Enzymatic Oxidation of Carbohydrate Byproducts for Use in Formation of Chitosan Hydrogels
Bibliographic record
Abstract
Chitosan hydrogels are used in diverse applications ranging from pharmaceuticals and biomedical materials to food and agriculture. This study introduces a biology‐inspired approach to create fully bio‐based hydrogels by combining chitosan with bio‐based di/polycarbonyl crosslinkers produced through the enzymatic oxidation of carbohydrates. Two such crosslinkers, Ox‐XOS and Ox‐Lac, were synthesized by oxidizing carbohydrates: Ox‐XOS was produced by oxidizing xylooligosaccharides (XOS) with pyranose dehydrogenase from Agaricus bisporus ( Ab PDH1), and Ox‐Lac was produced by oxidizing lactose (Lac) with galactose oxidase from Fusarium graminearum ( Fgr GalOx). The efficacy of enzymatic oxidation of lactose and XOS was analyzed using liquid chromatography and mass spectroscopy, showing high degrees of oxidation, and carbonyl groups were confirmed using ATR‐FTIR and 1 H NMR. Compared with unmodified XOS, Ox‐XOS showed a lower reaction temperature towards hexamethylenediamine by differential scanning calorimetry and demonstrated stronger gel formation ability with polyallylamine and chitosan. Rheological measurements showed – increases in the storage moduli () of chitosan hydrogels formed with Ox‐Lac and Ox‐XOS compared with unmodified lactose and XOS, indicating considerable increases in the hydrogels’ resistance to deformation. These findings demonstrate the potential of enzymatically oxidized carbohydrates as crosslinkers to enhance chitosan hydrogels with potential utility in both high‐value and large‐volume sectors.
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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".