Enhancement of the denitrification efficiency over low‐rank activated coke by doping with transition metal oxides
Bibliographic record
Abstract
Abstract Low‐rank activated coke (AC) is widely used for industrial flue gas purification due to its multipollutant cooperative removal capability. To enhance the denitrification capacity of AC for the selective catalytic reduction (SCR) of NO with NH 3 , several transition metal (Fe, Mn, Ce, V) oxides were uniformly loaded into AC by solvent impregnation. Compared to untreated AC, modified AC showed excellent denitrification efficiency above 90%. N 2 adsorption‐desorption and Raman spectroscopy techniques were used to characterize the pore size distribution and crystal structure of AC samples. The introduction of transition metal oxides had little effect on the pore structure of AC but increased the nitrogen‐containing functional groups, which facilitated NO removal. Moreover, x‐ray photoelectron spectroscopy (XPS) was used to analyze the valence changes of metal elements before and after denitrification. After the reaction, the content increase of the low‐valence metal oxides indicated that the transition metal oxides were involved in the reaction of NO with NH 3 . High‐valence metal oxides oxidized NO to NO 2 , which reacts more easily with NH 3 , thereby increasing the denitrification efficiency. Importantly, in the presence of SO 2 , modified AC still presented high denitrification performance. This transition metal oxides doping method can effectively improve the ability of low‐rank AC to remove NO in multi‐contaminant flue gas.
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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".