increasingly paired with intelligent experimental design.However,current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints.Here,we devise an Evolution-Guided Bayesian Optimization(EGBO)algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement(qNEHVI)optimizer;this not only solves for the Pareto Front(PF)efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space.
