An in-situ integrated material distinction sensor based on density and heat capacity

Abstract

Material distinction via artificial intelligence (AI) has been studied extensively and demonstrates strong capacity. However, a high-performance sensor and accurate theoretical model remain necessary to assist AI in precise identification. Here, we developed an in-situ integrated material distinction sensor, which depends on the density (ρ) and heat capacity (C) of measured matter. The device is fabricated using an outer nickel (Ni) thermistor and an inner laser-induced graphene (LIG) heater on a polyimide substrate. When a sample is placed on the surface of the sensor and a certain voltage is applied to the LIG heater, the Ni thermistor will perceive the temperature changing trends, which can reflect the heat absorption and conduction ability of the placed material. Notably, finite element analysis was conducted and the simulation results are perfectly combined with the experimental results, which implied that the ρ and C of the sample together determined the temperature distribution, instead of the thermal conductivity (κ). By establishing a heat absorption model, each kind of material can be identified with a feature value. A novel sensor and corresponding physical model are provided to perceive the material properties, improving the range and accuracy of material identification based on a contact-mode sensing mechanism.