journal article·2019-01-30

Development of a Highly Sensitive, Broad-Range Hierarchically Structured Reduced Graphene Oxide/PolyHIPE Foam for Pressure Sensing

Lei Yang, Yu Liu, Carlos D. M. Filipe, Darko Ljubic, Yingwu Luo, He Zhu, Jiaxing Yan, Shiping Zhu

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Abstract

Highly sensitive pressure sensors are usually made from soft materials that allow large deformations to be obtained when very small pressures are applied. Unfortunately, this current paradigm limits the ability to create sensors capable of high sensitivities and broad dynamic ranges as these materials are prone to saturation responses when attempting to obtain measurements involving high pressures. In this paper, we detail a piezoresistive pressure sensor that is capable of high sensitivity over a pressure range spanning from 0.6 Pa (a mosquito touching a surface) to 200 kPa (an elephant standing on the surface). The sensor's ability to cover such a broad dynamic range is made possible by the fairly hard foam used in its construction as this material is capable of propagating strain in a highly effective manner due to its hierarchical porous structure. The material was fabricated by using high-internal-phase emulsion (HIPE) as a template to generate a highly porous material consisting of small pores packed between larger ones whose inner walls are lined with reduced graphene oxide. The developed foam exhibits very fast response times (less than 15.4 ms) and excellent cyclic stability (at least 10,000 cycles). Furthermore, it is capable of responding to the entire tactile pressure range, and it can be formatted as pixelated arrays, which makes it highly suitable for integration into wearable electronic devices. Such arrays were built and used to identify and render the shape of objects with different geometries, including a sphere, a triangle, a square, and two nearly identical rods differing only by 0.4 mm in diameter.
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Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Tactile and Sensory Interactions · Neuroscience and Neural Engineering

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Materials and Textiles, Zhejiang Sci-Tech University, Hangzhou 310018, China

Department of Chemical Engineering, McMaster University, Hamilton L8S 4L7, Canada

State Key Lab of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China

School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China

Funding

Natural Sciences and Engineering Research Council of Canada · Natural Science Foundation of Zhejiang Province

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