Optimization of Microwave Assisted Alkaline Extraction of Xylan from Birch Wood Using Response Surface Methodology
Bibliographic record
Abstract
The main purpose of this study was to optimize microwave assisted alkaline extraction of the hemicellulose, xylan, from birch wood. The simultaneous effects of process variables such as time (10 - 30 minutes), concentration of sodium hydroxide solution (4 - 8 wt%), solid to liquid ratio (1:8 to 1:20, g:mL), and sample size (5 - 10 g) on the temperature of the wood slurry, wood dissolution, and yield of extraction were evaluated. A central composite design (CCD) and response surface methodology (RSM) were used for the optimization of the extraction process. Based on the CCD, quadratic models were developed to correlate the extraction process variables with the responses such as temperature of wood slurry, wood dissolution, and yield of xylan and the models were analyzed using appropriate statistical methods (ANOVA). Statistical analysis showed that all the models developed were found to be adequate for the prediction of the respective responses. Optimization of the process was performed using a numerical optimization available in the software to maximize the yield of xylan and the optimum process variables for the maximum yield of xylan was found to be: 10 g of wood fibres, 8 wt% of NaOH solution, 1:10 solid to liquid ratio (g:mL) and 25 minutes of irradiation time. About 72.5% of the xylan present in the birch wood was extracted using the optimized extraction parameters.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".