Influence of Interfacial Interactions on the Formation of Zein–Ethyl Cellulose Composite Films
Bibliographic record
Abstract
In this study, submicrometer zein particle suspensions (ZPS) were prepared by antisolvent precipitation from aqueous ethanol (Eth, 90% w/w) or isopropanol (Iso, 90% w/w) solutions. The resulting ZPS were incorporated into an ethyl cellulose solution (EC, 4% w/w), followed by casting to obtain continuous composite films. The zein particle size distribution was evaluated by laser diffraction, confocal laser scanning microscopy, and atomic force microscopy analyses. The ZPS prepared from 90% aqueous ethanol had zein particles with a smaller average particle size (Dv50 = 0.56 μm) compared to those prepared from 90% aqueous isopropanol (Dv50 = 3.74 μm). Transparent and coherent zein-EC composite films exhibited different scanning electron microscopy features depending on the types of solvent (ethanol = Eth or isopropanol = Iso) and surfactants (Span 85 or Tween 80) used. Atomic force microscopy analysis confirmed the affinity between zein particles and the EC phase when prepared in pure ethanol or in ethanol- or isopropanol-based formulations containing Tween 80. The Hansen Solubility Parameter concept was applied to elucidate how the compatibility of various components affected the microstructures and material properties of the zein-EC films. Overall, results from this study have contributed to increased understanding of the effects of solvents and surfactants on the formation of submicrometer zein particles and zein-EC composite films.
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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".