Using high throughput experimental data and in silico models to discover alternatives to toxic chromate corrosion inhibitors. (May 2016)

• Record Type: Journal Article
• Authors: Winkler, D.A.; Breedon, M.; White, P.; Hughes, A.E.; Sapper, E.D.; Cole, I.
• Is Part Of: Corrosion science. Volume 106(2016)
• Page Start: 229
• Page End: 235
• Publication Date: 2016-05
• Subjects: A. alloy -- A. aluminium -- B. modelling studies -- C. neutral inhibition; Corrosion and anti-corrosives -- Periodicals; 620.11223
• DOI: 10.1016/j.corsci.2016.02.008
• Languages: English
• ISSNs: 0010-938X

Abstract

Graphical abstract: Highlights: We screened a large library of organic compounds as replacements for toxic chromates. High throughput automated corrosion testing was used to assess inhibitor performance. Robust, predictive machine learning models of corrosion inhibition were developed. Models indicated molecular features contributing to performance of organic inhibitors. We also showed that quantum chemistry descriptors do not correlate with performance. Abstract: Restrictions on the use of toxic chromate-based corrosion inhibitors have created important issues for the aerospace and other industries. Benign alternatives that offer similar or superior performance are needed. We used high throughput experiments to assess 100 small organic molecules as potential inhibitors of corrosion in aerospace aluminium alloys AA2024 and AA7075. We generated robust, predictive, quantitative computational models of inhibitor efficiency at two pH values using these data. The models identified molecular features of inhibitor molecules that had the greatest impact on corrosion inhibition. Models can be used to discover better corrosion inhibitors by screening libraries of organic compounds for candidates with high corrosion inhibition.