Carbon Dot Sensitized Photoanodes for Visible Light Driven Organic Transformations
Bibliographic record
Abstract
Visible light photosensitization of metal oxides to create heterostructures for the conversion of solar-to-chemical energy is a promising approach to produce solar fuels and other valuable chemicals. Carbon dots have recently been considered as suitable candidates to sensitize wide bandgap metal oxide semiconductors due to their low cost and tunable optical properties. While photocatalytic systems using carbon dots as sensitizers have been reported, transformations involving the production of value-added chemicals as well as the electron transfer mechanisms underpinning photocatalysis within such heterostructures remain underexplored. Here we report the sensitization of zinc oxide nanowires with carbon dots for the α-heteroarylation of 1-phenylpyrrolidine with 2-chlorobenzothiazole under visible light illumination at room temperature. The carbon dots improve the light absorption of the nanowires in the visible region of the spectrum affording the use of white light to drive catalysis. From optical spectroscopy and electrochemistry investigations of the resulting nanohybrid material, the photocatalytic properties are explained by the band alignment at the zinc oxide-carbon dot junction where a series of single-electron transfers creates the necessary potential to oxidize 1-phenylpyrrolidine. The resulting cascade of electron transfers into and from the carbon dots drives the α-heteroarylation to a 97% yield after 24 hrs.
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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) | 0.002 | 0.001 |
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".