Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution

Coelho, Leonardo Bertolucci; Torres, Daniel Roberto Vega; López, Miguel Ángel Martín; Paldino, Gian Marco; Bontempi, Gianluca; Ustarroz, Jon

doi:10.1016/j.corsci.2023.111104

Cited by 20 publications

(33 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It could be observed that the conditional mean of log(j), as a function of E, was generally a poor representative of a polarisation curves set (in line with the log(j) distributions scrutinised in 29 ). As illustrated in Fig.…”

Section: Central Tendency Estimations Of Descriptors Based On the Mea...supporting

confidence: 62%

“…The conjecture of pitting corrosion should ideally be faced in the light of data-centric approaches. As shown in our previous work 29 , the distributions of the local current density at potential regions associated with pitting are potentially uniform (high randomness). This means that, as the observation error tends to decrease with increased sample size 39 , if only few samples are considered, the actual underlying distributions are not captured (subrepresentation).…”

Section: Introductionmentioning

confidence: 51%

“…The difference between the conditional mean and the conditional median tend to increase with E, because the high outliers present particularly high log(j) values at high potential regions (E > 1.15 V (Vs Ag/AgCl)). As demonstrated in 29 , the log(j) distributions become more positively skewed with increased corrosiveness (more positive potential and higher [Cl -]). The occurrence of high outliers with particularly high j values at high E regions results from pitting corrosion processes.…”

Section: Interpretating the Higher Robustness Of The Median Modelmentioning

confidence: 91%

“…One could observe the median curves generally crossing (or at least touching) the "target" markers (plot b, Figs. 2, S1, S2, S3); the only exception was the Epit in 0.05 M NaCl (50 mV/s), which was the most difficult value to be appraised out of the 10 descriptors considered (high uncertainty of j values in E regions associated to pitting 29 ).…”

Section: Superimpositions As a Function Of Potential (V) Of The: A) C...mentioning

confidence: 99%

“…In this work, the SECCM was selected as experimental tool mainly due to its proven high throughput capabilities 22,27,28 . The collection of statistically representative amounts of data is key when high variance is expected in the target feature 29 .…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Estimating pitting descriptors of 316L stainless steel by machine learning and statistical analysis

Torres,

vangrunderbeek,

Bernal

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

A hybrid rule-base/ML approach using linear regression and artificial neural networks (ANN) determined pitting corrosion descriptors from high-throughput data obtained with Scanning Electrochemical Cell Microscopy (SECCM) on 316L stainless steel. Non-parametric density estimation determined the central tendencies of the Epit/log(jpit) and Epass/log(jpass) distributions. Descriptors estimated using conditional mean or median curves were compared to their central tendency values, with the conditional medians providing more accurate results. Due to their lower sensitivity to high outliers, the conditional medians were more robust representations of the log(j) Vs E distributions. An observed trend of passive range shortening with increasing testing aggressiveness was attributed to delayed stabilisation of the passive film, rather than early passivity breakdown.

show abstract

Section: Central Tendency Estimations Of Descriptors Based On the Mea...supporting

confidence: 62%

Section: Introductionmentioning

confidence: 51%

Section: Interpretating the Higher Robustness Of The Median Modelmentioning

confidence: 91%

Section: Superimpositions As a Function Of Potential (V) Of The: A) C...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimating pitting descriptors of 316L stainless steel by machine learning and statistical analysis

Torres,

vangrunderbeek,

Bernal

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Probing passivity of corroding metals using scanning electrochemical probe microscopy

Skaanvik

Gateman

2023

Electrochemical Science Adv

View full text Add to dashboard Cite

Passive films are essential for the longevity of metals and alloys in corrosive environments. A great deal of research has been devoted to understanding and characterizing passive films, including their chemical composition, thickness, uniformity, thickness, porosity, and conductivity. Many characterization techniques are conducted under vacuum, which do not portray the true in‐service environments passive films will endure. Scanning electrochemical probe microscopy (SEPM) techniques have emerged as necessary tools to complement research on characterizing passive films to enable the in situ extraction of passive film parameters and monitoring of local breakdown events of compromised films. Herein, we review the current research efforts using scanning electrochemical microscopy, scanning electrochemical cell microscopy (or droplet cell measurements), and local electrochemical impedance spectroscopy techniques to advance the knowledge of local properties of passivated metals. The future use of SEPM for quantitative extraction of local film characteristics within in‐service environments (i.e., with varying pH, solution composition, and applied potential) is promising, which can be correlated to nanostructural and microstructural features of the passive film and underlying metal using complementary microscopy and spectroscopy methods. The outlook on this topic is highlighted, including exciting avenues and challenges of these methods in characterizing advanced alloy systems and protective surface films.

show abstract

Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy

Makogon

Galochkina

et al. 2023

Small Methods

View full text Add to dashboard Cite

Operando wide‐field optical microscopy imaging yields a wealth of information about the reactivity of metal interfaces, yet the data are often unstructured and challenging to process. In this study, the power of unsupervised machine learning (ML) algorithms is harnessed to analyze chemical reactivity images obtained dynamically by reflectivity microscopy in combination with ex situ scanning electron microscopy to identify and cluster the chemical reactivity of particles in Al alloy. The ML analysis uncovers three distinct clusters of reactivity from unlabeled datasets. A detailed examination of representative reactivity patterns confirms the chemical communication of generated OH− fluxes within particles, as supported by statistical analysis of size distribution and finite element modelling (FEM). The ML procedures also reveal statistically significant patterns of reactivity under dynamic conditions, such as pH acidification. The results align well with a numerical model of chemical communication, underscoring the synergy between data‐driven ML and physics‐driven FEM approaches.

show abstract

Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution

Cited by 20 publications

References 78 publications

Estimating pitting descriptors of 316L stainless steel by machine learning and statistical analysis

Estimating pitting descriptors of 316L stainless steel by machine learning and statistical analysis

Probing passivity of corroding metals using scanning electrochemical probe microscopy

Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy

Contact Info

Product

Resources

About