Wax Coatings for Paper Packaging Applications: Study of the Coating Effect on Surface, Mechanical, and Barrier Properties
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The aim of this study was to comprehensively assess the effect of environmentally friendly wax coatings, including beeswax, soywax, TopScreen biowax, and a conventional water-based emulsion wax on paper for food packaging applications. A food-grade paper was bar-coated with a single layer of molten wax on both sides, varying in the coating weight, coating thickness, and wax type. Waxes were thoroughly characterized in terms of their functional groups, thermal properties, degree of crystallinity, and crystal morphology using polarized optical microscopy. Thereafter, wax-coated papers were studied in terms of their morphological, mechanical, and water vapor barrier properties. Moreover, the water and oil contact angles were measured to determine the resistance of wax-coated papers to moisture and grease penetration. Wax coatings represent 10–25% of the weight of the coated paper, with a coating thickness of 5–10 μm. Scanning electron microscopy images showed that waxes penetrated the cellulosic fibers of the paper, thereby effectively reducing its porous structure. Water and oil contact angles of the uncoated paper increased after the wax coating. Among the four waxes, beeswax-coated paper was characterized as having superior capability in improving the water vapor barrier of the uncoated paper (by ∼77%). While the percentage elongation at break (EB %) decreased for all four wax-coated papers, tensile strength (TS) and elastic modulus ( E ) increased, with beeswax showing the highest percent improvement in TS (by ∼26%) and E (by ∼46%). Our results suggest that paper surface modification through ecofriendly wax coatings can be utilized as an alternative for petroleum-based paper coating materials for food packaging applications.
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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) | 0.001 | 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".