Geological disposal of high-level nuclear wastes (HLNW) is planned over hundreds of thousands of years, or even up to several million years. How can we predict the behavior of materials over such durations and, in particular, their corrosion?

We propose a global approach which can be implemented for the geological disposal of high-level nuclear wastes (HLNWs), and present this alongside the main corrosion-related issues. This approach was formalized by Digby's participation at the beginning of the 2000s. Some specificities of the French concept of a geological repository will be underlined: clay underground repository, reversibility concept, HLNWs mainly composed of a glass matrix, a carbon steel overpack, etc.

The main approach is based on “four pillars” which we have developed and which include:

• The use of experimental data obtained in laboratories for initial estimation of service lifetimes and corrosion mechanism investigations.
• Development of modelling (mechanistically based and also stochastic modelling) based on data and mechanisms found in laboratory experiments for robust and reliable prediction.
• The use of archaeological artefacts to provide data for long-term corrosion rates for the investigation of corrosion mechanisms and for validation of corrosion modelling.
• The importance of integrated experiments in underground laboratories is underlined to be sure that all the interactions are integrated.

The whole approach is iterative and includes the integration of the evolution of knowledge.

The development of environmentally benign corrosion inhibitors is critical to reduce the ecological impact of acid cleaning processes in industrial systems. This work presents an integrated approach to the discovery and application of green corrosion inhibitors for admiralty brass (AB), combining molecular-level studies of natural products with the valorization of agro-industrial waste.

In a first stage, latex extracts from Croton lechleri (Dragon’s blood), a native Amazonian species, were investigated as corrosion inhibitors in 0.5 M HCl. Phytochemical characterization revealed the presence of phenolic compounds and alkaloids, whose heteroatoms and π-electron systems promote adsorption on the metal surface. Electrochemical analyses, including potentiodynamic polarization and electrochemical impedance spectroscopy, demonstrated inhibition efficiencies up to 57% at low concentrations (50 ppm). Surface characterization (SEM, EDS, XPS) confirmed the formation of protective organic films that mitigate dezincification processes.

Building on this mechanistic understanding, a second stage explored low-cost inhibitor sources derived from agro-industrial residues. Extracts from Musa acuminata (banana) peels and Lupinus mutabilis debittering wastewater were screened using electrochemical methods. While banana extracts exhibited moderate efficiencies (~44%), the alkaloid-rich lupine extract achieved inhibition efficiencies above 80%, highlighting the effectiveness of waste-derived systems.

Finally, a techno-economic pre-feasibility analysis supports the scalability of the lupine-based inhibitor. This work bridges fundamental insights and applied screening, advancing sustainable corrosion protection strategies and promoting circular economy approaches based on Latin American biomass resources.

Organic corrosion inhibitors protect steel against degradation by forming chemisorbed molecular films, with inhibition efficiency governed by the interplay between electronic structure and adsorption behaviour. This work employs density functional theory (DFT)-based computational screening [1] to design and evaluate a novel series of oxadiazole‑based inhibitors for Fe surfaces, establishing a rigorous structure–property–performance relationship. Using the recently reported high‑performance inhibitor 2‑(5‑methylthiophen‑2‑yl)‑5‑(pyridin‑3‑yl)‑1,3,4‑oxadiazole (MTPO‑3) as a structural benchmark [2], a systematic library of isomers was generated through systematic rational modification of heteroatom positions and ring connectivity. DFT calculations identified 2‑(5‑methylthiophen‑2‑yl)‑5‑(pyridin‑2‑yl)‑ 1,3,4‑ oxadiazole (MTPO‑2) as the most stable isomer, exhibiting a higher HOMO energy, lower LUMO energy, and a narrower HOMO–LUMO gap—collectively indicative of superior electronic reactivity and enhanced electron-donor capacity toward Fe. Spin‑polarised DFT calculations were subsequently employed to evaluate the adsorption energetics of MTPO-2 and MTPO-3 on Fe(100) and Fe(110) surfaces. The computed adsorption energy of MTPO-3 on Fe(110) shows close agreement with published values [2], validating the reliability of the surface adsorption model. MTPO‑2 consistently exhibited stronger adsorption across both surface facets, corroborating its superior electronic profile. These results demonstrate that DFT‑guided isomer engineering is a powerful and systematic strategy for the rational design of high-performance oxadiazole corrosion inhibitors. Full computational details and mechanistic insights will be presented at the conference.

Magnesium and its alloys possess unique advantages in the fields of lightweight structural materials and biodegradable medical metallic materials. However, the chemically active nature of magnesium exacerbates localized corrosion of magnesium alloys. This issue significantly restricts the widespread application of magnesium and its alloys.

Corrosion inhibitor technology is an effective means to improve the corrosion resistance of metallic materials, achieved by introducing a small amount of compounds into the corrosive environment to markedly reduce the corrosion rate of materials. After years of research, a number of highly efficient magnesium alloy corrosion inhibitors have been reported. However, most studies primarily focus on the effect of inhibitors on the corrosion rate of magnesium alloys, failing to fully evaluate magnesium alloy corrosion inhibitors in combination with their actual service requirements.

For magnesium alloys, whether used as lightweight structural materials or biodegradable medical metallic materials, they need to maintain sufficient mechanical strength to bear corresponding loads during service. Studies have shown that the corrosion rate of magnesium alloys in different media environments cannot directly reflect their ability to maintain the original mechanical strength in corresponding environments, and the latter is directly related to the service reliability of magnesium alloys in such environments. Our recent research found that relying solely on corrosion rate measurement makes it difficult to objectively evaluate the actual effectiveness of magnesium alloy corrosion inhibitors.

This presentation will report the relevant research results of our team on magnesium alloy corrosion inhibitors. The most core highlight lies in taking the mechanical integrity and corrosion fatigue behavior of magnesium alloys under the action of corrosion inhibitors as reference indicators for the screening of corrosion inhibitors, which is of great significance for expanding the practical application of corrosion inhibitors.

In many industries, using corrosion inhibitors to protect piping systems remains a highly cost-effective strategy. While the primary goal is to reduce corrosion to acceptable levels, there is a growing demand for sustainable, "green" alternatives. Sodium silicate (SS) has emerged as a promising candidate, offering a non-toxic, eco-friendly, and readily available inorganic solution.

This study evaluates SS as a corrosion inhibitor for steel, copper, and zinc in flowing saltwater. To simulate the conditions of an open recirculating cooling water system, a rotating-cylinder electrode setup was used, with surface flow speeds ranging from 0.5 to 1 m/s. A comprehensive electrochemical evaluation was performed using potentiodynamic scans (PDS), electrochemical impedance spectroscopy (EIS), and Mott-Schottky (M-S) analysis.

The results from PDS and EIS demonstrated that SS drastically lowers corrosion rates by forming protective layers. Inhibition efficiencies reached up to 99% for steel, 97% for copper, and 88% for zinc. EIS and M-S curves provided additional insights into film structure, revealing a direct link between improved film properties and overall film stability. However, the interaction with zinc proved complex; at low concentrations, a competition emerged between the formation of the protective silicate layer and the development of non-protective insoluble species.

The study also highlighted a distinct difference in flow speed interaction for each metal. Combined EIS and M-S analysis revealed an interesting interplay between SS concentration and flow velocity. The resistance to flow-induced degradation varied significantly with substrate material and inhibitor bulk concentration. Ultimately, these findings confirm that sodium silicate is a highly effective and versatile green alternative for protecting multi-metal piping systems under dynamic flow conditions.

Corrosion in acidic environments represents a major challenge in many industrial sectors, particularly in pipelines, chemical processing units, and metallic equipment exposed to aggressive conditions. Metal degradation caused by corrosion results in economic losses, safety concerns, and reduced service life of industrial components. Therefore, environmentally friendly corrosion inhibitors are increasingly required. This study evaluates the effectiveness of Artemisia plant extract as a natural corrosion inhibitor for metals in a 1 M hydrochloric acid solution.

The Artemisia extract was prepared using water as a solvent and applied to metal samples immersed in the acidic medium. Gravimetric weight loss measurements were used to investigate the effects of inhibitor concentration, immersion time, and temperature on corrosion rate and inhibition efficiency. The results show that increasing inhibitor concentration significantly reduces corrosion rate while improving inhibition efficiency, indicating effective adsorption of extract components onto the metal surface.

Conversely, increasing temperature and acid molarity decreases inhibition efficiency and increases corrosion rate due to reduced stability of the protective film. Electrochemical techniques including Electrochemical Impedance Spectroscopy and Potentiodynamic Polarization will be employed to validate the gravimetric results. The inhibition mechanism is attributed to the formation of an adsorbed protective layer on the metal surface. These findings highlight the potential of plant-based inhibitors for sustainable corrosion protection in acidic industrial environments.

Stainless steel sheet pile walls are increasingly adopted in agricultural drainage channels as a corrosion-resistant alternative to conventional steel sheet piles. However, their high passive-film stability means that measurable thickness reduction does not occur within approximately ten years of installation, rendering conventional ultrasonic thickness measurement ineffective for early-stage condition assessment. This study proposes a deep learning-based automated detection system for pitting corrosion on stainless steel sheet pile surfaces using visible images acquired with a standard smartphone camera.

Two martensitic and ferritic stainless steel grades, SUS410 (Pitting Index: 11) and SUS430 (Pitting Index: 16), were sampled from an agricultural drainage channel in Niigata Prefecture, Japan, after five years of exposure in a brackish water environment with chloride ion concentrations of approximately 120 mg/L. Pixel-level annotation was performed on cropped regions of approximately 60 mm × 270 mm, and the U-Net semantic segmentation model was adopted as the detection model. Bayesian hyperparameter optimization using Optuna with the Tree-structured Parzen Estimator was applied across 100 independent trials to ensure robust performance evaluation. Data augmentation using vertical flip and blur operations was incorporated to improve generalization on the limited dataset.

The deep learning approach achieved F1-scores of 0.831 (SUS410) and 0.808 (SUS430), substantially outperforming the conventional binary thresholding baseline (F1-scores: 0.407 and 0.329, respectively). Data augmentation contributed improvements of approximately 1.2–2.8 percentage points. The results also confirmed the superior pitting resistance of SUS430, which exhibited markedly lower pit density and area ratio relative to SUS410. The proposed method enables non-destructive, quantitative assessment of early-stage pitting corrosion using readily available imaging equipment, offering a practical and cost-effective tool for infrastructure maintenance and long-term durability evaluation of agricultural water management facilities.

Natural carbonation of reinforced concrete is a major mechanism of steel depassivation and may lead to corrosion initiation once the carbonation front reaches the reinforcement, making it a critical durability issue for structures exposed to tropical urban climates. This study developed a machine learning framework to predict carbonation-induced reinforcement corrosion initiation risk under climate conditions representative of Kinshasa. The workflow combined a RILEM natural carbonation database containing 1,744 records, including 863 mix-level observations and 6,879 time-series measurements, with Kinshasa climate descriptors based on temperature, relative humidity, and an atmospheric CO₂ proxy. After filtering, 847 observations contained usable numerical climate information for model training. Four models were evaluated, namely Ridge regression, Random Forest, Gradient Boosting, and a median-based dummy baseline. Gradient Boosting achieved the best performance, with grouped cross-validation by reference yielding an RMSE of 0.0737 and an R² of 0.6101, while the reference-based holdout test produced an RMSE of 0.0668, an MAE of 0.0510, and an R² of 0.6488. For a 25 mm concrete cover, the median estimated time for the carbonation front to reach the reinforcement level was 18.30 years under a typical dry Kinshasa climate scenario and 16.05 years under an annual mean Kinshasa climate scenario; at 30 years, the proportion of profiles reaching corrosion initiation was 70.72% and 74.26%, respectively. These results show that an open-data, machine learning-based framework can provide a first quantitative estimate of carbonation-driven corrosion initiation risk in Kinshasa, while also highlighting the need for future local validation and improved representation of the most humid exposure conditions.

A dose–response function (DRF) incorporating time of wetness (TOW) as an input parameter was proposed by Ohara et al. (2024) based on outdoor exposure test data for carbon steel and zinc in Japan. The environmental input factors of the DRF include chloride deposition (S_d), time of wetness, and temperature. Corrosion rate maps were also generated by combining the proposed DRF with spatial distributions of these environmental factors. By expressing the S_d term in the DRF as an exponential function, the estimated corrosion rate maps successfully reproduced the decreasing trend with increasing distance from the coastline, indicating that the DRF is applicable to the evaluation of atmospheric corrosivity in Japan. In this study, the TOW-based dose–response functions proposed by Ohara et al. (2024) were examined with respect to their applicability to outdoor exposure test data for carbon steel and zinc obtained in multiple countries, including data from the ISOCORRAG program (Knotkova et al., 2010). Based on environmental data available in the ISOCORRAG database, the corrosion rates of carbon steel and zinc were estimated using TOW and S_d as input parameters. For data with S_d exceeding 200 mg m⁻² day⁻¹, primarily from coastal sites in Sweden, Norway and France, the corrosion rates were significantly overestimated. In contrast, for Sd below 200 mg m⁻² day⁻¹, the estimated corrosion rates showed better agreement with the measured values. These results indicate that the proposed TOW-based dose–response functions still face challenges in evaluating atmospheric corrosivity across multiple countries, particularly in regions with substantially higher chloride deposition.

This work explores the electrochemical behavior of carbon steel in highly alkaline solutions representative of concrete pore environments. The alkaline medium was prepared using NaOH, KOH, and Ca(OH)₂ to ensure conditions favorable to steel passivation. Chloride and sulfate ions were then added at different concentrations in order to study their individual and combined effects on corrosion behavior.

Electrochemical tests were carried out using open circuit potential (OCP), electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization measurements at several immersion times. In solutions with no chloride or low chloride content, the steel showed stable passive behavior. This was reflected by relatively steady OCP values, high impedance levels, large phase angles, and low corrosion current densities, all indicating the presence of a protective passive film in the alkaline environment. When the chloride concentration was increased, this passive state became less stable. A clear decrease in impedance and polarization resistance was observed, together with higher corrosion current densities, suggesting degradation of the passive film and the onset of localized corrosion.

Compared to chlorides, sulfate ions had a milder effect on corrosion but still influenced the electrochemical response by modifying the solution chemistry and the characteristics of the passive layer. In media containing both chloride and sulfate ions, the corrosion behavior was found to depend on their relative concentrations and the exposure time. In some cases, competitive effects were observed, while in others, the combined influence of the two ions affected the stability of the passive film and the overall corrosion kinetics.

Overall, the results highlight the key role played by aggressive anions in controlling the corrosion behavior of carbon steel in highly alkaline media. This study provides useful insight into corrosion processes in concrete-related environments and underlines the importance of ion composition when assessing the durability of steel in alkaline pore solutions.