Healable, Degradable, and Conductive MXene Nanocomposite Hydrogel for Multifunctional Epidermal Sensors.
Xiaobin LiLingzhang HeYanfei LiMingyuan ChaoMingkun LiPengbo WanLiqun ZhangPublished in: ACS nano (2021)
Conductive hydrogels have emerged as promising material candidates for epidermal sensors due to their similarity to biological tissues, good wearability, and high accuracy of information acquisition. However, it is difficult to simultaneously achieve conductive hydrogel-based epidermal sensors with reliable healability for long-term usage, robust mechanical property, environmental degradability for decreased electronic waste, and sensing capability of the physiological stimuli and the electrophysiological signals. Herein, we propose the synthesis strategy of a multifunctional epidermal sensor based on the highly stretchable, self-healing, degradable, and biocompatible nanocomposite hydrogel, which is fabricated from the conformal coating of a MXene (Ti3C2Tx) network by the hydrogel polymer networks involving poly(acrylic acid) and amorphous calcium carbonate. The epidermal sensor can be employed to sensitively detect human motions with the fast response time (20 ms) and to serve as electronic skins for wirelessly monitoring the electrophysiological signals (such as the electromyogram and electrocardiogram signals). Meanwhile, the multifunctional epidermal sensor could be degraded in phosphate buffered saline solution, which could not cause any pollution to the environment. This line of research work sheds light on the fabrication of the healable, degradable, and electrophysiological signal-sensitive conductive hydrogel-based epidermal sensors with potential applications in human-machine interactions, healthy diagnosis, and smart robot prosthesis devices.
Keyphrases
- wound healing
- drug delivery
- tissue engineering
- reduced graphene oxide
- hyaluronic acid
- low cost
- endothelial cells
- cancer therapy
- heavy metals
- risk assessment
- multiple sclerosis
- quantum dots
- gene expression
- healthcare
- human health
- ms ms
- machine learning
- induced pluripotent stem cells
- deep learning
- pluripotent stem cells
- highly efficient
- health information
- sewage sludge
- carbon nanotubes