NO<i><sub>x</sub></i> Reconstruction Triggered by Zeolite Reverses Alkali Metal Poisoning in NO Selective Catalytic Reduction
Bibliographic record
Abstract
Alkali metal poisoning represents a formidable challenge for the effective utilization of ammonia selective catalytic reduction (NH 3 –SCR) technology. Herein, we propose a versatile strategy to radically circumvent the deactivation effect from alkali metals via regulated activation of surface NO x species. Activity test showed that after physical mixing with a series of zeolites (ZSM-5, MOR, and SAPO-34), the activity of the K-poisoned CeO 2 –MnO x catalyst achieved complete recovery, and the observed NO conversion was found to be even superior to that of the fresh catalyst. Mechanism analysis from in situ DRIFTS and TPSR revealed that K deposition shut off the transformation of surface NO x from chelating bidentate nitrites to bidentate nitrates (both bridging and chelating bidentate nitrates), leading to catalyst deactivation. Zeolite coupling introduced labile NO + species, which interacted facilely with the chelating bidentate nitrites to generate chemically reactive bidentate nitrates, enabling a thorough regeneration of NH 3 –SCR performance. In addition to CeO 2 –MnO x, this strategy was also found to be valid for a variety of NH 3 –SCR catalysts, demonstrating great potential in reversing alkali metal poisoning.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".