Inexpensive but Highly Efficient Co–Mn Mixed‐Oxide Catalysts for Selective Oxidation of 5‐Hydroxymethylfurfural to 2,5‐Furandicarboxylic Acid
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A highly active and inexpensive Co–Mn mixed‐oxide catalyst was prepared and used for selective oxidation of 5‐hydroxymethylfurfural (HMF) into 2, 5‐furandicarboxylic acid (FDCA). Co–Mn mixed‐oxide catalysts with different Co/Mn molar ratios were prepared through a simple solid‐state grinding method—a low‐cost and green catalyst preparation method. The activity of these catalysts was evaluated for selective aerobic oxidation of HMF into FDCA in water. Excellent HMF conversion (99 %) and FDCA yield (95 % ) were obtained under the best reaction conditions (i.e., 120 °C, 5 h, Co–Mn mixed‐oxide catalyst with a Co/Mn molar ratio of 0.25 calcined at 300 °C (Co‐Mn‐0.25) and 1 MPa O 2 ). The catalyst could be reused five times without a significant decrease in activity. The results demonstrated that the catalytic activity and selectivity of the Co–Mn mixed‐oxide catalysts prepared through solid‐state grinding were superior to the same Co–Mn catalyst prepared through a conventional coprecipitation method. The high catalytic activity of the Co‐Mn‐0.25 catalyst was attributed to its high lattice oxygen mobility and the presence of different valence states of manganese. The high activity and low cost of the Co–Mn mixed‐oxide catalysts prepared by solid‐state grinding make it promising for industrial application for the manufacturing of polyethylene furanoate, a bioreplacement for polyethylene terephthalate, from sustainable bioresources.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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