Electrochemical impedance spectroscopy (EIS) is a powerful technique to study electrochemical processes and to perform screening tasks. Recently an integrated measuring and modeling methodology for EIS based on a multisine excitation signal was developed. A key issue in this methodology is the data analysis, allowing us to rapidly quantify the reliability of the measured data. In this paper, a comparison is made between classical single-sine and the proposed multisine measurements on the same system. The fitting of the impedance data obtained by single-or multisine excitation and using different weighting factors is also discussed. In addition to the advantages reported in earlier work, it is concluded that, of all investigated frequencies, the odd random phase multisine excitation yields the highest quality data in the shortest measurement time.

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This work provides a data-oriented overview of the rapidly growing research field covering machine learning (ML) applied to predicting electrochemical corrosion. Our main aim was to determine which ML models have been applied and how well they performed depending on the corrosion topic considered. From an extensive review of corrosion articles presenting comparable performance metrics, a ‘Machine learning for corrosion database’ was created, guiding corrosion experts and model developers in their applications of ML to corrosion. Potential research gaps and recommendations are discussed, and a broad perspective for future research paths is provided.

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The goal of this study is to obtain a deeper insight in the relation between hydrogen diffusion and hydrogen traps present in Armco pure iron. Cold deformation was applied to this material, which initially contained a limited amount of traps. The cold deformation was applied to increase the dislocation density and modify grain boundary characteristics. In this way, the hydrogen diffusivity decreased as the hydrogen trapping ability of the microstructure increased. A subsequent heat treatment allowed changing the density of microstructural defects again and consequently increased the hydrogen diffusion coefficient. In addition, studying blister formation showed that a higher degree of deformation caused more surface blisters, while recovery lowered the number of blisters. Electron backscatter diffraction characterisation provided the necessary input on the microstructural features and their evolution. Analysis of these samples allowed evaluating the correlation between hydrogen diffusion, blister formation and microstructural defects. This paper is part of a thematic issue on Hydrogen in Metallic Alloys

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