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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2397-2106 |