Chemistry of lignin and condensed tannins as aromatic biopolymers
Bibliographic record
Abstract
Aromatic biopolymers are the second largest group of biopolymers after polysaccharides. Depolymerization of aromatic biopolymers, as cheap and renewable substitutes for fossil-based resources, has been used in the preparation of biofuels, and a range of aromatic and aliphatic small molecules. Additionally, these polymers exhibit a robust UV-shielding function due to the high content of aromatic groups. Meanwhile, the abundance of phenolic groups in their structures gives these compounds outstanding antioxidant capabilities, making them well-suited for a diverse array of anti-UV and medical applications. Nevertheless, these biopolymers possess inherent drawbacks in their pristine states, such as rigid structure, low solubility, and lack of desired functionalities, which hinder their complete exploitation across diverse sectors. Thus, the modification and functionalization of aromatic biopolymers are essential to provide them with specific functionalities and features needed for particular applications. Aromatic biopolymers include lignins, tannins, melanins, and humic acids. The objective of this review is to offer a thorough reference for assessing the chemistry and functionalization of lignins and condensed tannins. Lignins represent the largest and most prominent category of aromatic biopolymers, typically distinguishable as either softwood-derived or hardwood-derived lignins. Besides, condensed tannins are the most investigated group of the tannin family. The electron-rich aromatic rings, aliphatic hydroxyl groups, and phenolic groups are the main functional groups in the structure of lignins and condensed tannins. Methoxy groups are also abundant in lignins. Each group displays varying chemical reactivity within these biopolymers. Therefore, the selective and specific functionalization of lignins and condensed tannins can be achieved by understanding the chemistry behavior of these functional groups. Targeted applications include biomedicine, monomers and surface active agents for sustainable plastics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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 teacher head, 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".