Structure and properties of starch nanocrystal‐reinforced soy protein plastics
Bibliographic record
Abstract
Abstract Pea starch nanocrystals (StNs) were incorporated into a soy protein isolate (SPI) matrix to produce a class of full‐biodegradable nanocomposites. The StN with low loading level (2 wt%) showed a predominant reinforcing function, resulting in an enhancement in strength and Young's modulus. This was attributed to uniform dispersion of StN in the amorphous region of the SPI matrix, as well as maintaining stress of the rigid StN and transfer of stress mediated by interfacial interaction between the active StN surface and the SPI matrix. As a result, the nanocomposite containing 2 wt% StN had the maximum strength and Young's modulus in all the materials. With an increase in StN content, the number and the size of StN domains simultaneously increased due to a strong self‐aggregation tendency of StN. It lowered the effective active StN surface for interaction with the SPI matrix and destroyed the ordered structure in the SPI matrix, resulting in a gradual decrease of strength and Young's modulus. The introduction of relatively hydrophilic StN did not cause an obvious decrease of water resistance for any of the nanocomposites. The water uptake behavior of all the nanocomposites similar to that of neat SPI material was attributed mainly to the strong interfacial interaction between the StN filler and the SPI matrix. POLYM. COMPOS., 2009. Published by the 2008 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) | 0.000 | 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".