Effect of Extrusion Pretreatment on Enzymatic Hydrolysis of Miscanthus for the Purpose of Ethanol Production
Bibliographic record
Abstract
Lignocellulosic biomass can be converted to energy via several routes. One of them is hydrolysis to sugars with subsequent transformation to fuels and chemicals. Due to the crystalline structure of lignocellulose, pretreatment is a prerequisite to achieving increased enzymatic hydrolysis’ rates. The objective of this study was to determine the optimum extrusion operating conditions for glucose and xylose production from Miscanthus. Extrusion was conducted in a high shear extruder (single screw type) with compression ratio 3:1. Barrel temperature and screw speed, along with sample moisture content and particle size were the parameters evaluated using Response surface methodology (RSM). Conversion rate to glucose and xylose was monitored after enzymatic hydrolysis with low enzyme loadings (5 FPU g-1 of cellulase complex and 18 CBU g-1 of B-glucosidase). The optimum conditions for the glucose production (3.63 g L-1) were: barrel temperature 150 °C, screw speed 2.5 Hz, moisture content 20% and particle size 2 mm; the optimum conditions for the xylose production (0.78 g L-1) were: barrel temperature 150 °C, screw speed 1.67 Hz, moisture content 15% and particle size 2 mm. Hence, under controlled conditions, extrusion resulted in better digestibility of Miscanthus and as such it can be utilized as a source of glucose and xylose in ethanol production.
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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) | 0.001 | 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 | 0.000 |
| 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 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".