A Multi‐Structured Performance‐Enhanced Triboelectric Yarn for Virtual Human‐Machine Interaction

Abstract Advances in flexible wearable technology have imposed higher demands on the performance of smart textiles. As a wearable, integrable, and self‐powered sensing component, triboelectric yarn demonstrates significant potential in constructing comfortable, sensitive, and low‐power human‐machine interactive interfaces. However, yarn‐based triboelectric nanogenerators still suffer from low charge generation efficiency and rapid charge dissipation under dynamic conditions, which limit their practical application. Therefore, this study proposes a multi‐level structured triboelectric yarn designed with a synergistic combination of a conductive core, a charge trapping interface, and a microstructured friction surface, significantly enhancing charge generation, retention, and output stability. Experimental results demonstrate that the proposed triboelectric yarn exhibits a tenfold increase in open‐circuit voltage and a sixfold improvement in short‐circuit current compared to conventional yarns. Furthermore, the yarn can be woven into a smart textile for spatial pressure mapping and human‐machine interaction. A fabric keyboard array based on this yarn is integrated with a virtual interaction platform, enabling real‐time control of a virtual character through distinguishing commands based on pressing location and force intensity. This work provides an effective design strategy for high‐performance yarn‐based sensors and demonstrates their potential for immersive interactive applications in next‐generation wearable human‐machine systems.

Authors

Institutions

Publication Details

Journal
Advanced Materials Technologies
Published
2025-11-08
DOI
https://doi.org/10.1002/admt.202501876
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Multi‐Structured Performance‐Enhanced Triboelectric Yarn for Virtual Human‐Machine Interaction

Zhishui Chen, Shengxin Xiang, Qiongfeng Shi, Jun Wu et al.
Advanced Materials Technologies
Advanced Sensor and Energy Harvesting Materials
article

A Multi‐Structured Performance‐Enhanced Triboelectric Yarn for Virtual Human‐Machine Interaction

Zhishui Chen, Shengxin Xiang, Qiongfeng Shi, Jun Wu, Lei Liu, Huiyun Zhang, Long Liu, Shuze Cao, Xiao Wei
article en

Abstract

Abstract Advances in flexible wearable technology have imposed higher demands on the performance of smart textiles. As a wearable, integrable, and self‐powered sensing component, triboelectric yarn demonstrates significant potential in constructing comfortable, sensitive, and low‐power human‐machine interactive interfaces. However, yarn‐based triboelectric nanogenerators still suffer from low charge generation efficiency and rapid charge dissipation under dynamic conditions, which limit their practical application. Therefore, this study proposes a multi‐level structured triboelectric yarn designed with a synergistic combination of a conductive core, a charge trapping interface, and a microstructured friction surface, significantly enhancing charge generation, retention, and output stability. Experimental results demonstrate that the proposed triboelectric yarn exhibits a tenfold increase in open‐circuit voltage and a sixfold improvement in short‐circuit current compared to conventional yarns. Furthermore, the yarn can be woven into a smart textile for spatial pressure mapping and human‐machine interaction. A fabric keyboard array based on this yarn is integrated with a virtual interaction platform, enabling real‐time control of a virtual character through distinguishing commands based on pressing location and force intensity. This work provides an effective design strategy for high‐performance yarn‐based sensors and demonstrates their potential for immersive interactive applications in next‐generation wearable human‐machine systems.

Advanced Materials Technologies
Northwestern Polytechnical University (CN), Southeast University (CN)
Openalex Percentile: Top 67%
Advanced Sensor and Energy Harvesting Materials
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.