A promoted charge separation and transfer system from Fe single atoms and g‐C <sub>3</sub> N <sub>4</sub> for efficient photocatalysis
Bibliographic record
Abstract
Abstract The introduction of metal single atoms (SAs) into semiconductors can effectively optimize their electronic configuration and enhance their photocatalytic properties. Therefore, it is crucial to clarify the corresponding principles and photocatalytic mechanisms for efficient and sustainable photocatalytic water remediation systems. Herein, a promising Fe single‐atom photocatalyst (Fe SA ‐CN) is obtained by anchoring Fe SAs in graphitic carbon nitride using a simple calcination strategy. Characterization and experimental results indicate that the modification of Fe SAs not only introduces a doping energy level, but also changes the valence band position, which expands the light absorption range, enhances the reduction ability of photogenerated electrons, and improves the separation and transfer of photogenerated charge carriers. Subsequently, contaminants adsorbed on the Fe SA ‐CN surface trigger their oxidation removal by h + , and the H 2 O 2 generated via two‐electron direct reductions is converted in situ into ·OH by self‐Fenton reaction for the synergistic contaminant degradation. In summary, Fe SA ‐CN offers a promising pathway for single‐atom photocatalysts in water remediation because of outstanding contamination removal efficiency, adaptability, and stability.
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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".