Self cleaning surfaces

Accelerate your research into self cleaning surfaces with resistivity, optical, and XRD measurements

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Self cleaning windows consist of a photocatalytic material which reacts with light to break down organic dirt, and a hydrophobic material which allows the water to form an even ‘sheet’ on the window which then can dry without forming droplets on the window. Finding the ideal materials for the window coatings is important so the window can have self cleaning properties. This database including millions of optical, sheet resistance, composition, XRD, and Raman measurements for thousands of materials which can accelerate research and development into coatings for self cleaning windows.

Accelerating self cleaning window R&D with machine learning

Machine learning algorithms in combination with novel experimental data can accelerate the process of materials design and discovery for self cleaning windows applications. Using machine learning algorithms on experimental data we can discover new hydrophobic transparent photocatalysts which can serve as coatings for glass, get ‘materials recipes’ for synthesizing materials, and build tools to automatically analyze new measurements to retrieve insights. This could help design new and improved materials for self cleaning windows. 

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CASE STUDY | Combinatorial Lab Accelerates Discovery with MZ

Bar-Ilan conducts hundreds of experiments to identify new photovoltaic compositions. Leveraging the MaterialsZone platform, researchers experienced an 85% cost reduction during innovation cycle.

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