Sugarcane Bagasse Hydrolysis Enhancement by Microwave-Assisted Sulfolane Pretreatment
Bibliographic record
Abstract
Sugarcane bagasse is the major by-product of the sugarcane industry and, due to its abundant availability, it has been extensively studied for lignocellulosic bioconversion in the production of bioethanol and other value-added commercial products. In the study presented herein, a combined pretreatment using sulfolane, TiO2 and alkali microwave irradiation (MW-A) was assessed for the dissolution of lignin prior to enzymatic saccharification of holocellulose. Total reducing sugars (TRS) and saccharinic acid yields were investigated. The increase in NaOH concentration up to 5% and in temperature from 120 °C to 140 °C were found to have a positive influence on both yields. While increasing the reaction time from 5 to 60 min only led to an increase in TRS yield <2%, a reaction time of 30 min almost doubled the saccharinic acids production. TRS yields and saccharinic acid production were approximately 5% and 33% higher when the sulfolane-TiO2 reaction medium was used, as compared to MW-A in water, reaching up to 64.8% and 15.24 g/L of saccharinic acids, respectively. The proposed MW-A pretreatment may hold promise for industrial applications, given the good TRS yields obtained, and the associated enzyme and time/energy savings. The use of sulfolane-TiO2 reaction medium is encouraged if saccharinic acids are to be recovered too.
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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) | 0.000 | 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) | 0.001 | 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".