Mechanical and barrier properties of nanocrystalline cellulose reinforced poly(caprolactone) composites: Effect of gamma radiation
Bibliographic record
Abstract
Abstract Nanocrystalline cellulose (NCC) reinforced poly(caprolactone) (PCL) composites were prepared by compression molding. The NCC content varied from 2 to 10% by weight. NCC played a significant role in improving the mechanical properties of PCL. The addition of 5 wt % NCC caused a 62% improvement of the tensile strength (TS) value of PCL films. Similarly, tensile modulus (TM) values were also improved by NCC reinforcement but elongation at break (Eb) values decreased montonically with NCC content. The water vapor permeability (WVP) of PCL was 1.51 g·mm/m2·day·kPa, whereas PCL films containing 5 wt % NCC showed a WVP of 1.22 g·mm/m2·day·kPa. The oxygen transmission rate (OTR) and carbon dioxide transmission rate (CO2TR) of PCL decreased by 19 and 17%, respectively, with 5 wt % NCC incorporation. It was found that the mechanical and barrier properties of both PCL and PCL‐NCC composites further improved with 10 kGy gamma irradiation treatment. The combination of NCC and radiation significantly increased the TS, TM, and Eb (by 156, 123, and 80%, respectively, compared to untreated PCL). The WVP, OTR, and CO2TR decreased by 25–35% with respect to untreated PCL. The surface and interface morphologies of the PCL‐NCC composites were studied by scanning electron microscopy and suggested homogeneous distribution of NCC within the PCL matrix. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013
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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".