Abstract
As an emerging 2D transition metal carbide material with metal-like conductivity, MXene exhibits significant potential in the development of piezoresistive sensors. Nevertheless, achieving favorable combined properties in MXene-based pressure sensors remains challenging. This research explores a method for the assisted deposition of Ag nanoparticles between the layers of waffle-structured MXene by template-directed growth strategy, preparing the chocolate-inlaid Ag@waffle-structured MXene (WSM-A8). With DFT calculations and finite element simulations, the theoretical analysis for the transition of energy bands and the increase of charge density is conducted for the WSM-A8. Meanwhile, the conducting pathways established in WSM-A8 are systematically simulated, verifying the construction of the oriented field-modulation piezoresistive structure proposed in this study. The pressure sensor prepared with WSM-A8 presents the highest ΔI/I(0) response intensity (507 in 210 kPa) among the reported MXene-based piezoresistive sensors with a satisfactory sensitivity (45/30 ms for response/recovery time), which also possesses outstanding structural stability (less than 4% attenuation of the response value after 500 bending cycles) and superior oxidation resistance. Utilizing the convolutional neural network and machine learning, the recognition accuracy of the integrated device is effectively improved. This study provides a feasible approach for realizing real-time pressure monitoring, demonstrating great potential in medical diagnosis, intelligent actuators, and human-computer interactions.