A Strand Entangled Supramolecular PANI/PAA Hydrogel Enabled Ultra‐Stretchable Strain Sensor. Issue 47 (10th October 2022)

• Record Type: Journal Article
• Authors: Liu, Dong; Zhou, Honghao; Zhao, Yuanyuan; Huyan, Chenxi; Wang, Zibi; Torun, Hamdi; Guo, Zhanhu; Dai, Sheng; Xu, Ben Bin; Chen, Fei
• Is Part Of: Small. Volume 18:Issue 47(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-10
• Subjects: doping effect -- entanglement -- hydrogels -- strain sensors -- ultra‐stretchable; Nanotechnology -- Periodicals; Nanoparticles -- Periodicals; Microtechnology -- Periodicals; 620.5
• DOI: 10.1002/smll.202203258
• Languages: English
• ISSNs: 1613-6810

Abstract

Abstract: Hydrogel electronics have attracted growing interest for emerging applications in personal healthcare management, human‐machine interaction, etc. Herein, a "doping then gelling" strategy to synthesize supramolecular PANI/PAA hydrogel with a specific strand entangled network is proposed, by doping the PANI with acrylic acid (AA) monomers to avoid PANI aggregation. The high‐density electrostatic interaction between PAA and PANI chains serves as a dynamic bond to initiate the strand entanglement, enabling PAA/PANI hydrogel with ultra‐stretchability (2830%), high breaking strength (120 kPa), and rapid self‐healing properties. Moreover, the PAA/PANI hydrogel‐based sensor with a high strain sensitivity (gauge factor = 12.63), a rapid responding time (222 ms), and a robust conductivity‐based sensing behavior under cyclic stretching is developed. A set of strain sensing applications to precisely monitor human movements is also demonstrated, indicating a promising application prospect as wearable devices. Abstract : A strand entangled supramolecular polyacrylic acid/polyaniline (PAA/PANI) hydrogel is developed via a "doping then gelling" strategy. The designed high‐density electrostatic interaction between PAA and PANI chains endows the PAA/PANI hydrogel with ultra‐stretchability (2830%) and rapid self‐healing properties, thus achieving high strain sensitivity (GF = 12.63) for precisely monitoring human movements.