Ti-Doped LiAlH<sub>4</sub> for Hydrogen Storage: Synthesis, Catalyst Loading and Cycling Performance
Bibliographic record
Abstract
The direct synthesis of LiAlH(4) from commercially available LiH and Al powders in the presence of TiCl(3) and Me(2)O has been achieved for the first time. The effects of TiCl(3) loadings (Ti/Al = 0, 0.01, 0.05, 0.2, 0.5, 1.0 and 2.0%) and various other additives (TiCl(3)/Al(2)O(3), metallic Ti, Nb(2)O(5), and NbCl(5)) on the formation and stability of LiAlH(4) have been systematically investigated. The yield of LiAlH(4) initially increases, and then decreases, with increasing TiCl(3) loadings. LiH + Al → LiAlH(4) yields above 95% were obtained when the molar ratios of Ti/Al were 0.05 and 0.2%. In the presence of a very tiny amount of TiCl(3) (Ti/Al = 0.01%), LiAlH(4) is still generated, but the yield is lower. In the complete absence of TiCl(3), LiAlH(4) does not form. Addition of metallic Ti, Nb(2)O(5), and NbCl(5) to commercial LiH and Al does not result in the formation of LiAlH(4). Preliminary tests show that TiCl(3)-doped LiAlH(4) can be cycled, making it a suitable candidate for hydrogen storage.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".