Nanocomposite fabrics with high content of boron nitride nanotubes for tough and multifunctional composites
Bibliographic record
Abstract
Abstract Herein, we apply a one-step filtration method to obtain boron nitride nanotube (BNNT)-based fabrics incorporating high content of BNNTs and an adhesive thermoplastic polyurethane (TPU). The adsorption behavior of TPU on BNNTs of different qualities and on functionalized BNNTs was evaluated in a two-solvent system and contrasted with carbon nanotubes, pointing to differences in surface interaction. BNNT quality affected not only the nanocomposite mechanical properties but also the trends as a function of increasing TPU content and the adhesion to substrates. Samples containing higher quality BNNT materials showed up to 12-fold improvement in Young’s modulus, while functionalization improved the tensile toughness. Thermal conductivity varied between 1.5 and 3 W m−1 K−1 depending primarily on the BNNT content and without a pronounced effect from the quality of BNNTs. The BNNT-TPU fabric offers a promising format to exploit BNNTs within tough, electrically insulating, thermally conductive materials for heat dissipation within packaging or adhesive materials in electronics. Graphical abstract
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".