DFT study of n-alkyl carboxylic acids on oxidized aluminum surfaces: From standalone molecules to self-assembled-monolayers

Chiter, Fatah; Milošev, Ingrid; Marcus, Philippe; Costa, Dominique; Kokalj, Anton

doi:10.1016/j.apsusc.2020.146156

Cited by 35 publications

(59 citation statements)

References 44 publications

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“…All of them consist of surfactant-type molecules, comprising a hydrophilic head (polar) group and a hydrophobic tail (long alkyl chain). The head group can spontaneously adhere by physical or chemical adsorption to the metal or metal oxide surface; the hydrophobic tails are kept together via weak noncovalent interactions and the results is the formation of a wellordered molecular layer (Jadhav, 2011;Costa et al, 2016;Telegdi, 2020;Poberžnik et al, 2020). It is interesting to observe that similar spontaneous assembly is also observed for ionic surfactants used in large extent for the corrosion protection of steel in acidic media of oil and gas industry (Shaban et al, 2020;Labena et al, 2020).…”

Section: Introductionmentioning

confidence: 81%

“…Therefore, understanding the conditions that contribute to the formation of a dense, ordered and stable SAM is of great importance. To this end, a significant contribution has been made by recent theoretical studies on the adsorption of n-alkyl carboxylic acids with varying chain length on Al oxide surface (Milošev et al, 2019;Poberžnik et al, 2020). Computational modeling was carried out with density functional theory (DFT) assuming that the following condensation reaction takes place:…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have shown that carboxylic acids and alkylphosphonic acids can adsorb onto the oxide surface of aluminum to form close-packed and highly oriented molecular assemblies (Liakos et al, 2004;Foster et al, 2006;Van den Brand et al, 2004a;Hauffman et al, 2008;Liascukiene et al, 2014). The carboxylic (or phosphonic) acid chemisorb to the aluminum oxide surface via hydroxyl groups, forming carboxylate (or phosphonate) functionalities at the surface (Foster et al, 2006;Van den Brand et al, 2004a, 2004bPoberžnik et al, 2020). Furthermore, as confirmed by different research groups, there is a direct relation between the density of hydroxyls on the oxide surface and the amount of chemisorbed molecules (Van den Brand et al, 2004a;Hauffman et al, 2008;Giza et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Corrosion inhibition of AA3003 aluminum alloy by self-assembled layers of myristic acid

Puc-Oxté

Pech‐Canul

2021

ACMM

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Purpose This paper aims to prepare highly hydrophobic films on aluminum AA3003 using myristic acid (MA) and evaluate its corrosion protection efficiency in a low-chloride solution. Design/methodology/approach The aluminum surface was initially treated with boiling water to develop a porous nanostructure, and then surface modification was carried out in ethanolic solutions with different concentrations of MA. The surface morphology, wetting behavior and film composition were first characterized, and then, the corrosion behavior was evaluated with electrochemical techniques. Findings The best hydrophobicity and corrosion resistance were obtained with 50 mM of MA. For such concentration, a water contact angle of 140° and protective efficiency of 96% were achieved. A multilayer structure was revealed by scanning electron microscope and X-ray photoelectron spectroscopy. Originality/value The results of this work shed light on the anticorrosion performance of fatty acid self-assembled multilayers on the surface of Al–Mn alloys.

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Section: Introductionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Corrosion inhibition of AA3003 aluminum alloy by self-assembled layers of myristic acid

Puc-Oxté

Pech‐Canul

2021

ACMM

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“…Hence, they offer a very efficient way to preselect a short list of promising candidates prior to experimental investigation. Additionally, computational techniques can provide deeper insight into the underlying chemical mechanisms and most important chemical functional moieties [41][42][43][44][45][46] . A combination of experimental and computational methods constitutes a sound foundation for a data-driven discovery of modulators.…”

Section: Introductionmentioning

confidence: 99%

Exploring structure-property relationships in magnesium dissolution modulators

et al. 2021

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Small organic molecules that modulate the degradation behavior of Mg constitute benign and useful materials to modify the service environment of light metal materials for specific applications. The vast chemical space of potentially effective compounds can be explored by machine learning-based quantitative structure-property relationship models, accelerating the discovery of potent dissolution modulators. Here, we demonstrate how unsupervised clustering of a large number of potential Mg dissolution modulators by structural similarities and sketch-maps can predict their experimental performance using a kernel ridge regression model. We compare the prediction accuracy of this approach to that of a prior artificial neural networks study. We confirm the robustness of our data-driven model by blind prediction of the dissolution modulating performance of 10 untested compounds. Finally, a workflow is presented that facilitates the automated discovery of chemicals with desired dissolution modulating properties from a commercial database. We subsequently prove this concept by blind validation of five chemicals.

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“…Furthermore, they greatly contribute to a comprehensive understanding of the underlying mechanisms. [24][25][26] There are two major strategies that can be adopted to identify compounds with suitable degradation modulating properties using computational techniques. The first is a data-driven machine learning (ML) approach 22,[27][28][29][30] that is based on quantitative structureactivity relationships to predict the experimental performance of untested compounds.…”

Section: Introductionmentioning

confidence: 99%

Rational Design of Effective Mg Degradation Modulators

Feiler

Mei

Luthringer-Feyerabend

et al. 2020

Corrosion

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Prerequisite to unlock the full potential of Mg-based materials is to gain control of its degradation properties. Here we present a proof of concept 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.

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DFT study of n-alkyl carboxylic acids on oxidized aluminum surfaces: From standalone molecules to self-assembled-monolayers

Cited by 35 publications

References 44 publications

Corrosion inhibition of AA3003 aluminum alloy by self-assembled layers of myristic acid

Corrosion inhibition of AA3003 aluminum alloy by self-assembled layers of myristic acid

Exploring structure-property relationships in magnesium dissolution modulators

Rational Design of Effective Mg Degradation Modulators

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