Comparative Study of PM Mass and Chemical Composition from Diesel and Biodiesel Fuelled CRDI SUV Engine
Bibliographic record
Abstract
Adverse health effects of particulate matter (PM) originating from diesel engine exhaust are largely attributed to the complex chemical composition of the exhaust species. This study was set out to characterize particulate emissions from a Euro-III-compliant modern automotive common rail direct injection (CRDI) sports utility vehicle (SUV) diesel engine operated at different loads at rated engine speed (1800 rpm), employing diesel and 20% biodiesel blends (B20) produced from Karanja oil. This study is mainly divided into two main sections, first one includes the gravimetric analysis in order to assess the amount of Benzene Soluble Organic Fraction (BSOF) and trace metals using Inductively Coupled Plasma-Optical Emission Spectrometer (ICPOES). The second section includes real-time measurements for Organic Carbon (OC), Elemental Carbon (EC) and total particle-bound Polycyclic Aromatic Hydrocarbons (PAHs). For diesel and biodiesel fuels, BSOF results showed decreasing trends with increasing engine load. B20 showed higher BSOF as compared to diesel for all loads. The concentration of the different trace metals analyzed also showed decreasing trends with increasing engine load. OC/EC data suggested that the ratio of OC to EC decreased with corresponding increase in engine load for both fuels. A peak in PAH concentration was observed at 60% rated engine load at 1800 rpm and was almost identical for both fuels. Comparison of chemical components of PM emitted from this CRDI engine provides new insights in terms of PM toxicity for B20 vis-à-vis diesel.
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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 | 0.001 | 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".