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Machine learning (ML) is providing a new design paradigm for many areas of technology, including corrosion inhibition. However, ML models require relatively large and diverse training sets to be most effective. This paper provides an overview of developments in corrosion inhibitor research, focussing on how corrosion performance data can be incorporated into machine learning and how large sets of inhibitor performance data that are suitable for training robust ML models can be developed through various corrosion inhibition testing approaches, especially high-throughput performance testing. It examines different types of environments where corrosion by-products and electrolytes operate, with a view to understanding how conventional inhibitor testing methods may be better designed, chosen, and applied to obtain the most useful performance data for inhibitors. The authors explore the role of modern characterisation techniques in defining corrosion chemistry in occluded structures (e.g., lap joints) and examine how corrosion inhibition databases generated by these techniques can be exemplified by recent developments. Finally, the authors briefly discuss how the effects of specific structures, alloy microstructures, leaching structures, and kinetics in paint films may be incorporated into machine learning strategies.

This work demonstrates an approach towards the understanding of multi-scale and open-circuit localised electrochemical processes of AA2024-T3 in the presence and absence of an environmentally friendly rare-earth inhibitor; cerium diphenyl phosphate (Ce(dpp)3). At high temporal resolution, a wire bean electrode (WBE) made from 100 identical AA2024-T3 wires revealed sudden increases in galvanic anodic and cathodic activities immediately after dosing of 50 and 100 ppm of the inhibitor and an overall suppression of macro-scale activities by increasing the inhibitor concentration to 200 ppm, suggesting it as a fast-screening tool for inhibitors and measuring inhibition efficiency. At high spatial resolutions, scanning probe electrochemical techniques confirmed local activation of corroding microstructures on individual AA2024-T3 wires similarly by dosing the inhibitor up to 100 ppm. In agreement with WBE findings, the effective shutdown of both anodic and cathodic activities occurred after increasing the inhibitor concentration to 200 ppm confirming the optimal concentration of the Ce(dpp)3 and the mixed mode inhibition mechanism of this selected inhibitor on AA2024-T3.

The protection of ferrous metals in acidic environments is important in many industries. Extending the pH range of organic inhibitors to low pH has been achieved with the addition of iodide ions, which facilitate adsorption. It was of interest to see whether similar outcomes could be achieved with inorganic inhibitors. To this end, this paper examines the influence of potassium iodide addition on the level of corrosion protection provided by Ce(NO3)3 in 3.5% NaCl electrolytes over a pH range of 2.5 to 8. Potentiodynamic polarization was used to assess percentage inhibitor efficiency (IE%), and scanning electron microscopy, energy dispersive X-ray spectrometry, and X-ray photoelectron spectroscopy were used to characterize the corrosion product. It was found that KI alone provided only poor corrosion inhibition except at pH 2.5, where nearly 85IE% was achieved. Its addition to the cerium electrolytes was generally in excess of 90% and over 97% for the optimum concentration. The addition of KI seemed to change the mechanism of formation of corrosion products from predominantly Fe2O3 to a mixture of FeOOH, Fe3O4, and Fe2O3, which were more adherent. Corrosion protection was extended to pH 4, but under the conditions explored here, no additional protection was evident at pH 2.5.

Abstract In situ X‐ray computed tomography (X‐ray CT) is used to investigate the effects of characteristic microstructural features on the pitting initiation and propagation in austenitic stainless steel specimens prepared with laser powder bed fusion (LPBF) additive manufacturing. In situ X‐ray CT in probing the mechanism and kinetics of localized corrosion is demonstrated by immersing two LPBF specimens with different porosities in an aggressive ferric chloride solution for the evaluation of corrosion. X‐ray CT images are acquired from the specimens after every 8 hours of immersion over an extended period of time (216 hours). Corrosion pit growth is then quantitatively analyzed with a data‐constrained modeling method. The pitting growth mechanism of LPBF stainless steel is found to be different from that of conventional stainless steels. More specifically, the mechanism of corrosion pit initiation is closely correlated with the original lack of fusion porosity (LOF) distribution on the surface of the specimens and preferential pit propagation through the LOF pores inside the specimens. Pit growth kinetics are derived from pit volume changes determined through 3D data analysis. The pit growth kinetics in LPBF specimens are found to vary in the initial pit formation, competitive pit propagation, and the dominant pit growth stages.

The major applications of PGMs are as catalysts in automotive industry, petroleum refining, environmental (gas remediation), industrial chemical production (e.g., ammonia production, fine chemicals), electronics, and medical fields. As the next generation energy technologies for hydrogen production, such as electrolysers and fuel cells for stationary and transport applications, become mature, the demand for PGMs is expected to further increase. Reserves and annual production of Ru, Rh, Pd, Ir, and Pt have been determined and reported. Based on currently available resources, there is around 200 years lifetime based on current demand for all PGMs, apart from Pd, which may be closer to 100 years. Annual primary production of 190 t/a for Pt and 217 t/a for Pd, in combination with recycling of 65.4 t/a for Pt and 97.2 t/a for Pd, satisfies current demand. By far, the largest demand for PGMs is for all forms of catalysis, with the largest demand in auto catalysis. In fact, the biggest driver of demand and price for Pt, Pd, and Rh, in particular, is auto emission regulation, which has driven auto-catalyst design. Recovery of PGMs through recycling is generally good, but some catalytic processes, particularly auto-catalysis, result in significant dissipation. In the US, about 70% of the recycling stream from the end-of-life vehicles is a significant source of global secondary PGMs recovered from spent auto-catalyst. The significant use of PGMs in the large global auto industry is likely to continue, but the long-term transition towards electric vehicles will alter demand profiles.

Computed tomography is making a significant impact in the field of materials science in recent years. In this paper the authors report on advances made in three areas of characterization and also identified where further research needs to be focused. First we report on a new approach to data analysis called “Data Constrained Modelling (DCM)” in which compositional tomography can be undertaken rather than adsorption or phase contrast tomography. This is achieved by collecting X-ray CT data at different energies and then combining the datasets to reconstruct 3D compositional tomography. Second, on the application of this approach to inhibited primers typical of those used in the aerospace industry. Aerospace primers are effectively composite materials containing inorganic phases which are bound together with a polymer. Understanding the materials science of these systems requires information over several orders of magnitude in length-scale. In this paper we report on how DCM can be used to extend our understanding at the smaller length scales at the limits of resolution of the technique. The third and final advance is in extending the approach to include 4-dimensional studies. In this case we examine the primer before and after leaching. This process causes changes in the primer which can be both detected and quantified using the above approach.