What question did this study set out to answer?

The aim is to investigate how zinc oxide nanoparticles in neem biodiesel affect engine performance, combustion, and emissions.

June 14, 2026Open Access

Integrated experimental and machine learning investigation of green-synthesized ZnO nanoparticle-dispersed neem biodiesel: effects on diesel engine performance, combustion, and emissions

Key Points

The aim is to investigate how zinc oxide nanoparticles in neem biodiesel affect engine performance, combustion, and emissions.
Neem oil was transesterified for biodiesel production.
Zinc oxide nanoparticles were synthesized using neem leaf extract at 50 ppm and 100 ppm concentrations.
Machine learning models (KNN, RF, SVR, XGB) were employed to predict engine performance and emissions.
Brake Thermal Efficiency (BTE) increased by 3.64% for NB25Zn100.
Specific Fuel Consumption (SFC) decreased by 5.28% for NB25Zn100.
Emission reductions were observed for CO, HC, and smoke opacity, with reduced NOx emissions at 100 ppm.

Abstract

This study explores the performance, combustion, and emission parameters of a neem biodiesel enhanced with zinc oxide (ZnO) Nanoparticles (NPs) synthesized through a green method in a compression ignition (CI) engine. Neem oil was transesterified to produce biodiesel, while ZnO NPs were synthesized using neem leaf extract and dispersed at concentrations of 50 ppm and 100 ppm, denoted as NB25Zn50 and NB25Zn100, respectively. The outcomes showed that the addition of ZnO NPs significantly enhanced combustion efficiency, resulting in Brake Thermal Efficiency (BTE) increases of 1.51% for NB25Zn50 and 3.64% for NB25Zn100, along with Specific Fuel Consumption (SFC) decreases of 2.54% and 5.28%, respectively. Combustion analysis revealed a higher peak cylinder pressure (CP), increased Heat Release Rate (HRR), and quicker Mass Fraction Burning (MFB) progression for fuels blended with NPs, indicating a shorter Ignition Delay (ID) and more complete combustion. Emission analysis showed considerable reductions in Carbon monoxide (CO), Hydrocarbons (HC), and smoke opacity, whereas the optimal ZnO concentration (100 ppm) effectively controlled Nitrogen Oxides (NO x ) emissions. Additionally, machine learning models such as K-Nearest Neighbors (KNN), Rrandom Forest (RF), Ssupport Vector Regression (SVR), and Extreme Gradient Boosting (XGB) were used to predict engine performance, emissions, and energy parameters. Among these, extreme gradient boosting demonstrated superior predictive accuracy with high correlation coefficients ( R ≈ 0.99) and minimal error. Overall, neem biodiesel blended with ZnO NPs, especially at 100 ppm, exhibited strong potential as a sustainable fuel for enhanced overall engine performance and cleaner emissions.

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Cite This Study

Srinivasarao et al. (Fri,) studied this question.

synapsesocial.com/papers/6a2e45adb1cc60ccdea8a9cd https://doi.org/https://doi.org/10.1038/s41598-026-57550-x

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