Hydrogen embrittlement of iron nanowires: investigating size and orientation dependence on loading behaviour
Bibliographic record
Abstract
With the ever-increasing use and applications of nanowires it has never been more imperative to understand how environmental interactions modify their unique mechanical properties and loading behaviour. While experimental research has shown that atomic hydrogen degrades mechanical properties through hydrogen embrittlement, results are limited and often do not directly quantify the hydrogen concentration or consider small diameter nanowires. In this study, we have used molecular dynamics simulations to the study the effect of atomic hydrogen on iron nanowires with various orientations and diameters. Results demonstrate that with increasing hydrogen concentration there is a clear reduction in the elastic modulus and yield stress as compared to the hydrogen free case for all diameters and orientations considered. In addition, this reduction in mechanical properties appears to exhibit a size dependence, with larger reductions being found in nanowires with larger cross-sectional diameters. We suggest that smaller diameter nanowires, with a higher ratio of surface to bulk atoms, are more influenced by free surface atoms than lattice distortions from atomic hydrogen. As this ratio of surface to bulk atoms is decreased, the larger diameter nanowires become less affected by free surfaces and more susceptible to the effect of atomic hydrogen.
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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".