Soy protein‐based nanocomposites reinforced by supramolecular nanoplatelets assembled from pluronic polymers/β‐cyclodextrin pseudopolyrotaxanes
Bibliographic record
Abstract
Abstract The self‐assembled rigid supramolecular nanoplatelets (SN) from Pluronic polymers with various lengths of polyethylene oxide (PEO) and β‐cyclodextrin have reinforced the soy protein isolate (SPI)‐based biodegradable plastics in terms of strength and modulus but at the expense of elongation. Meanwhile, the water resistance, which limited the application of the SPI plastics, was also enhanced. The structure and properties of nanocomposites were characterized by X‐ray diffraction, differential scanning calorimetry (DSC), scanning electron microscopy (SEM), tensile test, and water uptake test. The low loading of nanoplatelets was able to disperse into SPI matrix homogeneously, which resulted in reinforcement in nanocomposites. With an increase of nanoplatelets loading, the repulsion between nanoplatelets and SPI matrix occurred, accompanying with the formation of rectangle objects, resulted in a decrease of mechanical performance of the nanocomposites. The nanoplatelets with longest free PEO segments produced highest strength with least loss of elongation by virtue of enhanced association with SPI matrix mediated by PEO segments. Meanwhile, the nanoplatelets with moderate length of free PEO segments showed optimal water resistance. Herein, the reinforcing function of a supramolecular nanoplatelet, similar to the structure of layered silicate, was verified. © 2007 Government of Canada. Exclusive worldwide publication rights in the article have been transferred to 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".