Exceptionally low electrical hysteresis, soft, skin-mimicking gelatin-based conductive hydrogels for machine learning-assisted wireless wearable sensors
- Authors
- Wibowo, Anky Fitrian; Sasongko, Nurwarrohman Andre; Puspitasari, Anita; Vo, Truong Tien; Entifar, Siti Aisyah Nurmaulia; Sembiring, Yulia Shara; Kim, Jung Ha; Azizi, Muhamad Junda; Slamet, Muhammad Nur; Oh, Junghwan; Park, Jae-Seong; Kim, Soyeon; Lim, Dong Chan; Moon, Myoung-Woon; Kim, Min-Seok; Park, Myeongkee; Kim, 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
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.