Effect of Intermediate Washing on Ozonolysis Delignification and Enzymatic Hydrolysis of Wheat Straw
Bibliographic record
Abstract
Wheat straw was pretreated with ozone to increase the enzymatic hydrolysis yield. Ozonolysis pretreatment was performed in two stages with an intermediate washing step with water in between. Part of the delignification products (lignin fragments) were removed by a washing step so ozone was used to oxidize more lignin rather than oxidizing lignin fragments. Three parameters, i.e. Initial Water Content (IWC) in wheat straw, Washing Starting Time (WST), and Washing Contact Time (WCT), were optimized to minimize Acid Insoluble Lignin (AIL) content of ozonated wheat straw. Performing an experiment using optimal parameters' values, i.e. IWC equal to 45 wt. %, WST equal to 20 minutes and WCT equal to 80 seconds, showed a drop in AIL content to 9.35 % with an increase in enzymatic hydrolysis yield to 80 % of the theoretical. Readjusting water content of wheat straw to 45 % before performing the 2nd ozonolysis stage further reduced AIL content to 7.36 % and increased the hydrolysis yield to 90 %. Increasing wheat straw fiber size from < 2 mm to < 6 mm decreased the hydrolysis yield to 72 %. All results were considered significant improvements when compared to untreated wheat straw with AIL content of 20.5 % and hydrolysis yield of 23 %.
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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) | 0.001 | 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".