The Monte Carlo(MC)method is widely used to simulate kinetic processes involving particle hopping through probabilistic modeling and stochastic sampling,particularly in contexts relevant to electrochemical energy stor...The Monte Carlo(MC)method is widely used to simulate kinetic processes involving particle hopping through probabilistic modeling and stochastic sampling,particularly in contexts relevant to electrochemical energy storage,spanning material synthesis,microstructural evolution,and device-level operation.However,the broader applicability of MC simulations is often limited by the requirement for customized definitions of key parameters for each specific physical system.To address this limitation,we propose an adaptive Monte Carlo simulation framework(AMCSF),which adjusts hopping rates,interaction energies,and configuration state parameters on-the-fly in response to updating system states.We provide three representative examples of the kinetic process simulation to demonstrate its potential utility and broad applications,including effective carrier ion concentration analysis in garnet-type electrolytes,voltage plateau formation in phosphate-based mixed ionic conductor electrodes,and oxygen release in lithium-rich layered oxide cathodes.The work provides a paradigm towards synergizing modeling and experiments into the understanding of complex materials kinetics and lays the groundwork for hierarchically bridging multiscale modeling methods.展开更多
Temperature sensing is essential for human health monitoring.High-sensitivity(>1 nm∕℃)fiber sensors always require long interference paths and temperature-sensitive materials,leading to a long sensor and thus slo...Temperature sensing is essential for human health monitoring.High-sensitivity(>1 nm∕℃)fiber sensors always require long interference paths and temperature-sensitive materials,leading to a long sensor and thus slow response(6–14 s).To date,it is still challenging for a fiber optic temperature sensor to have an ultrafast(~ms)response simultaneously with high sensitivity.Here,a side-polished single-mode/hollow/single-mode fiber(SPSHSF)structure is proposed to meet the challenge by using the length-independent sensitivity of an anti-resonant reflecting optical waveguide mechanism.With a polydimethylsiloxane filled sub-nanoliter volume cavity in the SP-SHSF,the SP-SHSF exhibits a high temperature sensitivity of 4.223 nm/℃ with a compact length of 1.6 mm,allowing an ultrafast response(16 ms)and fast recovery time(176 ms).The figure of merit(FOM),defined as the absolute ratio of sensitivity to response time,is proposed to assess the comprehensive performance of the sensor.The FOM of the proposed sensor reaches up to 263.94(nm/℃)∕s,which is more than two to three orders of magnitude higher than those of other temperature fiber optic sensors reported previously.Additionally,a threemonth cycle test shows that the sensor is highly robust,with excellent reversibility and accuracy,allowing it to be incorporated with a wearable face mask for detecting temperature changes during human breathing.The high FOM and high stability of the proposed sensing fiber structure provide an excellent opportunity to develop both ultrafast and highly sensitive fiber optic sensors for wearable respiratory monitoring and contactless in vitro detection.展开更多
Data-driven approaches are attracting wide attention in the field of materials science due to their ca-pacity to unravel complex structure-activity relationships deriving from nonlinear interplay of materials properti...Data-driven approaches are attracting wide attention in the field of materials science due to their ca-pacity to unravel complex structure-activity relationships deriving from nonlinear interplay of materials properties across multiple scales.However,unlocking their potential in materials discovery and design requires addressing two main challenges:multi-disciplinary knowledge barriers across the entire ma-terials data lifecycle(acquisition,processing,and analysis),and the absence of an infrastructure that can accommodate the continuous proliferation of data volume,algorithms,and models.Here,we propose a multirole collaborative and co-constructive materials design ecosystem that restructures both the productive forces and the relations of production in materials design.By establishing a structured di-vision of labor and a customized materials design infrastructure with a workflow system that decouples control and data flows,our framework reduces inter-module dependencies and enables the flexible,scalable integration of heterogeneous resources.A case study on electrochemical storage materials design demonstrates that this approach can improve streamlined collaborative efficiency by at least 50%,highlighting its potential to accelerate materials design.This work establishes a new paradigm for building intelligent materials design platforms,characterized by dynamic composability instead of static integration,thereby fostering an open and sustainable ecosystem for future materials discovery.展开更多
基金supported by the National Natural Science Foundation of China(Nos.92472207,52372208,52472223)the Science and Technology Commission of Shanghai Municipality(Grant No.22160730100)+1 种基金the High Performance Computing Center of Shanghai University,Shanghai Engineering Research Center of Intelligent Computing System(Grant No.19DZ2252600)the Shanghai Technical Service Center for Advanced Ceramics Structure Design and Precision Manufacturing(Grant No.20DZ2294000)。
文摘The Monte Carlo(MC)method is widely used to simulate kinetic processes involving particle hopping through probabilistic modeling and stochastic sampling,particularly in contexts relevant to electrochemical energy storage,spanning material synthesis,microstructural evolution,and device-level operation.However,the broader applicability of MC simulations is often limited by the requirement for customized definitions of key parameters for each specific physical system.To address this limitation,we propose an adaptive Monte Carlo simulation framework(AMCSF),which adjusts hopping rates,interaction energies,and configuration state parameters on-the-fly in response to updating system states.We provide three representative examples of the kinetic process simulation to demonstrate its potential utility and broad applications,including effective carrier ion concentration analysis in garnet-type electrolytes,voltage plateau formation in phosphate-based mixed ionic conductor electrodes,and oxygen release in lithium-rich layered oxide cathodes.The work provides a paradigm towards synergizing modeling and experiments into the understanding of complex materials kinetics and lays the groundwork for hierarchically bridging multiscale modeling methods.
基金National Key Research and Development Program of China(2021YFB2800801)National Natural Science Foundation of China(12174155,12174156,61675092,62105125)+4 种基金Natural Science Foundation of Guangdong Province for Distinguished Young Scholars(2020B1515020024)Key-Area Research and Development Program of Guangdong Province(2019B010138004)Aeronautical Science Foundation of China(201708W4001,201808W4001)Special Project in Key Fields of the Higher Education Institutions of Guangdong Province(2020ZDZX3022)Project of STRPAT of EC Laboratory(ZHD201902)。
文摘Temperature sensing is essential for human health monitoring.High-sensitivity(>1 nm∕℃)fiber sensors always require long interference paths and temperature-sensitive materials,leading to a long sensor and thus slow response(6–14 s).To date,it is still challenging for a fiber optic temperature sensor to have an ultrafast(~ms)response simultaneously with high sensitivity.Here,a side-polished single-mode/hollow/single-mode fiber(SPSHSF)structure is proposed to meet the challenge by using the length-independent sensitivity of an anti-resonant reflecting optical waveguide mechanism.With a polydimethylsiloxane filled sub-nanoliter volume cavity in the SP-SHSF,the SP-SHSF exhibits a high temperature sensitivity of 4.223 nm/℃ with a compact length of 1.6 mm,allowing an ultrafast response(16 ms)and fast recovery time(176 ms).The figure of merit(FOM),defined as the absolute ratio of sensitivity to response time,is proposed to assess the comprehensive performance of the sensor.The FOM of the proposed sensor reaches up to 263.94(nm/℃)∕s,which is more than two to three orders of magnitude higher than those of other temperature fiber optic sensors reported previously.Additionally,a threemonth cycle test shows that the sensor is highly robust,with excellent reversibility and accuracy,allowing it to be incorporated with a wearable face mask for detecting temperature changes during human breathing.The high FOM and high stability of the proposed sensing fiber structure provide an excellent opportunity to develop both ultrafast and highly sensitive fiber optic sensors for wearable respiratory monitoring and contactless in vitro detection.
基金supported by the National Natural Science Foun-dation of China(Nos.92472207,52472223 and 12404265)the Science and Technology Commission of Shanghai Municipality(No.22160730100)the Shanghai Technical Service Center for Advanced Ceramics Structure Design and Precision Manufacturing(No.20DZ2294000).
文摘Data-driven approaches are attracting wide attention in the field of materials science due to their ca-pacity to unravel complex structure-activity relationships deriving from nonlinear interplay of materials properties across multiple scales.However,unlocking their potential in materials discovery and design requires addressing two main challenges:multi-disciplinary knowledge barriers across the entire ma-terials data lifecycle(acquisition,processing,and analysis),and the absence of an infrastructure that can accommodate the continuous proliferation of data volume,algorithms,and models.Here,we propose a multirole collaborative and co-constructive materials design ecosystem that restructures both the productive forces and the relations of production in materials design.By establishing a structured di-vision of labor and a customized materials design infrastructure with a workflow system that decouples control and data flows,our framework reduces inter-module dependencies and enables the flexible,scalable integration of heterogeneous resources.A case study on electrochemical storage materials design demonstrates that this approach can improve streamlined collaborative efficiency by at least 50%,highlighting its potential to accelerate materials design.This work establishes a new paradigm for building intelligent materials design platforms,characterized by dynamic composability instead of static integration,thereby fostering an open and sustainable ecosystem for future materials discovery.
