Length fractionation of boron nitride nanotubes using creamed oil-in-water emulsions
Bibliographic record
Abstract
The fractionation by length of multiwalled boron nitride nanotubes (BNNTs) was achieved by emulsification and creaming of an oil/water/surfactant mixture. The length separation is based on the fact that nanoparticle-coated oil droplets polydisperse in size move toward the upper surface or the bottom of an emulsified mixture depending on the density of the droplets, such that droplets of different sizes are located at different heights. By sampling heightwise an unstable hexane/water/Tween 20/BNNT (1-20 mum long) emulsion, we observed that the lengths of the BNNTs adsorbed on the droplets display a strong correlation with the droplets sizes, thus leading to selective separation of the BNNT lengths, as confirmed by dark-field optical imaging and dynamic light scattering. This method may potentially be extended to other high aspect ratio nanomaterials exhibiting emulsification properties similar to those of BNNTs.
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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) | 0.000 | 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".