Removal of elemental mercury by Ce and Co modified MCM‐41 catalyst from simulated flue gas
Bibliographic record
Abstract
Abstract Co and Ce based catalysts have been proven to possess a high Hg0 removal efficiency. However, these catalysts have a low dispersion of active components and low mass transfer rate, which limits their catalytic activity. MCM‐41 has a large surface area and a highly ordered mesoporous structure, which can improve the dispersion of Co and Ce, resulting in a high mass transfer rate. In this paper, different amounts of Co and Ce were loaded on MCM‐41 to synthesize Cox‐Cex/MCM‐41. The characteristic results of the catalyst showed that catalysts with a lower Co and Ce loading amount had a high dispersion and a low crystallinity, leading to more activity sites on the catalyst surface. Hence, the Hg0 removal efficiency of the catalysts first increased with the increase of the Co and Ce loading amount. However, with the further increase of the Co and Ce loading amount, the crystallinity of the catalysts increased, which might cause the blockage of the pores and the decrease of BET surface areas, leading to a low Hg0 removal efficiency. Along with the factors mentioned above, Co0.15‐Ce0.15/MCM‐41 had the best catalytic activity, which showed above 85 % Hg0 removal efficiency in the temperature range of 200–300 °C at 180 000 h−1. Flue gas components had different effects on catalytic activity. O2 and NO could promote, but H2O and SO2 inhibit the Hg0 removal efficiency. The simulated flue gas of 300 ppm SO2, 400 ppm NO, 6 % H2O, and 5 % O2 have serious effects on catalytic activity leading to a lower Hg0 removal efficiency of 20 % for Co0.15‐Ce0.15/MCM‐4.
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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) | 0.001 | 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 | 0.001 | 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".