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Abstract

Data Science Framework to Select Corrosion Inhibitors †

by
Tiago L. P. Galvão
*,
Gerard Novell-Leruth
,
Inês Ferreira
,
Alena Kuznetsova
,
José R. B. Gomes
and
João Tedim
CICECO-Aveiro Institute of Materials, University of Aveiro, 3810-193 Aveiro, Portugal
*
Author to whom correspondence should be addressed.
†
Presented at the First Corrosion and Materials Degradation Web Conference, 17–19 May 2021; Available online: https://cmdwc2021.sciforum.net/.
Mater. Proc. 2021, 6(1), 32; https://doi.org/10.3390/CMDWC2021-09935
Published: 8 May 2021
(This article belongs to the Proceedings of The 1st Corrosion and Materials Degradation Web Conference)
Versions Notes

Organic corrosion inhibitors embedded in coatings play a crucial role in substituting for traditional anti-corrosion pigments, which can cause acute toxicity problems to human health and the environment. However, it is still not well understood why some organic compounds inhibit corrosion and others do not. Therefore, we are currently developing two complementary technological approaches to help corrosion scientists and engineers working in academia and across different industries choose the optimal inhibitor for each specific problem. We (1) build an interactive exploratory data tool for the selection of the ideal corrosion inhibitor, taking into account different conditions (type of alloy, electrolyte, pH, etc.) based on previously published information (https://datacor.shinyapps.io/cordata/ accessed on 7 May 2021), and (2) develop machine learning models and an online tool to perform an initial virtual screen of potential molecules for the design of more efficient organic corrosion inhibitors [1]. These two approaches will contribute to the digitalization of the search for inhibitors, helping to speed up research in corrosion science and tailor corrosion-protective technologies in a more efficient and condition specific manner.

Supplementary Materials

The conference poster is available at https://www.mdpi.com/article/10.3390/CMDWC2021-09935/s1.

Author Contributions

Conceptualization, T.L.P.G., J.R.B.G. and J.T.; coding, T.L.P.G. and G.N.-L.; validation, T.L.P.G. and I.F.; formal analysis, T.L.P.G., G.N.-L., I.F. and A.K.; investigation, T.L.P.G., G.N.-L., I.F. and A.K.; resources, T.L.P.G., J.R.B.G. and J.T.; data curation, T.L.P.G., I.F. and A.K.; writing—original draft preparation, T.L.P.G.; writing—review and editing, G.N.-L., I.F., A.K., J.R.B.G. and J.T.; visualization, T.L.P.G. and I.F.; supervision, T.L.P.G., J.R.B.G. and J.T.; project administration, T.L.P.G. and J.R.B.G.; funding acquisition, T.L.P.G., J.R.B.G. and J.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by project CICECO-Aveiro Institute of Materials, UIDB/50011/2020 & UIDP/50011/2020, financed by national funds through the FCT/MEC and when appropriate co-financed by FEDER under the PT2020 Partnership Agreement. It was also funded by FCT project DataCor (refs. POCI-01-0145-FEDER-030256, PTDC/QUI-QFI/30256/2017 and https://datacorproject.wixsite.com/datacor, accessed on 7 May 2021).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Reference

  1. Galvão, T.L.P.; Novell-Leruth, G.; Kuznetsova, A.; Tedim, J.; Gomes, J.R.B. Elucidating Structure–Property Relationships in Aluminum Alloy Corrosion Inhibitors by Machine Learning. J. Phys. Chem. C 2020, 124, 5624–5635. [Google Scholar] [CrossRef]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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MDPI and ACS Style

Galvão, T.L.P.; Novell-Leruth, G.; Ferreira, I.; Kuznetsova, A.; Gomes, J.R.B.; Tedim, J. Data Science Framework to Select Corrosion Inhibitors. Mater. Proc. 2021, 6, 32. https://doi.org/10.3390/CMDWC2021-09935

AMA Style

Galvão TLP, Novell-Leruth G, Ferreira I, Kuznetsova A, Gomes JRB, Tedim J. Data Science Framework to Select Corrosion Inhibitors. Materials Proceedings. 2021; 6(1):32. https://doi.org/10.3390/CMDWC2021-09935

Chicago/Turabian Style

Galvão, Tiago L. P., Gerard Novell-Leruth, Inês Ferreira, Alena Kuznetsova, José R. B. Gomes, and João Tedim. 2021. "Data Science Framework to Select Corrosion Inhibitors" Materials Proceedings 6, no. 1: 32. https://doi.org/10.3390/CMDWC2021-09935

APA Style

Galvão, T. L. P., Novell-Leruth, G., Ferreira, I., Kuznetsova, A., Gomes, J. R. B., & Tedim, J. (2021). Data Science Framework to Select Corrosion Inhibitors. Materials Proceedings, 6(1), 32. https://doi.org/10.3390/CMDWC2021-09935

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