Photocatalytic Synthesis of <i>p</i>‐Anisaldehyde in a Mini Slurry‐Bubble Reactor under Solar Light Irradiation
Bibliographic record
Abstract
Dense photocatalyst slurry was employed for the synthesis of p‐anisaldehyde under solar light irradiation. An Fe‐modified rutile TiO 2 (Fe‐TiO 2, 34.5 m 2/g) photocatalyst was used as a visible‐light‐responsive photocatalyst. A conventional TiO 2 (P25, 35 m 2/g) photocatalyst was also examined as a reference catalyst. XRD patterns and diffuse reflectance spectra showed that Fe‐TiO 2 consists of 100 % rutile phase and absorbs more visible light compared to P25, respectively. The catalyst powder was suspended in an ethyl acetate solution of p‐methoxytoluene in the mini‐reactor, with oxygen bubbling, under a solar simulator, visible light, and UV LEDs. p‐anisaldehyde, as a reaction product, was analyzed by sampling using gas‐chromatograph. Regardless of the light source, Fe‐TiO 2 always outperformed P25 in terms of both generation rates (GR) of p‐anisaldehyde and energy requirements (ER). It was demonstrated that the highly dense Fe‐TiO 2 slurry was efficient for the synthesis under solar light owing to the small size of the reactor. The small amount of Pt and ZrO 2 cocatalysts significantly enhanced the GR under solar light. By adopting a visible light responsive Fe‐TiO 2 photocatalyst, the mini slurry‐bubble reactor under solar light achieved a high GR per catalyst mass (CM), which is one to two orders higher than that reported by most previous studies with high‐power lamps.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".