4.8 Article

Anisotropic, Flexible Wood Hydrogels and Wrinkled, Electrodeposited Film Electrodes for Highly Sensitive, Wide-Range Pressure Sensing

期刊

ACS APPLIED MATERIALS & INTERFACES
卷 12, 期 38, 页码 43024-43031

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acsami.0c13962

关键词

anisotropy; wood hydrogels; flexibility; capacitive pressure sensors; wide-range

资金

  1. National Key R&D Program of China [2018YFD0600302]
  2. Young Elite Scientists Sponsorship Program by CAST [2018QNRC001]
  3. Zhejiang Provincial Natural Science Foundation of China [LQ19C160015]
  4. National Innovation and Entrepreneurship Training Program for College Students [201910341010]

向作者/读者索取更多资源

Biological muscles generally possess well-aligned muscle fibers and thus excellent strength and toughness. Inspired by their microstructure, tough wood hydrogels with a preserved unique alignment of cellulose fibers, mechanical anisotropy, and desirable flexibility were developed by introducing chemically and ionically cross-linked poly(acrylic acid) (PAA) into the abundant pores of delignified wood. PAA chains well infiltrated the parallelly aligned cellulose fibers of wood and formed a layer-by-layer network structure, resulting in strong, elastic tangential, and radial wood hydrogel slices. The tangential slices had a high compressive strength of 1.73 MPa and a maximum strain at fracture of 69.4%, while those of the radial slices were 0.6 MPa and 47.0%. After sandwiching the radial and tangential hydrogel slices with reduced graphene oxide (rGO) film electrodes into capacitive pressure sensors (CPSs), the tangential CPS displayed the most desired, gradient sensitivity values in the whole stress range. Additionally, the wrinkling treatment of the rGO electrode greatly improved the capacitance responsiveness toward pressure. The real-time sensing stress values of our tangential CPS during monitoring practical human activities were calculated in the range of 0.1-1.3 MPa, demonstrating the achievement of ultrafast, highly sensitive, and wide-stress-range detection for potential applications in human-machine interfaces.

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