Next Article in Journal
Correction: Škugor Rončević, I.; et al. Effective and Environmentally Friendly Nickel Coating on the Magnesium Alloy. Metals 2016, 6, 316
Next Article in Special Issue
Interaction of Model Inhibitor Compounds with Minimalist Cluster Representations of Hydroxyl Terminated Metal Oxide Surfaces
Previous Article in Journal
A Numerical Study on the Excitation of Guided Waves in Rectangular Plates Using Multiple Point Sources
Previous Article in Special Issue
Inhibition of Brass (80/20) by 5-Mercaptopentyl-3-Amino-1,2,4-Triazole in Neutral Solutions
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessFeature PaperReview
Metals 2017, 7(12), 553; https://doi.org/10.3390/met7120553

Predicting the Performance of Organic Corrosion Inhibitors

1,2,3,4,5
1
La Trobe Institute for Molecular Science, La Trobe University, Kingsbury Drive, Bundoora 3086, Australia
2
Monash Institute of Pharmaceutical Sciences, Monash University, 392 Royal Parade, Parkville 3052, Australia
3
School of Chemical and Physical Sciences, Flinders University, Bedford Park 5046, Australia
4
CSIRO Manufacturing, Bayview Avenue, Clayton 3168, Australia
5
School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, UK
Received: 20 November 2017 / Revised: 4 December 2017 / Accepted: 5 December 2017 / Published: 8 December 2017
(This article belongs to the Special Issue Corrosion Inhibition)
Full-Text   |   PDF [695 KB, uploaded 8 December 2017]   |  

Abstract

The withdrawal of effective but toxic corrosion inhibitors has provided an impetus for the discovery of new, benign organic compounds to fill that role. Concurrently, developments in the high-throughput synthesis of organic compounds, the establishment of large libraries of available chemicals, accelerated corrosion inhibition testing technologies, and the increased capability of machine learning methods have made discovery of new corrosion inhibitors much faster and cheaper than it used to be. We summarize these technical developments in the corrosion inhibition field and describe how data-driven machine learning methods can generate models linking molecular properties to corrosion inhibition that can be used to predict the performance of materials not yet synthesized or tested. We briefly summarize the literature on quantitative structure–property relationships models of small organic molecule corrosion inhibitors. The success of these models provides a paradigm for rapid discovery of novel, effective corrosion inhibitors for a range of metals and alloys in diverse environments. View Full-Text
Keywords: corrosion inhibitors; high-throughput corrosion inhibition testing; machine learning; quantitative structure–property relationships (QSPR); organic molecules; molecular design; data-driven models corrosion inhibitors; high-throughput corrosion inhibition testing; machine learning; quantitative structure–property relationships (QSPR); organic molecules; molecular design; data-driven models
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Winkler, D.A. Predicting the Performance of Organic Corrosion Inhibitors. Metals 2017, 7, 553.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Metals EISSN 2075-4701 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top