Two‐Step Synthesis of 2,5‐Furandicarboxylic Acid Through Biomass‐Derived 5‐(Hydroxymethyl)furfural in a Green Isopropanol–Water Solvent System
Bibliographic record
Abstract
Abstract This study presents a one‐pot, two‐step process for synthesizing 2,5‐furandicarboxylic acid (FDCA) from whey permeate powder (WPP) using a green isopropanol–water solvent system. WPP, a cheese industry byproduct produced in large quantities, was chosen over glucose, fructose, or lactose due to its high biochemical oxygen demand and disposal challenges. In the first step, 5‐(hydroxymethyl)furfural (HMF) was produced from WPP, with process optimization via Box–Behnken design. Among various catalysts tested, a combination of AlCl 3 and FeCl 3 (6:1 weight ratio) was most effective, yielding 51.8% HMF under optimal conditions: 36.6 mg catalyst, 139.7 °C, and 1.48 h. Crude HMF was isolated via rotary evaporation with a 41.3% yield. In the second step, a non‐precious Cu─Fe─O catalyst was synthesized for oxidizing HMF to FDCA. Using pure HMF (1 wt%), the optimized conditions—2.8 equiv NaOH, 159.5 °C, and 5.7 h—achieved a 49.3% FDCA yield. Integration with WPP‐derived HMF (0.5 wt%) produced a 60.8% FDCA yield. Catalyst reuse studies showed consistent performance over four cycles without significant yield loss.
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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 | 0.001 |
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".