Simultaneous removal of elemental mercury and <scp>NO</scp> from flue gas by the <scp> CeO <sub>2</sub> </scp> / <scp> TiO <sub>2</sub> </scp> catalysts and the mechanism investigation
Bibliographic record
Abstract
Abstract Mercury pollution has become one of the high‐profile environmental problems in the world. A series of Ce y Ti catalyst were synthesized by sol–gel method, and the process of simultaneous conversion of NO and removal of mercury (Hg 0 ) in the flue gas was studied. BET, SEM, and XRD were used to systematically detect the physicochemical properties of the catalysts, which show that excessive Ce loading would make its specific surface area reduce. The NO conversion efficiency would increase with the increase of the Ce loading. The optimal Ce/Ti mass ratio was 0.3. A part of the adsorbed NO would be oxidized by the active oxygen species on the catalyst surface to produce a certain amount of nitrogen‐containing substances, such as NO + , NO 3− , or NO 2 . The produced substances could promote the adsorption and oxidation of Hg 0 . The NO conversion was very dependent on the oxygen in the flue gas. The activity of the Ce 0.3 Ti catalyst was promoted in the presence of O 2 . SO 2 and H 2 O would inhibit the reaction of NO conversion and Hg 0 removal. The mechanisms of the catalytic system have been proved via the Mars‐Maessen mechanism.
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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".