Surface stabilization treatment serves as a primary method to promote stable rust layer formation on weathering steel (WS). However, due to the complex and multicomponent chemical formulations of stabilization treatment agents (STA), the precise control over STA component ratios to achieve the best stabilization treatment effect remains highly challenging. This study combines high-throughput experiment and machine learning method to establish an optimization framework for designing rust layer STA formulation. By employing high-throughput droplet dispensing experiments and wire beam electrode electrochemical testing, a predictive model is constructed using the AdaBoost algorithm. Interpretability analysis is further integrated to guide Bayesian optimization for iterative formulation refinement. After two optimization cycles, the optimal STA formulation (0.70 g/L CuSO₄, 0.20 g/L MgSO₄, 0.60 g/L Na₂HPO₄, and 0.20 g/L tannic acid) is identified from over 2.8 million candidate formulations. The optimized STA promotes the generation of stable rust layer on Q420 WS, which effectively reduces rust layer defects, inhibits corrosive medium penetration, and significantly enhances the corrosion resistance of WS.

Molybdenum disulfide (MoS ₂) has emerged as a promising electrocatalyst for the electrochemical reduction of CO ₂, primarily yielding carbon monoxide. However, product selectivity is known to be highly sensitive to structural features such as edge termination and defect density. In this work, we report the formation of higher hydrocarbons (C ₂+ products) enabled by the presence of inherent sulfur vacancies in MoS ₂ when combined with various ionic liquids as co-catalysts. While MoS ₂ has traditionally shown limited hydrocarbon output, our findings demonstrate for the first time that native defect sites, interacting synergistically with the electrolyte environment, can facilitate the production of significant amounts of C ₂+ species. These results provide new insights into defect-mediated catalytic pathways and highlight the importance of electrolyte design in tuning product distribution during CO ₂ electroreduction.

Developing accelerated exposure tests that accurately predict the in-service performance of structural aircraft coatings remains challenging, largely due to the complexity of simulating real-world environmental conditions without altering key degradation mechanisms. This study evaluated four different coating systems under various accelerated exposure tests and compared their degradation behavior to in-service performance. Coating degradation was characterized using electrochemical impedance spectroscopy, scanning electron microscopy, and attenuated total reflectance Fourier transform infrared spectroscopy. Under in-service conditions, failure was primarily driven by the leaching of corrosion inhibitors, while the polymer matrix degraded predominantly through hydrolysis and thermo-oxidation. In contrast, during outdoor- or cyclic salt spray exposure, inhibitor leaching remained a key contributor to coating degradation although polymer degradation was mainly caused by ultraviolet radiation or hydrolysis. These findings emphasize the challenge of replicating real-world degradation in laboratory settings. Additionally, anodized oxide layers containing polymers within their pores played a critical role in maintaining protection during early coating failure. Chromate-based systems restored barrier properties, likely through chromate adsorption on hydrolyzed products within the oxide pores. In comparison, praseodymium-based systems failed to restore protection, while lithium-based systems sustained protection through an intact polymer.

De-icing road salts are widely employed for snow and ice mitigation in cold climate regions, with sodium chloride (NaCl) being the most commonly used salt. The extensive application of NaCl has raised significant infrastructure, sustainability, and environmental concerns, and it has led to the emergence of various alternative de-icing salts, including other chloride-based and organic salts and compounds. In this study, the effect of zinc and acetate species on the corrosion behaviour of steels was systematically investigated using a combination of atmospheric corrosion testing, immersion testing, electrochemical measurements, cross-sectional microscopy, Zn K-edge X-ray absorption spectroscopy (XANES), and thermodynamic speciation modelling. The effect of eight chloride and non-chloride salts and their mixtures on the corrosion of structurally important galvanized steel, mild steel, and high-strength steel was studied. The chloride-based salts were found to be more detrimental than the organic salts to the corrosion of mild and high-strength steels, but all the salts were similarly corrosive to galvanized steel. It was found that the presence of both zinc and acetate species significantly enhanced corrosion and the Fe dissolution rate in steels. >40 wt.% of the 20 µm-thick galvanized zinc layer was dissolved after one week of immersion in 0.5 M sodium chloride or sodium acetate. After this one-week immersion, or the 10-week atmospheric field exposure, any remaining zinc was entirely in the form of zinc oxide. Our findings call for further investigation before using organic de-icing salts, alone or in mixtures with NaCl, on galvanized steel.

Seagoing vessels operate in harsh environments which make them especially prone to progressive degradation mechanisms such as fatigue and corrosion. Acoustic emission (AE) monitoring is gaining interest from ship operators and inspectors for its potential as an early-warning structural health monitoring technique for these types of damage. A major challenge facing the implementation of AE is dealing with the background noise. This article presents an experimental study of ultrasonic noise levels in representative environments and conditions AE monitoring. The probability of detection (PoD) is proposed as a quantitative metric for the detection of damage in the presence of operational noise. Measurements were carried out in multiple locations on board of a vessel under different operational conditions. Measurements at cruising speed on hull plates inside the engine room suggest that the ultrasonic background noise level exceeded 90 dB under 100 kHz but rapidly reduced in the higher frequencies associated with the failure mode-related AE signals. The PoD was estimated to be 94% for damage signals above 100 kHz. These results suggest that acoustic emission monitoring has the potential to perform reliably under noisy conditions. This perspective is promising to the future of a structural health monitoring system based on AE measurement.

Understanding localized corrosion under atmospheric droplets is critical, yet previous studies have mostly focused on single-droplet systems or general trends, leaving the role of individual droplets within multi-droplet environments yet to be explored. Here, we present a fully automated, image-based, data-driven framework for analyzing corrosion progression under thousands of droplets simultaneously. Using time-resolved optical imaging and pre-trained large vision models for droplet segmentation, we construct per-droplet color features and propose a probability-based representation of corrosion product formation in inner and outer regions of interest. This approach overcomes the limitations of binary classification by capturing the continuous and spatially heterogeneous nature of corrosion product formation. Applied to carbon steel exposed to over 1500 pre-sprayed 1 M NaCl droplets of various sizes, the method reveals that the probability of corrosion product presence strongly depends on droplet size, with larger droplets more likely to exhibit products both under and around the droplet footprint. Moreover, corrosion products in the outer region can appear independently of under-droplet corrosion, suggesting a role for inter-droplet interactions. By transforming raw imaging data into physically meaningful per-droplet metrics, this work offers a scalable platform for investigating localized corrosion kinetics and morphology in complex, real-world droplet populations, opening new opportunities for connecting droplet formation and population behavior to local and overall atmospheric corrosion rates.

The increasing concentration of CO2 is a serious concern for the environment. Electrochemical conversion of CO2 into valuable products, including fuels, offers a viable solution and helps close the carbon-neutral cycle. Metal-organic framework (MOF) composites, due to their high porosity, large surface area, and significant chemical tunability, are considered to be a promising class of catalyst materials for the CO2 reduction reaction (CO2RR). This chapter focuses on the fundamentals of CO2RR and mechanism of the reaction followed by discussing the recent advancements in MOF composite electrocatalysts for CO2RR including MOF-supported electrocatalysts, conductive-supported MOF composites, graphene and carbonous MOF composites, MOF-MXenes, MOF-polymers, and polyoxometalate.

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.

XPS analysis is routinely used in corrosion studies to analyse corrosion product and protective layers on a range of metals. In the case of transition metals and especially iron, the extraction of information about chemical species including identification and quantification requires complex fitting of the metal 2p spectrum. Unfortunately, there is extensive misunderstanding of what is required for fitting of these metal 2p photoelectron peaks. In the case of high spin Fe 2p compounds there is a complex structure based on multiplet and satellite peaks which is often ignored. In this review of the application of XPS in the study of corrosion and protection of ferrous metals; we quantify the extent of misinterpretation of XPS Fe 2p spectra within the literature. It is found that in over 70 % of papers there is an adamant misunderstanding of the requirements for fitting Fe 2p, which can be divided into three groups. First, in the most serious case, there seems to be a lack of understanding of spin orbit coupling which gives rise to the major Fe 2p_3/2 and Fe 2p_1/2 peaks with the latter being incorrectly assigned to a different chemical species. Second, satellite structures are often assigned to a different chemical species. Third, single peaks are used to fit chemical components whereas a complex multiplet structure should be employed. We establish the extent to which these errors are made by critical appraisal of over 220 papers published in selected years between 2015 and 2024.

Despite the remarkable success of machine learning in materials science, challenges persist in gaining mechanistic insights, especially in low-data regimes where dataset sizes limit the precise applicability of machine learning. The prevailing reliance on high-confidence predictions from the models often leaves the underlying decision-making mechanisms opaque, limiting scientific understanding. This study presents an alternative approach that emphasizes understanding the model decision-making process over individual predictions, enabling the extraction of scientifically meaningful insights from small datasets. Focusing on 107 small organic molecules and their corrosion inhibition properties as a case study, we systematically evaluate 29 molecular featurization methods and 9 target representations, generating over 12 thousand model configurations to identify robust feature-target pairings. We reveal common trends by reverse engineering the best-performing models based on featurization methods of physicochemical descriptors, hashed fingerprints, and structural keys, which we integrate with domain knowledge to create a molecular substructure template for candidate molecules. Using this template, we filter a toxicity database to identify non-toxic corrosion inhibitors, aiming to replace the de facto but hazardous corrosion inhibitor hexavalent chromium. The resulting candidate’s efficacy is validated through electrochemical testing, illustrating the feasibility of achieving mechanistic insights from statistical models in data-scarce environments.