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.

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.

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.

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.

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.

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.

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.

The development of advanced catalysts with innovative nanoarchitectures is critical for addressing energy and environmental challenges such as the electrochemical CO₂ reduction reaction (CO₂ RR). Herein, the synthesis of an innovative copper–sulfur planar structure, Cu–S–BDC, within a metal–organic framework (MOF) catalyst is presented, which demonstrates 100% selectivity toward formate as the sole carbon product. Structural analysis and surface characterizations reveal that Cu–S–BDC exhibits quasi-2D inorganic building units, with Cu bonded to two S-CH (Formula presented.) groups and one BDC linker, while carboxylate groups adopt a bridging coordination mode. This unique arrangement not only imparts remarkable structural stability but also enhances the electronic properties of the MOF, as evidenced by a narrow bandgap of 1.203 eV that facilitates efficient charge transfer and increased electrochemical current density in CO (Formula presented.) RR. Notably, it offers a Faradaic efficiency of 92% for formate at an overpotential as low as −0.4 V versus the reversible hydrogen electrode (RHE) in an aqueous electrolyte of 1 m KOH, as well as a current density of −25.8 mA cm² at −0.9 V versus RHE, averaged over 24 h of electrolysis. This study highlights a fresh perspective in the field of MOF electrocatalysts by demonstrating that engineering the metal coordination environment can significantly enhance the electronic properties and consequently improve the electrocatalytic performance of these materials.

In the search for effective high-tech materials for energy conversion and storage devices, spinel-structured nickel ferrite (NiFe₂O₄) has been identified as a promising anode material for lithium-ion batteries (LIBs). However, the influence of different morphologies and surface properties of NiFe₂O₄ nanoparticles on battery performance is hardly addressed. To understand the effect of different morphologies and surface properties on the lithium-ion storage performance, NiFe₂O₄ nanoparticles were synthesized through four different synthesis conditions: NFO-S, NFO-U, NFO-G, and NFO-C. The formation of polycrystalline inverse spinel NiFe₂O₄ was confirmed through XRD, FTIR, and Raman spectroscopy. The morphologies of the obtained samples were studied using FESEM, and it was found that the four different synthesis conditions employed here enabled us to obtain NiFe₂O₄ with four different morphologies. The surface chemistry, surface area and porosity of the NiFe₂O₄ samples were respectively characterized using XPS and BET. The electrochemical performance of the four NiFe₂O₄ samples as anode material was studied by fabricating lithium-ion half-cells. NiFe₂O₄ sample obtained from surfactant-free synthesis condition (NFO-S) displayed a high initial discharge and charge capacity of 2258 mAh/g and 1815 mAh/g, respectively at the current density of 100 mA/g. Even after 100 cycles, NFO-S showed a better discharge capacity of 116 mAh/g at the current density of 100 mA/g, compared to the other samples studied here. The observed higher capacity of the NFO-S sample is attributed to the higher surface area (40.8 m²/g) and pore volume (0.190 cm³/g). The NiFe₂O₄ sample prepared with cationic CTAB surfactant (NFO-C) showed better cyclic stability with a stable coulombic efficiency of 98.5% at the 100th cycle, mainly attributed to its nanocube morphology with lower surface area (16.1 m²/g) and pore volume (0.087 cm³/g).

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.

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.

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.

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.

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.