Transition metals cobaltites spinel for depollution of NO<sub>x</sub> emissions using SCR technology
Bibliographic record
Abstract
Abstract The catalytic performances of various transition metal (M = Cu, Mn, and Ni) cobaltites prepared by the nanocasting method were investigated for the selective catalytic reduction (SCR) of NO under lean burn conditions. KIT‐6 was used as a hard template in the nanocasting method for preparation of catalysts. The catalyst samples were characterized by various techniques such as XRD, low‐temperature N2 physisorption, SEM‐EDS, and XPS. The catalysts were examined for the SCR of NO by NH3 and H2‐LPG in a packed bed tubular flow reactor under the following reaction conditions: 500 ppm NO, 8 % O2, (0.1 % NH3) or (1000 ppm LPG, 1 % H2) in Ar with 200 mg catalyst. The inlet and outlet gases of the reactor were analyzed by an Eco Physics CLD 62 chemiluminescence NO/NOx analyzer and online GC. NO and NO2 measurements were done by an Eco Physics CLD 62 chemiluminescence NO/NOx analyzer. Two separate GCs equipped with Porapak Q/capillary columns and FID/ECD detectors were used to analyze the hydrocarbons/−N2O respectively. The addition of 1 % H2 with LPG promoted NO reduction at a remarkably low temperature. It was found that the nature of the dispersed metal strongly affects the light‐off temperature (52 °C) and enhances NO conversion. The oxygen‐deficient MnCo2O4 spinel structure enhanced NO reduction (87.1 %) at a lower temperature of 250 °C as compared to Cu and Ni cobaltites using H2‐LPG‐SCR. The H2‐LPG reductant showed the best de‐NOx activity and the order of catalyst activity followed the sequence: MnCo2O4 > CuCo2O4 > NiCo2O4.
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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".