An Investigation on Nitrous Oxide and Ammonia Formation of Lean NOx Trap
Bibliographic record
Abstract
Abstract Automobiles are mandated to meet the increasingly stringent emission regulations as evident in EURO VII and EPA2027 requirements. Nitrogen oxides (NOx) are among the major regulated emissions under these standards. For tailpipe NOx emissions control, the current single-stage catalytic solutions like selective catalytic reduction (SCR) and lean-NOx trap (LNT) catalysts have demonstrated reduction efficiencies of 80% to 90%. For the LNT system, nitrous oxide (N2O) and ammonia (NH3) can be formed from NOx reduction, which need to be mitigated to align with the upcoming emission regulations. Renewable fuels, such as alcohols and ethers, are found to be highly effective as reductants for NOx conversion in LNT, while offering reasonable energy densities compared to conventional petroleum fuels and having no sulfur poisoning tendencies toward the catalyst. This paper investigates the formation of NOx, N2O, and NH3 during the regeneration period on a commercially available lean NOx trap. Dimethyl ether and ethanol are utilized as reductants in this research. The product selectivity of a lean NOx trap aftertreatment system is characterized on a heated flow bench platform with varying catalyst temperature and reductant quantity. Relevant engine-out exhaust conditions from a CI engine are simulated on a heated flow bench. A comprehensive analysis of species before and after the catalyst is performed using Fourier-transformed infrared (FTIR) and mass spectrometers to study the conversion of species, including ammonia, methane, and hydrogen, under different engine-out conditions. It is observed that, as catalyst temperatures or reductant quantities increase, nitrate species on LNT become progressively unstable, and eventually cause the stored NOx slipped at around 400°C LNT catalyst temperature. This extra-slipped NOx significantly influences the regeneration behavior of the LNT.
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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".