DRIFTS study of γFe<sub>2</sub>O<sub>3</sub> nano‐catalyst for low‐temperature selective catalytic reduction of NO<sub>x</sub> with NH<sub>3</sub>
Bibliographic record
Abstract
The catalysts used in the selective catalytic reduction (SCR) of NOx with NH3 were prepared from γFe2O3 nanoparticles. The NH3‐SCR activity measurement was carried out in a fixed bed reactor. The adsorption characteristics of γFe2O3 nano‐catalysts to NH3 and NO were studied with in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) measurements. Experimental activity measurement indicated that γFe2O3 used as a SCR de‐NOx catalyst exhibited excellent low‐temperature SCR de‐NOx performance. The DRIFTS measurements showed that there existed both NH4+ bonded to Brønsted acid sites and coordinated NH3 bonded to Lewis acid sites on the catalyst surface. Coordinated NH3 was the main adsorption product. O2 promoted H‐abstraction from coordinated NH3 to form NH2 species, as well as O2 greatly enhancing the adsorption of NO on the catalyst surface to form nitrate species and absorbed NO2. Possible reaction paths with the γFe2O3 catalyst were proposed as follows: the SCR process mainly formed NH2 from H‐abstraction of coordinated NH3 which reacted with NO to form N2 and H2O at high and medium temperatures. At low temperatures, however, the formation of adsorbed NO2 resulted from NO oxidation by O2 over Fe3+ sites played an important role. NH4NO3 and (NH4+)2NO2 as the key intermediate products reacted with NO to form N2 and H2O at low temperatures.
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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".