Valorizing corn stover waste into valuable bioproducts using subcritical water hydrolysis
Bibliographic record
Abstract
This study examined the behavior of the semi-continuous hydrolysis process of corn stover (CS) in subcritical water, focusing on the effects of varying pH levels (1, 2, 4.5, 7, and 8) and temperatures (113, 130, 170, 210, and 226°C). The results showed that the process at 170°C and pH 1 was able to recover the highest amount of phenolic compounds (76.82 mg Gallic Acid Equivalents g⁻¹), consequently demonstrating the highest antioxidant activities by the Ferric Reducing Antioxidant Power (FRAP) (423.85 μMol TEAC g⁻¹) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) (293.12 μMol TEAC g⁻¹) methods. Additionally, it was possible to obtain the highest concentration of sugars (cellobiose, glucose, and xylose) (460.92 mg g⁻¹). High temperatures (226°C and pH 4.5) produced the highest amount of organic acids (1,157.19 mg g⁻¹). The formation of inhibitors was observed only at temperatures of 170 and 210 °C at a pH of 1.0. The highest yields of sugars, organic acids, and inhibitors were 0.565, 1.123, and 0.665 mg g⁻¹ of carbohydrates, respectively. The statistical analysis identified the optimal conditions for the recovery of various compounds: phenolic compounds at 190.7 °C and pH 1, soluble proteins at 187.4 °C and pH 1, sugars at 134.9 °C and pH 1, organic acids at 223.59 °C and pH 4.1, and for minimizing inhibitors at 114.95 °C and pH 7.3. EcoScale analysis identified subcritical water as the most sustainable and efficient method for CS hydrolysis. Subcritical water treatment effectively recovers valuable compounds from CS, promoting a circular economy by valorizing waste and reducing resource dependence.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".