This study investigates the role of austenite domain size in governing the electrochemical corrosion behavior of UNS S32205 duplex stainless steel. Specimens with a quantified range of austenite domain sizes were subjected to electrochemical testing in 0.1 - 1.0 M NaCl solutions, complemented by analyses of post-corrosion morphology and surface composition. The results demonstrate that microstructures with finer austenite domains exhibit systematically superior corrosion resistance across all tested conditions. This is evidenced by a higher charge transfer resistance, a lower corrosion current density, and a more stable passive film compared to their coarse-structured counterparts. The enhancement is attributed to accelerated repassivation kinetics and the geometric confinement of corrosive attack within the refined dual-phase network, which effectively suppresses stable pit growth within the less-connected ferrite phase. This work establishes austenite domain size as a master microstructural variable for controlling localized corrosion in 2205 DSS, providing a scientific basis for optimizing the alloy's durability through microstructural refinement strategies during thermo-mechanical processing.
The influence of aluminum (Al) addition content on the high-temperature chlorine corrosion behavior of Fe-Ni-Cr-Mo alloy (254SMo) alloy modified by Al additions was investigated in a simulated waste incinerator environment containing N2-2.6 vol%, CO2-1.3 vol%, and O2-2,700 vppm HCl at 700 degrees C. The results demonstrate that as the Al content increases, the microstructure transforms from a single-phase austenite to an intermetallic compound phase structure comprising (Fe,Ni) and FeAlCr, accompanied by grain refinement. The corrosion kinetics curves of 254SMo alloys with 0 at%, 5 at%, 10 at%, and 15 at% Al addition after 80 h of corrosion all follow the parabolic law. The 254SMo:5Al alloy exhibited the highest corrosion weight gain of 1.3280 mg/cm2, while the 254SMo:15Al alloy showed the lowest weight gain of 0.1116 mg/cm2. The chlorine corrosion product layer exhibits a tri-layer structure of "FexOy/Cr2O3/Al2O3", where the density of the inner Al2O3 layer increases with higher Al content, effectively inhibiting the chlorine cycle during the "active oxidation" process. The 254SMo:5Al alloy demonstrated poor corrosion resistance due to the discontinuous Al2O3 layer, leading to localized oxide formation. In contrast, the 254SMo:15Al alloy forms a continuous Cr2O3/Al2O3 composite oxide layer, significantly enhancing the inhibition efficiency against Cl-diffusion and achieving optimal high temperature chlorine corrosion resistance.
Corrosion in aquatic environments causes significant economic losses and structural degradation. This study models the corrosion rate of S235 carbon steel using machine learning (ML) under real-world aquatic conditions. A field dataset from 46 locations along the Ghent-Terneuzen canal was used, encompassing exposure and environmental parameters such as temperature, pH, total dissolved oxygen (HDO%), chlorophyll concentration, oxidation-reduction potential (ORP), total dissolved solids, chloride concentration, specific conductivity, depth, and salinity. Six ML algorithms, including Light Gradient Boosting Machine (LightGBM), Gradient Boosting Regressor, Random Forest, Extreme Gradient Boosting, Neural Network, and Categorical Boosting were benchmarked before and after feature selection. This work demonstrates that environmental feature selection provides substantially greater predictive improvement than model architecture choice: feature selection enhanced all algorithms from poor (R2 <= 0.14) to strong performance (R2 = 0.70 to 0.80), reduced intermodel variation by 64%, and decreased prediction error by 48% (RMSE) and 74% (MSE). LightGBM achieved the best performance (MSE = 0.003, R2 = 0.80). Unexpectedly, feature importance analysis identified that salinityand depth, traditionally considered critical factors, showed minimal predictive influence, while exposure duration, pH, HDO%, temperature, chlorophyll concentration, and ORP-dominated corrosion behaviour. These findings emphasize the critical role of environmental parameters and feature selection over model complexity, supporting more efficient corrosion monitoring and management in marine and aquatic environments.
Aluminum (Al) alloys, especially the 5xxx and 7xxx series, are widely used in various applications due to their excellent mechanical properties achieved through alloying. Exploring the effect of alloying elements on the corrosion resistance of Al alloys is an important step in the development of high-performance Al materials. In this work, density functional theory (DFT) calculations were utilized to examine the role of alloying elements on galvanic corrosion behavior in 5xxx and 7xxx series Al alloys, and the impact of aggressive ions on the protective properties of their passive films. According to the DFT calculation results, the potential difference between the Al matrix and Mg2Al3 shows an initial decrease followed by an increase as the concentration of Mg increases in 5xxx series alloys (Al-Mg). For 7xxx series alloys (Al-Zn-MgCu), increasing Cu content enhances the susceptibility to galvanic corrosion. Additionally, competitive adsorption diagrams of various ions on the passive film of the alloyed Al in chloride-containing solutions were plotted. The Si-additive in the film promotes the adsorption of hydroxide ions (OH-) on the passive films, thus effectively blocking the penetration of harmful Cl- ions. However, when passive films are modified by Mg, Zn, or Cu additives, the film degradation is accelerated. This study explains the mechanisms of microgalvanic corrosion and ion adsorption on the passive film of Al alloys, thereby providing the theoretical basis for designing corrosion-resistant alloys.
