Role of Hydrogen in High-Yield Growth of Boron Nitride Nanotubes at Atmospheric Pressure by Induction Thermal Plasma
Bibliographic record
Abstract
We recently demonstrated scalable manufacturing of boron nitride nanotubes (BNNTs) directly from hexagonal BN (hBN) powder by using induction thermal plasma, with a high-yield rate approaching 20 g/h. The main finding was that the presence of hydrogen is crucial for the high-yield growth of BNNTs. Here we investigate the detailed role of hydrogen by numerical modeling and in situ optical emission spectroscopy (OES) and reveal that both the thermofluidic fields and chemical pathways are significantly altered by hydrogen in favor of rapid growth of BNNTs. The numerical simulation indicated improved particle heating and quenching rates (∼10 5 K/s) due to the high thermal conductivity of hydrogen over the temperature range of 3500–4000 K. These are crucial for the complete vaporization of the hBN feedstock and rapid formation of nanosized B droplets for the subsequent BNNT growth. Hydrogen is also found to extend the active BNNT growth zone toward the reactor downstream, maintaining the gas temperature above the B solidification limit (∼2300 K) by releasing the recombination heat of H atoms, which starts at 3800 K. The OES study revealed that H radicals also stabilize B or N radicals from dissociation of the feedstock as BH and NH radicals while suppressing the formation of N 2 or N 2 + species. Our density functional theory calculations showed that such radicals can provide faster chemical pathways for the formation of BN compared with relatively inert N 2 .
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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".