The surface engineering has been testified to be an effective strategy for optimizing oxygen evolution reaction(OER)activity.Nevertheless,many of these techniques involve complex and multiple synthesis process,which l...The surface engineering has been testified to be an effective strategy for optimizing oxygen evolution reaction(OER)activity.Nevertheless,many of these techniques involve complex and multiple synthesis process,which leads to potential safety hazards,raises the cost of production,and hinders the scaled-up application.Herein,a facile strategy(i.e.,quenching with lanthanum nitrate cold salt solution)was adopted to fabricate the surface of Co_(3)O_(4)grown on nickel foam,and boost the electrocatalytic performance for OER.Analyses of the experimental results show that the surface engineering strategy can induce many defects on the surface of Co_(3)O_(4),including microcracks and oxygen vacancies,which provides more active sites for electrochemical reaction.Consequently,the treated sample exhibits significantly improved OER electrocatalytic activity,requiring only 311 mV to deliver 100 mA·cm?2 for OER in alkaline solution.This work highlights the feasibility of designing advanced electrocatalysts towards OER via quenching and extends the use of quenching chemistry in catalysis.展开更多
Digital twin(DT)technology is currently pervasive in industrial Internet of things(IoT)applications,notably in predictive maintenance scenarios.Prevailing digital twin-based predictive maintenance methodologies are co...Digital twin(DT)technology is currently pervasive in industrial Internet of things(IoT)applications,notably in predictive maintenance scenarios.Prevailing digital twin-based predictive maintenance methodologies are constrained by a narrow focus on singular physical modeling paradigms,impeding comprehensive analysis of diverse factory data at scale.This paper introduces an improved method,federated continual learning with authentication for distributed digital twin-based industrial IoT(FCLA-DT).This decentralized strategy ensures the continual learning capability vital for adaptive and real-time decision-making in complex industrial predictive maintenance systems.An authentication scheme based on group signature is introduced to enable the verification of digital twin identities during inter-twin collaborations,avoiding unauthorized access and potential model theft.Security analysis shows that FCLA-DT can enable numerous nodes to collaborate learning without compromising individual twin privacy,thereby ensuring group authentication in the cooperative distributed industrial IoT.Performance analysis shows that FCLA-DT outperforms traditional federated learning methods with over 95% fault diagnosis accuracy and ensures the privacy and authentication of digital twins in multi-client task learning.展开更多
基金the support from the National Natural Science Foundation of China(52101215)the Natural Science Foundation of Liaoning Province,China(2024-BS-315).
文摘The surface engineering has been testified to be an effective strategy for optimizing oxygen evolution reaction(OER)activity.Nevertheless,many of these techniques involve complex and multiple synthesis process,which leads to potential safety hazards,raises the cost of production,and hinders the scaled-up application.Herein,a facile strategy(i.e.,quenching with lanthanum nitrate cold salt solution)was adopted to fabricate the surface of Co_(3)O_(4)grown on nickel foam,and boost the electrocatalytic performance for OER.Analyses of the experimental results show that the surface engineering strategy can induce many defects on the surface of Co_(3)O_(4),including microcracks and oxygen vacancies,which provides more active sites for electrochemical reaction.Consequently,the treated sample exhibits significantly improved OER electrocatalytic activity,requiring only 311 mV to deliver 100 mA·cm?2 for OER in alkaline solution.This work highlights the feasibility of designing advanced electrocatalysts towards OER via quenching and extends the use of quenching chemistry in catalysis.
基金supported by the National Natural Science Foundation of China under Grant 62472132Natural Science Foundation of Zhejiang Province under Grant LZ22F030004Key Research and Development Program Project of Zhejiang Province under Grant 2024C01179.
文摘Digital twin(DT)technology is currently pervasive in industrial Internet of things(IoT)applications,notably in predictive maintenance scenarios.Prevailing digital twin-based predictive maintenance methodologies are constrained by a narrow focus on singular physical modeling paradigms,impeding comprehensive analysis of diverse factory data at scale.This paper introduces an improved method,federated continual learning with authentication for distributed digital twin-based industrial IoT(FCLA-DT).This decentralized strategy ensures the continual learning capability vital for adaptive and real-time decision-making in complex industrial predictive maintenance systems.An authentication scheme based on group signature is introduced to enable the verification of digital twin identities during inter-twin collaborations,avoiding unauthorized access and potential model theft.Security analysis shows that FCLA-DT can enable numerous nodes to collaborate learning without compromising individual twin privacy,thereby ensuring group authentication in the cooperative distributed industrial IoT.Performance analysis shows that FCLA-DT outperforms traditional federated learning methods with over 95% fault diagnosis accuracy and ensures the privacy and authentication of digital twins in multi-client task learning.
