It remains a challenge to use a simple approach to fabricate a multi-shape memory material with high mechanical performances.Here,we report a triple crosslinking design to construct a multi-shape memory epoxy vitrimer...It remains a challenge to use a simple approach to fabricate a multi-shape memory material with high mechanical performances.Here,we report a triple crosslinking design to construct a multi-shape memory epoxy vitrimer(MSMEV),which exhibits high mechanical properties,multi-shape memory property and malleability.The triple crosslinking network is formed by reacting diglycidyl ether of bisphenol F(DGEBF)with 4-aminophenyl disulfide,γ-aminopropyltriethoxysilane(APTS)and poly(propylene glycol)bis(2-aminopropyl ether)(D2000).The triple crosslinking manifests triple functions:the disulfide bonds and the silyl ether linkages enable malleability of the epoxy network;the silyl ether linkages impart the network with high heterogeneity and broaden the glass transition region,leading to multi-shape memory property;a small amount of D2000 increases the modulus difference between the glassy and rubbery states,thereby improving the shape fixity ratio.Meanwhile,the high crosslinking density and rigid structure provide the MSMEV with high tensile strength and Young’s modulus.Moreover,integrating carbon fibers and MSMEV results in shape memory composites.The superior mechanical properties of the composites and the recyclability of carbon fiber derived from the dissolvability of MSMEV make the composites hold great promise as structural materials in varied applications.展开更多
To solve the problem of missing many valid triples in knowledge graphs(KGs),a novel model based on a convolutional neural network(CNN)called ConvKG is proposed,which employs a joint learning strategy for knowledge gra...To solve the problem of missing many valid triples in knowledge graphs(KGs),a novel model based on a convolutional neural network(CNN)called ConvKG is proposed,which employs a joint learning strategy for knowledge graph completion(KGC).Related research work has shown the superiority of convolutional neural networks(CNNs)in extracting semantic features of triple embeddings.However,these researches use only one single-shaped filter and fail to extract semantic features of different granularity.To solve this problem,ConvKG exploits multi-shaped filters to co-convolute on the triple embeddings,joint learning semantic features of different granularity.Different shaped filters cover different sizes on the triple embeddings and capture pairwise interactions of different granularity among triple elements.Experimental results confirm the strength of joint learning,and compared with state-of-the-art CNN-based KGC models,ConvKG achieves the better mean rank(MR)and Hits@10 metrics on dataset WN18 RR,and the better MR on dataset FB15k-237.展开更多
In the current study, Nation is adopted as a dispersant for assisting the water- phase exfoliation of MoS2. The completely ionized hydrophilic sulfonic groups and hydrophobic polytetrafluoroethylene backbone permit st...In the current study, Nation is adopted as a dispersant for assisting the water- phase exfoliation of MoS2. The completely ionized hydrophilic sulfonic groups and hydrophobic polytetrafluoroethylene backbone permit strong non-covalent bonding interactions between Nation and exfoliated nanosheets for stabilization and functionalization to obtain Nation-modified MoS2 （N-MoS2） nanocomposites. These interactions are stable in different pH environments. The concentration of Nation influences the exfoliation efficiency and the size of the exfoliated nanosheets. N-MoSdNation composite membranes are prepared. The N-MoS2 nanocomposite exhibits good dispersibility in a Nation matrix, benefitting from the functionalization of Nation. The N-MoSdNafion composite membrane shows excellent near-infrared light-controllable multi-shape-memory performance with convenient operation. The Nation-assisted water-phase exfoliation method shows good efficiency, convenient operation, environmental benignity, and broad application potential.展开更多
基金by the State Key Scientific Special Project of China(No.2016ZX05017-002)the National Natural Science Foundation of China(No.51873110).
文摘It remains a challenge to use a simple approach to fabricate a multi-shape memory material with high mechanical performances.Here,we report a triple crosslinking design to construct a multi-shape memory epoxy vitrimer(MSMEV),which exhibits high mechanical properties,multi-shape memory property and malleability.The triple crosslinking network is formed by reacting diglycidyl ether of bisphenol F(DGEBF)with 4-aminophenyl disulfide,γ-aminopropyltriethoxysilane(APTS)and poly(propylene glycol)bis(2-aminopropyl ether)(D2000).The triple crosslinking manifests triple functions:the disulfide bonds and the silyl ether linkages enable malleability of the epoxy network;the silyl ether linkages impart the network with high heterogeneity and broaden the glass transition region,leading to multi-shape memory property;a small amount of D2000 increases the modulus difference between the glassy and rubbery states,thereby improving the shape fixity ratio.Meanwhile,the high crosslinking density and rigid structure provide the MSMEV with high tensile strength and Young’s modulus.Moreover,integrating carbon fibers and MSMEV results in shape memory composites.The superior mechanical properties of the composites and the recyclability of carbon fiber derived from the dissolvability of MSMEV make the composites hold great promise as structural materials in varied applications.
基金Supported by the National Natural Science Foundation of China(No.61876144)。
文摘To solve the problem of missing many valid triples in knowledge graphs(KGs),a novel model based on a convolutional neural network(CNN)called ConvKG is proposed,which employs a joint learning strategy for knowledge graph completion(KGC).Related research work has shown the superiority of convolutional neural networks(CNNs)in extracting semantic features of triple embeddings.However,these researches use only one single-shaped filter and fail to extract semantic features of different granularity.To solve this problem,ConvKG exploits multi-shaped filters to co-convolute on the triple embeddings,joint learning semantic features of different granularity.Different shaped filters cover different sizes on the triple embeddings and capture pairwise interactions of different granularity among triple elements.Experimental results confirm the strength of joint learning,and compared with state-of-the-art CNN-based KGC models,ConvKG achieves the better mean rank(MR)and Hits@10 metrics on dataset WN18 RR,and the better MR on dataset FB15k-237.
文摘In the current study, Nation is adopted as a dispersant for assisting the water- phase exfoliation of MoS2. The completely ionized hydrophilic sulfonic groups and hydrophobic polytetrafluoroethylene backbone permit strong non-covalent bonding interactions between Nation and exfoliated nanosheets for stabilization and functionalization to obtain Nation-modified MoS2 （N-MoS2） nanocomposites. These interactions are stable in different pH environments. The concentration of Nation influences the exfoliation efficiency and the size of the exfoliated nanosheets. N-MoSdNation composite membranes are prepared. The N-MoS2 nanocomposite exhibits good dispersibility in a Nation matrix, benefitting from the functionalization of Nation. The N-MoSdNafion composite membrane shows excellent near-infrared light-controllable multi-shape-memory performance with convenient operation. The Nation-assisted water-phase exfoliation method shows good efficiency, convenient operation, environmental benignity, and broad application potential.
