Influence of hydrogen on deformation and embrittlement mechanisms in a high Mn austenitic steel: In-Situ neutron diffraction and diffraction line profile analysis
Bibliographic record
Abstract
Austenitic steels have relatively high resistance to hydrogen embrittlement and play a critical role in hydrogen service applications. In particular, high Mn austenitic steels are considered economically viable alloy alternatives for these applications. The current study employed in-situ and ex-situ neutron diffraction techniques combined with diffraction line profile analysis (DLPA) to investigate the influence of hydrogen on deformation and embrittlement mechanisms in a high Mn (approximately 30 wt pct) austenitic steel. Investigation using both neutron diffraction and electron backscatter diffraction revealed the presence of extensive deformation twins and stacking faults within the steel microstructure after tensile deformation in the non-charged condition. These microstructural features suggest planar deformation behavior, which is expected from the relatively low stacking fault energy (SFE) of the alloy (approximately 29 mJ/m 2 ). Hydrogen pre-charging resulted in apparent increases in both dislocations and stacking faults, contributing to macroscopic hardening and embrittlement mechanisms. Overall, numerical parameters obtained through neutron DLPA were used to elucidate the underlying mechanisms associated with hydrogen effects on the mechanical behavior, i.e. macroscopic strengthening, strain hardening rate, and embrittlement.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".