Catalytic oxidation removal of gaseous elemental mercury in flue gas over niobium‐loaded catalyst
Bibliographic record
Abstract
Nb‐Co‐Ce/Al2O3 catalysts prepared by impregnation, sol‐gel method, and co‐precipitation were examined for elemental mercury (Hg0) oxidation in a simulated coal combustion flue gas. The catalysts were characterized by SEM‐EDS, BET, XRD, FTIR, and XPS techniques. The performances of different niobium‐loaded catalysts on Hg0 oxidation efficiency with regard to preparation method, assistant, reaction temperature, and typical individual flue gas were investigated. The results showed that the Hg0 oxidation efficiency performed at the highest value for niobium‐loaded catalysts at 523 K. SO2 was observed to have a negative effect on Hg0 oxidation. The niobium‐loaded catalysts prepared by the sol‐gel method and co‐precipitation exhibited higher Hg0 oxidation efficiency than that prepared by impregnation, due to higher specific surface area and more exposure of active sites. However, it also results in these catalysts being affected by SO2 more easily. An enhancing effect of O2 was observed and an addition of HCl would promote the Hg0 oxidation further. Results also indicated that synergistic effects between NbOx, CoOx, and CeOx could promote the sustainable capacity of Hg0 oxidation for Nb‐Co‐Ce/Al2O3 catalysts. Hg0 oxidation over Nb‐Co‐Ce/Al2O3 catalysts is thought to follow a Mars‐Maessen mechanism in which lattice oxygen derived from NbOx would react with absorbed Hg0.
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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".