Hydrogen-Catalyzed, Pilot-Scale Production of Small-Diameter Boron Nitride Nanotubes and Their Macroscopic Assemblies
Bibliographic record
Abstract
Boron nitride nanotubes (BNNTs) exhibit a range of properties that are as compelling as those of carbon nanotubes (CNTs); however, very low production volumes have prevented the science and technology of BNNTs from evolving at even a fraction of the pace of CNTs. Here we report the high-yield production of small-diameter BNNTs from pure hexagonal boron nitride powder in an induction thermal plasma process. Few-walled, highly crystalline small-diameter BNNTs (∼5 nm) are produced exclusively and at an unprecedentedly high rate approaching 20 g/h, without the need for metal catalysts. An exceptionally high cooling rate (∼10(5) K/s) in the induction plasma provides a strong driving force for the abundant nucleation of small-sized B droplets, which are known as effective precursors for small-diameter BNNTs. It is also found that the addition of hydrogen to the reactant gases is crucial for achieving such high-quality, high-yield growth of BNNTs. In the plasma process, hydrogen inhibits the formation of N2 from N radicals and promotes the creation of B-N-H intermediate species, which provide faster chemical pathways to the re-formation of a h-BN-like phase in comparison to nitridation from N2. We also demonstrate the fabrication of macroscopic BNNT assemblies such as yarns, sheets, buckypapers, and transparent thin films at large scales. These findings represent a seminal milestone toward the exploitation of BNNTs in real-world applications.
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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".