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Flexible Pyroresistive Graphene Composites for Artificial Thermosensation Differentiating Materials and Solvent Types. | LitMetric

AI Article Synopsis

  • Thermosensation helps us detect not just temperature but also wetness and materials, posing challenges for robots and wearables since these abilities come from personal sensory experience.
  • A new artificial thermosensation system, called TCM, uses advanced materials to accurately differentiate contact materials and solvents based on their thermal properties.
  • The TCM can monitor skin temperature and assess properties like coolness and wetness in skincare products, proving effective when combined with machine learning algorithms.

Article Abstract

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) 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.

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Source
http://dx.doi.org/10.1021/acsnano.1c08993DOI Listing

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