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INDUSTRY

TITLE Highly Oxidation-Resistant and Self-Healable MXene-Based Hydrogels for Wearable Strain Sensor
AUTHOR Ari Chae, G. Murali, Seul-Yi Lee, Jeonghwan Gwak, Seon Joon Kim, Yong Jin Jeong, Hansol Kang, Seongmin Park, Albert S. Lee, Dong-Yeun Koh, Insik In, Soo-Jin Park
YEAR 2023
JOURNAL Advanced Functional Materials
ABSTRACT

Very recently, MXene-based wearable hydrogels have emerged as promising candidates for epidermal sensors due to their tissue-like softness and unique electrical and mechanical properties. However, it remains a challenge to achieve MXene-based hydrogels with reliable sensing performance and prolonged service life, because MXene inevitably oxidizes in water-containing system of the hydrogels. Herein, catechol-functionalized poly(vinyl alcohol) (PVA-CA)-based hydrogels is proposed to inhibit the oxidation of MXene, leading to rapid self-healing and superior strain sensing behaviors. Sufficient interaction of hydrophobic catechol groups with the MXene surface reduces the oxidation-accessible sites in the MXene for reaction with water and eventually suppresses the oxidation of MXene in the hydrogel. Furthermore, the PVA-CA-MXene hydrogel is demonstrated for use as a strain sensor for real-time motion monitoring, such as detecting subtle human motions and handwriting. The signals of PVA-CA-MXene hydrogel sensor can be accurately classified using deep learning models.

 
FULL ARTICLE https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202213382
INSTRUMENT FTIR-4600
KEYWORDS deep learning, hydrogels, MXenes, oxidation, poly(vinyl alcohol), sensors
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