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Xiaohan Bie

Publications and source records attributed to Xiaohan Bie.

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Atomic-Scale Insights into Solute Drag Effects on Grain Boundary Motion in Mg-Al and Mg-Ca Alloys

The slip behavior of dislocations and grain boundaries critically governs recrystallization and plastic deformation in Mg alloys and can be strongly influenced by solutes. However, the quantitative effects of solute distribution on defect mobility remain unclear. Using molecular dynamics and Monte Carlo simulations, we systematically investigate how Al and Ca solutes affect the motion of dislocations, low-angle grain boundaries (LAGBs), and high-angle grain boundaries (HAGBs) in Mg. Within the idealized framework of random solid-solution, solute drag is dominated by elastic interactions arising from atomic size mismatch, resulting in a stronger resistance from Ca than from Al. In contrast, under the more realistic condition where solute segregation occurs, the dominant mechanism shifts to chemically driven pinning, whose effectiveness is governed by the attainable segregation density. Owing to strong Ca-Ca repulsion, Al achieves substantially higher segregation concentrations than Ca and therefore exerts much stronger pinning effects. Notably, solute-induced retardation is significantly more pronounced for HAGBs than for LAGBs, leading to amplified solute effects during the late stages of recrystallization, where grain growth is controlled primarily by HAGB migration. These results provide atomic-scale insight into experimentally observed grain refinement in Mg alloys.

cond-mat.mtrl-sci

Segregation-Controlled Diffusion-Induced Grain Boundary Migration in Alloy 690

Grain boundary (GB) migration accompanied by Cr depletion is widely observed in Alloy 690 and is closely linked to intergranular degradation and stress corrosion cracking. However, the fundamental driving force for GB migration and its link with Cr depletion remains unclear. In this work, hybrid molecular dynamics and semi-grand canonical Monte Carlo simulations were employed to investigate GB migration in Alloy 690 under coupled solute diffusion and segregation effects across a range of GB characters. The results show that Cr segregation at GBs, while generally considered favorable for GB stability, can facilitate diffusion-induced GB migration and Cr depletion. Cr diffusion along GBs produces localized Cr depletion zones that are energetically incompatible with positively segregating GBs, generating a chemical driving force that drives GB migration toward the Cr-rich matrix, which ultimately results in persistent GB migration accompanied by a Cr depletion. By quantifying solute-GB interaction energetics, we demonstrate that GB migration is quantitively controlled by the coupled effects of solute diffusivity and segregation strength. These mechanistic insights provide a unified framework that rationalizes experimentally observed correlations between GB character, Cr depletion, and GB migration in Cr-containing alloys.

cond-mat.mtrl-sci

Hydrogen trapping in sub-stoichiometric niobium and vanadium carbide precipitates in high-strength steels

High-strength steel is a structural metal crucial for load-bearing components yet is known to be highly susceptible to hydrogen embrittlement (HE). Vanadium (V) and niobium (Nb) containing precipitated carbides introduce strong hydrogen traps to immobilize hydrogen, thus mitigating HE. However, variations in intrinsic vacancy concentrations in these carbides affect hydrogen thermodynamics and kinetics but remain poorly understood. Employing first-principles calculations, hydrogen trapping and diffusion in V/Nb carbides were investigated. Hydrogen dissolution energies are composition-dependent, revealing a transition from reversible to irreversible trapping with increasing carbon vacancy content, prescribed by the strength of covalent bonds with neighboring V/Nb atoms. Meanwhile, the diffusion energy barrier decreases with increasing carbon vacancy content, attributed to changes in vacancy patterns within carbides. The findings contribute new and critical knowledge for understanding hydrogen trapping and diffusion in sub-stoichiometric V/Nb carbides, providing valuable guidance for process and composition innovation of high-strength alloy steels for better HE resistance.

cond-mat.mtrl-sci

MatSegNet: a New Boundary-aware Deep Learning Model for Accurate Carbide Precipitate Analysis in High-Strength Steels

Lower Bainite (LB) and Tempered Martensite (TM) are two common microstructures in modern high-strength steels. LB and TM can render similar mechanical properties for steels, yet LB is often considered superior to TM in resistance to hydrogen embrittlement. Such performance difference has conventionally been attributed to their distinction in certain microstructural features, particularly carbides. The present study developed, MatSegNet, a new contour-aware deep learning (DL) architecture. It is tailored for comprehensive segmentation and quantitative characterization of carbide precipitates with complex contours in high-strength steels, shown to outperform existing state-of-the-art DL architectures. Based on MatSegNet, a high-throughput DL pipeline has been established for precise comparative carbide analysis in LB and TM. The results showed that statistically the two microstructures exhibit similarity in key carbide characteristics with marginal difference, cautioning against the conventional use of carbide orientation as a reliable means to differentiate LB and TM in practice. Through MatSegNet, this work demonstrated the potential of DL to play a critical role in enabling accurate and quantitative microstructure characterization to facilitate development of structure-property relationships for accelerating materials innovation.

cs.CV