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.

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.

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.

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.

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.

Chromium-based functional coatings (CFCs) are widely recognized for their outstanding wear and corrosion resistance across diverse industrial sectors. However, despite advancements in deposition techniques and microstructural enhancements, many contemporary CFCs remain vulnerable to degradation in highly corrosive environments. For the first time, this research delivers a thorough characterization of the corrosion resistance of advanced CFCs, focusing on the performance of a 5 μm thin dense chromium (TDC) coating. These TDCs exhibit a distinctive, uniform nodular microstructure, characterized by approximately 3.6 μm nodules composed of defect-free near-nanocrystalline grains (227 ± 75 nm) plus enhanced electrochemical nobility. This structure promotes the rapid formation of a stable, dense bilayer oxide, resulting in a remarkably low corrosion susceptibility, effectively impeding both charge transfer and mass transport, particularly the diffusion of Cl^- ions. Furthermore, the coating sustains an exceptionally high polarization resistance over extended exposure times in aqueous NaCl electrolyte. These findings offer critical insights into the design of CFCs optimized for extreme environmental durability.

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.

The discovery of synergistic strategies effectively improves the corrosion inhibition capability of amino acids. However, the wide variety of amino acid formulations and the time-consuming nature of corrosion tests make combinatorial discovery challenging to achieve. Herein, a library of 70 amino acids was created and tested in a high-throughput manner. Benefiting from a vast amount of labeled data of amino acid formulations, an interpretable machine learning approach was used to reveal the contribution of molecular features to inhibition performance of amino acids and the synergisms in the optimal formulation. The synergism was verified by electrochemical tests and quantum chemical calculations.

Medical devices contribute to the carbon footprint generated by the healthcare sector. The development of implants and biomaterials using recycled waste materials promotes sustainable advances in tissue engineering. Additively manufactured (AM) bone-substituting biomaterials with multifunctional properties, e.g., biodegradability, antibacterial and osteogenic potential, can contribute to sustainable healthcare. Biodegradable biomaterials eliminate secondary surgeries to remove implants, reduce post-surgical complications, and enhance patient recovery, thus decreasing the energy usage and waste associated with medical treatments. Herein, we present porous iron (Fe) scaffolds incorporating 20 vol% waste-derived eggshell particles for bone substitution. The Fe-eggshell scaffolds were fabricated using direct ink writing (DIW) technique and underwent post-AM heat treatment. During sintering, the eggshell's main component – CaCO₃, transformed into CaO. Atomic diffusion between α-Fe and CaO phases resulted in the formation of Ca₂Fe₂O₅ phase at the interface. The scaffolds were 70 % porous and displayed a biodegradation rate of 0.11 mm/year. The mechanical properties were comparable to trabecular bone and the scaffolds endured 3 million loading cycles at 0.7σ_y in r-SBF. The scaffolds showed apatite-forming ability, evidenced by the formation of (carbonaceous) hydroxyapatite, which are conducive to preosteoblast adhesion, proliferation, and differentiation. RT-qPCR analysis confirmed the osteogenic potential of the specimens as evidenced by the upregulated expression of osteopontin and osteocalcin as compared to Ti6Al4V controls. Furthermore, the scaffolds exhibited bactericidal activity (>3.9-log CFU reduction) against methicillin-sensitive and multidrug-resistant strains of Staphylococcus aureus and delayed their biofilm formation. Our research showcases the exceptional multifunctionality of DIW Fe-eggshell composite scaffolds for the sustainable development of orthopedic biomaterials. Statement of significance: We aim to improve the biofunctionalities and sustainability of biodegradable bone substitutes, by developing the extrusion-based 3D printed porous Fe composite scaffolds containing eggshell-derived CaO bioceramics. Our results demonstrated that Fe-eggshell scaffolds exhibited hydroxyapatite-forming ability in simulated body fluid, having mechanical properties in the range of trabecular bone even after 4 weeks biodegradation, supported the proliferation of preosteoblasts and upregulated the expression of osteogenic genes. Moreover, the scaffolds were bactericidal against methicillin-sensitive and multi-drug resistant strains Staphylococcus aureus and delayed their biofilm formation.

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.

Corrosion is a leading damage mechanisms in the degradation of marine assets. Acoustic emission (AE) monitoring has gained increasing interest as a technique for continuous monitoring of corrosion damage. This study numerically and experimentally investigates the feasibility of wall thickness loss estimation from the AE signals due to localized corrosion. The interaction of the elastic waves emitted due to the evolution of corrosion damage are influenced by the local thickness and material properties of the structure. A steel plate of (500 mm x 500 mm x 10 mm) with a localized wall thickness loss between 0 and 80% in the center of the plate was considered. The numerical investigation was conducted using a higher-order finite element model. Laboratory experiments were performed on a carbon steel specimen instrumented with 7 AE transducers (40 - 250 kHz). Corrosion damage was artificially introduced in the steel plate by progressively milling a pit in the center. At different stages of wall thickness loss, simulated AE sources were generated. The response of the structure was evaluated based on signal characteristics such as amplitude, rise-time, frequency content, and waveform. A correlation between the signal amplitudes and the wall thickness loss was observed in both experimental and numerical results. This perspective is promising for the feasibility of corrosion-induced wall thickness loss estimation based on AE measurements.

This research provides detailed insights into the correlation of microstructural and morphological characteristics of a Cr/CrN multilayer coating deposited onto steel and its corrosion behavior, by examining its local surface electronic properties, nanomechanical behavior, and electrochemical activity in a 3.5 % NaCl solution. A key focus of the study is the influence of physicochemical surface evolution on nano-mechanical properties of Cr/CrN coating. This is investigated by correlating electrochemical data from electrochemical impedance spectroscopy (EIS) with findings from X-ray photoelectron spectroscopy (XPS) and nanoindentation analysis. The integrated approach shed light on physicochemical evolution of the coating, and its resistance to corrosion in demanding environments.

2D materials, characterized by their extensive surface area and customizable chemical and electronic properties, offer compelling advantages as advanced materials. These unique attributes pave the way for the development of next-generation electronics and optoelectronics, photo- and electro-catalysis, energy storage and conversion devices, and sensors. The most prominent and commonly available 2D transition metal dichalcogenide, molybdenum disulfide (MoS₂), has already shown its potential for advanced applications. However, its relatively unfavorable electronic structure and limited intrinsic conductivity lower its suitability for applications that require high conductivity, such as electrocatalysts. One way to enhance its conductivity is by electrochemically intercalating alkali metal ions, e.g., Na⁺ and K⁺, into its layered structure, potentially adjusting its electronic structure. Here, we present a comprehensive investigation into the atomic-scale intercalation mechanism using molecular dynamics simulations, complemented by experimental analysis of structural and electronic properties at the macro scale through various characterization techniques. It is demonstrated that the hydration shell of ions serves as an energy barrier to intercalation as it undergoes a structural change during the intercalation. When alkali metal ions are intercalated into MoS₂, they introduce more defects and enhance conductivity. Notably, these effects are more pronounced for potassium than for sodium.

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.

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.