Phyto‐synthesis of magnetic CuFe <sub>2</sub> O <sub>4</sub> catalyst using pitaya peel extract for the selective persulphate‐driven oxidation of benzyl alcohol at room temperature
Bibliographic record
Abstract
Abstract The development of heterogeneous catalysts for the selective oxidation of benzyl alcohol to high‐value benzaldehyde under mild conditions remains a highly desirable research area. To address this, magnetic CuFe 2 O 4 catalysts were synthesized for the first time via an eco‐friendly sol–gel method utilizing pitaya peel extract as a green agricultural resource at various calcination temperatures. The experimental results revealed that the synthesized CuFe 2 O 4 microparticles effectively activated Na 2 S 2 O 8 in aqueous solution, enabling the efficient conversion of benzyl alcohol to benzaldehyde at room temperature (30°C). Among the samples, the catalyst calcined at 900°C demonstrated superior performance, attributed to the optimized distribution of Cu 2+ and Fe 3+ ions in tetrahedral sites and an increased concentration of hydroxyl groups on the catalyst surface. After 24 h of reaction, this catalyst achieved a benzyl alcohol conversion of 80.94% and a benzaldehyde selectivity of 86.31%, with a pseudo‐second‐order reaction rate constant of 4.69 mol L −1 h −1 . However, at higher calcination temperatures, the catalytic activity declined due to the particle growth and reduced surface metal content. Furthermore, all CuFe 2 O 4 samples demonstrated strong ferromagnetic properties, allowing for easy recovery and reuse, thereby highlighting their potential as sustainable catalysts for benzaldehyde synthesis.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".