The construction of biomass-based conductive hydrogel e-skins with high mechanical properties is the research hotspot and difficulty in the field of biomass materials.Traditional collagen-based conductive hydrogels,co...The construction of biomass-based conductive hydrogel e-skins with high mechanical properties is the research hotspot and difficulty in the field of biomass materials.Traditional collagen-based conductive hydrogels,constructed by the typical"bottom-up"strategy,normally have the incompatible problem between high mechanical property and high collagen content,and the extraction of collagen is often necessary.To solve these problems,inspired by the high mechanical properties and high collagen content of animal skins,this work proposed a"top-down"construction strategy,in which the extraction of collagen was unnecessary and the skin collagen skeleton(SCS)with the 3D network structure woven by natural collagen fibers in goatskin was preserved and used as the basic framework of hydrogel.Following a four-step route,namely,pretreatment→soaking in AgNPs(silver nanoparticles)solution→soaking in the mixed solution containing HEA(2-hydroxyethyl methacrylate)and AlCl_(3)→polymerization,this work successfully achieved the fabrication of a new skin-based conductive hydrogel e-skin with high mechanical properties(tensile strength of 2.97 MPa,toughness of 6.23 MJ·m^(-3)and breaking elongation of 428%)by using goatskin as raw material.The developed skin hydrogel(called PH@Ag)possessed a unique structure with the collagen fibers encapsulated by PHEA,and exhibited satisfactory adhesion,considerable antibacterial property,cytocompatibility,conductivity(3.06 S·m^(-1))and sensing sensitivity(the maximum gauge factor of 5.51).The PH@Ag e-skin could serve as strain sensors to accurately monitor and recognize all kinds of human motions such as swallowing,frowning,walking,and so on,and thus is anticipated to have considerable application prospect in many fields including flexible wearable electronic devices,health and motion monitoring.展开更多
To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination predi...To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model.Specifically,the characteristics of load components are analyzed for different seasons,and the corresponding models are established.First,the improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)method is employed to decompose the system load for all four seasons,and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component.Second,the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy.Finally,different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm,with a prediction resolution of 15 min,and the predicted values are accumulated to obtain the final results.According to the experimental findings,the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods.The results demonstrate that the proposedmethod can effectively address the load power variation induced by seasonal differences in different regions.展开更多
基金supported by the National Natural Science Foundation of China(No.21978180)the Universite de Bordeaux and the Centre National de la Recherche Scientifique(CNRS).
文摘The construction of biomass-based conductive hydrogel e-skins with high mechanical properties is the research hotspot and difficulty in the field of biomass materials.Traditional collagen-based conductive hydrogels,constructed by the typical"bottom-up"strategy,normally have the incompatible problem between high mechanical property and high collagen content,and the extraction of collagen is often necessary.To solve these problems,inspired by the high mechanical properties and high collagen content of animal skins,this work proposed a"top-down"construction strategy,in which the extraction of collagen was unnecessary and the skin collagen skeleton(SCS)with the 3D network structure woven by natural collagen fibers in goatskin was preserved and used as the basic framework of hydrogel.Following a four-step route,namely,pretreatment→soaking in AgNPs(silver nanoparticles)solution→soaking in the mixed solution containing HEA(2-hydroxyethyl methacrylate)and AlCl_(3)→polymerization,this work successfully achieved the fabrication of a new skin-based conductive hydrogel e-skin with high mechanical properties(tensile strength of 2.97 MPa,toughness of 6.23 MJ·m^(-3)and breaking elongation of 428%)by using goatskin as raw material.The developed skin hydrogel(called PH@Ag)possessed a unique structure with the collagen fibers encapsulated by PHEA,and exhibited satisfactory adhesion,considerable antibacterial property,cytocompatibility,conductivity(3.06 S·m^(-1))and sensing sensitivity(the maximum gauge factor of 5.51).The PH@Ag e-skin could serve as strain sensors to accurately monitor and recognize all kinds of human motions such as swallowing,frowning,walking,and so on,and thus is anticipated to have considerable application prospect in many fields including flexible wearable electronic devices,health and motion monitoring.
文摘To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model.Specifically,the characteristics of load components are analyzed for different seasons,and the corresponding models are established.First,the improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)method is employed to decompose the system load for all four seasons,and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component.Second,the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy.Finally,different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm,with a prediction resolution of 15 min,and the predicted values are accumulated to obtain the final results.According to the experimental findings,the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods.The results demonstrate that the proposedmethod can effectively address the load power variation induced by seasonal differences in different regions.
