Human motion-driven self-powered stretchable sensing platform based on laser-induced graphene foams

APPLIED PHYSICS REVIEWS(2022)

引用 76|浏览13
暂无评分
摘要
Practical applications of next-generation stretchable electronics hinge on the development of sustained power supplies to drive highly sensitive on-skin sensors and wireless transmission modules. Although the manufacture of stretchable self-charging power units has been demonstrated by integrating stretchable energy harvesters and power management circuits with energy storage units, they often suffer from low and unstable output power especially under mechanical deformation and human movements, as well as complex and expensive fabrication processes. This work presents a low-cost, scalable, and facile manufacturing approach based on laser-induced graphene foams to yield a self-powered wireless sensing platform. 3D porous foams with high specific surface area and excellent charge transport provide an efficient flow of triboelectric electrons in triboelectric nanogenerators. The surface coating or doping with second laser irradiation on these foams can also form a 3D composite to provide high energy density in micro-supercapacitor arrays. The integration of a triboelectric nanogenerator and power management circuits with micro-supercapacitor arrays can efficiently harvest intermittent mechanical energy from body movements into stable power output. 3D foams and their composites patterned into various geometries conveniently create various deformable sensors on large scale at low cost. The generated stable, yet high, power with adjustable voltage and current outputs drives various stretchable sensors and wireless transmission modules to wirelessly measure pulse, strain, temperature, electrocardiogram, blood pressure, and blood oxygen. The self-powered, wireless, wearable sensing platform paves the way to wirelessly detect clinically relevant biophysical and biochemical signals for early disease diagnostics and healthy aging.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要