Enzymatic hydrolysis of biologically pretreated sorghum husk for bioethanol production
Why this work is in the frame
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Bibliographic record
Abstract
Biological pretreatment of lignocellulosic biomass is considered to be energy-efficient and cost-effective. In the present study, sorghum husk was biologically pretreated with a white-rot fungus Phanerochaete chrysosporium (MTCC 4955) under submerged static condition. Ligninolytic enzymes like lignin peroxidase (0.843 U/mL) and manganese peroxidase (0.389 U/mL) played an important role in the biological pretreatment of sorghum husk. Activities of different hydrolytic enzymes such as endoglucanase (57.25 U/mL), exoglucanase (4.76 U/mL), filter paperase (0.580 U/mL), glucoamylase (153.38 U/mL), and xylanase (88.14 U/mL) during biological pretreatment of sorghum husk by P. chrysosporium were evaluated. Enzymatic hydrolysis of untreated sorghum husk and biologically pretreated sorghum husk produced 20.07 and 103.0 mg/g reducing sugars, respectively. This result showed a significant increase in reducing sugar production in the biologically pretreated sorghum husk as compared to its untreated counterpart. Biologically pretreated sorghum husk hydrolysate was further fermented for 48 h using Saccharomyces cerevisiae (KCTC 7296), Pachysolen tannophilus (MTCC 1077), and their co-culture resulting in ethanol yields of 2.113, 1.095, and 2.348%, respectively. The surface characteristics of the substrate were evaluated after the delignification and hydrolysis, using FTIR, XRD, and SEM, confirming the effectiveness of the biological pretreatment process.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it