Hydrogenation Properties of TiFe Doped with Zirconium
Bibliographic record
Abstract
The goal of this study was to optimize the activation behaviour of hydrogen storage alloy TiFe. We found that the addition of a small amount of Zr in TiFe alloy greatly reduces the hydrogenation activation time. Two different procedural synthesis methods were applied: co-melt, where the TiFe was melted and afterward re-melted with the addition of Zr, and single-melt, where Ti, Fe and Zr were melted together in one single operation. The co-melted sample absorbed hydrogen at its maximum capacity in less than three hours without any pre-treatment. The single-melted alloy absorbed its maximum capacity in less than seven hours, also without pre-treatment. The reason for discrepancies between co-melt and single-melt alloys was found to be the different microstructure. The effect of air exposure was also investigated. We found that the air-exposed samples had the same maximum capacity as the argon protected samples but with a slightly longer incubation time, which is probably due to the presence of a dense surface oxide layer. Scanning electron microscopy revealed the presence of a rich Zr intergranular phase in the TiFe matrix, which is responsible for the enhanced hydrogenation properties of these Zr-doped TiFe alloys.
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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".