In-Flight Plasma Functionalization of Boron Nitride Nanotubes with Ammonia for Composite Applications
Bibliographic record
Abstract
Surface functionalization is an essential step to successfully harness the properties of boron nitride nanotubes (BNNTs) in several applications. Currently available functionalization methods are prohibitively costly for commercial use and have significant environmental impacts. Here, we show that a low-pressure, capacitively coupled radio-frequency (RF, 13.56 MHz) glow discharge plasma reactor can be used to effectively carry out surface chemical functionalization of BNNTs. This low-pressure vacuum and gas handling system incorporates a solenoid valve to periodically fluidize the free-standing BNNTs, which provides for functionalizing BNNTs in-flight. Ammonia, which generates ·H, ·NH, and ·NH 2 radicals by energetic electron impact, was employed as the functionalization gas. The functionalization begins by anchoring the ·H, ·NH, and ·NH 2 radicals to the surface of BNNTs, which is promoted by the presence of free electrons forming reduced BNNTs. On the basis of density functional theory (DFT) calculations, we propose a mechanism wherein BNNTs are functionalized to the final product of BNNT–NH 3, a weakly bonded complex, through subsequent cascade reactions with H radicals abundant in the plasma reactor. BNNT surface functionalization with ammonia was confirmed by tandem thermogravimetric analysis–infrared spectroscopy (TGA-IR), Fourier transform infrared (FTIR) spectroscopy, dynamic vapor sorption (DVS), our recently reported BNNT quality assessment method based on the formation of regiorandom poly(3-hexylthiophene) (rra-P3HT) aggregates on BNNTs, and solubility in water. This solvent-free and environmentally friendly approach can be applied by using other functionalizing gases, and the in-flight functionalization of fluidized BNNT powder is also advantageous in terms of scalability. These features offer the potential to dramatically advance BNNT composites research efforts and the development of BNNT 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".