Transport effects and chemical effects on NO removal by SCR with NH<sub>3</sub>over iron‐based catalyst in a magnetically fluidized bed
Bibliographic record
Abstract
Abstract Selective catalytic reduction (SCR) of NO by NH3on iron‐based catalysts was investigated with a magnetically fluidized bed (MFB). Magnetic fields promoted the NO conversion. The optimal efficiency of 95 % was attained under a magnetic field of 0.01–0.015 T at 250 °C. Magnetic fields yielded transport effects and chemical effects on SCR of NO over Fe2O3catalyst. The transport effects are reflected by the enhancement of physical transfer in a MFB. Magnetic fields can check and eliminate bubbles, increase gas‐solid contact probabilities, and thereby improve heat and mass transfer characteristics in a MFB. The chemical effects can be summarized into three points. First, the magnetization ofγ‐Fe2O3by uniform magnetic fields gives rise to boundary effects, which results in Faraday force on paramagnetic NO molecules and yields NO movement to the catalyst surface, and hence increases NO chemisorption. Second, the synergy of magnetic fields and ferrimagnetic iron‐based catalyst can boost the transformation of antimagnetic reactant into paramagnetic products, and accelerate electron transport in the reaction, which enhances the activation of NH3on magnetic Fe (III) sites. Third, magnetic fields can alter the energy dispersal of a free radicals reaction system, and thereby promotes the free radicals reaction between NH2· and NO·.
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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".