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 NO x with NH 3 were prepared from γFe 2 O 3 nanoparticles. The NH 3 ‐SCR activity measurement was carried out in a fixed bed reactor. The adsorption characteristics of γFe 2 O 3 nano‐catalysts to NH 3 and NO were studied with in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) measurements. Experimental activity measurement indicated that γFe 2 O 3 used as a SCR de‐NO x catalyst exhibited excellent low‐temperature SCR de‐NO x performance. The DRIFTS measurements showed that there existed both NH 4 + bonded to Brønsted acid sites and coordinated NH 3 bonded to Lewis acid sites on the catalyst surface. Coordinated NH 3 was the main adsorption product. O 2 promoted H‐abstraction from coordinated NH 3 to form NH 2 species, as well as O 2 greatly enhancing the adsorption of NO on the catalyst surface to form nitrate species and absorbed NO 2 . Possible reaction paths with the γFe 2 O 3 catalyst were proposed as follows: the SCR process mainly formed NH 2 from H‐abstraction of coordinated NH 3 which reacted with NO to form N 2 and H 2 O at high and medium temperatures. At low temperatures, however, the formation of adsorbed NO 2 resulted from NO oxidation by O 2 over Fe 3+ sites played an important role. NH 4 NO 3 and (NH 4 + ) 2 NO 2 as the key intermediate products reacted with NO to form N 2 and H 2 O at low temperatures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".