Bio-Based Adhesives Formulated from Tannic Acid, Chitosan, and Shellac for Packaging Materials
Bibliographic record
Abstract
The aim of this study was to develop bio-based adhesives that can be used for various packaging papers. In addition to commercial paper samples, papers produced from harmful plant species in Europe, such as Japanese Knotweed and Canadian Goldenrod, were used. In this research, methods were developed to produce bio-based adhesive solutions in combinations of tannic acid, chitosan, and shellac. The results showed that the viscosity and adhesive strength of the adhesives were best in solutions with added tannic acid and shellac. The tensile strength with adhesives of tannic acid and chitosan was 30% better than with commercial adhesives and 23% for combinations of shellac and chitosan. For paper from Japanese Knotweed and Canadian Goldenrod, the most durable adhesive was pure shellac. Because the surface morphology of the invasive plant papers was more open and had numerous pores compared to the commercial papers, the adhesives penetrated the paper structure and filled the voids. There was less adhesive on the surface and the commercial papers achieved better adhesive properties. As expected, the bio-based adhesives also showed an increase in peel strength and exhibited favorable thermal stability. In summary, these physical properties support the use of bio-based adhesives use in different 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".