Experimental and Kinetic Study on the Production of Furfural and HMF from Glucose
Bibliographic record
Abstract
Furfural and 5-hydroxymethylfurfural (HMF) have been identified as promising bio-platform furans that have a wide range of potential applications as biofuels, bioplastics, and biochemicals. Furfural and HMF are typically synthesized from the substrates of C5 sugars and C6 sugars, respectively. Furfural can also be produced from C6 sugars, which is technically more challenging owing to the higher energy requirement for carbon–carbon bond cleavage. In this study, the simultaneous production of furfural and HMF from glucose was conducted over different binary catalyst systems of Brønsted acids and Lewis acids using γ-valerolactone (GVL) as the solvent. A promising performance was achieved by a SnSO4-H2SO4 coupling catalyst, with an optimized furfural yield of 42% and an HMF yield of 34% at 443 K in GVL. In addition, a kinetics study was performed in order to understand the mechanism of the simultaneous formation of furfural and HMF from glucose at different temperatures and GVL/water ratios. The results showed that the ratio of furfural to HMF production rate at different temperatures (433 to 463 K) or GVL/water ratios (90 to 80%) was constant close to 1, suggesting that the production of furfural and HMF might follow similar reaction pathways. Finally, the reaction pathway of glucose conversion to furfural and HMF was proposed based on the experimental and kinetics studies.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".