The Potential of Using Immobilized Xylanases to Enhance the Hydrolysis of Soluble, Biomass Derived Xylooligomers
Bibliographic record
Abstract
Earlier work had indicated that enzyme-mediated hydrolysis of xylooligomer-rich water-soluble streams (derived from steam pre-treated wheat straw) resulted in the effective production of xylose which was subsequently used to produce bio-glycol. In the work reported here, both the thermostability and recyclability of xylanases were significantly improved by covalent immobilizing the enzymes onto alginate beads. The immobilized xylanases showed a lower hydrolytic potential (~55% xylooligomer conversion) compared to the commercial xylanase cocktail HTec3 (~90% xylooligomer conversion) when used at the same protein loading concentration. This was likely due to the less efficient immobilization of key higher molecular weight enzymes (>75 kDa), such as β-xylosidases. However, enzyme immobilization could be improved by lowering the glutaraldehyde loading used to activate the alginate beads, resulting in improved hydrolysis efficacy (~65% xylooligomer conversion). Enzyme immobilization improved enzyme thermostability (endoxylanase and β-xylosidase activities were improved by 80% and 40%, respectively, after 24 h hydrolysis) and this allowed the immobilized enzymes to be reused/recycled for multiple rounds of hydrolysis (up to five times) without any significant reduction in their hydrolytic potential.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".