Green composites reinforced with hemp nanocrystals in plasticized starch
Bibliographic record
Abstract
Abstract New nanocomposite films were prepared from a mixed suspension of hemp cellulose nanocrystals (HCNs) and thermoplastic starch, or plasticized starch (PS), by the casting and evaporating method. The morphology, thermal behavior, mechanical properties, and water sensitivity of the films were investigated by means of scanning electron microscopy, wide‐angle X‐ray diffraction, differential scanning calorimetry, tensile testing, contact angle measurements, and water absorption. The results indicate that the cellulose nanocrystals dispersed in the PS matrix homogeneously and resulted in an increase in the glass‐transition temperature ascribed to the fact that the flexibility of the starch molecular chains in the starch‐rich phase was reduced because of the strong intermolecular interactions between the starch and stiff HCNs. The films exhibited significant increases in the tensile strength and Young's modulus, from 3.9 to 11.5 MPa and from 31.9 to 823.9 MPa, respectively, with increasing HCN content from 0 to 30 wt %. In addition to the improvement in mechanical properties, the incorporation of HCNs into the PS matrix also led to a decrease in the water sensitivity of the final composite materials. Therefore, the HCNs played an important role in improving the mechanical properties and water resistance of the starch‐based material. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008
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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".