Preparation and Characterization of Cellulose Nanofibril Films from Wood Fibre and Their Thermoplastic Polycarbonate Composites
Bibliographic record
Abstract
The aim of this study was to develop cellulose-nanofibril-film-reinforced polycarbonate composites by compression molding. Nano fibres were prepared from wood pulp fibres by mechanical defibrillation, and diameter distribution of the fibres produced was in the range of 1–100 nm. Nanofibre films were prepared from the nanofibre suspensions and were characterized in terms of strength properties, crystallinity, and thermal properties. Strength and modulus of the nano fibre films prepared were 240 MPa and 11 GPa, respectively. Thermal properties of the sheets demonstrated the suitability of processing fibre sheets at high temperature. Tensile properties of the films subjected to composite-processing conditions demonstrated the thermal stability of the fibre films during the compression molding process. Nanocomposites of different fibre loads were prepared by press-molding nano fibre sheets with different thickness in between polycarbonate sheet at 205°C under pressure. The tensile modulus and strength of the polycarbonate increased with the incorporation of the fibres. The strength of the thermoplastic increased 24% with 10% of the fibres and is increased up to 30% with 18% of the fibres. Tensile modulus of the polycarbonate demonstrated significant enhancement (about 100%).
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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".