Sustainable Hydrophobic and Moisture-Resistant Coating Derived from Downstream Corn Oil
Bibliographic record
Abstract
Sustainable coating as a replacement for petroleum-derived materials is highly required for packaging and other applications to reduce the generation of land and ocean pollution. In this study, a novel approach has been introduced to incorporate hydrolyzable silane (−Si–OCH 2 CH 3 ) groups in the downstream corn oil backbone (one of the coproducts of bioethanol industry) which has been consequently used for the fabrication of hydrophobic and moisture-resistant coating for paper substrate. Scanning electron microscopy (SEM) images confirmed the uniform coating of the silane-modified epoxidized downstream corn oil (SECO) over the paper. The condensation reaction has taken place with the hydroxyl group of paper and SECO, and as a result, the number of surface hydroxyl group was reduced to make a hydrophobic surface on coated paper with static water contact angle (WCA) of 129.4 ± 1.12°. SECO-coated paper showed almost 67% decrease in specific water vapor transmission rate compared to that of the uncoated filter paper. These results indicate that the SECO coating delivers excellent moisture barrier and hydrophobicity to the paper substrate. Thus, SECO might be a competitive substitute to petroleum-based polymers for fluorine-free hydrophobic and moisture-resistant coating. This research finding can find application especially in huge paper packaging industries as well as cellulose-based products such as fiber and foam.
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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".