Atomic-scale insights on hydrogen trapping and exclusion at incoherent interfaces of nanoprecipitates in martensitic steels
Bibliographic record
Abstract
Hydrogen is well known to embrittle high-strength steels and impair their corrosion resistance. One of the most attractive methods to mitigate hydrogen embrittlement employs nanoprecipitates, which are widely used for strengthening, to trap and diffuse hydrogen from enriching at vulnerable locations within the materials. However, the atomic origin of hydrogen-trapping remains elusive, especially in incoherent nanoprecipitates. Here, by combining in-situ scanning Kelvin probe force microscopy and aberration-corrected transmission electron microscopy, we unveil distinct scenarios of hydrogen-precipitate interaction in a high-strength low-alloyed martensitic steel. It is found that not all incoherent interfaces are trapping hydrogen; some may even exclude hydrogen. Atomic-scale structural and chemical features of the very interfaces suggest that carbon/sulfur vacancies on the precipitate surface and tensile strain fields in the nearby matrix likely determine the hydrogen-trapping characteristics of the interface. These findings provide fundamental insights that may lead to a better coupling of precipitation-strengthening strategy with hydrogen-insensitive designs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".