Hydrogen trapping and diffusion in sub-stoichiometric vanadium and niobium carbide precipitates in high-strength steels
Bibliographic record
Abstract
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. • VC x /NbC x precipitates show distinct hydrogen embrittlement resistance in steels. • H–V/Nb bonds strengthen H trapping with rising carbon vacancy content. • H activation barriers vary with different carbon vacancy arrangements in VC x /NbC x . • 1NN vacancies ease H diffusion, making M 4 C 3 /M 3 C 2 effective against embrittlement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".