Stable Cu (I) single copper atoms supported on porous carbon nitride nanosheets for efficient photocatalytic degradation of antibiotics
Bibliographic record
Abstract
Abstract Exploration of stable metal single‐site supported porous graphitic carbon nitride (PCN) nanostructures and the development of maximum atom utilization for enhanced photocatalytic oxidation of antibiotics remains a challenge in current research. This work proposed a one‐step thermal copolymerization to obtain Cu (I) doping porous carbon nitride (CUCN) through a spontaneously reducing atmosphere by urea in a covered crucible. The obtained CUCN had crumpled ultrathin nanosheets and mesoporous structures, which possessed higher specific surface areas than PCN. From X‐ray absorption near edge structure (XANES) and Fourier transform extended X‐ray absorption fine structure (FT‐EXAFS) spectra analysis, the Cu doping existed in the oxidation state of Cu (I) as single atoms anchored on the 2D layers of CN through two N neighbors, thereby facilitating efficient pathways for the transfer of photoexcited charge carriers. Furthermore, the photoluminescence (PL) spectra, electrochemical impedance spectra (EIS) and transient photocurrent response test proved the improved separation and transfer of photoexcited charge carriers for Cu (I) introduction. Consequently, the photocatalytic activity of CUCN was much better than that of PCN for antibiotics norfloxacin (NOR), with 4.7‐fold higher degradation reaction rate constants. From species‐trapping experiments and density function theory (DFT) calculations, the Cu single atoms in Cu–N 2 served as catalytic sites that could accelerate charge transfer and facilitate the adsorption of molecular oxygen to produce active species. The stable Cu (I) embedded in the layer structure led to the excellent recycling test and remained stable after four runs of degradation and even thermal regenerated treatment. The degradation paths of NOR by CUCN under visible light were also demonstrated. Our work sheds light on a sustainable and practical approach for achieving stable metal single‐atom doping and enhancing photocatalytic degradation of aqueous pollutants.
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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) | 0.000 | 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".