A5047126698- Corrosion Behavior and Inhibition
- Hydrogen embrittlement and corrosion behaviors in metals
- Non-Destructive Testing Techniques
- Anodic Oxide Films and Nanostructures
- Concrete Corrosion and Durability
- Electrochemical Analysis and Applications
- Machine Learning in Materials Science
- Metal and Thin Film Mechanics
- Infrastructure Maintenance and Monitoring
- Electrodeposition and Electroless Coatings
- Aluminum Alloys Composites Properties
- Magnesium Alloys: Properties and Applications
- Force Microscopy Techniques and Applications
- Welding Techniques and Residual Stresses
- Conducting polymers and applications
- Aluminum Alloy Microstructure Properties
- Material Properties and Failure Mechanisms
- Magnesium Oxide Properties and Applications
- Electron and X-Ray Spectroscopy Techniques
- Lubricants and Their Additives
- Advanced Electron Microscopy Techniques and Applications
- Advanced Materials Characterization Techniques
- Dental Research and COVID-19
- Marine Biology and Environmental Chemistry
- High-Temperature Coating Behaviors
Université Libre de Bruxelles
2022-2025
University of Mons
2016-2023
Vrije Universiteit Brussel
2022-2023
Universidade Federal de Santa Catarina
2015
Abstract This work provides a data-oriented overview of the rapidly growing research field covering machine learning (ML) applied to predicting electrochemical corrosion. Our main aim was determine which ML models have been and how well they performed depending on corrosion topic considered. From an extensive review articles presenting comparable performance metrics, ‘Machine for database’ created, guiding experts model developers in their applications Potential gaps recommendations are...
Abstract A hybrid rule-based/ML approach using linear regression and artificial neural networks (ANNs) determined pitting corrosion descriptors from high-throughput data obtained with Scanning Electrochemical Cell Microscopy (SECCM) on 316 L stainless steel. Non-parametric density estimation the central tendencies of E pit /log( jpit ) pass jpass distributions. Descriptors estimated conditional mean or median curves were compared to their tendency values, medians providing more accurate...
Abstract Plasma electrolytic oxidation (PEO) coatings were produced on AZ80 magnesium alloy in a solution containing silicates and phosphates working at high current densities with short treatment times. The effect of sealing boiling water corrosion mechanical properties the investigated. Moreover, mechanism samples without was evaluated. microstructure characterized scanning electron microscope observation X‐ray diffraction analysis. evaluated nanoindentation tests resistance studied by...
Accurately forecasting the locations of future pit formation sites on stainless steels (SS) holds significant practical value in both fundamental science and corrosion industry, yet it poses a experimental challenge due to need localize imperfections passive film at sub-nanometer scale. In this study, we tackle issue by utilizing combination in-situ Reflective Microscopy (RM) instrumentation, optical modeling, predictive machine learning (ML) methods, focusing prediction pits location SS316L...
<title>Abstract</title> This study introduces a fractal-inspired PCA-based framework to distinguish stable pitting pathways in 316L stainless steel chloride media. By transforming potentiodynamic polarisation data into Mandelbrot space, the approach reveals two distinct scenarios of growth: directly following passivity breakdown (case I) and preceded by metastable activity II). Clustering identifies critical potentials (E_pit E_sp) with high accuracy, effectively capturing rare...
Forecasting stainless steel (SS) pitting corrosion remains challenging due to the need identify nanometer-scale imperfections in surface passive films. Traditional analytical methods are costly, time-consuming, and limited model systems with adequate signal-to-noise ratios. We propose an alternative approach that leverages optical signatures of layer properties which, when enhanced unsupervised machine learning (ML) extract signals even at noise level, successfully identifies...
The forecasting of stainless steel (SS) pitting corrosion remains challenging due to the need identify nanometer-scale imperfections in surface passive films. Traditional analytical methods are costly, time-consuming, and limited model systems with adequate signal- to-noise ratios. This study proposes an alternative approach that leverages optical signatures layer properties which, when enhanced unsupervised machine learning (ML) extract signals even at noise level, successfully identifies...
In suspected cases of COVID-19 infection, the World Health Organization has recommended thorough cleaning surfaces and application commonly used hospital-level disinfectants are effective procedures. The new virus situation changed sanitary habits industries this will potentially have anadverse effect on highly exposed to that often chlorinated. Therefore, corrosion community should be concerned if sudden increased use certain would alter their rate/mechanism in short-medium term. This...