Viability of Boron Nitride Nanotubes as a Support Structure for Metal Nanoparticle Catalysts for the Plasma-Catalytic Synthesis of Ammonia
Bibliographic record
Abstract
Nonthermal plasma-catalytic processes are being investigated as an alternative method to the energy-intensive and environmentally impactful Haber-Bosch (H-B) process for ammonia synthesis. Due to the large-scale production of this commodity chemical, the H-B process uses 1-2% of the world's energy, 3-5% of the world's refined natural gas and corresponding CO2emissions. With the recent development of large-scale electrolysers for H2production powered by renewable electricity, the integration of a plasma process at scale may be the last step toward the all-electric and environment-friendly green ammonia synthesis. However, the most encouraging results are still an order of magnitude below the H-B process in terms of energy efficiency. Key to success are the nanocatalysts, the plasma excitation and gas mixing, with reaction kinetics as the coupling between these variables. Boron nitride nanotubes (BNNTs) are a dielectric material with high chemical and thermal stability, and a unique affinity to ammonia. These properties make them interesting substrates for nanocatalysts. We report on the surface modification of BNNTs and the deposition of metal catalyst nanoparticles by two sequential plasma processing steps; plasma functionalization and pulsed laser ablation to produce BNNT-supported nanoparticle catalysts. We also report our additional findings on the morphology, activity, and stability of the produced catalysts.
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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 | 0.001 |
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".