Comparison of Composite Materials Designed to Optimize Heterogeneous Decatungstate Oxidative Photocatalysis
Bibliographic record
Abstract
Catalysis plays a pivotal role in green chemistry practices, particularly in reducing waste generated during chemical synthesis. Decatungstate (DT) emerges as a potent photocatalyst for Type I oxidations, exhibiting remarkable resilience to oxygen quenching, a characteristic that sets it apart from other excited triplet state photocatalysts. While homogeneous DT catalysis demonstrates effectiveness, its solubility poses challenges for its separation and recycling. To address these limitations, we focus on the development and comparison of heterogeneous DT photocatalysts, aiming to optimize their yield, recovery, and reusability. We synthesized tetrabutylammonium decatungstate (TBADT)-supported catalysts using silica, alumina, titanium dioxide, and glass wool and characterized them using diffuse reflectance measurements. Subsequently, we evaluated their photocatalytic performance by monitoring the oxidation of 1-phenylethanol and cyclohexanol under UVA irradiation. Our findings reveal that TBADT@silica emerges as the most effective catalyst, achieving approximately 20% conversion of cyclohexanol and 50% conversion of 1-phenylethanol with good reusability. Interestingly, we observed that 3-aminopropyl-triethoxysilane (APTES) treatment, intended to enhance DT anchoring, unexpectedly quenches the 3DT* triplet state, reducing catalytic activity. This unexpected finding underscores the importance of careful consideration in designing robust and recyclable heterogeneous decatungstate catalysts. Our research contributes significantly to the advancement of heterogeneous photocatalysis, paving the way for future applications in flow photochemistry. Further, we share a Python code (Google 3.12.11) to correct spectra obtained in Cary spectrometers.
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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".