Effect of addition of lanthanum on the hydrogen storage properties of TiFe alloy
Bibliographic record
Abstract
TiFe is one of the good candidates for the solid-state hydrogen storage.Despite having fast kinetics and fairly good capacity, it's first hydrogenation is difficult.In this study, we investigated the effect of addition of lanthanum on the hydrogen storage properties of TiFe alloy.As the melting point of lanthanum is much lower than the ones of the other two elements, synthesis by casting was impossible.Instead ball milling was used to synthesize the compound.It was found that the TiFe alloy is formed after 5 hours of milling.The hydrogen storage properties were measured at room temperature and at a pressure of up to 40 bars on a home-made Sievert's apparatus.For the first hydrogenation, the alloy absorbed 1 wt.% of hydrogen in less than 5 minutes.But, the first de-hydrogenation showed a reduced capacity from 1 wt% to 0.65 wt% i.e., a reduction of 0.35 wt%.Further hydrogenation and dehydrogenation shows no further loss in capacity.To understand the loss in capacity, the X-ray diffraction of fully hydrogenated and fully dehydrogenated samples were performed.But from these diffraction patterns a secondary phase was observed.The presence of this secondary phase may explain the loss of capacity.Possible crystal structure of this phase will be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".