Triangle Cl−Ag<sub>1</sub>−Cl Sites for Superior Photocatalytic Molecular Oxygen Activation and NO Oxidation of BiOCl
Bibliographic record
Abstract
Abstract BiOCl photocatalysis shows great promise for molecular oxygen activation and NO oxidation, but its selective transformation of NO to immobilized nitrate without toxic NO 2 emission is still a great challenge, because of uncontrollable reaction intermediates and pathways. In this study, we demonstrate that the introduction of triangle Cl−Ag 1 −Cl sites on a Cl‐terminated, (001) facet‐exposed BiOCl can selectively promote one‐electron activation of reactant molecular oxygen to intermediate superoxide radicals (⋅O 2 − ), and also shift the adsorption configuration of product NO 3 − from the weak monodentate binding mode to a strong bidentate mode to avoid unfavorable photolysis. By simultaneously tuning intermediates and products, the Cl−Ag 1 −Cl‐landen BiOCl achieved >90 % NO conversion to favorable NO 3 − of high selectivity (>97 %) in 10 min under visible light, with the undesired NO 2 concentration below 20 ppb. Both the activity and the selectivity of Cl−Ag 1 −Cl sites surpass those of BiOCl surface sites (38 % NO conversion, 67 % NO 3 − selectivity) or control O−Ag 1 −O sites on a benchmark photocatalyst P25 (67 % NO conversion and 87 % NO 3 − selectivity). This study develops new single‐atom sites for the performance enhancement of semiconductor photocatalysts, and also provides a facile pathway to manipulate the reactive oxygen species production for efficient pollutant removal.
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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 | 0.001 | 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".