Morphological, physical and mechanical properties of nanocrystalline cellulose filled Nylon 6 foams
Bibliographic record
Abstract
Nanocomposite foams based on Nylon 6 and nanocrystalline cellulose were prepared via extrusion and injection molding to study the effect of nanocrystalline cellulose concentration (0 to 5%), chemical foaming agent content (0, 1%, and 2%), and mold temperature (30℃ and 80℃) on the morphological, physical, and mechanical properties of the samples. Nanocrystalline cellulose content, especially between 1 and 3 wt%, was very effective in reducing the cell size and increasing the cell density of the foam structure. Nanocrystalline cellulose addition (0–5%) was found to increase density (4% for composites and 20% for foams), tensile strength (10% for composite and 13% for foams), tensile modulus (20% for composites and 34% for foams), and flexural modulus (37% for composites and 29% for foams), but decreased the impact strength (35–40% for composites and 20–40% for foams). Foaming agent addition (1%) was able to improve the specific tensile (10%) and flexural (12%) moduli, tensile strength (14%), elongation at break (6%), and impact strength (27%). Finally, higher mold temperature decreased skin thickness and, consequently, decreased the mechanical properties, mostly tensile strength of the foam samples (1% for composites and 18% for foams).
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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".