Polyurethane sponges-based ultrasensitive pressure sensor via bioinspired microstructure generated by pre-strain strategy. (12th April 2022)

• Record Type: Journal Article
• Authors: Yang, Heng; Shang, Jia-Chen; Wang, Wen-Feng; Yang, Yi-Fan; Yuan, Ya-Nan; Lei, Hong-Shuai; Fang, Dai-Ning
• Is Part Of: Composites science and technology. Volume 221(2022)
• Publication Date: 2022-04-12
• Subjects: A. Flexible composites -- B. Electro-mechanical behavior -- B. Sensing -- Pressure sensor -- Sensing model; Composite materials -- Periodicals; Composite materials; Fibrous composites; Periodicals; 620.118
• DOI: 10.1016/j.compscitech.2022.109308
• Languages: English
• ISSNs: 0266-3538

Abstract

Abstract: Highly sensitive flexible composite-based pressure sensors have recently attracted extensive attention. However, the realization of ultrasensitive pressure sensors is still challenging owing to their cumbersome and expensive preparation processes. This paper reports a design and preparation method for an excellently sensitive and easy-to-manufacture flexible composites-based pressure sensor using a pre-strain strategy. This is achieved by the combined effects of the first applied pre-strain and the subsequent dip-coating treatment of graphene for the polyurethane (PU) sponges to generate bionic structures such as micro-wrinkles and micro-cracks. The sponges-based sensors achieved an outstanding sensitivity of 158.1 kPa −1 owing to the presence of these bioinspired microstructures, which also demonstrate a broad sensing range of 11 kPa, a quick response time of 100 ms, and satisfactory stability in 500 cycles. The effect of pre-strain and graphene content on the sensing performances of pressure sensors was systematically studied. An analytical model was proposed based on the theory of contact mechanics and tunneling effect, providing a theoretical basis for designing and improving conductive sponge-based flexible pressure sensors. The ultrasensitive pressure sensors can detect human pulse and process the ability to identify surfaces with different roughness, demonstrating great potential for improving robotic hands and human wearable devices. Graphical abstract: Image 1