Conversion of <i>Symphytum officinale</i> and <i>Panicum virgatum</i> plant extracts to 5-hydroxymethylfurfural catalysed by metal chlorides in ionic liquids
Bibliographic record
Abstract
The present work examined the potential for two plants grown on Canadian soil, Symphytum officinale L. (common comfrey) and Panicum virgatum L. (switchgrass), to produce 5-hydroxymethylfurfural using metal chloride catalysis in two ionic liquids, 1-butyl-3-methylimidazolium chloride or 1-ethyl-3-methylimidazolium chloride. Furthermore, two pre-treatments, namely the dilute sulfuric acid treatment and the methanol extraction, were studied as a way to improve sugar availability and increase 5-hydroxymethylfurfural yields compared with untreated biomass. The 0.5 mol/L H2SO4 hydrolysis under autoclave conditions produced sugar-rich extracts containing 230 ± 23 mg of sugars per gram of hydrolysed biomass for comfrey and 425 ± 13 mg of sugars per gram of hydrolysed biomass for switchgrass. The methanol extraction produced extracts high in simple sugars with concentration of 300 ± 60 mg of sugars per gram of dry extract for comfrey and 202 ± 16 mg of sugars per gram of dry extract for switchgrass. The yield of 5-hydroxymethylfurfural was improved from less than 1% using untreated biomass to 6.04% and 18.0% using dry methanol extracts of comfrey and switchgrass, respectively. These yields, although small, are important, as they show for the first time that a methanol extract could enhance the metal chloride catalysis in ionic liquids for 5-hydroxymethylfurfural production from biomass.
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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) | 0.001 | 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".