Driving selective aerobic oxidation of alkyl aromatics by sunlight on alcohol grafted metal hydroxides
Bibliographic record
Abstract
It is very difficult to selectively oxidise stable compounds such as toluene and xylenes to useful chemicals with molecular oxygen (O2) under moderate conditions. To achieve high conversion and less over-oxidised products, a new class of photocatalysts, metal hydroxide nanoparticles grafted with alcohols, is devised. They can efficiently oxidise alkyl aromatic compounds with O2 using visible or ultraviolet light or even sunlight to generate the corresponding aldehydes, alcohols and acids at ambient temperatures and give very little over-oxidation. For example toluene can be oxidised with a 23% conversion after a 48-hour exposure to sunlight with 85% of the product being benzaldehyde, and only a trace of CO2.The surface complexes grafted onto metal hydroxides can absorb light, generating free radicals on the surface, which then initiate aerobic oxidation of the stable alkyl aromatic molecules with high product selectivity. This mechanism is distinctly different from those of any known catalysts. The use of the new photocatalysts as a controlled means to generate surface radicals through light excitation allows us to drive the production of fine organic chemicals at ambient temperatures with sunlight. The process with the new photocatalysts is especially valuable for temperature-sensitive syntheses and a greener process than many conventional thermal reactions.
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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".