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.

Designing Materials to Revolutionize and Engineer our Future (DMREF)