Creating gas sensors that are highly selective and function at low temperatures based on semiconductor metal oxides(SMOs)is considered a difficult endeavor,and these sensors are extensively applied in medical diagnosi...Creating gas sensors that are highly selective and function at low temperatures based on semiconductor metal oxides(SMOs)is considered a difficult endeavor,and these sensors are extensively applied in medical diagnosis,industrial manufacturing,and in spacecraft within the aerospace sector.This review article delves into the emerging horizons of chemiresistive gas sensing,particularly focusing on the synergy between polyoxometalates(POMs)and block copolymers in self-assembly for the construction of ordered mesoporous metal oxides(MMOs).It highlights the advancements in gas sensing technology,emphasizing the role of POMs as precursors for MMOs,which offer high sensitivity and selectivity due to their unique physicochemical properties.The review covers various synthetic strategies and their impact on sensor performance,including low-temperature operation,high sensitivity,and selectivity towards specific gases.It also underscores the importance of nanostructure control,heteroatom doping,and the integration of noble metal catalysts in enhancing sensor capabilities.The article concludes with future research directions,suggesting the exploration of a broader range of detectable compounds and the integration of these materials into practical devices for real-world applications.展开更多
Most multimodal multi-objective evolutionary algorithms(MMEAs)aim to find all global Pareto optimal sets(PSs)for a multimodal multi-objective optimization problem(MMOP).However,in real-world problems,decision makers(D...Most multimodal multi-objective evolutionary algorithms(MMEAs)aim to find all global Pareto optimal sets(PSs)for a multimodal multi-objective optimization problem(MMOP).However,in real-world problems,decision makers(DMs)may be also interested in local PSs.Also,searching for both global and local PSs is more general in view of dealing with MMOPs,which can be seen as generalized MMOPs.Moreover,most state-of-theart MMEAs exhibit poor convergence on high-dimension MMOPs and are unable to deal with constrained MMOPs.To address the above issues,we present a novel multimodal multiobjective coevolutionary algorithm(Co MMEA)to better produce both global and local PSs,and simultaneously,to improve the convergence performance in dealing with high-dimension MMOPs.Specifically,the Co MMEA introduces two archives to the search process,and coevolves them simultaneously through effective knowledge transfer.The convergence archive assists the Co MMEA to quickly approach the Pareto optimal front.The knowledge of the converged solutions is then transferred to the diversity archive which utilizes the local convergence indicator and the-dominance-based method to obtain global and local PSs effectively.Experimental results show that Co MMEA is competitive compared to seven state-of-the-art MMEAs on fifty-four complex MMOPs.展开更多
基金financial support from the Program for the Development of Science and Technology of Jilin Province(YDZJ202401335ZYTS)the Fundamental Research Funds for the Central Universities
文摘Creating gas sensors that are highly selective and function at low temperatures based on semiconductor metal oxides(SMOs)is considered a difficult endeavor,and these sensors are extensively applied in medical diagnosis,industrial manufacturing,and in spacecraft within the aerospace sector.This review article delves into the emerging horizons of chemiresistive gas sensing,particularly focusing on the synergy between polyoxometalates(POMs)and block copolymers in self-assembly for the construction of ordered mesoporous metal oxides(MMOs).It highlights the advancements in gas sensing technology,emphasizing the role of POMs as precursors for MMOs,which offer high sensitivity and selectivity due to their unique physicochemical properties.The review covers various synthetic strategies and their impact on sensor performance,including low-temperature operation,high sensitivity,and selectivity towards specific gases.It also underscores the importance of nanostructure control,heteroatom doping,and the integration of noble metal catalysts in enhancing sensor capabilities.The article concludes with future research directions,suggesting the exploration of a broader range of detectable compounds and the integration of these materials into practical devices for real-world applications.
基金supported by the Open Project of Xiangjiang Laboratory(22XJ02003)the National Natural Science Foundation of China(62122093,72071205)。
文摘Most multimodal multi-objective evolutionary algorithms(MMEAs)aim to find all global Pareto optimal sets(PSs)for a multimodal multi-objective optimization problem(MMOP).However,in real-world problems,decision makers(DMs)may be also interested in local PSs.Also,searching for both global and local PSs is more general in view of dealing with MMOPs,which can be seen as generalized MMOPs.Moreover,most state-of-theart MMEAs exhibit poor convergence on high-dimension MMOPs and are unable to deal with constrained MMOPs.To address the above issues,we present a novel multimodal multiobjective coevolutionary algorithm(Co MMEA)to better produce both global and local PSs,and simultaneously,to improve the convergence performance in dealing with high-dimension MMOPs.Specifically,the Co MMEA introduces two archives to the search process,and coevolves them simultaneously through effective knowledge transfer.The convergence archive assists the Co MMEA to quickly approach the Pareto optimal front.The knowledge of the converged solutions is then transferred to the diversity archive which utilizes the local convergence indicator and the-dominance-based method to obtain global and local PSs effectively.Experimental results show that Co MMEA is competitive compared to seven state-of-the-art MMEAs on fifty-four complex MMOPs.
