Starch-based nanocomposites reinforced with flax cellulose nanocrystals
Bibliographic record
Abstract
In this study, the cellulose crystals, prepared by acid hydrolysis of flax fiber, consisted of slender rods with lengths ranging from 100 to 500 nm and diameters ranging from 10 to 30 nm, respectively. After mixing the suspension of flax cellulose nanocrystals (FCNs) and plasticized starch (PS), the nanocomposite films were obtained by the casting method. The effects of FCNs loading on the morphology, thermal behaviour, mechanical properties and water sensitivity of the films were investigated by means of wide-angle X-ray diffraction, differential scanning calorimetry, tensile testing, and water absorption testing. Scanning electron microscopy photographs of the failure surfaces clearly demonstrated a homogeneous dispersion of FCNs within the PS matrix and strong interfacial adherence between matrix and fillers, which led to an increase of glass transition temperature ascribed to the starch molecular chains in the starch-rich phase. In particular, these nanocomposite films exhibited a significant increase in tensile strength and Young’s modulus from 3.9 to 11.9 MPa and from 31.9 to 498.2 MPa, respectively, with increasing FCNs content from 0 to 30 wt%. Also, with a loading of FCNs, the resulting nanocomposite films showed a higher water resistance. Therefore, FCNs played an important role in improving the mechanical properties and water resistance of the starch-based materials.
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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) | 0.000 | 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".