Wibowo, AF;
Sasongko, NA;
Puspitasari, A;
Vo, TT;
Entifar, SAN;
Sembiring, YS;
Kim, JH;
... Kim, YH; + view all
(2025)
Exceptionally low electrical hysteresis, soft, skin-mimicking gelatin-based conductive hydrogels for machine learning-assisted wireless wearable sensors.
Chemical Engineering Journal
, 526
, Article 170741. 10.1016/j.cej.2025.170741.
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Abstract
Hydrogels are promising candidates for sustainable wearable sensors due to their intrinsic stretchability, conductivity, and biocompatibility. Here, we present a gelatin (Gel)-based hydrogel reinforced with a hybrid conductive filler of silver nanowires (AgNWs) and poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS). Strategic crosslinking with glutaraldehyde (GA) provides enhanced mechanical robustness and electromechanical stability. The optimized hydrogel exhibits a working strain of up to 200 % with ultralow hysteresis (<3.5 % at 200 % strain), surpassing many reported conductive hydrogels. Mechanistic insights from Raman spectroscopy and ab initio calculations reveal that glycerol/polyethylene glycol-induced helix-to-coil transitions, together with GA crosslinking, increase molecular flexibility and stabilize the conductive network. As a wearable on-skin sensor, the hydrogel reliably monitors diverse physiological activities, including handwriting, arterial pulses, and facial expressions. Furthermore, integration with a wireless system and machine learning enables accurate motion classification. This study represents one of the first systematic demonstrations of gelatin-based conductive hydrogels with ultralow hysteresis and high stretchability, highlighting their potential for next-generation intelligent and eco-friendly wearable sensors.
| Type: | Article |
|---|---|
| Title: | Exceptionally low electrical hysteresis, soft, skin-mimicking gelatin-based conductive hydrogels for machine learning-assisted wireless wearable sensors |
| DOI: | 10.1016/j.cej.2025.170741 |
| Publisher version: | https://doi.org/10.1016/j.cej.2025.170741 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Gelatin, Ultralow hysteresis, Silver nanowires, Sensors, Machine learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10219304 |
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