Oxygenated Fuels as Reductants for Lean NOx Trap Regeneration
Bibliographic record
Abstract
The push for environmental protection and sustainability has led to strict emission regulations for automotive manufacturers as evident in EURO VII and 2026 EPA requirements. The challenge lies in maintaining fuel efficiency and simultaneously reducing the carbon footprint while meeting future emission regulations. Alcohol (primarily methanol, ethanol, and butanol) and ether (dimethyl ether) fuels, owing to their comparable energy density to existing fuels, the comparative ease of handling, renewable production, and suitable emission characteristics may present an attractive drop-in replacement, fully or in part as an additive, to the gasoline/diesel fuels, without extensive modifications to the engine geometry. Additionally, lean and diluted combustion are well-researched pathways for efficiency improvement and reduction of engine-out emissions of modern engines. Modern internal combustion engines typically employ various in-cylinder emission reduction techniques along with a multi-stage exhaust after-treatment system to comply with emission standards. Lean NOx trap (LNT) is one such aftertreatment system that can reduce the tailpipe NOx under lean conditions at a cost of fuel efficiency penalty due to regeneration. This penalty can be partially mitigated by using in-cylinder NOx reduction methodologies. In the present study, the impact of oxygenated fuels (ethanol and dimethyl Ether) on the regeneration of LNT catalyst under various lean burn exhaust conditions is investigated. The regeneration characteristics of the oxygenated fuels are compared to those of conventional gasoline fuels. Relevant engine-out exhaust conditions from SI and CI engines, including flow, temperature, and exhaust species, operating at different dilution conditions were replicated on a heated aftertreatment flow bench. A comprehensive analysis of species before and after the catalyst sections was performed using Fourier-transformed infrared (FTIR) and mass spectrometers to study and quantify the conversion and formation of species, including ammonia, methane, and hydrogen, under different catalyst conditions. The conversion selectivity of different species is also investigated. The results show that gasoline, ethanol and DME can act as effective reductants for LNT regeneration. LNT catalyst achieves the maximum storage efficiency at 350°C regardless of the reductant used. The NOx conversion efficiency of LNT increases unidirectionally with temperature.
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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".