Performance evaluation of CNT-COOH coated dual-intertwined yarn for wearable resistive strain sensing

Abstract

Stretchable resistive strain sensors are generating significant interest in wearable electronics due to their exceptional stretchability, rapid response, and ability to detect complex human movements. While yarn-based strain sensors show enormous potential for textile integration, they typically suffer from low sensitivity, poor interfacial adhesion between conductive materials and yarn surfaces, and limited mechanical durability under repeated strain, undermining their long-term applicability and fruitful use. This study presents a novel high-performance yarn strain sensor created by depositing multi-walled carboxyl-functionalized carbon nanotubes (MWCNT-COOH) onto a dual-intertwined yarn (DIY) substrate, consisting of a spandex core wrapped with nylon monofilaments using a scalable, cost-effective layer-by-layer (LBL) assembly without surfactants, followed by encapsulation with Ecoflex to enhance mechanical resilience and environmental endurance. The CNT-COOH dispersion exhibited excellent coverage and adsorption on the yarn surface, resulting in a reliable and conductive sensing layer. The CNT-COOH/DIY sensor demonstrated exceptional strain responsiveness, achieving a gauge factor (GF) of 11.40 for 10%–40% strain and 8.75 for 40%–60% strain. It also exhibited a response time of 263 ms and an electrical conductivity of 6.03 × 10⁻³ S/m. The Ecoflex/CNT-COH/DIY sensor retained stable sensing performance across 2000 stretching cycles at 30% strain. Real-time assessments on the finger, wrist, elbow, and knee confirmed its practical viability for wearable motion sensors and smart textiles.

Keywords

Strain sensor / Stretchable yarn sensor / Functionalized carbon nanotubes (CNTs) / Dual intertwined yarn (DIY) / Human motion monitoring

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Rezaul Karim Khan Alave: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Mahin Ahmed Shishir: Visualization, Investigation, Formal analysis, Data curation. Sheikh Mohammad Rahat: Visualization, Data curation. Fazle Rabbe: Visualization, Data curation. Gazi Farhan Ishraque Toki: Writing – review & editing, Formal analysis. Jiyong Hu: Writing – review & editing, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Acknowledgements

This project was funded by Natural Science Foundation of Shanghai (Grant No. 22ZR1400800).

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