Optical and surface properties of whey protein isolate coatings on plastic films as influenced by substrate, protein concentration, and plasticizer type
Bibliographic record
Abstract
Abstract Composite film structures of common plastic polymers including polypropylene (PP) or poly(vinyl chloride) (PVC) with whey protein isolate (WPI) coatings may be obtained by a casting method. Optical and surface properties of the resulting WPI‐coated plastic films, as affected by protein concentration and plasticizer type, were investigated to examine the biopolymer coating effects on surface modification with polymeric substrates of opposite polarity. The measured properties involved specular gloss, color, contact angle, and critical surface energy. Regardless of the substrates, WPI‐coated films possessed excellent gloss and no color, as well as good adhesion between the coating and the substrate when an appropriate plasticizer was added to the coating formulations. The protein concentration did not significantly affect gloss of WPI‐coated plastic films. Among five plasticizers applied, sucrose conferred the most highly reflective and homogeneous surfaces to the coated films. The WPI coatings were very transparent and the coated films with various protein concentrations and plasticizers showed no noticeable changes in color. Experimental results suggest that WPI coatings formulated with a proper plasticizer can improve the visual characteristics of the polymeric substrate and enhance water wettability of the coated plastic films. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 335–343, 2004
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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".