ZnO–TiO<sub>2</sub> Nanoparticles Coated by the Dioxomolybdenum (VI) <i>bis</i>-Schiff Base Complex for Catalytic Oxidation of Sulfides
Bibliographic record
Abstract
The oxygenation processes for the organic compounds have great applications in fine organic fabrications. To speed up progress in the industrialization of such oxidation systems, a monometallic dioxomolybdenum (VI) bis -Schiff base complex (MoO 2 L 2 ) was synthesized through coordination of easily accessible 4-hydroxyiminophenol ligand (HL) with cis -MoO 2 2+ ion in an octahedral geometry. The structural conformation of MoO 2 L 2 and its coordinated ligand (HL) was determined using modern spectrophotometric tools. A heterogeneous catalyst (MoO 2 L 2 @ZnO–TiO 2 ) was successfully prepared by immobilization of MoO 2 L 2 on the surface of ZnO–TiO 2 nanostructured particles through the deprotonation of 4-OH group in its coordinated ligand. Various analytical techniques were used to examine the surface morphology and internal structure features of ZnO–TiO 2 and MoO 2 L 2 @ZnO–TiO 2 nanocomposites. To study the catalytic efficiency of MoO 2 L 2 and MoO 2 L 2 @ZnO–TiO 2 -based catalysts, they were employed in the oxygenation process of Ph 2 S (diphenyl sulfide) and MePhS (methylphenyl sulfide) using the most environmentally friendly oxidant (an aqueous H 2 O 2 ) under an aerobic environment. A high degree of selectivity was observed for the productivity of diphenyl sulfoxide (Ph 2 SO) and methylphenyl sulfoxide (MePhSO), with some contamination of overoxidation side products (Ph 2 SO 2 and MePhSO 2 ). The catalytic oxidative ability was not observably superior for the heterogeneous coated catalyst by ZnO–TiO 2 over its homogeneous one (MoO 2 L 2 ) due to the less redox activity of ZnO and TiO 2 nanoparticles in the catalytic cycles for the oxygen-transfer mechanism. Both catalysts were successfully recycled, in which the homogeneous catalyst sustained its high catalytic potential for up to three cycles, while the heterogeneous catalyst was able to be reused up to six times.
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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 | 0.001 |
| 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 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".