Covalent functionalization of boron nitride nanotubes through photo‐initiated chemical vapour deposition
Bibliographic record
Abstract
Abstract Boron nitride nanotubes (BNNTs) are analogous nanostructures to carbon nanotubes (CNTs), possessing similar properties such as Young's modulus and thermal conductivity, but superior resistance to oxidation and thermal stability. In addition, BNNTs are insulating materials, whereas CNTs are electrically conductive. They could be used as reinforcements in polymeric matrices as heat dissipators or as protective coatings in harsh environments. However, when incorporating them into polymers, one main drawback is their tendency to agglomerate. To improve their dispersion, covalent surface modification can be applied, with solvent‐free approaches being preferred. Herein, we used syngas photo‐initiated chemical vapour deposition (PICVD) to incorporate oxygen functionalities on the surface of BNNT. X‐ray photoelectron spectroscopy analysis showed a highly oxidized BNNT surface after treatment. In addition, a decrease in water contact angle and an increase in surface energy were observed for the treated material. These results open new possibilities to incorporate hydrophilic BNNTs surfaces into polar polymers or other matrices of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".