The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Biocompatible, self-healing, highly stretchable polyacrylic acid/reduced graphene oxide nanocomposite hydrogel sensors via mussel-inspired chemistry
Abstract
Abstract Biocompatible and self-healing hydrogels that mimic the functions of human skin have attracted much attention for skin-like electronics and human motion detection. Integrating high stretchability and improving sensing sensitivity are current challenges. In this study, a nanocomposite hydrogel comprised of polyacrylic acid (PAA) and reduced graphene oxide (rGO) prepared via mussel-inspired chemistry integrates high stretchability (higher than 600%), strong mechanical strength (as high as 400 kPa), excellent self-healing properties, and superior sensing abilities. The outstanding performance of this autonomous self-healing hydrogel originates from its dual-crosslinked networks, which include both physically crosslinked networks and chemically crosslinked networks. The physical crosslinking formed by the ionic interactions between the carboxylic groups of polyacrylic acid and the ferric ions provide reversible self-healing properties for the hydrogel, whereas the covalent bonds provide a stable and strong chemical network for the hydrogel. Due to the effective electric pathways provided via rGO, the hydrogel exhibited strain sensitivity and was able to detect multiple human motions. Moreover, HEF1 fibroblasts differentiated from human embryonic stem cells showed a flourishing living state on the biocompatible hydrogel. The preparation method is simple, easily scaled-up, which will allow for the low cost fabrication of electronic skins and bio-sensors.
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Cites
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Cited by
Mussel-inspired hydrogels: From design principles to promising applications
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Nanocomposite hydrogel-based strain and pressure sensors: a review
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Advances in graphene-based flexible and wearable strain sensors
Chen, H., Zhuo, F., Zhou, J., Liu, Y., Zhang, J., Dong, S., Liu, X., Elmarakbi, A., Duan, H., & Fu, Y. (2023) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.142576 · Cited by 386
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Metadata
- Authors
- Jing, X., Mi, H.-Y., Peng, X.-F., & Turng, L.-S.
- Venue
- Carbon
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 136
- Pages
- 63-72
- ISSN
- 0008-6223 (ISSN-L)
- OpenAlex
- W2802811491
- Cited by
- 351
- References
- 8
- FWCI
- 16.61
- Type
- journal article
- Language
- en
- Open access
- closed
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