Robust Hydrogel Sensors Induced by Intermolecular Mechanical Interlocking of Covalent Organic Frameworks for Non‐Invasive Health Monitoring

The compromise of tensile strength and toughness is essential for conductive hydrogels to enhance their cycling stability and mechanical durability in wearable strain sensors. It is effective but challenging to balance energy dissipation. Herein, a new strategy based on the intermolecular mechanical interlocking of the hydrogels has been proposed for improving their tensile strength and toughness. The hydrogels are readily fabricated through non-covalently threading linear polymer chains into covalent organic frameworks (COFs). The mechanical interlocking between polyacrylamide and COFs significantly promotes the energy dissipation with synchronous increment of ion transport, which endows hydrogels with superior mechanical performance and high conductivity. The tensile strength, toughness, and ionic conductivity are as high as 0.19 ± 0.03 MPa, 2.11 ± 0.33 MJ m<sup>-3</sup> and 2.30 ± 0.55 S m<sup>-1</sup>, respectively, which greatly surpass those in the polyacrylamide counterpart and rank the top in the reported hydrogels. The resultant hydrogel sensor achieves a fast strain response of 4.8 ms and high cyclic stability over 100 cycles, and could non-invasively monitor human health. This work provides one promising approach to the construction of robust hydrogels based on COFs for the development of advanced wearable sensors.

Authors

Institutions

Publication Details

Journal
Advanced Materials
Published
2025-08-21
DOI
https://doi.org/10.1002/adma.202420271
Citations
7
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
4.72

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Robust Hydrogel Sensors Induced by Intermolecular Mechanical Interlocking of Covalent Organic Frameworks for Non‐Invasive Health Monitoring

Ruihu Wang, Mingwang Pan, Guohui Liu, Dan Wang et al.
7 citations
Advanced Materials
Advanced Sensor and Energy Harvesting Materials
4.72
article

Robust Hydrogel Sensors Induced by Intermolecular Mechanical Interlocking of Covalent Organic Frameworks for Non‐Invasive Health Monitoring

Ruihu Wang, Mingwang Pan, Guohui Liu, Dan Wang, He Li, Zhaoyu Liu, Xiaoju Li
article en
7 citations

Abstract

The compromise of tensile strength and toughness is essential for conductive hydrogels to enhance their cycling stability and mechanical durability in wearable strain sensors. It is effective but challenging to balance energy dissipation. Herein, a new strategy based on the intermolecular mechanical interlocking of the hydrogels has been proposed for improving their tensile strength and toughness. The hydrogels are readily fabricated through non-covalently threading linear polymer chains into covalent organic frameworks (COFs). The mechanical interlocking between polyacrylamide and COFs significantly promotes the energy dissipation with synchronous increment of ion transport, which endows hydrogels with superior mechanical performance and high conductivity. The tensile strength, toughness, and ionic conductivity are as high as 0.19 ± 0.03 MPa, 2.11 ± 0.33 MJ m<sup>-3</sup> and 2.30 ± 0.55 S m<sup>-1</sup>, respectively, which greatly surpass those in the polyacrylamide counterpart and rank the top in the reported hydrogels. The resultant hydrogel sensor achieves a fast strain response of 4.8 ms and high cyclic stability over 100 cycles, and could non-invasively monitor human health. This work provides one promising approach to the construction of robust hydrogels based on COFs for the development of advanced wearable sensors.

Advanced MaterialsVol. 37(44)
Hebei University of Technology (CN), Chinese Academy of Sciences (CN), Fujian Institute of Research on the Structure of Matter (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hebei Province, State Key Laboratory of Structural Chemistry
Openalex Percentile: Top 5%
Advanced Sensor and Energy Harvesting Materials
4.72
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.