Micro-scale potentiodynamic polarisation (log(j)) curves of 316L stainless steel
This database comprises 5 Potentiodynamic Polarisation (PP) datasets. Each dataset consists of a pair of CSVs: 1 file containing the values of the applied potential E (Vs Ag/AgCl); and 1 containing the corresponding log of the current density log(j) (µA/cm²) values. This database was deployed as the source dataset in the following scientific article, accepted for publication in npj Materials Degradation journal on 25 September 2023: "Estimating pitting descriptors of 316L stainless steel by machine learning and statistical analysis". Leonardo Bertolucci Coelho1,2,∗, Daniel Torres1, Vincent Vangrunderbeek2, Miguel Bernal1, Gian Marco Paldino3, Gianluca Bontempi3, Jon Ustarroz 1,2 1 ChemSIN – Chemistry of Surfaces, Interfaces and Nanomaterials, Université libre de Bruxelles (ULB), Brussels, Belgium 2 Research Group Electrochemical and Surface Engineering (SURF), Vrije Universiteit Brussel, Brussels, Belgium 3 Machine Learning Group (MLG), Université libre de Bruxelles (ULB), Brussels, Belgium *firstname.lastname@example.org These datasets are almost identical to the ones available at https://data.mendeley.com/datasets/78rz8vw46x/2 The only difference is that eventual missing j values were filled with an iterative imputer (Python 3.7 language). The IterativeImputer class (from sklearn.impute) models each feature with missing values as a function of other features and uses that estimate for imputation.
Steps to reproduce
The iterative imputer method for filling missing j values is accessible in the Code file (also made available on GitHub) that can be downloaded with the published article (“Estimating pitting descriptors of 316L stainless steel by machine learning and statistical analysis”). For further details on the acquisition of the PP curves (5 different combinations of [NaCl] and scan rates), please refer to: Bertolucci Coelho, Leonardo (2023), “Micro-scale potentiodynamic polarisation curves of 316L stainless steel ”, Mendeley Data, V3, doi: 10.17632/78rz8vw46x.3 Coelho, L. B. et al. Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution. Corros. Sci. 217, 111104 (2023). As in https://doi.org/10.1016/j.corsci.2023.111104, the present log(j) Vs E datasets were sliced from 0.5 V (considerably more positive than the open circuit potential (OCP)) upward.
Fonds De La Recherche Scientifique - FNRS
Chargé de recherches - CR