Gas barrier properties of polyurethane nanocomposites
Bibliographic record
Abstract
Abstract Polyurethane nanocomposites with varying concentration of different fillers are produced through hot melt extrusion by using nanotalc and Cloisite 30B as fillers. The TEM images show good dispersion of 30B while moderate agglomeration in nanotalc composite. The result is supported by the respective nanostructures (exfoliated in 30B vs. intercalated in nanotalc composites). A slight decrease in degradation temperature is observed but the nanocomposites are thermally stable upto 300°C. Permeability significantly decreases for nanocomposites. Young's modulus increases with increasing filler concentration while the toughness improvement exhibits a maximum at 4 and 6 wt% of 30B and nanotalc, respectively. Halpin–Tsai model is employed to predict the mechanical properties of the composites. The mechanical, thermal and gas barrier properties are better in 30B as compared to nanotalc nanocomposites, due to greater interaction in 30B nanocomposites evident from the large shift of peak position in UV–vis and FTIR measurements along with its good dispersion.
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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".