The method creates materials used in wearable heating applications. (Credit: University of Maryland)

Researchers have developed a machine learning model that eliminates hassles in materials design to yield green technologies used in wearable heaters. The model could automate design processes by leveraging machine learning and collaborative robotics.

Similar to water-based gels, but instead made using air, aerogels are lightweight and porous materials used in thermal insulation and wearable technologies for their mechanical strength and flexibility. But despite their seemingly simplistic nature, their assembly line is quite complex. Researchers rely on endless experiments and experience-based approaches to explore the vast design space and design these materials.

To solve these issues, the researchers combined robotics, machine-learning algorithms, and material science expertise to enable aerogel design with programmable mechanical and electrical properties — breaking through scientific barriers at full speed. The prediction model is built to generate sustainable products with a 95% accuracy rate.

The resulting strong and flexible aerogels were made using conductive titanium nanosheets, as well as naturally occurring components such as cellulose; an organic compound found in plant cells, and gelatin; a collagen-derived protein found in animal tissue and bones.

Source 

Topics:
Medical Wearables

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