Super-slick Coatings: Data Driven Design to Application
(Top left) Slices of a machine learning model designed to capture self-assembly of molecular monolayers on perovskite surfaces. (Top right) Photograph of a superslick surface comprising ZnO tetrapods functionalized with a molecular monolayer. (Lower) Lacy Douglas, Bill Tolar, Erick Braham, and Sarbajit Banerjee at I-corps Kick-off in Chicago.
Sarbajit Banerjee
Nature has many examples of non-wettable surfaces such as shark skin and the lotus leaf. However, there are no natural examples of universally non-wettable coatings that can glide both water and viscous oil. This DMREF project is focused on the design of earth-abundant photocatalysts for water splitting. Machine learning methods are being used to rapidly and efficiently navigate the design space of mesoscale textures and surface chemistry. In order to selectively perform reactions other than water splitting, we have sought to design surfaces that are non-wettable by water or other solvents and that allow for directed flow profiles of reactants. While these coatings were initially designed to facilitate selectivity in electrocatalytic transformations, it is clear that they have great potential for the transportation and processing of oils and viscous liquids. Through participation in the NSF I-corps program, team Laminel was created. At the conclusion of the program, the team received the “Cohort Effect” Award for excellence in fostering connections, supporting teams, and leading by example. The team is continuing to move forward through the path of commercialization with support from large extractors and transporters of bitumen.