Pea starch‐based composite films with pea hull fibers and pea hull fiber‐derived nanowhiskers
Bibliographic record
Abstract
Abstract New applications of both pea hull fiber (PHF) and PHF‐derived nanowhiskers (PHFNW), isolated from PHF by acid‐hydrolysis, as fillers in starch‐based biocomposite films were explored in this work. Two series of films were prepared by blending pea starch (PS), respectively, with PHF and PHFNW. The effects of PHF and PHFNW as filler on the structure and properties of the composite films were comparatively investigated by observation of morphology and analysis of thermal, optical, and mechanical properties. The results revealed that the PS/PHFNW nanocomposite films exhibited improved physical properties over both the neat PS film and PS/PHF microcomposite films. The light transmittance at 800 nm, tensile strength, elongation at break, and Young's modulus were 56.0%, 4.1 MPa (Megapascal), 30.1%, 40.3 MPa, respectively, for the PS film without filler; 58.0%, 7.6 MPa, 41.8%, and 415.2 MPa for the PS/PHFNW film containing 10 wt% filler; and 37.2%, 2.8 MPa, 17.0%, and 29.8 MPa for the PS/PHF film containing 10 wt% filler. The improvement to the properties of PS/PHFNW nanocomposite films may be attributed to the nanometer size effect of PHFNW, which resulted in the homogeneous dispersion of PHFNW within the PS, and the strong interactions between the matrix and the nanoscale filler. POLYM. ENG. SCI., 2009. Published by the Society of Plastics Engineers
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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".