Bark tannins: Extraction methods, characterization, and reactivity
Bibliographic record
Abstract
Tannins are compounds that occur naturally in a wide range of plants. They play an essential role in regulating plant growth and protecting plants from predators. Tannins are classified into two types: hydrolyzable and condensed. Among the many methods used to extract tannin are solvent extraction, solid-liquid extraction, and supercritical fluid extraction, depending on the target application. Supercritical fluid extraction is widely considered superior because it preserves tannin integrity while minimizing solvent use, but the steep operational costs and sophisticated equipment requirements constitute limitations. Tannins have extensive industrial uses, including leather tanning and the production of pharmaceuticals, processed food, adhesives, wine, biomaterials, and biobased polymers. Recently, tannins have gained attention as bioactive agents, with potential applications for advanced technologies such as 3D printing, biomedical devices, and therapeutic interventions for various diseases. This paper provides a detailed description of tannin extraction methods, with a focus on efficiency factors such as temperature, solvent selection, and plant material preparation. Extracted tannins are characterized based on their chemical properties, which are determinant for assessing the application potential in diverse fields. The sustainability and environmental benefits of tannins position them as valuable compounds for use in innovative industrial and environmental solutions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".