Matrix Swelling-InducedPrecracking in Graphene WovenFabric for Ultrasensitive Strain Sensors

The growing demand for highly sensitive flexible strain sensors in applications such as wearable electronics, healthcare monitoring, and environmental sensing has driven the development of materials capable of detecting subtle deformations with high precision. Herein, we introduce a precracked strain sensor based on solvent-swollen graphene woven fabric/polydimethylsiloxane (sGWF/PDMS) composites, designed to achieve ultrahigh gauge factors (GFs) and enhanced responsiveness to minor deformations. By utilizing PDMS swelling to induce network microcracks within the graphene structure, the sGWF/PDMS composites exhibit substantially improved sensitivity compared to traditional graphene-based strain sensors. Systematic <i>in situ</i> SEM analyses reveal that these preexisting microcracks expand readily under minor strain, resulting in rapid resistance changes that underpin the high sensitivity achieved. With GFs reaching up to 82,378 at only 2.8% stretching strain, the sGWF/PDMS composites demonstrate excellent performance across various applications, including human motion detection such as monitoring pulses, eye blinks, and speech-related movements, as well as detecting environmental disturbances such as water surface ripples. These findings highlight matrix-swollen composites as a promising platform for high-sensitivity, low-strain detection, offering great potential for advancements in wearable electronics, environmental monitoring, and other precision sensing applications.

Publication Details

Journal
Figshare
Published
2025-01-15
DOI
https://doi.org/10.1021/acsomega.4c10403.s001
Primary Topic
Mycorrhizal Fungi and Plant Interactions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Matrix Swelling-InducedPrecracking in Graphene WovenFabric for Ultrasensitive Strain Sensors

Figshare
Mycorrhizal Fungi and Plant Interactions
article

Matrix Swelling-InducedPrecracking in Graphene WovenFabric for Ultrasensitive Strain Sensors

article en

Abstract

The growing demand for highly sensitive flexible strain sensors in applications such as wearable electronics, healthcare monitoring, and environmental sensing has driven the development of materials capable of detecting subtle deformations with high precision. Herein, we introduce a precracked strain sensor based on solvent-swollen graphene woven fabric/polydimethylsiloxane (sGWF/PDMS) composites, designed to achieve ultrahigh gauge factors (GFs) and enhanced responsiveness to minor deformations. By utilizing PDMS swelling to induce network microcracks within the graphene structure, the sGWF/PDMS composites exhibit substantially improved sensitivity compared to traditional graphene-based strain sensors. Systematic <i>in situ</i> SEM analyses reveal that these preexisting microcracks expand readily under minor strain, resulting in rapid resistance changes that underpin the high sensitivity achieved. With GFs reaching up to 82,378 at only 2.8% stretching strain, the sGWF/PDMS composites demonstrate excellent performance across various applications, including human motion detection such as monitoring pulses, eye blinks, and speech-related movements, as well as detecting environmental disturbances such as water surface ripples. These findings highlight matrix-swollen composites as a promising platform for high-sensitivity, low-strain detection, offering great potential for advancements in wearable electronics, environmental monitoring, and other precision sensing applications.

Figshare
Openalex Percentile: Top 53%
Mycorrhizal Fungi and Plant Interactions
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.