Hydrogen Trapping at Fe/Cu Interfaces
Bibliographic record
Abstract
Copper (Cu) in steel production can be a residual element, causing challenges during steel processing, as well as an alloying element, improving corrosion resistance and providing hardenability by nanosized precipitates. For the transition toward a green economy, increased recycling rates in steel production and alternative energy carriers, such as hydrogen, are of vital importance. As hydrogen is known for its embrittling effect on high-strength steels, this work sought to explore possible mitigation strategies for hydrogen embrittlement (HE) with the help of Cu precipitates. Hydrogen trapping at Cu/Fe interfaces following the complex phase transformations in the Cu precipitation sequence from body-centered cubic (bcc) to the so-called 9R structure to face-centered cubic (fcc) was addressed by a series of systematic density functional theory calculations. In combination with thermodynamic calculations, the pressing question regarding which of the precipitate structures was most desirable for the tackling of HE was alluded to. We found that hydrogen trapping at the Cu/Fe interfaces increased from -0.05 to -0.18 eV following the precipitation sequence. Despite this relatively weak hydrogen trapping, which was in the range of dislocations, we showed through thermodynamic calculations that fcc Cu precipitates could still contribute to lowering the risk of triggering the hydrogen-enhanced localized plasticity (HELP) mechanism of HE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.033 | 0.009 |
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; both teacher heads agree on what is shown here.
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".