A high recognition accuracy tactile sensor based on boron nitride nanosheets/epoxy composites for material identification.

Tactile sensors based on triboelectric nanogenerators (TENGs) showed great potential for self-driven sensing in material identification. The existing TENG devices used strongly electrophilic materials as friction layers. For test materials with electrophilicity, their output signals are weak and difficult to efficiently recognize. Here, a TENG-based sensor with boron nitride nanosheets/waterborne epoxy (BNNSs/WEP) composites as the friction layer was proposed for improving the accuracy of identifying negative charged materials. During the process of contact friction with negative charged objects, the as-fabricated TENG device displayed excellent output performance, with a maximum output voltage of 2.7 V and a charge density of 88.32 nC m^-2. Combining deep machine learning and the friction electric effect, we developed a material recognition system for TENG sensors with integrated fatigue testing, data processing, and display modules. Following the training of the convolutional neural network (CNN) model with friction electrical signals generated by TENGs, the model demonstrated high accuracy in recognizing eight different materials, with a confusion matrix accuracy of 100%. Then, a sensor was developed for real-time device monitoring, with recognition accuracy of 100%, 100%, 55% and 49% for four kinds of materials. This work will further facilitate the development of a material perception system in the machine intelligence field.