Catalytic NO<sub>x</sub> Aftertreatment—Towards Ultra-Low NO<sub>x</sub> Mobility
Bibliographic record
Abstract
Article Catalytic NOx Aftertreatment—Towards Ultra-Low NOx Mobility Navjot Sandhu * , Xiao Yu, and Ming Zheng Department of Mechanical, Automotive and Materials Engineering, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada * Correspondence: sandh12p@uwindsor.ca Received: 26 January 2024 Accepted: 13 March 2024 Published: 20 March 2024 Abstract: The push for environmental protection and sustainability has led to strict emission regulations for automotive manufacturers as evident in EURO VII and EPA2027 requirements. The challenge lies in maintaining fuel efficiency and simultaneously reducing the carbon footprint while meeting future emission regulations. Nitrogen oxides represent one of the major and most regulated components of automotive emissions. The need to meet the stringent requirements regarding NOx emissions in both SI and CI engines has led to the development of a range of in-cylinder strategies and after-treatment techniques. In-cylinder NOx control strategies including charge dilution (fresh air and EGR), low-temperature combustion, and use of alternative fuels (as drop-in replacements or dual fuel operation) have proven to be highly effective in thermal NOx abatement. Aftertreatment methods are required to further reduce NOx emissions. Current catalytic aftertreatment systems for NOx mitigation in SI and CI engines include the three-way catalyst (TWC), selective catalytic reduction (SCR) and lean NOx trap (LNT). This review summarizes various approaches to NOx abatement in IC engines using aftertreatment catalysts. The mechanism, composition, operation parameters and recent advances in each after-treatment system are discussed in detail. The challenges to the current after-treatment scenario, such as cold start light off, catalyst poisoning and the limits of current aftertreatment solutions in relevance to the EURO VII and 2026 EPA requirements are highlighted. Lastly, recommendations are made for future aftertreatment systems to achieve ultra-low NOx emissions.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".