An MXene/cellulose nanofiber-based multi-parametric tactile sensor for object material identification

We propose a novel multi-parametric tactile sensor that can realize force and slip sensing during the contact between a manipulator and a target. The pressure-sensitive unit of this sensor is prepared by homemade MXene and cellulose nanofiber (CNF) composite conductive film, which has high sensitivity of pressure-sensitive response. The triboelectric unit is made of a chemically stable PTFE film with high electron-acquiring ability as the triboelectric sensing layer together with metal electrodes. Experiments have demonstrated that the prepared sensors have high sensitivity, fast response, and excellent durability and stability over 5000 cycles. Combined with the established machine learning algorithm model, the pressure and triboelectric signals of this multi-parametric tactile sensor were used to recognize 15 types of materials with similar characteristics and with 94.33% identification accuracy. This work shows the great potential of multi-parametric tactile sensors for applications in item sorting, human-machine interaction, and other fields.