Impact of titanium dioxide (TiO2) nanoparticles addition in Eichhornia Crassipes biodiesel used to fuel compression ignition engine at variable injection pressure
Why this work is in the frame
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Bibliographic record
Abstract
Power production through combustion of fuels plays a vital role in the progress of a nation. In liquid fuels, biodiesel has emerged as a potential replacement of fossil fuel. In this regard, the improvement of fuel properties or adjustment of the operating parameters can improve the efficiency and lower the emission for a biodiesel run diesel engine. In this regard, the current study focuses on the use of novel nano blended biodiesel, prepared by blending titanium dioxide nano particles along Eichhornia Crassipes biodiesel. Three different biodiesel blends are prepared having composition of titanium dioxide by 50 ppm, 100 ppm and 150 ppm which is tested in a four stroke, single cylinder, naturally aspirated water cooled diesel engine of 3.5 kW rated power for different loading conditions for performance, emission, and combustion evaluation. Further, at the same engine conditions, the biodiesel blend having 150 ppm of titanium dioxide is tested at fuel injection pressure of 220 bar. The findings suggests that the brake thermal efficiency improves and emissions lowers with the addition of nano particles at high fuel injection pressure. The maximum improvement of brake thermal efficiency of 1.01% in comparison to diesel mode has been found for nano blended biodiesel composition of 150 ppm of titanium dioxide at fuel injection pressure of 220 bar under full loading condition. For the same fuel injection pressure of 220 bar and same nano blended biodiesel composition, the hydrocarbon and carbon monoxide emission were found to minimum at 60% load.
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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 | 0.001 |
| 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 it