Bibliographic record
Abstract
The impacts of automotive pollutants on local air quality, human health, and climate change are a major concern worldwide. Therefore, the internal combustion engine (ICE) powered automobiles are expected to be able to meet the increasingly stringent emission and fuel efficiency standards. The reduction in oxides of nitrogen (NOx) in lean burn and diesel-fueled compression ignition (CI) engines is a major challenge. In this research, the use of alcohol fuel such as n-butanol and ethanol is studied in a CI engine. With the application of moderate exhaust gas recirculation (EGR), low engine-out NOx and soot emissions are achieved simultaneously. However, to meet NOx emission regulations, the use of only alternative fuels is not sufficient for a wide range of engine operating conditions. Therefore, lean NOx trap (LNT) after-treatment system is used for further NOx reduction using a reductant. In this study, an investigation on the performance of long breathing LNT is performed using ethanol, n-butanol, and diesel as reductants. The LNT regeneration experiments are conducted on a heated flow bench under simulated engine exhaust like conditions. Ethanol and n-butanol are found to be more effective compared to diesel as a reductant in terms of NOx conversion, ammonia (NH3) slip, nitrogen dioxide (N2O) slip, and hydrogen (H2) formation, during the LNT regeneration period at the tested conditions. The formation of NH3 and N2O during the LNT regeneration is not desired. Albeit, the NH3 generated during the LNT regeneration can be utilized to further reduce NOx using a selective catalytic reduction (SCR) convertor downstream of the LNT catalyst. In this study, the combined LNT-SCR tests are performed to investigate overall NOx reduction using n-butanol as a reductant.
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 | 0.000 | 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".