Mechanochemical Fractionation of Straw Biomass to Maximize Biorefinery Value Using Alkaline Deep Eutectic Solvents at Room Temperature
Bibliographic record
Abstract
Thoughtful utilization of the entire lignocellulose is important to achieve sustainable and cost-effective biorefineries. However, there is a trade-off between efficiently utilizing carbohydrates and stabilizing the lignin aryl ether structure due to the recalcitrance and heterogeneity of biomass. This work proposed the ball-milling-assisted alkaline deep eutectic solvents (quaternary ammonium hydroxides with monoethanolamine) mechanochemical fractionation process to extract high-value amphiphilic lignin-carbohydrate complexes (LCCs) by preserving the phenyl glycosidic bonds while coproducing high-quality cellulose and Xylan. Results showed that high cellulose saccharification efficiency (74.2–96.4%) was achieved with nearly 100% cellulose recovery. The resulting LCCs and Xylan were not subjected to undesired chemical structural modifications, exhibiting extremely high β-O-4 bond contents (48.8/100 Ar) and favorable carbohydrate properties, respectively. It was calculated that 100 g of original straw could produce 31.1 g of glucose, 9.6 g of LCCs, and 12.3 g of Xylan in a cascade using this process route. It was proposed that mechanochemical effects could enhance the selective destruction of lignocellulose hydrogen-bonding networks, which allowed the subsequent cascade fractionation of the three value-added products by adjusting solution properties. In short, this work provided a mild and efficient mechanochemical fractionation process for whole-component utilization of lignocellulose, which has great potential in sustainable biorefinery applications.
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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".