Embedding computation in a material that cures cold.
Consumer electronics rely on materials that are high in carbon emissions and difficult to recycle. As demand for embedded computing grows, so does the need for alternatives that reduce environmental impact while still supporting electronic function.
This work advances the machine-learning-assisted formulation and application of geopolymers, and the techniques for embedding sensors and components as the material sets.