Efficient Fractionation of Corn Stover for Biorefinery Using a Sustainable Pathway
Bibliographic record
Abstract
This study presents a sustainable pathway for fractionating corn stover to produce glucose, high-quality lignin nanoparticles (LNPs), and levulinic acid (LA) based on the use of p -toluenesulfonic acid ( p -TsOH), a solvent having strong acidity and surface activity at a high concentration. At a moderate pretreatment temperature (100 °C), 85% of xylose, 88% of arabinose, and 83% of lignin were removed from the substrate and present in the hydrolysate, while the cellulose yield in the solid residue fraction was 93%. The cellulose fraction exhibited much reduced “biomass recalcitrance” and was readily enzyme-hydrolyzed, with its glucose yield reaching up to 93% at a high solid concentration of 15% (w/w). The hydrolysate, involving the p -TsOH catalyst, was recycled for further hydrolysis of fresh corn stover: after recycling the hydrolysate four times, the cellulose fraction still had a high glucose yield of 81%. The lignin fraction in the hydrolysate was utilized in the form of LNPs, which were prepared as a result of diluting the recycled hydrolysate. The as-prepared LNPs were spherical and uniform, with an average particle size of 147 nm. The application of LNPs in the preparation of chitosan film significantly improved its strength. After LNP preparation, the diluent containing monomeric sugars was directly heated to 180 °C to produce LA in the presence of p -TsOH (an effective catalyst) with an LA yield of 57.1%. The LA was easily separated from the spent acid based on methyl isobutyl ketone (MIBK) extraction, and the p -TsOH/water mixture was recycled in the process.
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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) | 0.000 | 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".