Enhancing Diesel Engine Performance with Different Concentrations of Copper Oxide Nanoparticles in Biodiesel Blends
Bibliographic record
Abstract
This study explores the impact of adding copper oxide (CuO) nanoparticles to biodiesel derived from sunflower oil on the performance of a diesel engine.Various concentrations of CuO nanoparticles were tested.Using a Korean-origin KiaBongo2701 Model 12 diesel engine, seven fuel types were examined, including pure diesel and biodiesel blends with different ratios.The effects of these blends and the performance of CuO nanoparticle additives were evaluated at various speeds and loads.Both mechanical mixing and ultrasonic devices were employed to prepare the fuel blends for comparison.Copper oxide nanoparticles significantly enhanced the performance of the D85B15 fuel blend, narrowing the performance gap with pure diesel.The results demonstrated that increasing the nanoparticle concentration led to a marked improvement in thermal efficiency at 1600 RPM, with D85B15PPM50 showing a 4.63% increase, D85B15PPM75 achieving a 6.41% improvement, and D85B15PPM100 experiencing a 7.54% boost.Diesel-like performance was attained using D85B15PPM100.D85B15's reduced calorific value raised fuel consumption, but nanoparticle integration improved brake-specific fuel consumption, especially at full load with 100 ppm copper oxide.The emissions tests showed that biodiesel blends containing nanoparticles produced less carbon monoxide and nitrogen oxides than the basic mix, reducing environmental impact.At the highest concentration (100 ppm), nanoparticles virtually equaled pure diesel performance, suggesting a way to produce biodiesel with equivalent performance and reduced environmental effects.These findings demonstrate the potential of nanoparticle additions to bridge the performance gap between biodiesel and conventional diesel and make biodiesel usage in diesel engines more sustainable and efficient.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".