Enhanced Hydrogen Storage Properties of LiAlH<sub>4</sub> by Excellent Catalytic Activity of XTiO<sub>3</sub>@<i>h</i>‐BN (X = Co, Ni)
Bibliographic record
Abstract
Abstract The high desorption temperature and slow kinetics still restrict the applications of LiAlH4 in hydrogen storage. To solve the above problems, NiTiO3@h‐BN and CoTiO3@h‐BN prepared for the first time are introduced into LiAlH4 by ball milling. LiAlH4 doped with 7 wt% NiTiO3@h‐BN, selected as an optimal doping sample, starts to release hydrogen at 68.1 °C, and the total amount of hydrogen released is 7.11 wt% below 300 °C. The activation energies (Ea) of the two‐step hydrogen release reactions are 55.93 and 59.25 kJ∙mol−1, which are 45.8% and 69.0% lower than those of as‐received LiAlH4, respectively. Under 30 bar hydrogen pressure and 300 °C constant temperature, LiAlH4 doped with 7 wt% NiTiO3@h‐BN after dehydrogenation can absorb ≈1.05 wt% hydrogen. Based on density functional theory calculations, AlNi3 and NiTi, in situ formed nanoparticles during ball milling, can decrease the desorption energy barrier of AlH bonding in LiAlH4 and accelerate the breakdown of AlH bonding due to the interfacial charge transfer and the dehybridization. Furthermore, NiTi can enhance the adsorption and splitting of H2, promoting the activation of H2 molecules during the rehydrogenation process.
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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".