Base-Metal Nanoparticle-Catalyzed Hydrogen Release from Ammine Yttrium and Lanthanum Borohydrides
Bibliographic record
Abstract
Solid ammine metal borohydrides [M(BH 4 ) m (NH 3 ) n, AMBs] are promising materials for low temperature, high capacity hydrogen generation. Retention of metal halide co-products, arising from typical mechanochemical synthetic methods, is shown to have negative impacts on dehydrogenation properties of yttrium AMB. Halide-free yttrium and lanthanum AMBs, M(BH 4 ) 3 (NH 3 ) 4, have been synthesized directly by treatment of MCl 3 with 3 equiv of NaBH 4 in thf followed by filtration, cooling, and exposure to liquid ammonia. The peak dehydrogenation temperature of the Y analog decreased from previously reported 179 to 160 °C while the ammonia peak temperature increased from 86 to 165 °C. To enhance the dehydrogenation properties and increase the selectivity of gas formation from these AMBs, base-metal nanoparticle catalysts, M′NPs; M′ = Fe, Co, Ni, and Cu) were employed. Preparation of the M′NPs from M′Cl 2 and liquid hexylamine–borane allowed for separation of the B–Cl byproducts by subsequent solvent washing. Sonification of the M′NPs in toluene followed by addition of the solid AMB afforded composite AMB–M′NP–BN solids. Thermolysis data indicated a threefold reduction in ammonia release from the Y–Co and fourfold for the La–Fe composite. The purity of the released hydrogen was estimated to be 97.9 mol % for Y–Co and 98.9 mol % for La–Fe.
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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".