In a recent study published in Science Advances, researchers from the California Institute of Technology, led by Dr. Wei Gao, have developed a machine learning (ML)–powered 3D-printed epifluidic electronic skin for multimodal health surveillance. This wearable platform enables real-time physical and chemical monitoring of health status.
The team used the MXene to address a limitation with current wearable systems. In the words of Dr. Gao,"Most current wearable systems rely on batteries, which are rigid, bulky, and insufficient, necessitating frequent replacement."-skin integrates a solar cell, harvesting energy from ambient light and efficiently storing it in 3D-printed MXene-based micro-supercapacitors.
MXene nanosheets possess properties such as negatively charged surfaces and hydrophilicity, which enable them to disperse and remain stable in water. This allows for precise printing, with MXene filaments having adjustable line widths and the ability to adhere to flexible substrates, like human skin.
Dr. Gao further emphasized,"The printed MXene filaments can form uniform arrays with intricate patterns, enabling the creation of complex structures within the eMXene's versatility extends to temperature sensing, with sensors exhibiting a negative temperature coefficient and wear stability. For pulse monitoring, MXene, in combination with carbon nanotubes, forms sensors with customizable foam designs, ensuring high sensitivity and durability. Notably, this enables reliable radial pulse monitoring on human subjects.-skin's capabilities extend to predicting behavioral responses to alcohol consumption, which they demonstrated. Dr.
ML analyzes this data to predict an individual's response time and degree of impairment. Sweat alcohol plays a pivotal role in predicting response time, while heart rate complements sweat alcohol for more accurate impairment prediction.
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