Akademska digitalna zbirka SLovenije - logo
E-viri
Celotno besedilo
Recenzirano Odprti dostop
  • Porous graphene foam compos...
    Chen, Xue; Li, Runze; Niu, Guangyu; Xin, Mingyang; Xu, Guizhi; Cheng, Huanyu; Yang, Li

    Chemical engineering journal (Lausanne, Switzerland : 1996), 09/2022, Letnik: 444
    Journal Article

    Display omitted •Porous graphene foam composites are explored for the waterproof dual-mode sensor.•The sensor has high sensitivity over large strain range with ultralow detection limit.•The sensor also exhibits high temperature sensitivity over a wide linear range.•The waterproof sensor detects movement and temperature in dry and wet conditions.•It captures subtle pulse waveforms from distal arteries and skeletal muscle motions. Soft multimodal sensors in practical applications for health monitoring and human–machine interfaces require them to show sufficiently high sensitivity over wide sensing rages, rapid response and excellent durability, and waterproof property. Herein, we demonstrate a dual-mode sensor based on a porous graphene foam composite to achieve the aforementioned challenging yet attractive performance parameters for strain and temperature sensing. The resulting dual-mode sensor exhibits a strain sensitivity of 2212.5 in the wide piecewise linear range of 0–65%, a rapid response of 0.11 s, an ultralow detection limit of 0.0167%, and outstanding stability over 15,000 cycles. The sensor can also detect temperature with a high sensitivity of 0.97 × 10-2 °C -1 over a wide linear range of 10–185 °C and a small detection limit for sensing both low- and high-temperature environments. Taken together with the waterproof property, the dual-mode sensor can accurately monitor the large, small, and even subtle motions and temperature variations in both dry and underwater conditions. The capability to detect the subtle yet rapidly changing motions from distal arteries and skeletal muscles paves the way for the development of future soft multimodal, waterproof electronic sensing devices toward human–computer interaction, health monitoring and early disease prevention, and personalized medicine.