Synthesis of Ni–Ru Alloy Nanoparticles and Their High Catalytic Activity in Dehydrogenation of Ammonia Borane
Bibliographic record
Abstract
We report the synthesis and characterization of new Ni(x)Ru(1-x) (x = 0.56-0.74) alloy nanoparticles (NPs) and their catalytic activity for hydrogen release in the ammonia borane hydrolysis process. The alloy NPs were obtained by wet-chemistry method using a rapid lithium triethylborohydride reduction of Ni(2+) and Ru(3+) precursors in oleylamine. The nature of each alloy sample was fully characterized by TEM, XRD, energy dispersive X-ray spectroscopy (EDX), and X-ray photoelectron spectroscopy (XPS). We found that the as-prepared Ni-Ru alloy NPs exhibited exceptional catalytic activity for the ammonia borane hydrolysis reaction for hydrogen release. All Ni-Ru alloy NPs, and in particular the Ni(0.74)Ru(0.26) sample, outperform the activity of similar size monometallic Ni and Ru NPs, and even of Ni@Ru core-shell NPs. The hydrolysis activation energy for the Ni(0.74)Ru(0.26) alloy catalyst was measured to be approximately 37 kJ mol(-1). This value is considerably lower than the values measured for monometallic Ni (≈70 kJ mol(-1)) and Ru NPs (≈49 kJ mol(-1)), and for Ni@Ru (≈44 kJ mol(-1)), and is also lower than the values of most noble-metal-containing bimetallic NPs reported in the literature. Thus, a remarkable improvement of catalytic activity of Ru in the dehydrogenation of ammonia borane was obtained by alloying Ru with a Ni, which is a relatively cheap metal.
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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) | 0.000 | 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".