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.

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.

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.

This study investigates the microbiologically influenced corrosion (MIC) of X80 steel accelerated by the phototrophic bacterium Rhodopseudomonas palustris TIE-1. The photorespiration plays a key role in promoting extracellular electron transfer (EET)-induced MIC. In the early corrosion stage, unstable localized corrosion dominated in the dark, while intense diffusion-controlled corrosion occurs in light. Compared to the sterile anaerobic medium, R. palustris TIE-1 accelerated corrosion of X80 steel, with a significantly higher corrosion rate under light conditions, approximately three times that of dark conditions. Inhibition of photosynthetic electron transfer or cessation of photostimulation resulted in pronounced reduction in the corrosion rate.

Machine learning is a powerful means for the rapid development of high-performance functional materials. In this study, we presented a machine learning workflow for predicting the corrosion resistance of a self-healing epoxy coating containing ZIF-8@Ca microfillers. The orthogonal Latin square method was used to investigate the effects of the molecular weight of the polyetheramine curing agent, molar ratio of polyetheramine to epoxy, molar content of the hydrogen bond unit (UPy-D400), and mass content of the solid microfillers (ZIF-8@Ca microfillers) on the low impedance modulus (lg|Z|_0.01Hz) values of the scratched coatings, generating 32 initial datasets. The machine learning workflow was divided into two stages: In stage I, five models were compared and the random forest (RF) model was selected for the active learning. After 5 cycles of active learning, the RF model achieved good prediction accuracy: coefficient of determination (R ²) = 0.709, mean absolute percentage error (MAPE) = 0.081, root mean square error (RMSE) = 0.685 (lg(Ω·cm²)). In stage II, the best coating formulation was identified by Bayesian optimization. Finally, the electrochemical impedance spectroscopy (EIS) results showed that compared with the intact coating ((4.63 ± 2.08) × 10¹¹ Ω·cm²), the |Z|_0.01Hz value of the repaired coating was as high as (4.40 ± 2.04) × 10¹¹ Ω·cm². Besides, the repaired coating showed minimal corrosion and 3.3% of adhesion loss after 60 days of neutral salt spray testing.

Following the construction of a dataset of cross-category corrosion inhibitors at different concentrations based on 1241 data from 184 research papers, a performance prediction model incorporating 2D–3D molecular graph representation and corrosion inhibitor concentration information was established. This model was shown to effectively predict the inhibition efficiency (IE) of different categories of corrosion inhibitors for carbon steel in 1 mol/L HCl solution. The model was also able to predict IEs of corrosion inhibitors at different concentrations. The results demonstrated that 3D features of corrosion inhibitors, especially those of large molecules, had a significant impact on the prediction precision of IEs.

In this work, an active protective epoxy coating with weathering resistant, corrosion-warning, and self-healing properties was developed by incorporating tannic acid (TA) loaded mesoporous silica (MSN-TA) nanocontainers. The introduction of MSN-TA nanocontainers could alleviate the coating degradation via scavenging the radicals generated during UV irradiation. Compared with the blank coating, the coating containing 5 wt.% MSN-TA nanocontainers exhibited much less degradation in surface morphology, wettability and glossiness, and maintained a good barrier property after 384 h of accelerated weathering. Once the coating was damaged, the released TA could react with the Fe³⁺ ions to form a chelate that endowed the coating scratch with a visible black coloration, i.e. triggering a self-warning capability to indicate the initial onset of corrosion. In addition, the generated chelate could inhibit extensive corrosion propagation, offering a significant self-healing effect demonstrated by the stabilized impedance modulus values during 28 days of immersion in NaCl solution.

Organic coatings are one of the most used and versatile technologies to mitigate corrosion of metals. However, organic coatings are susceptible to defects and damages that may not be easily detected. If not repaired timely, these defects may develop into major coating failures due to corrosion occurring in the damaged region, thereby limiting the lifetime of the to be protected structure. Thus, the development of smart coatings that can accurately identify corrosion location and reliably recover the damage autonomously is of particular interest. Herein, we reported a robust, corrosion-sensing and self-healing coating which incorporated pH-sensitive ZIF-8-capped CaCO₃ microcontainers containing the healing agent tung oil (TO) and the corrosion indicator/inhibitor 1,10-phenanthrolin-5-amine (APhen). The spontaneous leakage of incorporated TO and APhen was restrained, and the release initiated when local pH variation occurred. The corrosion protection performance of the coatings implanted with different contents of smart microcontainers were evaluated. The intact epoxy coating containing 7.5 wt% of the microcontainers exhibited the best protection performance with low water absorption (0.65 wt%), low O₂ permeability (0.21 × 10^–15 cm³ cm cm⁻² s⁻¹ Pa⁻¹), and a high storage modulus (3.0 GPa). Electrochemical impedance spectroscopy (EIS) measurements in 3.5 wt% NaCl solution demonstrated superior durability of the composite coating after self-healing. The immersion test and neutral salt spray test confirmed the coating can accurately report corrosion sites via coloration.

The microbiologically influenced corrosion of pure iron was investigated in the presence of Shewanella oneidensis MR-1 with various levels of exogenous riboflavin (RF) serving as electron shuttles for extracellular electron transfer (EET). With more RF available, a larger and denser phosphate layer was formed on the surface of pure iron by the bacteria. The results of electrochemical impedance spectroscopy, linear polarization resistance and potentiodynamic polarization tests showed that the product layer provided good corrosion protection to the pure iron. Using electrochemical noise, we observed that the addition of RF accelerated the corrosion at the initial stage of immersion, thereby accelerating the deposition of products to form a protective layer subsequently.

Herein, we report the development of a self-sensing and active corrosion protection coating which incorporates pH-sensitive multilayer chitosan/alginate-covered CaCO₃ microcontainers containing 1,10-phenanthrolin-5-amine (APhen). The microcontainers can respond to pH variation to release APhen which serves not only as a corrosion indicator but also as an inhibitor. An epoxy coating doped with 5 wt% microcontainers exhibited improved corrosion performance and was capable of inhibiting corrosion spreading from the damaged area in a 3.5 wt% NaCl solution. The salt spray test showed that corrosion damage can be quickly detected by the appearance of a red color within 2 min.

Current experimental verification, computational modeling, and machine learning methods for predicting corrosion inhibition efficiency (IE) are limited to specific inhibitor categories with high cost and poor generalization. In this study, a cross-category corrosion inhibitor dataset is constructed and a three-level direct message passing neural network (3 L–DMPNN) model using molecular structure information that integrates atomic-level, chemical bond-level, and molecular-level features to predict the IEs of compounds in a specific environment is established. This work demonstrates that the 3 L–DMPNN model can predict IEs of cross-category corrosion inhibitors from other independent literature and experimental dataset effectively and quickly.

Shewanella oneidensis MR-1 is an attractive model microbe for elucidating the biofilm-metal interactions that contribute to the billions of dollars in corrosion damage to industrial applications each year. Multiple mechanisms for S. oneidensis-enhanced corrosion have been proposed, but none of these mechanisms have previously been rigorously investigated with methods that rule out alternative routes for electron transfer. We found that S. oneidensis grown under aerobic conditions formed thick biofilms (∼50 µm) on stainless steel coupons, accelerating corrosion over sterile controls. H₂ and flavins were ruled out as intermediary electron carriers because stainless steel did not reduce riboflavin and previous studies have demonstrated stainless does not generate H₂. Strain ∆mtrCBA, in which the genes for the most abundant porin-cytochrome conduit in S. oneidensis were deleted, corroded stainless steel substantially less than wild-type in aerobic cultures. Wild-type biofilms readily reduced nitrate with stainless steel as the sole electron donor under anaerobic conditions, but strain ∆mtrCBA did not. These results demonstrate that S. oneidensis can directly consume electrons from iron-containing metals and illustrate how direct metal-to-microbe electron transfer can be an important route for corrosion, even in aerobic environments.

Corrosion jeopardizes the materials longevity and engineering safety, hence the corrosion rate needs to be forecasted so as to better guide materials selection. Although field exposure experiments are dependable, the prohibitive cost and their time-consuming nature make it difficult to obtain large dataset for machine learning. Here, we propose a strategy Integrating Ab-initio Calculations with Random Forest (IACRF) to optimize the model, thereby estimating the corrosion rate of Al alloys in diverse environments. Based on the thermodynamic assessment of the secondary phases, the ab-initio calculation quantities, especially the work function, significantly improved the prediction accuracy with respect to small-sample Al alloys corrosion dataset. To build a better generic prediction model, the most accessible and effective features are identified to train IACRF. Finally, the independent field exposure experiments in Southeast Asia have proven the generalization ability of IACRF in which the average prediction accuracy is improved up to 91%.

The influence of outward extracellular electron transfer (EET) of Pseudomonas aeruginosa in accelerating corrosion of 304 stainless steel was investigated. With less NO₃⁻ available as electron acceptor, P. aeruginosa biofilm accelerated the pitting corrosion. The ICP-MS and XPS results indicated that P. aeruginosa promoted the bioreductive dissolution of iron oxides in the passive film of stainless steel. Using in situ scanning electrochemical microscopy, we established a relationship between this accelerated deterioration of the passive film and the EET process mediated by the conversion of the redox states of pyocyanin secreted by P. aeruginosa.

This study investigated the deterioration of a lubricant-infused anodic aluminium oxide surface in a 1 M NaCl solution for ∼200 days. Direct observation by cryo-SEM and quantitative analyses by UV spectroscopy and EIS revealed that the long-term deterioration of the lubricant-infused surface was divided into two stages: the surface-adhered lubricant layer gradually dissolved at a constant rate until the substrate was exposed; afterwards the lubricant infused in the nanochannels began to diffuse and was depleted after ∼200 days. The EIS results also revealed that the defects reduced the corrosion resistance of the lubricant-infused surface considerably.

Microbiologically influenced corrosion of metals is prevalent in both natural and industrial environments, causing enormous structural damage and economic loss. Exactly how microbes influence corrosion remains controversial. Here, we show that the pitting corrosion of stainless steel is accelerated in the presence of Shewanella oneidensis MR-1 biofilm by extracellular electron transfer between the bacterial cells and the steel electrode, mediated by a riboflavin electron shuttle. From pitting measurements, X-ray photoelectron spectroscopy and Mott-Schottky analyses, the addition of an increased amount of riboflavin is found to induce a more defective passive film on the stainless steel. Electrochemical impedance spectroscopy reveals that enhanced bioanodic and biocathodic process can both promote the corrosion of the stainless steel. Using in situ scanning electrochemical microscopy, we observe that extracellular electron transfer between the bacterium and the stainless steel is bidirectional in nature and switchable depending on the passive or active state of the steel surface.

In this study, a new self-healing shape memory polymer (SMP) coating was prepared to protect the aluminum alloy 2024-T3 from corrosion by the incorporation of dual-function microspheres containing polycaprolactone and the corrosion inhibitor 8-hydroxyquinoline (8HQ). The self-healing properties of the coatings were investigated via scanning electron microscopy, electrochemical impedance spectroscopy, and scanning electrochemical microscopy following the application of different healing conditions. The results demonstrated that the coating possessed a triple-Action self-healing ability enabled by the cooperation of the 8HQ inhibitor, the SMP coating matrix, and the melted microspheres. The coating released 8HQ in a pH-dependent fashion and immediately suppressed corrosion within the coating scratch. After heat treatment, the scratched coating exhibited excellent recovery of its anticorrosion performance, which was attributed to the simultaneous initiation of scratch closure by the shape memory effect of the coating matrix, sealing of the scratch by the melted microspheres, and the synergistic effect of corrosion inhibition by 8HQ.

This work introduces a new self-healing superhydrophobic coating based on dual actions by the corrosion inhibitor benzotriazole (BTA) and an epoxy-based shape memory polymer (SMP). Damage to the surface morphology (e.g., crushed areas and scratches) and the corresponding superhydrophobicity are shown to be rapidly healed through a simple heat treatment at 60 °C for 20 min. Electrochemical impedance spectroscopy (EIS) and scanning electrochemical microscopy (SECM) were used to study the anti-corrosion performance of the scratched and the healed superhydrophobic coatings immersed in a 3.5 wt% NaCl solution. The results revealed that the anti-corrosion performance of the scratched coatings was improved upon the incorporation of BTA. After the heat treatment, the scratched superhydrophobic coatings exhibited excellent recovery of their anti-corrosion performance, which is attributed to the closure of the scratch by the shape memory effect and to the improved inhibition efficiency of BTA. Furthermore, we found that the pre-existing corrosion product inside the coating scratch could hinder the scratch closure by the shape memory effect and reduce the coating adhesion in the scratched region. However, the addition of BTA effectively suppressed the formation of corrosion products and enhanced the self-healing and adhesion performance under these conditions. Importantly, we also demonstrated that these coatings can be autonomously healed within 1 h in an outdoor environment using sunlight as the heat source.

We present a concealing method in which an anti-point spread function (APSF) is generated using binary optics, which produces a large-scale dark area in the focal region that can hide any object located within it. This result is achieved by generating two identical PSFs of opposite signs, one consisting of positive electromagnetic waves from the zero-phase region of the binary optical element and the other consisting of negative electromagnetic waves from the pi-phase region of the binary optical element.