4.8 Article

Wearable Capacitive Pressure Sensor Based on MXene Composite Nanofibrous Scaffolds for Reliable Human Physiological Signal Acquisition

Journal

ACS APPLIED MATERIALS & INTERFACES
Volume 12, Issue 19, Pages 22212-22224

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsami.0c05819

Keywords

MXene/PVDF-TrFE; wearable sensor; composite nanofiber; capacitive pressure sensor; physiological signal monitoring

Funding

  1. Bio AMP
  2. Medical Technology Development Program of the NRF - Korean government (MSIT) [NRF-2017M3A9F1031270]

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In recent years, highly sensitive pressure sensors that are flexible, biocompatible, and stretchable have attracted significant research attention in the fields of wearable electronics and smart skin. However, there has been a considerable challenge to simultaneously achieve highly sensitive, low-cost sensors coupled with optimum mechanical stability and an ultralow detection limit for subtle physiological signal monitoring devices. Targeting aforementioned issues, herein, we report the facile fabrication of a highly sensitive and reliable capacitive pressure sensor for ultralow-pressure measurement by sandwiching MXene (Ti3C2Tx)/poly(vinylidene fluoride-trifluoroethylene) (PVDF-TrFE) composite nanofibrous scaffolds as a dielectric layer between biocompatible poly-(3,4-ethylenedioxythiophene) poly- styrene sulfonate /polydimethylsiloxane electrodes. The fabricated sensor exhibits a high sensitivity of 0.51 kPa(-1) and a minimum detection limit of 1.5 Pa. In addition, it also enables linear sensing over a broad pressure range (0-400 kPa) and high reliability over 10,000 cycles even at extremely high pressure (>167 kPa). The sensitivity of the nanofiber-based sensor is enhanced by MXene loading, thereby increasing the dielectric constant up to 40 and reducing the compression modulus to 58% compared with pristine PVDF-TrFE nanofiber scaffolds. The proposed sensor can be used to determine the health condition of patients by monitoring physiological signals (pulse rate, respiration, muscle movements, and eye twitching) and also represents a good candidate for a next generation human-machine interfacing device.

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