The strategic dispersion of carbon nanotubes(CNTs)within triblock copolymer matrix is key to fabricating nanocomposites with the desired electrical properties.This study investigated the self-assembly and electrical b...The strategic dispersion of carbon nanotubes(CNTs)within triblock copolymer matrix is key to fabricating nanocomposites with the desired electrical properties.This study investigated the self-assembly and electrical behavior of a polystyrene-polybutadiene-polystyrene(SBS)matrix with CNTs of different aspect ratios using hybrid particle-field molecular dynamics simulations.Structural factor analysis of the nanocomposites indicated that CNTs with higher aspect ratios promoted the transition of the SBS matrix from a bicontinuous to a lamellar phase.The resistor network algorithm method showed that the electrical conductivity of SBS and CNTs nanocomposites was influenced by the interplay between the CNTs aspect ratios,concentrations,and domain sizes of the triblock copolymer SBS.Our research sheds light on the relationship between CNTs dispersion and the electrical behavior of SBS/CNTs nanocomposites,guiding the engineering of materials to achieve desired electrical properties through the modulation of CNTs aspect ratios and tailored sizing of triblock copolymer domains.展开更多
Conductive polymer nanocomposites have emerged as essential materials for wearable devices.In this study,we propose a novel approach that combines graph attention networks(GAT)with an improved global pooling strategy ...Conductive polymer nanocomposites have emerged as essential materials for wearable devices.In this study,we propose a novel approach that combines graph attention networks(GAT)with an improved global pooling strategy and incremental learning.We train the GAT model on homopolymer/carbon nanotube(CNT)nanocomposite data simulated by hybrid particle-field molecular dynamics(hPF-MD)method within the CNT concentration range of 1–8%.We further analyze the conductive network structure by integrating the resistor network approach with the GAT’s attention scores,revealing optimal connectivity at a 7%concentration.The comparative analysis of trained data and the reconstructed network,based on the attention scores,underscores the GATmodel’s ability in learning network structural representations.This work not only validates the efficacy of the GAT model in property prediction and interpretable network structure analysis of polymer nanocomposites but also lays a cornerstone for the reverse engineering of polymer composites.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.52273019,62173065,22133002,22273031,and 12274056)Fundamental Research Funds for the Central Universities(No.04442024074)+2 种基金NationalKey R&D Program of China(No.2022YFB3707300)Beijing Natural Science Foundation(No.4242040)Scientific Research Funds Project of Liaoning Provincial Department of Education(No.LJKZ0034)。
文摘The strategic dispersion of carbon nanotubes(CNTs)within triblock copolymer matrix is key to fabricating nanocomposites with the desired electrical properties.This study investigated the self-assembly and electrical behavior of a polystyrene-polybutadiene-polystyrene(SBS)matrix with CNTs of different aspect ratios using hybrid particle-field molecular dynamics simulations.Structural factor analysis of the nanocomposites indicated that CNTs with higher aspect ratios promoted the transition of the SBS matrix from a bicontinuous to a lamellar phase.The resistor network algorithm method showed that the electrical conductivity of SBS and CNTs nanocomposites was influenced by the interplay between the CNTs aspect ratios,concentrations,and domain sizes of the triblock copolymer SBS.Our research sheds light on the relationship between CNTs dispersion and the electrical behavior of SBS/CNTs nanocomposites,guiding the engineering of materials to achieve desired electrical properties through the modulation of CNTs aspect ratios and tailored sizing of triblock copolymer domains.
基金support from the National Natural Science Foundation of China(52273019)Fundamental Research Funds for the Central Universities(044420250076)+2 种基金General Program of National Natural Science Foundation of Liao Ning Province(2025-MS-105)Scientific Research Funds Project of Liaoning Provincial Department of Education(LJKZ0034)Beijing Natural Science Foundation(4242040).
文摘Conductive polymer nanocomposites have emerged as essential materials for wearable devices.In this study,we propose a novel approach that combines graph attention networks(GAT)with an improved global pooling strategy and incremental learning.We train the GAT model on homopolymer/carbon nanotube(CNT)nanocomposite data simulated by hybrid particle-field molecular dynamics(hPF-MD)method within the CNT concentration range of 1–8%.We further analyze the conductive network structure by integrating the resistor network approach with the GAT’s attention scores,revealing optimal connectivity at a 7%concentration.The comparative analysis of trained data and the reconstructed network,based on the attention scores,underscores the GATmodel’s ability in learning network structural representations.This work not only validates the efficacy of the GAT model in property prediction and interpretable network structure analysis of polymer nanocomposites but also lays a cornerstone for the reverse engineering of polymer composites.
