Development of a Soybean Wax Pickering Emulsion for Sustainable Hydrophobic Modification of Paper
Bibliographic record
Abstract
A hydrophobic coating layer on paper is needed to prevent liquid water transmission in many packaging applications. Currently, most paper-based packaging that requires a water barrier utilizes petroleum-derived polymer coatings. However, such coatings are nonsustainable and are persistent in the environment and landfills. As such, a pressing need exists for the development of coatings for packaging materials that have excellent water repellency and are also biodegradable. To address this challenge, the authors developed a one-step coating method to prepare hydrophobic paper by templating stable oil-in-water (O/W) Pickering emulsions of soybean wax. The emulsion is stabilized by employing cellulose nanocrystals (CNC) and sodium alginate in the aqueous phase, while chitosan (CS) was incorporated into the liquid soybean wax phase to enhance emulsification. The CNC-SA/wax Pickering emulsion coating significantly improves the hydrophobic and water vapor barrier properties of the paper that it is applied to. Indeed, the water contact angle (WCA) increased from 37° to 92° for northern bleached kraft (NBK) paper, 77° to 93° for copy paper, and 17° to 94° for CNF-coated NBK paper after coating, demonstrating enhanced hydrophobicity. Additionally, the Cobb test values of NBK, copy, and CNF-coated NBK papers decreased from 125 g/m 2 to 39 g/m 2, 97 g/m 2 to 34 g/m 2, and 75 g/m 2 to 32 g/m 2, respectively, indicating a substantial reduction in water absorption. The water vapor transmission rate decreased from 731 to 513 g/m 2 ·day for NBK paper, 754 to 487 g/m 2 ·day for copy paper, and 571 to 475 g/m 2 ·day for CNF-coated NBK paper after application of the coating, demonstrating a notable enhancement in gaseous moisture resistance. Mechanical tests revealed that coated paper maintained over 93% of its tensile strength while the tensile modulus decreased by 17%, 26%, and 25% for NBK, copy, and CNF-coated NBK papers respectively, indicating greater flexibility of the coated paper compared to the base paper. The research provides a naturally sourced and biodegradable water barrier coating formulation for paper-based packaging components as an alternative to conventional synthetic polymer-derived systems.
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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".