Current state of electrochemical techniques and corrosion rate analysis for next-generation materials

Han, Junsoo; Gharbi, Oumaïma

doi:10.1016/j.coelec.2022.101131

Cited by 10 publications

(7 citation statements)

References 54 publications

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“…14,15 Having a high social impact, industrially relevant metal alloys also offer a wealth of complexity due to a wide range of size or compositional distribution of particles. 16 Industrial Al alloys are the typical example of such a complex system that is considered in this work.…”

Section: ■ Introductionmentioning

confidence: 99%

“…For example, in heterogeneous systems, a cooperative chemical communication can be established in a solution via overlapping of diffusion fields of species (as shown in a recent example between Pd and Au nanoparticles), as well as on a surface (via spillover effects in catalysis) . Corroding metal interfaces are another example of heterogeneous systems where communication can occur. ,− The origin of this communication emerges from the dissimilar metals composing the alloy being in electrical contact with each other, forming myriads of local galvanic cells and sites of local degradation. , Having a high social impact, industrially relevant metal alloys also offer a wealth of complexity due to a wide range of size or compositional distribution of particles . Industrial Al alloys are the typical example of such a complex system that is considered in this work.…”

Section: Introductionmentioning

confidence: 99%

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Imaging and Quantifying the Chemical Communication between Single Particles in Metal Alloys

et al. 2023

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The communication within particle agglomerates in industrial alloys can have a significant impact on the macroscopic reactivity, putting a high demand on the adaptation of wide-field methodologies to clarify this phenomenon. In this work, we report the application of correlated optical microscopies probing operando both local pH and local surface chemical transformation correlated with identical location scanning electron microscopy to quantify in situ the structure reactivity of particle agglomerates of foreign elements in the Al alloy. The optical operando analyses allow us (i) to reveal and quantify the local production of OH– from proton and oxygen reduction at individual Si- or Fe-rich microparticles and (ii) to quantify (and model) the chemical communication between these active sites, within a few micrometer range, on the local chemical transformation of the material. Wide-field image analysis highlights the statistical importance of chemical communication that may introduce a new conceptual framework for the understanding of the mechanisms in related fields of charge transfer, electrocatalysis, and corrosion.

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Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Imaging and Quantifying the Chemical Communication between Single Particles in Metal Alloys

et al. 2023

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“…FEM). Notice that the development of the envisioned multi-fidelity models without developing ML-based surrogate models for the FEM predictions will be quite challenging despite the fact that most ML models of corrosion are based on experimental data 19,21,[44][45][46][47][48][49][50] or a combination of experimental corrosion data and some other form of modeling such as thermodynamic predictions, 51,52 a combination of field data and ab-initio calculations, 53 atomic level modeling, 53,54 or experimental data and lattice constants. 55 Furthermore, ML-based surrogate models for FEM are critical for the development of multi-fidelity models since corrosive applications are often complex and potentially contain changing environments that can alter corrosion rates, 56 yielding difficulties in analyzing with traditional statistical or computational methods.…”

Section: Discussion: Application Of Machine Learning To Corrosionmentioning

confidence: 99%

Accelerating FEM-Based Corrosion Predictions Using Machine Learning

Montes de Oca Zapiain,

Maestas,

Roop

et al. 2024

J. Electrochem. Soc.

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Atmospheric corrosion of metallic parts is a widespread materials degradation phenomena that is challenging to predict given its dependence on many factors (e.g. environmental, physiochemical, and part geometry). For materials with long expected service lives, accurately predicting the degree to which corrosion will degrade part performance is especially difficult due to the stochastic nature of corrosion damage spread across years or decades of service. The finite element method (FEM) is a computational technique capable of providing accurate estimates of corrosion rate by numerically solving complex differential equations characterizing this phenomena. Nevertheless, given the iterative nature of FEM and the computational expense required to solve these complex equations, FEM is ill-equipped for an efficient exploration of the design space to identify factors that accelerate or deter corrosion, despite its accuracy. In this work, a machine-learning-based surrogate model capable of providing accurate predictions of corrosion with significant computational savings is introduced. Specifically, this work leverages AdaBoosted Decision trees to provide an accurate estimate of corrosion current per width given different values of temperature, water layer thickness, molarity of the solution, and the length of the cathode for a galvanic couple of aluminum and stainless steel.

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“…The use of the Fresnel model has already transformed RM into a powerful nanoscale quantitative methodology for investigating surface film conversion, as demonstrated on Febased substrates. We anticipate this methodology will be expanded to a wider range of metal systems, such as Mg alloys, [43,44] high entropy alloys, [4,45] and multi-principal element alloys, [3,46] where in-situ localized characterization is urgently needed but would be costly and time-consuming using traditional surface characterization approaches (XPS, TEM, etc.). Furthermore, fine-tuning experimental conditions and applying other optical models is expected to enable the characterization of particle chemical conversion, bubble formation, and other phenomena, which are already established in the fields of batteries and electrocatalysis.…”

Section: Prospects Of Rm Application In Corrosion Sciencementioning

confidence: 99%