An integrated and scalable biorefinery approach for producing high purity xylo-oligosaccharides from prehydrolysis liquor
Bibliographic record
Abstract
Pre-hydrolysis liquor (PHL), a by-product of the kraft pulping process, contains abundant dissolved hemicellulose (mainly includes xylose, xylo-oligosaccharides (XOS), and xylan). Biorefinery of XOS from the PHL using an efficient and time-saving strategy remains a long-standing challenge. Herein, a sustainable and scalable biorefinery approach including sequential calcium hydroxide (CH) pretreatment, laccase assisted xylanase (LX) treatment, and activated carbon (AC) adsorption was proposed for producing XOS with high purity. CH pretreatment effectively enhanced xylanase hydrolysis efficiency by removing the acetyl side chains of xylan, converting approximately 86 % xylan to XOS. LX synergistic treatment outperformed single laccase and xylanase treatments, which could simultaneously remove lignin and concentrate XOS without catalytic efficiency loss. Overall, 91.5 % lignin and 100 % furfural were removed, and 87.9 % xylosugars were recovered. XOS content was significantly increased by 27.6 %, resulted in a considerable increase in XOS purity from 42.0 % to 75.2 %. The purified XOS contained a small amount of side chains (4-O-methyl-D-glucuronic acids and acetyls) and lignin carbohydrate complex (PhGlc3). This work demonstrates an efficient, eco-friendly, and time-saving strategy to prepare high purity XOS and provides theoretical guidance for purifying XOS from PHL via enzyme-based biorefining process. • XOS with high purity was obtained from PHL via biochemical process. • CH pretreatment enhanced xylanase hydrolysis, converting ∼86 % of xylan to XOS. • LX synergistic treatment outperformed single laccase and xylanase treatments. • 91.5 % lignin and 100 % furfural removal, and 27.6 % XOS increase were achieved.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".