Mechanistic Studies of the Catalytic Chemistry of NOx in Laboratory Plasma-Catalyst Reactors
Bibliographic record
Abstract
Several reactor systems have been used to study the catalytic chemistry of a particular proprietary zeolitic catalyst in conditions that mimic those found in light-duty diesel exhaust after a non-thermal plasma generator. Very similar catalytic results were obtained with NO + plasma or NO2 as the source of NOx using propene (C3H6) as the reductant. The formation of nitrogen, carbon dioxide, and other products were studied from 150°C to 250°C using a He balance gas and NOx in the form of NO2. The results demonstrate that nitrogen is formed by the selective catalytic reduction of NO2 by propene. The highest activity for N2 formation from NO2 was near 50% conversion at 200°C for a space velocity of 12,600 h-1. The NOx conversion by adsorption and by catalytic reduction was quantified. By performing studies with and without the presence of water, a clear separation in behavior between adsorption processes and catalytic reaction was observed. Good carbon and nitrogen balances were obtained in these experiments. While much still needs to be done to demonstrate the viability of a plasma-catalyst system in light-duty vehicles, mechanistic studies should help in the optimization of the catalysts that will be used in these exhaust aftertreatment systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".