Rational Design of Effective Mg Degradation Modulators

Prerequisite to unlock the full potential of Mg-based materials is to gain control of their degradation properties. Here a proof of concept is presented for an efficient and robust alternative to the data-driven machine learning approaches that are currently on the rise to facilitate the discovery o...

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Bibliographic Details
Published in:Corrosion (Houston, Tex.) Tex.), 2021-02, Vol.77 (2), p.204-208
Main Authors: Feiler, C., Mei, D., Luthringer-Feyerabend, B.J.C., Lamaka, S.V., Zheludkevich, M.L.
Format: Article
Language:English
Subjects:
Corrosion
Corrosion effects
Degradation
Density functional theory
Electrolytes
Functional groups
Hydrogen
Investigations
Learning algorithms
Ligands
Machine learning
Magnesium
Mathematical analysis
Modulators
Nitrogen
Properties
Quantum chemistry
Software
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Summary:Prerequisite to unlock the full potential of Mg-based materials is to gain control of their degradation properties. Here a proof of concept is presented for an efficient and robust alternative to the data-driven machine learning approaches that are currently on the rise to facilitate the discovery of corrosion modulating agents. The electronic properties of bipyridine were tuned by its substitution with electron donating and electron withdrawing functional groups to regulate the degradation modulators interaction with different ions and the effect on the corrosion inhibition of pure Mg was predicted based on density functional theory calculations. Bipyridine and two of its derivatives were subsequently investigated experimentally to validate the trend predicted by the quantum chemical calculations.
ISSN:0010-9312
1938-159X
DOI:10.5006/3597