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

This investigation proposes using Scanning Electrochemical Cell Microscopy (SECCM) as a high throughput tool to collect corrosion activity from randomly probed locations on 316 L SS. In the presence of chloride, poten-tiodynamic polarisation tests triggered the development of pitting corrosion. Data science methods were deployed to handle and explore 955 j Vs E curves. Normality tests and fitting with theoretical functions were used to understand the conditional log(j) distributions at different potentials. Unimodal and uniform distributions were assigned to the passive and pitting regions. Our big-data local strategy revealed a potential-dependent distribution of log(j), with the randomness increasing with testing aggressiveness. Data availability: All data generated or analysed during this study are included in this published article (and its supplementary information files) and are available in the Mendeley Data repository, [https://data.mendeley. com/datasets/78rz8vw46x/2]. Code availability: The code required to reproduce these findings is included in this published article (and its supplementary information files) and is available to download from GitHub: https://github.com/bcoelho-leona rdo/Data-driven-analysis-of-the-local-current-distributions-of-316L-corrosion-in-NaCl-solution/blob/4efff485b 115468840b25ea56ad81b31711c0f51/local%20current%20distributions%20of%20316L%20corrosion.ipynb.

Automated summary

This study proposes the use of Scanning Electrochemical Cell Microscopy (SECCM) to collect corrosion activity from random locations on 316 L SS. Pitting corrosion was triggered by potentiodynamic polarization tests in the presence of chloride. Data science methods were employed to analyze 955 j Vs E curves and understand the conditional log(j) distributions at different potentials. The study found that the log(j) distribution was potential-dependent and became more random with increasing testing aggressiveness.