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Multi-Level Pyramidal Microstructure-Based Pressure Sensors with High Sensitivity and Wide Linear Range for Healthcare Monitoring.

Tongge AnYongjun ZhangJiahong WenZhichao DongQifeng DuLong LiuYaxin WangGuozhong XingXiaoyu Zhao
Published in: ACS sensors (2024)
Flexible pressure sensors have garnered significant attention in the field of wearable healthcare due to their scalability and shape variability. However, a crucial challenge in their practical application for various healthcare scenarios is striking a balance between the sensitivity and sensing range. This limitation arises from the reduced compressibility of the microstructures on the surface of pressure-sensitive materials under high pressure, resulting in progressive saturation of the sensor's response and leading to a restricted and nonlinear pressure sensing range. In this study, we present a novel approach utilizing multi-level pyramidal microstructures in flexible pressure sensors to achieve both high sensitivity (8775 kPa -1 ) and linear response ( R 2 = 0.997) over a wide pressure range (up to 1000 kPa). The effectiveness of the proposed design stems from the compensatory behavior of the lower pyramidal microstructures, which counteracts the declining sensitivity associated with the gradual hardening of the higher pyramidal microstructures. Furthermore, the sensor demonstrates a fast response time of 11.6 ms and a fast relaxation time of 3.8 ms and can reliably detect pressures as low as 30.2 Pa. Our findings highlight the applicability of this flexible pressure sensor in diverse human body health detection tasks, ranging from weak pulses to finger flexion and plantar pressure distribution. Notably, the proposed sensor design eliminates the need for replacing flexible pressure sensors with varying ranges, thereby enhancing their practical utility.
Keyphrases
  • healthcare
  • multiple sclerosis
  • randomized controlled trial
  • mass spectrometry
  • endothelial cells
  • mental health
  • climate change
  • risk assessment
  • heart rate
  • neural network