FabricatingRemote-Controllable Dynamic Ionomer/CNTNetworks via Cation–π Interaction for Multi-ResponsiveShape Memory and Self-Healing Capacities

Shape memory polymers (SMPs) with remotely controllable triggering capabilities are crucial for actuating applications in biomedical and aeronautic devices. This work presents a novel ionomer/carbon nanotube (CNT) composite network with exceptional remotely controllable shape memory effects (SMEs) and self-healing capabilities. By integrating quaternary ammonium (QA) units covalently bonded to crystalline polycaprolactone (PCL) segments through a chain extension reaction, we not only enabled the formation of ion clusters that act as netpoints in PCLQA ionomers to achieve superior SMEs, but also facilitated the generation of cation–π interactions when the multiresponsive CNTs were incorporated into the PCLQA ionomer matrix. This resulted in a robust physical PCLQA@CNT network stabilized by ionic clusters and cation–π interactions, along with significantly enhanced CNT dispersion. The PCLQA@CNT composites demonstrated remarkably improved mechanical performance (tensile strength, σ<sub>b</sub> > 40 MPa; elongation at break, ε<sub>b</sub> > 1900%), excellent thermally induced SME (shape fixity ratio of 99.6% and shape recovery ratio of 92.3%), and exceptional antibacterial effects (>99% against Escherichia coli and Staphylococcus aureus). Furthermore, the physical dynamic interactions endowed PCLQA@CNT networks with reproducibility and welding capability. The remotely controllable shape memory and self-healing behaviors via NIR and electrical stimulation were verified and demonstrated. This work paves the way for developing remotely controllable shape memory materials for advanced intelligent devices and applications.

Publication Details

Journal
Figshare
Published
2025-03-05
DOI
https://doi.org/10.1021/acsami.5c00788.s001
Primary Topic
Polymer composites and self-healing
Type
article
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article

FabricatingRemote-Controllable Dynamic Ionomer/CNTNetworks via Cation–π Interaction for Multi-ResponsiveShape Memory and Self-Healing Capacities

Figshare
Polymer composites and self-healing
article

FabricatingRemote-Controllable Dynamic Ionomer/CNTNetworks via Cation–π Interaction for Multi-ResponsiveShape Memory and Self-Healing Capacities

article en

Abstract

Shape memory polymers (SMPs) with remotely controllable triggering capabilities are crucial for actuating applications in biomedical and aeronautic devices. This work presents a novel ionomer/carbon nanotube (CNT) composite network with exceptional remotely controllable shape memory effects (SMEs) and self-healing capabilities. By integrating quaternary ammonium (QA) units covalently bonded to crystalline polycaprolactone (PCL) segments through a chain extension reaction, we not only enabled the formation of ion clusters that act as netpoints in PCLQA ionomers to achieve superior SMEs, but also facilitated the generation of cation–π interactions when the multiresponsive CNTs were incorporated into the PCLQA ionomer matrix. This resulted in a robust physical PCLQA@CNT network stabilized by ionic clusters and cation–π interactions, along with significantly enhanced CNT dispersion. The PCLQA@CNT composites demonstrated remarkably improved mechanical performance (tensile strength, σ<sub>b</sub> > 40 MPa; elongation at break, ε<sub>b</sub> > 1900%), excellent thermally induced SME (shape fixity ratio of 99.6% and shape recovery ratio of 92.3%), and exceptional antibacterial effects (>99% against Escherichia coli and Staphylococcus aureus). Furthermore, the physical dynamic interactions endowed PCLQA@CNT networks with reproducibility and welding capability. The remotely controllable shape memory and self-healing behaviors via NIR and electrical stimulation were verified and demonstrated. This work paves the way for developing remotely controllable shape memory materials for advanced intelligent devices and applications.

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Polymer composites and self-healing
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FabricatingRemote-Controllable Dynamic Ionomer/CNTNetworks via Cation–π Interaction for Multi-ResponsiveShape Memory and Self-Healing Capacities · Figshare (2025) | TGRS Research Map | TGRS