Enhanced ethanol and glucosamine production from rice husk by NAOH pretreatment and fermentation by fungus Mucor hiemalis
Bibliographic record
Abstract
Ethanol production from rice husk by simultaneous saccharification and fermentation using Mucor hiemalis was investigated. To reach the maximum ethanol production yield, the most important influencing factors in the pretreatment process, including temperature (0-100°C), NaOH concentration (1-3 M), and the pretreatment time (30-180 min), were optimized using an experimental design by a response surface methodology (RSM). The maximum ethanol production yield of 86.7 % was obtained after fungal cultivation on the husk pretreated with 2.6 M NaOH at 67°C for 150 min. This was higher than the yield of 57.7% obtained using Saccharomyces cerevisiae as control. Furthermore, fermentation using M. hiemalis under the optimum conditions led to the production of a highly valuable fungal biomass, containing 60 g glucosamine (GlcN), 410 g protein, and 160 g fungal oil per each kg of the fungal biomass.
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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 | 0.000 |
| 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".