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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".