Understanding the Effects of Ethylene Glycol-Assisted Biomass Fractionation Parameters on Lignin Characteristics Using a Full Factorial Design and Computational Modeling
Bibliographic record
Abstract
Contributing to recent lignin valorization efforts, this study uses an integrative approach to explore the effects of fractionation parameters on lignin characteristics. The following reaction parameters are explored: water content of the water-organic solvent mixture, reaction temperature, and sulfuric acid content. Ethylene glycol (EG) was selected as the fractionation solvent because of its promising lignin solubility and extractability. This study takes a novel approach in conducting EG-assisted biomass fractionation; instead of removing lignin from the biomass, lignin was extracted and characterized. Lignin characteristics involving recovery and linkages were analyzed. A maximum of 27 wt % lignin recovery was achieved at a low water content (25%) and high reaction temperature (180 °C) in the presence of sulfuric acid (1 wt %). From NMR analysis, aryl-ether linkages, which are important to preserve for lignin valorization, were decomposed as a result of relatively high temperature and the presence of sulfuric acid. Statistical analysis showed that all individual parameters and their interactions had significant effects on lignin recovery. Computational analysis revealed that hydrogen bonding between the EG and lignin macromolecules greatly decreased with an increasing amount of water.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".