Driven by the rapid development of wearable electronics and smart healthcare, alongside the progressive realization of “dual-carbon” objectives, the quest for flexible piezoresistive sensors that combine high performance with environmental sustainability has emerged as a pivotal research focus within the flexible sensing domain. Conventional flexible piezoresistive sensors, however, often fall short in achieving a delicate balance between superior sensing capabilities and ecological friendliness. Drawing inspiration from the gradient structures prevalent in natural flora, this study innovatively incorporates an environmentally benign and highly efficient supercritical carbon dioxide (sc-CO2) foaming technique. Utilizing biodegradable poly(butylene succinate) (PBS) as the matrix material and carbon nanotubes (CNTs) as conductive functional fillers, we fabricated a PBS/CNTs composite foam piezoresistive sensor (FPS) with a symmetric gradient cell structure (SGCS) through precise modulation of gas saturation pressure. This fabrication process obviates the need for chemical foaming agents, thereby circumventing potential issues associated with chemical residues and environmental contamination. The resultant composite foam exhibits both gradient modulus and lightweight properties, facilitating a synergistic enhancement of sensing sensitivity and pressure detection range. The sensor demonstrates a remarkable sensitivity of up to 0.706 kPa–1 (0–20 kPa), coupled with a rapid response time of merely 90 ms. Furthermore, the FPS is capable of accurately monitoring human motions of varying amplitudes, including pulse signals, speech-induced vibrations, and joint flexion and extension, thereby showcasing its extensive potential applications in wearable electronics, early disease diagnosis, and motion monitoring. This study offers a design paradigm for the development of high-performance, eco-friendly FPS, providing valuable technical insights for the advancement of wearable electronics, motion monitoring, healthcare, and human-machine interaction.
Zhao et al. (Wed,) studied this question.