Cogrinding Wood Fibers and Tannins: Surfactant Effects on the Interactions and Properties of Functional Films for Sustainable Packaging Materials
Bibliographic record
Abstract
Abstract We report on the combination of cellulose nanofibrils (CNFs) and condensed tannins from Acacia mearnsii for the development of hybrid, functional films. The tannins are fractionated and concentrated in polyphenolics that are used for functional components in the hybrid materials. Cogrinding of wood fibers with the tannins in aqueous media allows simultaneous fiber deconstruction and in situ binding of tannins on the freshly exposed cellulosic surfaces. Hence, a tightly bound bicomponent system is produced, which is otherwise not possible if typical adsorption protocols are followed, mainly due to the extensive hydration typical of CNFs. A nonionic surfactant is used to tailor the cellulose–tannin interactions. The proposed strategy not only enables the incorporation of tannins with CNFs but also endows a high and prolonged antioxidant effect of films formed by filtration. Compared to tannin-free films, those carrying tannins are considerably more hydrophobic. In addition, they show selective absorption of ultraviolet light while maintaining optical transparency in the visible range. The proposed simple protocol for incorporating tannins and surfactants with CNFs is suitable to produce functional materials. This is possible by understanding associated interfacial phenomena in the context of sustainable materials within the concept of the circular bioeconomy.
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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".