Exceptionally low electrical hysteresis, soft, skin-mimicking gelatin-based conductive hydrogels for machine learning-assisted wireless wearable sensors

Authors
Wibowo, Anky FitrianSasongko, Nurwarrohman AndrePuspitasari, AnitaVo, Truong TienEntifar, Siti Aisyah NurmauliaSembiring, Yulia SharaKim, Jung HaAzizi, Muhamad JundaSlamet, Muhammad NurOh, JunghwanPark, Jae-SeongKim, SoyeonLim, Dong ChanMoon, Myoung-WoonKim, Min-SeokPark, MyeongkeeKim, Yong Hyun
Issue Date
2025-12
Publisher
Elsevier BV
Citation
Chemical Engineering Journal, v.526
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.
Keywords
Gelatin; Ultralow hysteresis; Silver nanowires; Sensors; Machine learning
ISSN
1385-8947
URI
https://pubs.kist.re.kr/handle/201004/153820
DOI
10.1016/j.cej.2025.170741
Appears in Collections:
KIST Article > 2025
Export
RIS (EndNote)
XLS (Excel)
XML

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE