Self-Adhesive and Reprocessable Ionogel Sensor from Controllable Ionized Corncob Cellulose

TL;DR

This paper introduces a self-adhesive, reprocessable ionogel sensor made from corncob cellulose, offering strong mechanical and thermal properties for use in wearable electronics.

Contribution

The study pioneers the use of corncob cellulose as a substrate for ionogel sensors through novel chemical modification techniques.

Findings

The ionogel exhibits high tensile strength (1.28 MPa) and elongation (573%) with excellent thermal and cryogenic stability.

It demonstrates strong adhesion (4.23 MPa) to various surfaces and high sensing sensitivity (GF = 1.23–2.08) for wearable electronics.

The sensor can detect subtle strains and accurately acquire electrocardiogram (ECG) signals.

Abstract

In recent years, the disposal of agricultural lignocellulosic residues has been accompanied by problems such as resource waste and environmental pollution. Therefore, the development of valorization technologies has emerged as a strategic priority in sustainable materials science. This study pioneered the use of corncob cellulose as the substrate (a representative agricultural lignocellulosic residue) and transformed it into ionized cellulose by grafting methacryloxyethyl trimethyl ammonium chloride (DMC) via atom transfer radical polymerization (ATRP) and UV-initiated polymerization. Characterizations demonstrated exceptional properties: robust mechanical strength (1.28 MPa tensile strength with 573% elongation); outstanding thermal stability (stable to 278 °C); cryogenic tolerance (retaining flexibility at −25 °C); and universal adhesion capability (4.23 MPa to glass substrates, with adequate interfacial bonding across diverse surfaces). Meanwhile, the ionogel exhibited exceptional sensing sensitivity (gauge factor, GF = 1.23–2.08), demonstrating versatile application potential in wearable electronics. It achieved the precise detection of subtle strains (1–5% strain range) and the high-fidelity acquisition of electrocardiogram (ECG) signals. This study broadens the design paradigm of agricultural lignocellulosic residue-based functional materials. It also provides a novel technical pathway to develop eco-friendly intelligent sensors.

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