Interaction between dislocations, precipitates and hydrogen atoms in a 2000 MPa grade hot-stamped steel
Bibliographic record
Abstract
Inhibiting the diffusion and aggregation of hydrogen atoms can effectively improve the resistance to hydrogen embrittlement (HE) of high strength hot-stamped steel. Here we investigate the interaction between dislocations, precipitates and hydrogen atoms in a 2000 MPa grade hot-stamped steel through a combination of microstructure characterization and HE sensitivity tests. Results show that HE susceptibility indexes increase with the increase of hydrogen charging current density, and the corresponding HE mechanism transfers from hydrogen-enhanced localized plasticity (HELP) dominates to hydrogen-enhanced decohesion (HEDE) dominates. Additionally, dislocations as reversible hydrogen traps with an activation energy of 36.3 kJ/mol, and through calculation, dislocations can carry hydrogen atoms to move. Moreover, we find that dispersed V-rich (Ti, V)C precipitates can refine grain to increase the number of reversible hydrogen traps, pin dislocations to inhibit H–dislocation interaction, and act as irreversible hydrogen traps to capture hydrogen atoms and, consequently, raise the resistance to HE.
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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) | 0.001 | 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".