Photocatalysis Meets Magnetism: Designing Magnetically Recoverable Supports for Visible-Light Photocatalysis
Bibliographic record
Abstract
While magnetic supports have been widely used to immobilize homogeneous catalysts in organic chemistry, this strategy has so far found very little application in photocatalysis. Indeed, magnetic supports are dark colored, and thus compete for photon absorption with photocatalysts themselves. We have developed a series of core–shell Fe(0)-silica nanoparticles as supports for immobilizing the photosensitizer Ru(bpy) 3 2+, featuring various silica shell thicknesses—16–34 nm SiO 2 —on 9 nm Fe cores. The supports and the resulting photocatalytic systems were studied for their magnetic, optical, and catalytic properties in the context of the photooxidation of citronellol, and we found that thicker silica shells lead to higher catalytic activity. We correlated this effect as well as Ru(bpy) 3 2+ fluorescence and singlet oxygen generation to the absorption properties of the supports. We were able to reuse our optimal system three times with minimal loss of activity and achieved turnover numbers largely surpassing the performance of homogeneous Ru(bpy) 3 2+ . This work highlights the role of material design in the conception of new supports for applications in heterogeneous photocatalysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".