Solute segregation at grain boundaries (GBs) has emerged as a promising strategy for alloy design. While most previous studies on GB segregation have assumed that solute atoms occupy substitutional sites within GBs, recent first-principles studies have revealed that undersized elements, such as Ni, Cu, and Fe, can also segregate to hollow interstitial sites in certain Al symmetric-tilt GBs. In this study, we employ hybrid molecular dynamics (MD)/Monte Carlo (MC) simulations to investigate the segregation behavior of Ni in Al coincidence-site lattice (CSL) GBs. The results demonstrate that Ni atoms preferentially segregate to interstitial sites within the kite-like GB structures in Al CSL GBs. Building on these findings, we develop a robust method to systematically identify potential interstitial sites at CSL GBs. This method consists of two main steps: detecting candidate interstitial sites and filtering them based on their structural properties. The identified interstitial sites show a distribution that is consistent with the results of hybrid MD/MC simulations. By applying this method to nanocrystalline alloys, we calculate the interstitial segregation energies, significantly improving the accuracy of GB segregation predictions. Furthermore, machine learning models using smooth overlap of atomic positions (SOAP) descriptors successfully predict these segregation energies. This study underscores the importance of GB interstitial segregation in enhancing our understanding of solute behavior and provides valuable guidance for the design of advanced metal alloys.

Any metal component failures (e.g., corrosion, fatigue) start from surface/subsurface defects (e.g., surface cracks, troughs, and pores). It is thus crucial to strengthen surface integrity by altering surface/subsurface properties in ways which provide protection from premature failure. To address this, in this paper, we presented a synergistic plasticity burnishing plus tumbling treatment to enhance the surface integrity and mechanical properties of cold-rolled AISI 304 steel alloys. A total of 304 steel specimens were ball-burnished, followed by a rotary tumbling process. The treated specimens were characterised in terms of surface roughness, 2D/3D topography hardness, and microstructures using laser confocal microscopy, SEM, a micro-hardness tester and EBSD analysis, respectively. Tensile tests were performed in accordance with ASTM standards to evaluate the Young’s modulus, yield strength, UTS and elongation. To evaluate the efficacy of the proposed synergistic approach, the results were compared and analysed across four types of specimens referred to as (1) “Untreated (as rolled)”, (2) “Burnished”, (3) “Tumbled” and (4) ‘Burnished+Tumbled”. The results showed that the ball burnishing resulted in grain modification and dislocation within the microstructure, improving the hardness and surface finish. But when the tumbling was applied to the burnished surface, the process augmented further hardness, grain modification, and yield strength, while there was a negligible impact on the fracture strength and elongation. These improved surface integrity properties are expected to enhance corrosion and fatigue life. The findings indicate that the combined burnishing–tumbling approach can extend the operational life of components subject to extreme loading conditions, thus saving huge costs by minimising premature failure.

This study presents a thorough exploration of borosilicate microglass balloons (MGBs) with a focus on their potential application in aerospace engineering. The research encompasses the preparation, characterization, and modification of MGBs utilizing advanced techniques aimed at enhancing their functionality and performance within aerospace composite materials. Initial efforts involved the synthesis of MGBs according to established protocols, followed by meticulous characterization employing Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS). SEM analysis provided insights into the particle size distribution, with a particular emphasis on the AlSi10Mg alloy, revealing an average size of 35 µm. Concurrent EDS examination confirmed the elemental composition of the alloy. Subsequent utilization of MGBs as reinforcement within the AlSi10Mg matrix revealed an average particle size of 75 µm, with EDS analysis identifying key constituent elements, including Si, O, Na, Ca, and Au. To further enhance the properties of MGBs, systematic surface treatments were conducted under varying chemical compositions and temperatures, guided by Taguchi methods. Post-treatment SEM analysis demonstrated a notable reduction in average particle size to 60 µm, corroborated by EDS analysis of the altered chemical makeup. Fourier Transform Infrared (FTIR) spectroscopy provided valuable insights into the formation of essential chemical bonds, particularly C-C and C-O bonds, indicative of improved covalent bonding and mechanical properties. Additionally, activation treatments were implemented to introduce functional groups onto the MGB surface, as confirmed by SEM analysis, revealing a particle size distribution averaging 75 µm. EDS analysis further detected the presence of catalytic elements such as Cu, Ag, and Bi, contributing to enhanced layer adhesion. FTIR analysis confirmed the establishment of crucial C-O and CH3 bonds, significantly augmenting surface wettability and adhesion properties.

Microbially influenced corrosion (MIC) poses a significant threat to the oil and gas industry, leading to costly failures and disruptions across production, transportation, and storage systems. Traditional microbial assessment and mitigation strategies often rely on culture-based methods, potentially underestimating the complexity of corrosion-driving microbial communities. Here, we present an integrative methodology that combines third-generation sequencing technologies, quantitative polymerase chain reaction (qPCR), physicochemical analyses of operational environments, and 3D reconstructions of defects on metallic probes or coupons, complemented by a thorough examination of design and operational parameters. By targeting both planktonic and sessile bacterial populations, this approach enables a comprehensive evaluation of corrosion-inducing potential under site-specific conditions. Crucially, shifts in microbial community composition across diverse corrosive taxa provide early warnings of elevated corrosion risk, guiding adjustments to biocides, corrosion inhibitors, and failure analyses. This framework’s inclusion of unculturable microorganisms underscores the importance of quantitative molecular methods in conjunction with or as an alternative to traditional culture-based techniques. Moreover, the integration of high-throughput sequencing data with advanced analytics supports the development of robust monitoring protocols, while the 3D mapping of metallic defects refines the detection of localized corrosion hotspots. By iteratively combining microbial profiling, defect reconstruction, and physicochemical parameters, this methodology refines monitoring strategies and defines key performance indicators (KPIs) that inform continuous optimization of corrosion management. Ultimately, this holistic and data-driven approach enhances the reliability and longevity of oil and gas infrastructure by enabling proactive, evidence-based decisions aimed at mitigating MIC effectively. ​​

Corrosion in oil wells results from the interaction of factors such as chemical composition, temperature, pressure, and fluid flow. Accurately predicting corrosion rates is crucial for operational efficiency and risk mitigation. Traditional models often have limitations in adapting to new operating conditions and capturing complex interactions between variables, while machine learning models can uncover these interactions. This study aims to develop a hybrid model that combines physical and chemical understanding of corrosion with machine learning approaches. Integrating fundamental corrosion principles with machine learning adaptivity, the model seeks to improve accuracy in predicting corrosion rates, thereby reducing operational costs, downtime, and safety risks. The methodology we applied began with the collection of historical data from a representative set of oil wells (more than 30,000 logs), including corrosion rates, well operating parameters, tubing parameters, and fluid compositions. The dataset was carefully cleaned and transformed, addressing missing values and outliers. To integrate first principles, the NORSOK corrosion model, based on fundamental chemical and physical concepts, was applied to calculate theoretical corrosion rates. Subsequently, machine learning models—such as Linear Models, Bootstrap Forest, and Boosted Tree—were trained using metrics like RSquare and Mean Average Error (MAE) to evaluate performance. Among the tested models, a Boosted Tree model (XGBoost) achieved the best performance, achieving an RSquare of 0.163 and an MAE of 1.39, capturing complex parameter relationships. Incorporating the NORSOK-based first principles ensured the model remained robust under varying conditions. Finally, a hybrid approach combining predictions from both physics- and chemistry-based and machine learning models was deployed in the cloud for real-time corrosion rate predictions. This deployment offers significant cost savings—up to a 30% reduction in chemical treatment costs—and enhances operational efficiency by minimizing downtime due to corrosion events.

The Al content plays a critical role in determining the corrosion and mechanical properties of AZ series Mg alloys. These properties have been widely investigated; however, the effects of high Al content above 9 wt.% on corrosion behavior, mechanical properties, and their relationship have not been explored. In this study, Mg–xAl–0.8Zn–0.1Mn–0.3Ca–0.2Y (x: 6, 9, 11, 13) alloys were prepared via mold casting (with a lower cooling rate) and high-pressure die casting (with a higher cooling rate) to investigate the effects of Al content on microstructure, corrosion behavior, and tensile properties. In particular, the tensile properties were evaluated before and after the salt spray test (SST). The corrosion rate was lower for the high-pressure die-casting alloy with a higher cooling rate than for the mold-casting alloy with a lower cooling rate. For mold-cast alloys, the corrosion rate increased significantly with increasing Al content. In contrast, for die-cast alloys, the corrosion rate initially increased and then decreased with higher Al content. The corrosion behavior was revealed to be related to the fraction and continuity of the β-Mg₁₇Al₁₂ phase and the Zn content within this phase through the EPMA and SKPFM analysis. Regarding mechanical properties, in both their initial state and their SST-exposed state, die-cast alloys exhibited improved strength with increasing Al content. Although the mechanical properties decreased after SST exposure, the extent of deterioration diminished with higher Al content.

Metastable high-entropy alloys (HEAs) represent a significant advancement in material science, addressing the longstanding challenge of balancing strength and ductility in alloys. The study of metastable HEAs offers a valuable framework for exploring the influence of γ-f.c.c.→ε-h.c.p. phase transformations on mechanical performance, particularly concerning volumetric changes during deformation. Structural changes, such as variations in the c/a ratio, provide critical insights into the interplay between phase stability and mechanical behavior in HEAs. We investigated the microstructure, mechanisms underlying phase transformations, and mechanical behavior of metastable Fe₄₀Mn₂₀Co₂₀Cr₁₅Si₅ and Fe₄₀Mn₂₀Co₂₀Cr₁₅Si₅ + 0.25wt.% B₄C HEAs fabricated via laser powder bed fusion (LBPF). Special attention was given to comparing how the presence of B₄C affects phase transformations, including variations in the c/a ratio, volumetric changes, and overall phase stability during deformation. The HEAs without B₄C demonstrated an increase in the c/a ratio, primarily due to a significant expansion of the c-axis, while introducing B₄C altered this response, leading to a decrease in the c/a ratio. This reduction was driven by a contraction of the c-axis rather than an expansion. Furthermore, the a-axis remained unchanged for both alloys. The contrasting behaviors highlight how adding B₄C fundamentally alters the stress-induced dimensional changes during the γ-f.c.c.→ε-h.c.p. phase transformation.