Mechanoluminescent/Electric Dual‐Mode Sensors Enabled by Trace Carbon Nanotubes

Mechanoluminescence (ML)-based sensors are emerging as promising wearable devices, attracting attention for their self-powered visualization of mechanical stimuli. However, challenges such as weak brightness, high activation threshold, and intermittent signal output have hindered their development. Here, a mechanoluminescent/electric dual-mode strain sensor is presented that offers enhanced ML sensing and reliable electrical sensing simultaneously. The strain sensor is fabricated via an optimized dip-coating method, featuring a sandwich structure with a single-walled carbon nanotube (SWNT) interlayer and two polydimethylsiloxane (PDMS)/ZnS:Cu luminescence layers. The integral mechanical reinforcement framework provided by the SWNT interlayer improves the ML intensity of the SWNT/PDMS/ZnS:Cu composite film. Compared to conventional nanoparticle fillers, the ML intensity is enhanced nearly tenfold with a trace amount of SWNT (only 0.01 wt.%). In addition, the excellent electrical conductivity of SWNT forms a conductive network, ensuring continuous and stable electrical sensing. These strain sensors enable comprehensive and precise monitoring of human behavior through both electrical (relative resistance change) and optical (ML intensity) methods, paving the way for the development of advanced visual sensing and smart wearable electronics in the future.

Authors

Institutions

Publication Details

Journal
Small
Published
2024-10-02
DOI
https://doi.org/10.1002/smll.202406398
Citations
9
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
1.66

Funders

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

Mechanoluminescent/Electric Dual‐Mode Sensors Enabled by Trace Carbon Nanotubes

Lin Dong, Shuge Dai, Yuan Deng, Danni Peng et al.
9 citations
Small
Advanced Sensor and Energy Harvesting Materials
1.66
article

Mechanoluminescent/Electric Dual‐Mode Sensors Enabled by Trace Carbon Nanotubes

Lin Dong, Shuge Dai, Yuan Deng, Danni Peng, Yuanyuan Shang, Shulong Chang, Wenjing Liu, Chongxin Shan, Kaiying Zhang
article en
9 citations

Abstract

Mechanoluminescence (ML)-based sensors are emerging as promising wearable devices, attracting attention for their self-powered visualization of mechanical stimuli. However, challenges such as weak brightness, high activation threshold, and intermittent signal output have hindered their development. Here, a mechanoluminescent/electric dual-mode strain sensor is presented that offers enhanced ML sensing and reliable electrical sensing simultaneously. The strain sensor is fabricated via an optimized dip-coating method, featuring a sandwich structure with a single-walled carbon nanotube (SWNT) interlayer and two polydimethylsiloxane (PDMS)/ZnS:Cu luminescence layers. The integral mechanical reinforcement framework provided by the SWNT interlayer improves the ML intensity of the SWNT/PDMS/ZnS:Cu composite film. Compared to conventional nanoparticle fillers, the ML intensity is enhanced nearly tenfold with a trace amount of SWNT (only 0.01 wt.%). In addition, the excellent electrical conductivity of SWNT forms a conductive network, ensuring continuous and stable electrical sensing. These strain sensors enable comprehensive and precise monitoring of human behavior through both electrical (relative resistance change) and optical (ML intensity) methods, paving the way for the development of advanced visual sensing and smart wearable electronics in the future.

SmallVol. 20(50)
Hunan Institute of Engineering (CN), Zhengzhou University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 17%
Advanced Sensor and Energy Harvesting Materials
1.66
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.