posted on 2022-01-12, 20:05authored byYoungoh Lee, Jonghwa Park, Ayoung Choe, Young-Eun Shin, Jinyoung Kim, Jinyoung Myoung, Seungjae Lee, Youngsu Lee, Young-Kyung Kim, Sung Won Yi, Jin Nam, Jeongeun Seo, Hyunhyub Ko
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.