Construction of a Two-Dimensional Response Network in Three-Dimensional Composites to Dramatically Enhance Sensor Sensitivity: A Simple, Feasible, and Green Regulating Strategy

A simple, feasible, and environmentally friendly method for supercritical fluid-assisted construction of a two-dimensional (2D) response network in three-dimensional (3D) composites is proposed to improve sensor performance so as to meet the requirement of new-generation strain sensors including high sensitivity, large strain range, and other auxiliary properties, such as light weight and thermal insulation. The mechanism is that cell wall stretching in the supercritical fluid foaming process changes the conductive fiber dispersive status by eliminating agglomeration and facilitating orientation in 3D cells. For the thermoplastic polyurethane (TPU)/carbon nanofiber (CNF) foam strain sensors manufactured in this work, there is an optimal cellular structure, that is, an optimal 2D response network in 3D composites to maximize sensitivity, and the gauge factor (GF) of the foamed TPU/CNF strain sensor is increased by 89 times compared to that of the unfoamed composite, together with increased thermal insulation and lightweight performance than the unfoamed composite. The obtained TPU/CNF foam strain sensor shows good stability and repeatability in human motion detection. To enlighten the structural design, a 2D response network model in a 3D foam for a foam strain sensor is established for the first time. The model reveals that filler content and aspect ratio are the key points to regulate the response network and there is an optimal value for these two factors to maximize the sensitivity in a foam strain sensor. Therefore, the knowledge on foam strain sensors based on the experimental and theoretical results in this work provides a new structural design strategy for sensing materials and guides the optimization of the sensing structure to improve sensor sensitivity.

Automated summary

A novel method for constructing a two-dimensional response network in three-dimensional composites using supercritical fluids is proposed to enhance sensor performance, including high sensitivity and large strain range. By establishing a 2D response network model for foam strain sensors, it is found that filler content and aspect ratio are crucial factors affecting sensor performance.