Mild Fractionation of Biomass into Three Binary-Component Fractions
Bibliographic record
Abstract
The effective fractionation technology route is a crucial aspect for the full valorization of lignocellulose. The current fractionation technologies primarily focus on single-component fractionation of lignocellulose, with the technical dilemma between each component conversion availability and overall product yields, especially under harsh fractionation conditions. This work proposed the mild fractionation process of a mechanochemistry-assisted choline hydroxide-ethylene glycol alkaline deep eutectic solvent (Ch-Ely DES) to fractionate straw biomass, with the goal of higher-value binary-component fractions pursuing, i.e., holocellulose, lignin-carbohydrate complexes (LCC), and arabinoxylan (AX). Through the synergistic effect of mechanochemical deconstruction and alkaline deep eutectic solvent swelling, 93% holocellulose recovery, corresponding to a 53.2/100 Ar β-O-4 bond content in the LCC fraction, was achieved. The resulting holocellulose hydrolysis values at a 5 wt % solid loading were 96 and 55% for glucose and xylose respectively. The mild fractionation process could yield fractions with well-preserved structures by selective hydrogen bond network interaction and lignin-carbohydrate complex covalent bond preservation. The proposed binary-component fractionation route is promising to diversify current fractionation technologies and product streams.
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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 | 0.001 |
| 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".