Enhanced Hydrogen Generation by LiBH4 Hydrolysis in MOH/water Solutions (MOH: C2H5OH, C4H8O, C4H9OH, CH3COOH) for Micro Proton Exchange Membrane Fuel Cell Application
Bibliographic record
Abstract
LiBH4 has high hydrogen storage capacity, and its high gravimetric hydrogen density reaches 18.36%. However, LiBH4 exhibits poor hydrolysis performance in water because the abrupt ending caused by the agglomeration of its hydrolysis products limits its full utilization [1, 2]. In this paper, four kinds of organics, namely, ethanol, tetrahydrofuran, acetic acid, and butanol (referred to MOH) were added to water, and the effect of MOH species and amount on the hydrolysis performances of LiBH4 was evaluated. Results show that agglomeration can be avoided and that LiBH4 has a controllable hydrogen generation rate and high hydrogen generation amount inMOH/water solutions compared with that in pure water. The order in terms of the hydrolysis performance of LiBH4 in MOH/water solutions is as follows: acetic acid >butanol> tetrahydrofuran >ethanol. From XRD, SEM, and other analyses, the enhancement performance is explained by the diluting and solvent effects. Moreover, the addition of MOH alters the hydrolysis route of LiBH4. MOH acts as not only a carrier for water and LiBH4 but also as a reactant to form intermediate LiBH4·[MOH(H2O)x]y, which slows the hydrolysis kinetics of LiBH4. Hydrolysis conditions were optimized, and high hydrogen amount was achieved correspondingly. The experimental data presents the potential application of LiBH4 as a highly efficiency and stable hydrogen source for fuel cells.
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 | 0.000 |
| 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".