The search for non-toxic alternatives to hexavalent chromium based corrosion inhibitors requires a comprehensive understanding of the factors critical to effective corrosion protection. Key considerations include the evolution of corrosion inhibition with inhibitor concentrations and exposure times, the inhibition efficacy in the presence and following absence of inhibitors, and the stability of inhibition upon polarisation. In our electrochemical comparison of promising organic molecules with sodium dichromate, we found that even top-performing candidates can lead to premature conclusions if such critical factors are overlooked. While organic molecules can match the inhibition performance of chromates under specific conditions, this can be misleading when considering concentration, time, and polarisation dependent behaviour. Initial high performance can also be deceptive in dynamic environments, as we observed that the inhibition provided by most organic molecules drastically decreases when the inhibitor is absent in the electrolyte. These observations call for broader comprehensive inhibitor robustness studies that take into account factors including time, concentration, stability, and polarisation effects in inhibitor efficacy analysis.

A 2D finite element model is developed to simulate the protection of active protective coatings in a defect. The leaching kinetics, inhibitor-electrolyte reactions and electrochemical surface reactions on homogenized AA2024-T3, are considered. Changes in local oxygen distribution, pH, and current density over time are validated using surface scanning micro-probe techniques. The limitations posed by the prediction of surface current density at the metal interface using a FEM model with a homogenized microstructure are identified and addressed. The validated FEM is then used to predict inhibitor concentration and pH dependent on the initial inhibitor pigment concentration in an organic coating.

The dream corrosion inhibitor would work for every substrate–environment combination, and the protection would be sustained indefinitely with an irreversible barrier layer when exposed to aggressive and changing environmental conditions. However our prior electrochemical experiments on AA2024-T3 have shown that despite the initial inhibition, all of the tested molecules had reversible bonds that limit their inhibition performance and applicability in dynamic environments, with the exception of 3-amino-1,2,4-triazole-5-thiol, which still showed 42% inhibition efficiency after being exposed to 0.1M NaCl only for three days. To our knowledge, this is the first mechanistic study that explains the origin of such quasi-sustained inhibition by an organic molecule under dynamic and aggressive conditions relevant to aerospace alloys. Potentiodynamic polarization, atomic force microscopy and scanning Kelvin probe force microscopy (AFM/SKPFM), X-ray photoelectron spectroscopy (XPS), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) complemented by density functional theory (DFT) calculations were used to identify the molecular mechanism responsible for the quasi-stable adsorption provided by 3-amino-1,2,4-triazole-5-thiol. Our findings suggest that a sulphatization of the Al-(hydr)oxide is the key contributor to the quasi-sustained corrosion inhibition. Sustained molecule adsorption over intermetallics in trace amounts was also observed, but their presence was insufficient to inhibit corrosion.

The targeted removal of efficient but toxic corrosion inhibitors based on hexavalent chromium has provided an impetus for discovery of new, more benign organic compounds to fill that role. Developments in high-throughput synthesis of organic compounds, the establishment of large libraries of available chemicals, accelerated corrosion inhibition testing technologies, the increased capabilities of machine learning (ML) methods, and a better understanding of mechanisms of inhibition provide the potential to make discovery of new corrosion inhibitors faster and cheaper than ever before. These technical developments in the corrosion inhibition field are summarized herein. We describe how data-driven machine learning methods can generate models linking molecular properties to corrosion inhibition that can be used to predict the performance of materials not yet synthesized or tested. The literature on inhibition mechanisms is briefly summarized along with quantitative structure–property relationships models of small organic molecule corrosion inhibitors. The success of these methods provides a paradigm for the rapid discovery of novel, effective corrosion inhibitors for a range of metals and alloys, in diverse environments. A comprehensive list of corrosion inhibitors tested for various substrates that was curated as part of this review is accessible online https://excorr.web.app/database and available in a machine-readable format.

Creating durable, eco-friendly coatings for long-term corrosion protection requires innovative strategies to streamline design and development processes, conserve resources, and decrease maintenance costs. In this pursuit, machine learning emerges as a promising catalyst, despite the challenges presented by the scarcity of high-quality datasets in the field of corrosion inhibition research. To address this obstacle, we have created an extensive electrochemical library of around 80 inhibitor candidates. The electrochemical behaviour of inhibitor-exposed AA2024-T3 substrates was captured using linear polarisation resistance, electrochemical impedance spectroscopy, and potentiodynamic polarisation techniques at different exposure times to obtain the most comprehensive electrochemical picture of the corrosion inhibition over a 24-h period. The experimental results yield target parameters and additional input features that can be combined with computational descriptors to develop quantitative structure–property relationship (QSPR) models augmented by mechanistic input features.