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, potentiodynamic 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. 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 ]. 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-leonardo/Data-driven-analysis-of-the-local-current-distributions-of-316L-corrosion-in-NaCl-solution/blob/4efff485b115468840b25ea56ad81b31711c0f51/local%20current%20distributions%20of%20316L%20corrosion.ipynb. [Display omitted] • SECCM collected nearly 955 j Vs E curves of 316 L stainless steel in NaCl medium. • The unimodal log(j) distributions as a function of potential were rarely normal. • The more positive the potential, the higher the randomness (variances) of log(j). • Test aggressiveness is critical for the shift from unimodal to uniform distributions. • The uniform distribution was mainly assigned to E regions related to stable pitting. [ABSTRACT FROM AUTHOR]