Affiliation:
1. Robert Frederick Smith School of Chemical and Biomolecular Engineering Cornell University Ithaca NY 14853 USA
2. Advanced Materials and Healthcare Technologies University of Nottingham Nottingham NG7 2RD UK
Abstract
AbstractAmphiphilic copolymers (AP) represent a class of novel antibiofouling materials whose chemistry and composition can be tuned to optimize their performance. However, the enormous chemistry‐composition design space associated with AP makes their performance optimization laborious; it is not experimentally feasible to assess and validate all possible AP compositions even with the use of rapid screening methodologies. To address this constraint, a robust model development paradigm is reported, yielding a versatile machine learning approach that accurately predicts biofilm formation by Pseudomonas aeruginosa on a library of AP. The model excels in extracting underlying patterns in a “pooled” dataset from various experimental sources, thereby expanding the design space accessible to the model to a much larger selection of AP chemistries and compositions. The model is used to screen virtual libraries of AP for identification of best‐performing candidates for experimental validation. Initiated chemical vapor deposition is used for the precision synthesis of the model‐selected AP chemistries and compositions for validation at solid–liquid interface (often used in conventional antifouling studies) as well as the air–liquid–solid triple interface. Despite the vastly different growth conditions, the model successfully identifies the best‐performing AP for biofilm inhibition at the triple interface.
Funder
Office of Naval Research
Cornell Center for Materials Research
University Of Nottingham
Wellcome Trust
Subject
Industrial and Manufacturing Engineering,Mechanics of Materials,General Materials Science
Cited by
2 articles.
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