Impact of pre-extraction on xylose recovery from two lignocellulosic agro-wastes
Bibliographic record
Abstract
A dilute acid hydrolysis of rice husk (RH), wheat straw (WS), and their extractive-free residues was investigated with the objective of recovering the highest yield of xylose while keeping at minimum its conversion into furfural. The hydrolysis conditions were determined for different concentrations of sulfuric acid and different reaction times at 121 °C. The pre-extraction with ethanol-water (1:1, v/v) was also examined as a parameter. Using response surface methodology, the optimum conditions for xylose production were identified as 1.8% of acid and 41.4 min of hydrolysis time for RH, while those for its counterpart EF-RH (extractives-free rice husks) were 1.0% acid concentration, for 60 min. The same conditions were also predicted for WS and its EF-WS. Under these conditions, the xylose yield was 79.6%, 82.8%, 94.3%, and 88.6% for RH, EF-RH, WS, and EF-RW, respectively. Under these conditions the minimal furfural yields obtained were 1.2% and 1.3% for RH and EF-RH, and 0.8% and 1.5%, for WS and EF-WS, respectively. These results suggested that the pre-extraction step before the acid hydrolysis affected, at least in part, the xylose recovery from RH, but it was not necessary for a better xylose yield of WS for its bioconversion into valuable bioproducts like xylitol.
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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) | 0.000 | 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 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".