Cold-rolling of Ti based alloys: How does plastic deformation improve the hydrogen sorption?
Bibliographic record
Abstract
Different amounts of cold rolling were applied to a commercial pure hexagonal Ti – taken as a “model material” - to gain new insights into the effect of plastic deformation on hydrogen absorption properties (activation and kinetics). Reproducible specific H 2 charging procedures were set to discriminate between different material parameters such as structural defects (dislocations, vacancies), grain boundaries, texture and native oxide stability. The positive effect of increasing the amount of plastic deformation on improving the absorption kinetics was maintained despite the occurrence of a recrystallisation process - that removes most of the structural defects induced by rolling - before any H 2 loading. Decisive factors responsible for improved absorption kinetics such as higher proportion of high-angle grain boundaries, texture strengthening, as well as surface oxide layer modification induced by increasing cold rolling are analysed and thoroughly discussed. The H-absorption curves in Ti are characterized by three different stages, having possibly very different durations. It is essentially the formation of the hydrogen (hcp) α-Ti solid solution (stage I) and its transformation into the (bcc) β-Ti phase (stages II) that are affected by these decisive parameters. In particular, cold rolling has a major effect on destabilizing the surface oxide which, in turn, shows faster reduction towards its metal form. The subsequent kinetics, towards the formation of the δ-TiH 2 hydride (stage III), being related to the volume expansion cracking and the formation of fresh surfaces, is always rather fast even at low (400 – 450°C) charging temperatures.
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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".