Probing interfacial interactions and dynamics of polymers enclosed in boron nitride nanotubes
Bibliographic record
Abstract
Abstract Understanding interfacial interactions in polymer systems is crucial for their applicability for instance in adhesives and coatings. Enclosing polymers in a cylindrical volume provides a system for studying interactions dictated by a continuous interfacial layer and a bulk‐like volume in the middle of the cylinders. Here, we describe a simple method for enclosing polymers into boron nitride nanotubes (BNNTs) and establishing the effect of the interfacial interactions on the glass transition temperature (Tg) of the polymers by infrared spectroscopy. The volume of the inner channel is large in comparison to the volume of the loaded polymer coils, allowing the polymer to expand along the inner channel, resulting in the effect of interfacial interactions on polymer dynamics dominating over confinement effects. As examples, we loaded poly(4‐vinyl pyridine), poly(methyl methacrylate), poly(vinyl pyrrolidone), and poly(disperse red 1 acrylate) in BNNTs. The strongest interaction between the studied polymer and BNNTs was observed for poly(4‐vinyl pyridine), which also caused a significant increase of Tg. In addition to characterizing the effect of interfacial interactions on the thermal transitions of the polymers, this method, which is generalizable to most soluble polymer materials, can be used for studying photoinduced transitions in photoactive polymers thanks to the transparency of the BNNTs at visible wavelengths.
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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".