Microscopic study of EPS-infused biodiesel with DEE and its performance and emissions in a diesel engine
Bibliographic record
Abstract
The main objective of this study is to investigate some important fuel properties of expanded polystyrene (EPS)-infused biodiesel blends without and with the addition of a certain percentage of diethyl ether (DEE), a cetane number (CN) enhancer. The originality of this study is the use of high-CN DEE as an additive for EPS-infused canola biodiesel to investigate a diesel engine’s performance and emissions. Furthermore, we performed a microscopic study (of structure and particle size distribution) of EPS-infused canola biodiesel blends without and with DEE as an additive. Biodiesel is an effective solvent for EPS. EPS-infused biodiesel can be used in diesel engines for power production, and it can be a supplementary energy source for IC engines. This study investigates the EPS-infused biodiesel blend performance and emission in a direct injection (DI) diesel engine at low, medium, and high loads, each at 1000, 2100, and 3000 rpm. The EPS was dissolved in biodiesel at varying concentrations (2, 6, and 10 g/L) at room temperature (25 °C). The results for different EPS-infused biodiesel blends are compared with those of pure diesel and biodiesel, and EPS dissolution and performance improvements with DEE were achieved.
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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".