Noncovalent functionalization of boron nitride nanotubes using poly(2,7‐carbazole)s
Bibliographic record
Abstract
Abstract To fully actualize the potential of boron nitride nanotubes (BNNTs), it is necessary to overcome the inherent insolubility of this nanomaterial. Drawing on the successes realized in the analogous carbon nanotube field, noncovalent functionalization with conjugated polymers offers a simple, scalable route toward the production of stable dispersions of BNNTs. 2,7‐carbazoles were chosen as our core monomer based on density functional theory (DFT) predictions, which suggest superior interactions with BNNTs when compared to fluorene‐BNNT interactions. Homo poly(2,7‐carbazole)s and copolymers with fluorenes were synthesized and used successfully to disperse BNNTs into organic solvents. Thermogravimetric analysis and atomic force microscopy results confirm the proficiency of these polymers to disperse large amounts (> 80% by weight) of individualized BNNTs. Analysis of absorbance data shows that the choice of solvent is critical, with stability enhanced in THF compared to CHCl3 due to the more efficient planarization of polymer chains on the surface of BNNTs, particularly for the homopolymers. The utility of these highly‐soluble poly(2,7‐carbazole)‐BNNT complexes for printed electronics and transparent composites was demonstrated by the fabrication of simple capacitors and incorporation into poly(methyl methacrylate) composites, respectively.
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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".