Graphene Oxide‐Modulated CMC/PVA Films with on‐Demand Shape Recovery and Self‐Healing Performance

With the advancement of artificial intelligence technologies, shape-memory polymers (SMPs) have attracted significant research interest due to their remarkable environmental responsiveness. However, conventional SMPs are limited to single-stimulus responsiveness and constrained performance, failing to meet practical application demands. This study develops a novel shape-memory carboxymethyl cellulose (CMC) and polyvinyl alcohol (PVA) composite film modulated by graphene oxide (GO) with multi-stimuli responsive capabilities and water-assisted self-healing properties. The introduction of GO significantly enhances the mechanical properties, and provides efficient swelling-resistant ability and thermal conversion efficiency of the composite film, endowing it with on-demand shape-memory performance under thermal and water stimulation. It can achieve a shape fixation rate of 94.45% and a recovery rate of 97.22%. Meanwhile, the shape-memory behaviors can be precisely tailored by modulating the content of GO and PVA as well as interfacial interactions. Furthermore, the composite films achieve efficient water-triggered self-healing with a remarkable 90.6% recovery efficiency, which is attributed to their inherent hydrophilic nature and dynamic physical crosslinking architecture. This research demonstrates a novel strategy for creating stimuli-adaptive and shape-memory materials, providing a pivotal platform for their application in flexible actuation devices and intelligent medical systems.

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Publication Details

Journal
Macromolecular Rapid Communications
Published
2025-05-20
DOI
https://doi.org/10.1002/marc.202500345
Citations
1
Primary Topic
Polymer composites and self-healing
Type
article
Field-Weighted Citation Impact
0.61

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Graphene Oxide‐Modulated CMC/PVA Films with on‐Demand Shape Recovery and Self‐Healing Performance

Jiayou Ji, Xianghua Yu, Liang Li, Huabo Huang et al.
1 citations
Macromolecular Rapid Communications
Polymer composites and self-healing
0.61
article

Graphene Oxide‐Modulated CMC/PVA Films with on‐Demand Shape Recovery and Self‐Healing Performance

Jiayou Ji, Xianghua Yu, Liang Li, Huabo Huang, Changdong Ju
article en
1 citations

Abstract

With the advancement of artificial intelligence technologies, shape-memory polymers (SMPs) have attracted significant research interest due to their remarkable environmental responsiveness. However, conventional SMPs are limited to single-stimulus responsiveness and constrained performance, failing to meet practical application demands. This study develops a novel shape-memory carboxymethyl cellulose (CMC) and polyvinyl alcohol (PVA) composite film modulated by graphene oxide (GO) with multi-stimuli responsive capabilities and water-assisted self-healing properties. The introduction of GO significantly enhances the mechanical properties, and provides efficient swelling-resistant ability and thermal conversion efficiency of the composite film, endowing it with on-demand shape-memory performance under thermal and water stimulation. It can achieve a shape fixation rate of 94.45% and a recovery rate of 97.22%. Meanwhile, the shape-memory behaviors can be precisely tailored by modulating the content of GO and PVA as well as interfacial interactions. Furthermore, the composite films achieve efficient water-triggered self-healing with a remarkable 90.6% recovery efficiency, which is attributed to their inherent hydrophilic nature and dynamic physical crosslinking architecture. This research demonstrates a novel strategy for creating stimuli-adaptive and shape-memory materials, providing a pivotal platform for their application in flexible actuation devices and intelligent medical systems.

Macromolecular Rapid CommunicationsVol. 47(4)
Wuhan Institute of Technology (CN)
National Natural Science Foundation of China
Clean water and sanitation
Openalex Percentile: Top 36%
Polymer composites and self-healing
0.61
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Graphene Oxide‐Modulated CMC/PVA Films with on‐Demand Shape Recovery and Self‐Healing Performance — Jiayou Ji, Xianghua Yu, et al. · Macromolecular Rapid Communications (2025) | TGRS Research Map | TGRS