A Promoted Charge Separation/Transfer System from Cu Single Atoms and C<sub>3</sub>N<sub>4</sub> Layers for Efficient Photocatalysis
Bibliographic record
Abstract
Abstract Establishing highly effective charge transfer channels in carbon nitride (C 3 N 4 ) for enhancing its photocatalytic activity is still a challenging issue. Herein, for the first time, the engineering of C 3 N 4 layers with single‐atom Cu bonded with compositional N (CuN x ) is demonstrated to address this challenge. The CuN x is formed by intercalation of chlorophyll sodium copper salt into a melamine‐based supramolecular precursor followed by controlled pyrolysis. Two groups of CuN x are identified: in one group each of Cu atoms is bonded with three in‐plane N atoms, while in the other group each of Cu atoms is bonded with four N atoms of two neighboring C 3 N 4 layers, thus forming both in‐plane and interlayer charge transfer channels. Importantly, ultrafast spectroscopy has further proved that CuN x can greatly improve in‐plane and interlayer separation/transfer of charge carriers and in turn boost the photocatalytic efficiency. Consequently, the catalyst exhibits a superior visible‐light photocatalytic hydrogen production rate (≈212 µmol h −1 /0.02 g catalyst), 30 times higher than that of bulk C 3 N 4 . Moreover, it leads to an outstanding conversion rate (92.3%) and selectivity (99.9%) for the oxidation of benzene under visible light.
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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".