Selective Aerobic Oxidation of 5‐Hydroxymethylfurfural to 2,5‐Diformylfuran or 2‐Formyl‐5‐furancarboxylic Acid in Water by using MgO⋅CeO<sub>2</sub> Mixed Oxides as Catalysts
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Bibliographic record
Abstract
Abstract Mixed oxides based on MgO ⋅ CeO 2 were used as efficient catalysts in the aerobic oxidation of 5‐hydroxymethylfurfural (5‐HMF) to afford, with very high selectivity, either 2,5‐diformylfuran (DFF, 99 %) or 2‐formyl‐5‐furancarboxylic acid (FFCA, 90 %), depending on the reaction conditions. 5‐Hydroxymethyl‐2‐furancarboxylic acid (HMFCA, 57–90 %) was formed only at low concentration of 5‐HMF (<0.03 m ) or in presence of external bases. The conversion of 5‐HMF ranged from a few percent to 99 %, according to the reaction conditions. The oxidation was performed in water, with O 2 as oxidant, without any additives. The surface characterization of the catalysts gave important information about their acid–base properties, which drive the selectivity of the reaction towards DFF. FFCA was formed from DFF at longer reaction times. Catalysts were studied by XPS and XRD before and after catalytic runs to identify the reason why they undergo reversible deactivation. XRD showed that MgO is hydrated to Mg(OH) 2 , which, even if not leached out, changes the basic properties of the catalyst that becomes less active after some time. Calcination of the recovered catalyst allows recovery of its initial activity. The catalyst is thus recoverable (>99 %) and reusable. The use of mixed oxides allows tuning of the basicity of the catalysts, avoiding the need for external bases for efficient and selective conversion of 5‐HMF and waste formation, resulting in an environmentally friendly, sustainable process.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it