Effects of particle size of cerium oxide nanoparticles on the combustion behavior and exhaust emissions of a diesel engine powered by biodiesel/diesel blend
Bibliographic record
Abstract
Meeting the emission norms specified by governing bodies is one of the major challenges faced by engine manufacturers, especially without sacrificing engine performance and fuel economy. Several methods and techniques are being used globally to reduce engine emissions. Even though emissions can be reduced, doing so usually entails a deterioration in performance. To address this problem, nanoadditives such as cerium oxide (CeO2) nanoparticles are used to reduce engine emissions while improving engine performance. However, some aspects of the application of these nanoadditives are still unknown. In light of that, three sizes of CeO2 nanoparticles (i.e., 10, 30, and 80 nm) and at a constant volume fraction of 80 ppm were added to a 20% blend of waste cooking oil biodiesel and diesel (B20). A single-cylinder diesel engine operating at a 1500 rpm speed and 180 bar fuel injection pressure was used to compare the performance and emission characteristics of the investigated fuel formulations. The results showed that the addition of CeO2 nanoparticles led to performance improvements by reducing brake specific fuel consumption. Moreover, the catalytic action of CeO2 nanoparticles on the hydrocarbons helped achieve effective combustion and reduce the emission of carbon monoxide, unburnt hydrocarbon, oxides of nitrogen, and soot. Interestingly, the size of the nanoadditive played an instrumental role in the improvements achieved, and the use of 30 nm-sized nanoparticles led to the most favorable performance and the lowest engine emissions. More specifically, the fuel formulation harboring 30 nm nanoceria reduced brake specific fuel consumption by 2.5%, NOx emission by 15.7%, and smoke opacity by 34.7%, compared to the additive-free B20. These findings could shed light on the action mechanism of fuel nanoadditives and are expected to pave the way for future research to develop more promising fuel nanoadditives for commercial applications.
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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".