Advances in Continuous Flow Production of 5-(Hydroxymethyl)furfural, 2,5-Furandicarboxylic Acid, 2,5-Diformylfuran, and 2,5-Dimethylfuran
Bibliographic record
Abstract
The synthesis of biobased molecules from biomass to produce fine chemicals, fuels, and commodity chemicals offers a sustainable alternative to petrochemical-based products. Biomass is rich in carbohydrates, which can be converted to 5-(hydroxymethyl)furfural (HMF), a highly functionalized platform molecule. Chemical modifications of HMF can yield other valuable molecules such as 2,5-furandicarboxylic acid (FDCA), 2,5-diformylfuran (DFF), and 2,5-dimethylfuran (DMF). FDCA and DFF are typically obtained by the catalytic oxidation of HMF, usually over metal catalysts, and serve as polymer precursors. DMF, which can be blended with gasoline due to its similar octane number and energy density, is produced by the hydrogenation of HMF, typically with the assistance of metallic catalysts. Laboratory-scale synthesis of these platform chemicals has primarily been performed under batch conditions by using various solvents and catalysts. However, scaling up production requires more effort to make synthesis pathways as economical and efficient as petrochemical processes. One promising approach is the use of continuous-flow reactors, which offer advantages in heat and mass transfer. These reactors facilitate the simple separation of products from solid catalysts and can be used for complex reactions. This review focuses on the laboratory-scale synthesis of HMF in continuous-flow reactors and its conversion into platform chemicals, such as FDCA, DFF, and DMF, through oxidation, hydrogenation, and hydrogenolysis reactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".