How Alkyl Chain Length of Alcohols Affects Lignin Fractionation and Ionic Liquid Recycle During Lignocellulose Pretreatment
Bibliographic record
Abstract
Alcohols of increasing alkyl chain length were investigated as precipitants in an ionic liquid (IL) pretreatment system. Switchgrass samples pretreated by 1-ethyl-3-methylimidazolium acetate were characterized after the use of different alkyl chain lengths of alcohols as antisolvents. The resulting IL-pretreated switchgrass (PSG) samples were characterized by enzymatic hydrolysis, cross polarization/magic angle spinning (CP/MAS) 13 C nuclear magnetic resonance (NMR), Fourier transform infrared spectroscopy (FTIR), and 2D NMR spectroscopy. Glucan digestibilities of PSG samples were ∼80 % after 72 h at 5 mg protein g −1 glucan regardless of the antisolvent used. The use of 1-octanol as an antisolvent, with 10 % water to allow for use of wet biomass, enabled a partial lignin fractionation and multiphase separation for the IL recycle without compromising the chemical structure of the carbohydrates and lignin from the PSG. Lignin fragments were observed in the IL after pretreatment by gel permeation chromatography (GPC). After separation, both the IL and the octanol antisolvent were reused for switchgrass pretreatment and precipitation for an additional 3 cycles. The PSG samples derived from recycled IL were rapidly hydrolyzed, and a high glucan digestibility of 80 % was obtained even at a low enzyme loading of 5 mg protein g −1 glucan. 2D NMR analysis of residual solids of PSG post-enzymatic hydrolysis revealed that lignin in these residual solids was depolymerized. This strategy enables an ease in separation of pretreated lignocellulosic solids, reduced water use, and recycle of both IL and the antisolvent.
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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".