Identifying stable pitting pathways in 316 L stainless steel via fractal-inspired PCA-based clustering
Abstract This study introduces a fractal-inspired PCA-based framework to distinguish stable pitting pathways in 316 L stainless steel in chloride media. By transforming potentiodynamic polarisation data into a Mandelbrot space, the approach reveals two distinct scenarios of stable pitting growth: di...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Materials Degradation |
| Online Access: | https://doi.org/10.1038/s41529-025-00594-7 |
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| author | Leonardo Bertolucci Coelho Thibaut Amand Daniel Torres Marjorie Olivier Jon Ustarroz |
| author_facet | Leonardo Bertolucci Coelho Thibaut Amand Daniel Torres Marjorie Olivier Jon Ustarroz |
| author_sort | Leonardo Bertolucci Coelho |
| collection | DOAJ |
| description | Abstract This study introduces a fractal-inspired PCA-based framework to distinguish stable pitting pathways in 316 L stainless steel in chloride media. By transforming potentiodynamic polarisation data into a Mandelbrot space, the approach reveals two distinct scenarios of stable pitting growth: directly following passivity breakdown (case I) and preceded by metastable activity (case II). Clustering identifies critical pitting potentials (E_pit and E_sp) with high accuracy, effectively capturing rare metastability-driven events often overlooked in traditional analyses. The method demonstrates robust performance across varying chloride concentrations, with classification metrics highlighting its ability to detect low-frequency E_sp events. Results show that metastability-driven stable pitting (case II) occurs at higher activity levels and potentially at lower potentials. This work advances the understanding of the probabilistic nature of pitting and provides a scalable, data-driven strategy for predicting stable growth regimes. |
| format | Article |
| id | doaj-art-cd7cb327dd634aa3b976573c71a972e1 |
| institution | OA Journals |
| issn | 2397-2106 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Materials Degradation |
| spelling | doaj-art-cd7cb327dd634aa3b976573c71a972e12025-08-20T02:25:08ZengNature Portfolionpj Materials Degradation2397-21062025-05-019111210.1038/s41529-025-00594-7Identifying stable pitting pathways in 316 L stainless steel via fractal-inspired PCA-based clusteringLeonardo Bertolucci Coelho0Thibaut Amand1Daniel Torres2Marjorie Olivier3Jon Ustarroz4ChemSIN – Chemistry of Surfaces, Interfaces and Nanomaterials, Université libre de Bruxelles (ULB)Materials Science Department, Faculty of Engineering, Place du Parc, University of Mons-UMONS, 20ChemSIN – Chemistry of Surfaces, Interfaces and Nanomaterials, Université libre de Bruxelles (ULB)Materials Science Department, Faculty of Engineering, Place du Parc, University of Mons-UMONS, 20ChemSIN – Chemistry of Surfaces, Interfaces and Nanomaterials, Université libre de Bruxelles (ULB)Abstract This study introduces a fractal-inspired PCA-based framework to distinguish stable pitting pathways in 316 L stainless steel in chloride media. By transforming potentiodynamic polarisation data into a Mandelbrot space, the approach reveals two distinct scenarios of stable pitting growth: directly following passivity breakdown (case I) and preceded by metastable activity (case II). Clustering identifies critical pitting potentials (E_pit and E_sp) with high accuracy, effectively capturing rare metastability-driven events often overlooked in traditional analyses. The method demonstrates robust performance across varying chloride concentrations, with classification metrics highlighting its ability to detect low-frequency E_sp events. Results show that metastability-driven stable pitting (case II) occurs at higher activity levels and potentially at lower potentials. This work advances the understanding of the probabilistic nature of pitting and provides a scalable, data-driven strategy for predicting stable growth regimes.https://doi.org/10.1038/s41529-025-00594-7 |
| spellingShingle | Leonardo Bertolucci Coelho Thibaut Amand Daniel Torres Marjorie Olivier Jon Ustarroz Identifying stable pitting pathways in 316 L stainless steel via fractal-inspired PCA-based clustering npj Materials Degradation |
| title | Identifying stable pitting pathways in 316 L stainless steel via fractal-inspired PCA-based clustering |
| title_full | Identifying stable pitting pathways in 316 L stainless steel via fractal-inspired PCA-based clustering |
| title_fullStr | Identifying stable pitting pathways in 316 L stainless steel via fractal-inspired PCA-based clustering |
| title_full_unstemmed | Identifying stable pitting pathways in 316 L stainless steel via fractal-inspired PCA-based clustering |
| title_short | Identifying stable pitting pathways in 316 L stainless steel via fractal-inspired PCA-based clustering |
| title_sort | identifying stable pitting pathways in 316 l stainless steel via fractal inspired pca based clustering |
| url | https://doi.org/10.1038/s41529-025-00594-7 |
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