Optimization of Organosolv Pretreatment with Acid Catalyst to Enhance Enzymatic Saccharification of Corn Husk
Bibliographic record
Abstract
Due to awareness of global warming and the devastation of environmental resources, the management of agricultural residues after each harvesting season has been integrated into the biorefining process to increase its value and mitigate environmental pollution caused by burning or combustion. This research focuses on the process development to utilize agricultural biomass residues for renewable energy production in the form of bioethanol. The study employed organosolv pretreatment with sulfuric acid as a catalyst to promote the enzymatic conversion of corn husk into reducing sugars. To determine the optimal conditions for the process, a one-factor-at-a-time method was initially employed to assess the influence of temperature (80-140 ºC), time (40-60 min), and sulfuric acid concentration (0.01-0.5% w/w). Subsequently, Response Surface Methodology (RSM) was conducted based on the Box-Behnken design (BBD) to identify the optimal pretreatment conditions. The predicted optimal pretreatment conditions were found to be 135.4 ºC, 57 min, and 0.46% w/w, resulting in a reducing sugar yield of 20.69% with a margin of error of 1.2%. Additionally, biomass composition analysis and Fourier Transform Infrared spectroscopy (FTIR) were performed to decipher the mechanism of organosolv pretreatment on enzymatic saccharification. This study demonstrated the potential of corn husk as an alternative raw material for the production of value-added products like bioethanol. The obtained reducing sugars serve as vital substrates for the fermentation process required to produce bioethanol as an alternative fuel to meet the target of sustainable development goals (SDGs).
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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) | 0.000 | 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 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".