Facile and Mild Strategy Toward Biopolymer-Coated Boron Nitride Nanotubes via a Glycine-Assisted Interfacial Process
Bibliographic record
Abstract
We report a simple way to obtain polymer-coated multiwalled boron nitride nanotubes (BNNTs) conducted under mild conditions compatible with fragile biopolymers. The approach converts aggregated pristine BNNTs into colloidally stable dispersions in water without requiring treatment at high temperature or in strongly oxidative conditions. The method relies on our experimental observation that glycine (NH 2 –CH 2 –COOH, Gly) interacts with BNNTs, in accordance with theoretical calculations. The role of glycine in this process is 2-fold: the Gly amine group binds to the B-sites of BNNTs, while the Gly carboxylic acid function provides ionic anchoring sites for interactions with polyelectrolytes. The formation of Gly-BNNTs proved to be essential, since they readily disperse in water as disentangled objects and spontaneously adsorb polycations, such as chitosan (CH), polyanions, such as hyaluronan (HA), and polyzwitterionic polymers, such as chitosan-phosphorylcholine (CH-PC). Treatment of aqueous dispersions of coarsely coated BNNTs with an immiscible solvent (hexane) resulted in the complete coverage of the BNNT surfaces via oil/water interfacial assembly. This work provides a rapid, mild, and scalable route to water-dispersible biofunctional BNNTs that may serve as drug delivery vehicles or scaffolds in tissue engineering.
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) | 0.001 | 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".