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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".