When we touch an object, thermosensation allows us to perceive not only the temperature but also wetness and types of materials with different thermophysical properties (i.e., thermal conductivity and heat capacity) of objects. Emulation of such sensory abilities is important in robots, wearables, and haptic interfaces, but it is challenging because they are not directly perceptible sensations but rather learned abilities via sensory experiences. Emulating the thermosensation of human skin, we introduce an artificial thermosensation based on an intelligent thermo-/calorimeter (TCM) that can objectively differentiate types of contact materials and solvents with different thermophysical properties. We demonstrate a TCM based on pyroresistive composites with ultrahigh sensitivity (11.2% °C-1) and high accuracy (<0.1 °C) by precisely controlling the melt-induced volume expansion of a semicrystalline polymer, as well as the negative temperature coefficient of reduced graphene oxide. In addition, the ultrathin TCM with coplanar electrode design shows deformation-insensitive temperature sensing, facilitating wearable skin temperature monitoring with accuracy higher than a commercial thermometer. Moreover, the TCM with a high pyroresistivity can objectively differentiate types of contact materials and solvents with different thermophysical properties. In a proof-of-principle application, our intelligent TCM, coupled with a machine-learning algorithm, enables objective evaluation of the thermal attributes (coolness and wetness) of skincare products.
Keywords: calorimeter; high sensitivity; intelligent sensor; pyroresistivity; thermometer; thermosensation.