A Sulfur‐Tolerant MOF‐Based Single‐Atom Fe Catalyst for Efficient Oxidation of NO and Hg<sup>0</sup>
Bibliographic record
Abstract
Abstract Catalytic oxidation of NO and Hg0 is a crucial step to eliminate multiple pollutants from emissions from coal‐fired power plants. However, traditional catalysts exhibit low catalytic activity and poor sulfur resistance due to low activation ability and poor adsorption selectivity. Herein, a single‐atom Fe decorated N‐doped carbon catalyst (Fe1‐N4‐C), with abundant Fe1‐N4 sites, based on a Fe‐doped metal–organic framework is developed to oxidize NO and Hg0. The results demonstrate that the Fe1‐N4‐C has ultrahigh catalytic activity for oxidizing NO and Hg0 at low and room temperature. More importantly, Fe1‐N4‐C exhibits robust sulfur resistance as it preferably adsorbs reactants over sulfur oxides, which has never been achieved before with traditional catalysts. Furthermore, SO2 boosts the catalytic oxidation of NO over Fe1‐N4‐C through accelerating the circulation of active sites. Density functional theory calculations reveal that the Fe1‐N4 active sites result in a low energy barrier and high adsorption selectivity, providing detailed molecular‐level understanding for its excellent catalytic performance. This is the first report on NO and Hg0 oxidation over single‐atom catalysts with strong sulfur tolerance. The outcomes demonstrate that single‐atom catalysts are promising candidates for catalytic oxidation of NO and Hg0 enabling cleaner coal‐fired power plant operations.
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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".