Performance and Emission Multi-Objective Optimization of γ-Al2O3 Nanoparticle-Enhanced E10 Bioethanol–Gasoline Combustion in Spark-Ignition Engines

Performance and Emission Multi-Objective Optimization of γ-Al2O3 Nanoparticle-Enhanced E10 Bioethanol–Gasoline Combustion in Spark-Ignition Engines | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Performance and Emission Multi-Objective Optimization of γ-Al 2 O 3 Nanoparticle-Enhanced E10 Bioethanol–Gasoline Combustion in Spark-Ignition Engines Ramtin Elkaei Behjati, Moein Navvab Kashani, Fathollah Ommi, Kiumars Mazaheri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7703508/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract With the increasing demand for green energy, bioethanol is widely recognized as a alternative to fossil fuels in spark-ignition (SI) engines. While aluminum oxide (Al 2​ O 3 ) nanoparticles have been studied with other fuels, this research is the first to investigate the impact of γ-Al 2 ​O 3 ​ nanoparticles on E10 blends at concentrations of 10, 20, and 30 ppm. Experimental tests utilized Response Surface Methodology (RSM) with a D-optimal design to model and optimize engine performance and emissions. Multi-objective optimization identified 2471.87 rpm and 5.88 ppm as the most balanced operating conditions. Results showed that at 20 ppm γ-Al 2 ​O 3 ​, brake torque (BT) and brake power (BP) increased by 7.67% and 6.94%, respectively, while brake specific fuel consumption (BSFC) decreased by 5.91%. Emissions analysis revealed reductions of 14.88% in carbon monoxide (CO) and 5.84% in hydrocarbon (HC) emissions, accompanied by a 5.68% increase in carbon dioxide (CO 2 ​). Nitrogen Oxides (NO x ​) emissions increased by 17.08% at higher engine speeds, consistent with elevated flame temperatures. Exceeding the optimal nanoparticle threshold resulted in a decline in performance and increased pollutant emissions. The findings confirm the potential of alumina nanoparticles to boost combustion efficiency and emission characteristics in SI engines, providing a data-driven framework for optimizing these fuel blends. Physical sciences/Chemistry Physical sciences/Energy science and technology Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Materials science gasoline bioethanol γ- Al2O3 nanoparticles emissions spark ignition engine Multi-objective Optimization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights γ-Al₂O₃ nanoparticles improved E10 fuel performance in SI engines and lowered CO, HC. Increased CO₂ emissions indicated more complete combustion despite higher NOₓ. Optimal 20 ppm concentration enhanced brake torque (+7.67%) and brake power (+6.94%). Brake specific fuel consumption decreased by 5.95% at the optimal nanoparticle level. RSM models (R² > 0.97) enabled multi-objective optimization of performance and emissions. 1. Introduction The global demand for sustainable energy has accelerated research into clean and efficient alternatives to conventional fossil fuels. Spark-ignition (SI) engines, which are still predominantly fueled by gasoline, remain a significant source of greenhouse gas emissions. Accordingly, improved fuel formulations that enhance combustion efficiency while reducing harmful exhaust emissions are of growing scientific and industrial interest [ 1 – 3 ]. Among liquid biofuels, ethanol is widely recognized as a renewable, oxygenated additive that improves combustion efficiency and reduces pollutant formation [ 4 – 9 ]. Ethanol–gasoline blends containing 3–10% ethanol are now mandated or encouraged in many countries [ 10 ]. In particular, E10 (10% ethanol, 90% gasoline) has demonstrated higher octane ratings, improved oxygen content, and reduced regulated emissions compared with E5 [ 6 ]. However, ethanol levels above 10% often reduce brake thermal efficiency due to ethanol’s lower heating value, leading to increased fuel consumption and diminished engine performance [ 7 , 8 ]. Despite drawbacks such as higher Reid vapor pressure [ 11 ], E10 is considered the most practical compromise between efficiency and compatibility, without requiring significant engine modifications [ 12 ]. Recent research has explored the addition of nanoparticles as fuel additives further to improve the thermo-physical and catalytic properties of biofuel blends [ 13 , 14 ]. Nanoparticles such as MgO, CeO₂, Mn₂O₃, Fe₂O₃, and TiO₂ have been reported to improve combustion efficiency, reduce unburned hydrocarbons and CO emissions, and in some cases enhance power output [ 15 – 19 ]. However, most prior studies suffer from two limitations: (i) they focused on limited nanoparticle–fuel combinations (often gasoline with butanol, methanol, or higher ethanol blends), and (ii) they did not systematically optimize nanoparticle concentration across a range of engine operating conditions. Without rigorous optimization, the comparative significance of the results is difficult to establish. For example, Zamakhan et al. [ 15 ] studied MgO and CeO 2 nanoparticles in E10, reporting improved performance but without concentration optimization. Amirabadi et al. [ 16 ] found that Mn₂O₃ nanoparticles improved brake power by nearly 20% but at the expense of increased CO₂ emissions. Valihesari et al. [ 17 ] examined Fe₂O₃ and TiO₂ in methanol–gasoline blends, while Taghayifar et al. [ 19 ] investigated TiO₂-based nanocomposites in E20. Other studies incorporated hydrogen, exhaust gas recirculation, or oxygenates such as MTBE alongside nanoparticles [ 18 , 20 ], complicating direct comparison. Although some works employed RSM to identify optimal concentrations [ 15 , 17 , 21 ], most focused on different fuels (butanol, methanol, or propanol) and on non-γ phases of Al₂O₃. Although Al₂O₃ has been studied in different alcohol–gasoline blends [ 21 , 22 ], the specific γ-phase remains underexplored. The γ-phase of Al₂O₃ offers unique advantages, including high surface area, superior thermal stability, and moisture adsorption capacity, which make it especially suitable for stabilizing ethanol-containing fuels and promoting efficient combustion. At the same time, excessive nanoparticle concentrations may induce agglomeration, raise viscosity, and pose health or environmental concerns [ 23 – 25 ], underlining the need to identify precise optimal levels. In this context, the present study systematically investigates the effect of low concentrations of γ-Al₂O₃ nanoparticles (10, 20, and 30 ppm) in E10 on engine performance and emissions. Using a D-optimal design under Response Surface Methodology (RSM), the combined influence of nanoparticle concentration and engine speed (1500, 2000, 2500, and 3000 rpm) was analyzed for brake torque (BT), brake power (BP), brake-specific fuel consumption (BSFC), and regulated emissions (CO₂, CO, HC, and NOₓ). The objective is to develop robust predictive models and identify optimal trade-offs between efficiency gains and emission reductions. By focusing on the underexplored γ-phase of Al₂O₃, systematically varying concentrations across multiple engine speeds, and applying a rigorous optimization framework, this study provides new insights into nanoparticle-assisted biofuel combustion and establishes a methodology for clean fuel innovation. 2. Materials and Sample Preparation The gasoline used in this study represents commercially available fuel commonly distributed at local petrol stations, ensuring both scientific relevance and industrial applicability, and bioethanol with a volumetric purity of 99.8% was also used. The key physicochemical properties of gasoline and bioethanol are listed in Table 1 . The γ-Al 2 O 3 nanoparticles (purity: 99.5%) employed in this work were selected on account of their high surface area, robust thermal stability, and capacity for moisture adsorption (Table 2). These properties make γ-Al 2 O 3 particularly suitable for stabilizing ethanol-containing fuels, as they help mitigate degradation phenomena typically associated with ethanol blends. In addition, their catalytic activity and heat transfer characteristics enhance combustion efficiency under practical engine operating conditions [ 26 ]. Table 1 The physical and chemical characteristics of the gasoline and bioethanol used in this study. Specifications Unit Gasoline Bioethanol Chemical symbol \(\:-\) C x H y C 2 H 5 OH Molecular weight g/mol \(\:-\) 46.01 Density at 16°C Kg/m 3 747.5 785 Specific Gravity g/ml 0.713 0.98 Octane Index (MON) \(\:-\) 83.6 \(\:-\) Octane Index (RON) \(\:-\) 87.3 109 Octane Index (AKI) \(\:-\) 85.45 \(\:-\) Flash point ̊C -43 13 Lower heating value MJ/kg 43.5 26.8 Latent heat of vaporization kJ/kg 350 854 Table 2 Physicochemical properties of γ-Al 2 O 3 nanoparticles. Specifications Unit Values structure \(\:-\) Gamma (γ) Type \(\:-\) Powder Average particle size nm 20 Morphology \(\:-\) Almost spherical Density Kg/m 3 3.97 Specific surface area m 2 /g 85.45 melting point ̊C 2040 boiling point ̊C 2980 2.2. Engine and Experimental Set-up The experiment was carried out on a spark-ignition (SI) engine (model XU7JP/L3) with a displacement of 1,761 cm³, a compression ratio of 9.25:1, four cylinders, and eight poppet valves. The engine is rated for a maximum power output of 74.57 kW and a peak torque of 153 N.m. To maintain stable operation, a cooling tower was incorporated into the system, and an oil temperature sensor was installed to ensure that the lubricant temperature remained below 95°C. Engine torque and power were measured using a 130 kW eddy current dynamometer, which was mechanically coupled to the engine. The dynamometer system was controlled through a panel and computer interface, as shown in Fig. 1 . Fuel consumption was monitored using a precision system that included a fuel pump, a return fuel cooling fan, and a high-accuracy fuel flow sensor positioned in the lower section of the fuel tank. This system is illustrated in Fig. 1 . Exhaust gas emissions were assessed with a CAP 3200 gas analyser (Capelec), equipped with a non-dispersive infrared (NDIR) sensor for quantifying CO 2 , CO, and HC. NO x emissions were determined using electrochemical detection methods. 2.3. Fuel Preparation and Experimental Procedure All experimental procedures were performed under ambient laboratory conditions at approximately 25°C. The base fuel blend was prepared by combining 10% (v/v) bioethanol with 90% commercial gasoline. To ensure accurate dosing, the displacement caused by solid nanoparticles was taken into account when preparing each batch. Fuel blends were prepared in 1 L volumes due to limitations of the ultrasonic homogenizer. γ- Al 2 O 3 nanoparticles were incorporated at concentrations of 10, 20, and 30 ppm, corresponding to 10, 20, and 30 mg of nanoparticles per liter of fuel, respectively. The preparation process began by ultrasonically dispersing γ-Al 2 O 3 into the bioethanol fraction using a probe ultrasonic homogenizer (Bandelin SONOPULS HD 3200). The stability of the nanoparticle–bioethanol suspension was confirmed before blending with gasoline. Homogenization of the complete mixture was homogenized with a magnetic stirrer (IKA C-MAG HS 7), which ensured a uniform distribution of nanoparticles. The resulting blends were stored for 24 hours under static conditions and visually inspected for sedimentation. No visible sedimentation was observed, confirming adequate colloidal stability of the nanoparticle-enhanced fuels. For engine testing, the prepared batches were combined to achieve the total required fuel volume. Trials were conducted under wide-open throttle conditions at four engine speeds: 1,500, 2,000, 2,500, and 3,000 rpm. After steady-state operation was achieved, key performance parameters—BT, BP, and BSFC—along with exhaust gas emissions, were recorded for subsequent analysis. 2.4. Statistical analysis and model construction A D-optimal experimental design was created with the aid of Design-Expert® software (version 11.0). This design strategy was selected for its ability to maximize information gained from a limited number of experimental runs due to a minimized determinant of the covariance matrix of estimated parameters (D-criterion). Compared to conventional factorial or central composite designs, the D-optimal approach provides more precise parameter estimates while reducing the total number of required experiments [ 27 ]. It is particularly suitable for irregular experimental domains and bounded factor ranges, making it well aligned with this study, where the independent variables—engine speed and nanoparticle concentration—were constrained within specific limits [ 28 ]. Thus, the design enabled significant reductions in experimental time and cost while maintaining high model accuracy and predictive reliability [ 29 , 30 ]. To facilitate regression analysis, the actual experimental variables ( \(\:{\text{X}}_{\text{a}\text{c}\text{t}\text{u}\text{a}\text{l}}\) ) were transformed into dimensionless coded values ( \(\:{\text{X}}_{\text{C}\text{o}\text{d}\text{e}\text{d}}\) ) according to Eq. ( 1 ): $$\:{X}_{Coded}\:=\:\frac{{X}_{actual}-\stackrel{-}{X}}{d}$$ 1 where x̅ denotes the average of the actual variable, and d is defined as the step size (i.e., the absolute difference between levels). The response variable Y was modeled as a function of the coded variables using multiple regression and the least squares method, which was based on the second-order polynomial shown in Eq. (2): Y = \(\:{\beta\:}_{0}\) + \(\:\:\sum\:_{i=1}^{n}{\beta\:}_{i}{X}_{i}\:\) + \(\:{\sum\:}_{i=1}^{n-1}{\sum\:}_{j=i+1}^{n}{\beta\:}_{ij}{X}_{i}{X}_{j}\:\) + \(\:\sum\:_{i=1}^{n}{\beta\:}_{ii}{{X}_{i}}^{2}\) (2) In this equation, Y represents the predicted response, n denotes the number of factors, X i​ and X j ​ are the independent (coded) variables, while the coefficients ( β 0 ​, β i ​, β ii ​ , and β ij ​ ) correspond to the intercept, linear, quadratic, and interaction effects, respectively. In this study, X 1 and X 2 correspond to engine speed (rpm) and nanoparticle concentration (ppm). The adequacy of the regression models was evaluated using statistical indicators, including p-values (with model terms considered significant at p < 0.05), lack-of-fit tests to verify model assumptions, and diagnostic metrics such as the coefficient of determination (R 2 ), adjusted R², predicted R², and the coefficient of variation (CV). These statistical measures were applied to select the most appropriate polynomial models and validate their predictive reliability. The factor levels and variable coding applied in the design are summarized in Table 3 . Table 3 The independent variables used for response surface modeling, showing both their actual and coded values. Variable Type Level Actual Coded Engine speed (rpm) X 1 1500 -1 2000 − 0.333 2500 0.333 3000 1 concentration of nanoparticles (ppm) X 2 0 -1 10 − 0.333 20 0.333 30 1 2.5. Error analysis An uncertainty analysis was carried out to verify the accuracy and reliability of the experimental findings. This process quantifies the potential deviation between measured and actual values by considering both instrument precision and measurement resolution. The measurement accuracies and associated uncertainties for all parameters are summarized in Table 4 , based on the manufacturers’ specifications. The measurement accuracies and associated uncertainties for all parameters are presented in Table 4 , according to the manufacturers’ specifications. Each directly measured parameter was recorded in triplicate to assess repeatability. The standard deviation (SD) of these repeated measurements was incorporated into the combined uncertainty, determined via the root sum of squares (RSS) method. This approach integrates both the instrument accuracy and the variability of repeated tests. For derived parameters such as BT, BP, and BSFC, uncertainties were calculated by propagating the input measurement errors according to the Guide to the Expression of Uncertainty in Measurement (GUM) framework. For example, the uncertainty in BSFC was determined by propagating the uncertainties in the fuel flow rate and brake power. Full details of the uncertainty propagation procedure are provided in Appendix A.1, A.2, and A.3. The final dataset, incorporating these uncertainty estimates, was processed using Design-Expert® software to develop regression models and identify the optimal operating conditions of the nanoparticle-enhanced fuel blends. Table 4 Measurement parameters, their operating ranges, instrument accuracies, and associated percentage uncertainties used in the experimental setup. Measurements Operating Range Instrument Accuracy \(\:(\mp\:\) ) Relative Uncertainty (%) Engine speed (rpm) 0-7000 1 0.13 Engine load (N.m) 0-600 0.01 1.5 Fuel flow measurement (g/hr) 1000–20000 0.2% 0.20 Time (s) \(\:-\) 0.1 \(\:-\) Temperature (°C) \(\:-\) 1 \(\:-\) CO (% vol.) 0–15 0.01 4.5 CO₂ (% vol.) 0–20 0.1 1.97 HC (ppm) 0-10000 1 1.72 NOₓ (ppm) 0-5000 1 0.36 BT (N.m) \(\:-\) \(\:-\) 1.5 BP (kW) \(\:-\) \(\:-\) 1.4 BSFC (g/kW.hr) \(\:-\) \(\:-\) 3.13 3. Results and discussion Before each experimental run, the fuel system was thoroughly flushed to remove any residual blend from previous tests and eliminate the risk of cross-contamination. All experiments were conducted in triplicate under identical operating conditions, and the reported values in Table 5 represent the arithmetic mean of the three trials. The experimental dataset was analyzed using Design-Expert software to develop regression models that describe how engine speed and nanoparticle concentration affect performance and emissions. These models were subsequently employed to identify the optimal operating conditions for the γ-Al2O3–E10 blends. The following subsections present the statistical evaluation of model adequacy, analysis of variance (ANOVA), and the detailed discussion of engine performance and emission characteristics. Table 5 Experimental conditions and corresponding results for engine performance and exhaust emissions. Each value represents the average of three repeated trials conducted under steady-state operation at wide-open throttle. Variable Response Engine Performance Emissions Run Speed (rpm) Concentration (ppm) BT (N.m) BP (kW) BSFC (g/kW.hr) CO 2 (%Vol.) CO (%Vol.) HC (ppm) NO x (ppm) 1 1500 0 106.46 16.56 232.4 9.1 3.03 178 739 2 1500 0 106.31 16.69 231.3 8.8 2.87 182 743 3 2000 0 106.82 22.38 234.7 9.5 3.46 175 791 4 2500 0 111.73 29.3 245.2 10.8 3.21 163 789 5 3000 0 109.38 34.36 248.8 11.4 3.83 137 814 6 1500 10 111.63 17.53 230.2 9.2 2.63 177 822 7 2000 10 111.87 23.41 235.2 9.6 3.2 170 869 8 3000 10 115.75 36.34 242.8 11.3 3.26 128 922 9 1500 20 112.45 17.66 225.6 9.3 2.76 178 849 10 2000 20 113.06 23.66 230.3 9.8 3.35 173 897 11 2500 20 118.12 30.96 236.7 11.3 3.04 159 889 12 3000 20 117.08 36.76 234.1 11.6 3.42 130 953 13 1500 30 112.27 17.59 226.6 9.1 2.75 179 843 14 1500 30 112.2 17.62 226 9.2 2.82 180 841 15 2000 30 112.06 23.45 233.4 9.5 3.46 173 857 16 2500 30 116.81 30.62 241.3 11.1 3.37 166 840 17 3000 30 115.03 36.08 238.2 11.2 4.03 143 911 18 3000 30 115.12 36.13 238.8 11.4 4.12 139 907 3.1. The engine performance results 3.1.1. Brake Torque (BT) The response surface equation for BT, obtained through regression analysis of the experimental dataset, is expressed in Eq. (3): BT = 115.7 + 8.735 X₁ + 1.218 X₂ − 0.031 X₁X₂ − 1.113 X₁² − 3.782 X₂² + 0.323 X₁²X₂ − 0.806 X₁X₂² − 6.462 X₁³ + 1.333 X₂³ (3) where X₁ and X₂ represent the coded variables for engine speed and Al 2 O 3 nanoparticle concentration, respectively. The regression coefficients indicate that BT decreases with increasing engine speed (negative coefficient for X₁ ), whereas nanoparticle addition initially enhances BT (positive coefficient for X₂ ). The presence of quadratic, cubic, and interaction terms confirms the nonlinear relationship between these parameters, consistent with the observed performance trends. The three-dimensional response surface plot in Fig. 2a illustrates this interaction. BT exhibits a distinct maximum at an engine speed of approximately 2500 rpm and a nanoparticle concentration near 20 ppm. At this point, torque for both the baseline E10 fuel and its nanoparticle-enhanced blends reaches a peak before declining at higher speeds and concentrations. This peak reflects the balance between improved combustion and volumetric efficiency at moderate speeds versus reduced cylinder filling time, increased friction, and shorter combustion duration at elevated speeds. The results demonstrate that γ-Al 2 O 3 nanoparticles enhance BT up to an optimal concentration (≈ 20 ppm), beyond which performance declines, reinforcing the importance of careful dosage optimization. Examination of the nanoparticle concentration axis in Fig. 2a shows that the addition of γ-Al 2 O 3 nanoparticles consistently enhanced BT across all engine speeds. Torque increased progressively from the baseline E10 fuel to 10 ppm and further to 20 ppm, reaching a maximum of 118.12 N·m at 2500 rpm, which corresponds to a relative increase of 7.67%. At 30 ppm, however, BT exhibited a slight decline or plateau, indicating that the optimal nanoparticle concentration was approximately 20 ppm. The decrease at higher concentrations can be attributed to nanoparticle agglomeration or an increase in fuel viscosity, both of which can impair atomization and reduce combustion efficiency. Several mechanisms can explain the observed improvements in torque. First, Al 2 O 3 nanoparticles likely act as combustion catalysts, accelerating oxidation reactions and increasing in-cylinder pressures, thereby promoting higher torque generation. Second, their high thermal conductivity enhances heat transfer within the combustion chamber, contributing to improved thermal efficiency. Ultimately, the formation of tribological boundary layers on engine surfaces may reduce internal friction and parasitic losses, resulting in more efficient power delivery. These findings are consistent with previous investigations that have reported improved performance in nanoparticle-fuel blends [ 15 , 17 , 20 ]. The statistical robustness of the regression analysis was confirmed by the model selection criteria summarized in Table 6 . The estimated parameters and goodness-of-fit metrics obtained via the least-squares method are presented in Table 7 . The same modeling framework was subsequently applied to other engine performance parameters, ensuring consistency and reliability across the analysis. Table 6 Analysis of variance (ANOVA) results for the response surface model predicting BT as a function of engine speed and − Al2O3 ​nanoparticle concentration. γ Model Sum of squares Degree of freedom Mean square F-value p value Prob > F Mean vs. total 2.276E + 05 1 2.276E + 05 Linear vs. mean 149.88 2 74.94 17.50 0.0001 2FI vs. linear 0.6712 1 0.6712 0.1478 0.7064 Quadratic vs. 2FI 39.81 2 19.90 10.05 0.0027 Cubic vs. quadratic 23.57 4 5.89 240.12 < 0.0001 (Suggested) Quartic vs Cubic 0.1234 3 0.0411 2.82 0.1465 Residual 0.0729 5 0.0146 Total 2.278E + 05 18 12657.52 Table 7 Least-squares regression coefficients and statistical parameters for the response surface model of BT as a function of engine speed and -Al2O3 nanoparticle concentration. γ Source Sum of squares Degree of freedom Mean square F-value p value Prob > F Model 213.92 9 23.77 968.76 < 0.0001 (significant) X₁ 38.88 1 38.88 1584.69 < 0.0001 X₂ 0.7571 1 0.7571 30.86 0.0005 X₁X₂ 0.0076 1 0.0076 0.3083 0.5939 X₁² 3.95 1 3.95 160.80 < 0.0001 X₂² 45.54 1 45.54 1855.89 < 0.0001 X₁²X₂ 0.2171 1 0.2171 8.85 0.0177 X₁X₂² 1.35 1 1.35 55.13 < 0.0001 X₁³ 22.28 1 22.28 908.00 < 0.0001 X₂³ 0.9490 1 0.9490 38.68 0.0003 Residual 0.1963 8 0.0245 Cor Total 214.12 17 3.1.2. Brake Power (BP) Bioethanol has a higher latent heat of vaporization than gasoline, which typically lowers combustion chamber temperatures and can reduce thermal efficiency in spark-ignition engines [ 31 ]. The addition of γ-Al 2 O 3 nanoparticles helps counteract this drawback through several synergistic mechanisms. First, γ-Al 2 O 3 nanoparticles adsorb residual moisture in bioethanol, mitigating its adverse effects on combustion stability and reducing the risk of cylinder corrosion. Second, the nanoparticles promote micro-explosions within the combustion chamber, which enhance localized ignition and accelerate flame propagation [ 32 ]. Third, they lower fuel viscosity, producing finer droplets and improving atomization [ 33 ], which in turn promotes more complete combustion and higher energy release. The experimental results confirm these effects. As illustrated in Fig. 2b, the baseline E10 fuel (10% bioethanol without nanoparticles) delivered a maximum BP of 34.36 kW at 3000 rpm. With the addition of γ-Al 2 O 3 nanoparticles at the optimal concentration of 20 ppm, BP increased to 36.76 kW, representing a 6.94% improvement. At concentrations above 20 ppm, BP declined slightly, suggesting that excessive nanoparticle loading may promote agglomeration or alter flame characteristics, thereby limiting combustion efficiency. Across all tested conditions, BP showed a strong positive correlation with engine speed, primarily due to enhanced turbulence and air–fuel mixing at higher rotational speeds. The influence of γ-Al 2 O 3 nanoparticles was more pronounced at higher speeds, where the benefits of improved atomization and micro-explosion effects were maximized. At lower engine speeds, these enhancements were less significant. This highlights the importance of the interactive effects of engine speed and nanoparticle concentration in determining brake power. The observed trends were accurately captured by the cubic polynomial regression model expressed in Eq. (4), which incorporates both nonlinear and interaction terms of the independent variables. BP = 27.367 + 11.228 X₁ + 0.289 X₂ + 0.186 X₁X₂ − 0.227 X₁² − 0.942 X₂² + 0.089 X₁²X₂ − 0.466 X₁X₂² − 1.698 X₁³ + 0.298 X₂³ (4) The regression model expressed in Eq. (4) defines a cubic relationship between BP and the independent variables, capturing both nonlinear and interaction effects. The inclusion of interaction terms indicates that the influence of each factor—engine speed and nanoparticle concentration—varies depending on the level of the other. In particular, the negative coefficients of the higher-order speed terms highlight the dominant role of engine speed, with gains in BP diminishing at elevated rpm. These findings emphasize the necessity of simultaneously optimizing engine speed and nanoparticle concentration to achieve maximum BP. 3.1.3. Brake Specific Fuel Consumption (BSFC) BSFC represents the ratio of the fuel mass flow rate to the engine's BP output, and it is a key metric for assessing fuel efficiency. A lower BSFC value signifies a more efficient conversion of the chemical energy "A lower BSFC value signifies a more efficient conversion of the chemical energy of the fuel into usable mechanical output. The following cubic polynomial expresses the empirical model representing BSFC as a function of X₁ and X₂ in Eq. (5): BSFC = 235.865 + 11.279 X₁ − 8.015 X₂ − 1.335 X₁X₂ − 2.869 X₁² + 3.289 X₂² − 2.971 X₁²X₂ + 2.432 X₁X₂² − 6.363 X₁³ + 7.02 X₂³ (5) The regression model highlights the nonlinear influence of nanoparticle concentration ( X₂ )​ on BSFC. The positive cubic term ( X ₂ 3 , coefficient: +7.02) indicates that moderate nanoparticle additions initially reduce BSFC, but exceeding the optimal threshold causes a sharp rise, reflecting diminishing returns. This behavior is illustrated in the three-dimensional response surface plot shown in Fig. 2c, which displays a characteristic valley representing the region of minimum BSFC. BSFC tended to increase with engine speed across all nanoparticle concentrations, primarily due to greater mechanical losses (e.g., friction) and reduced volumetric efficiency at higher rpm [ 30 ]. These effects offset the combustion improvements provided by ethanol and nanoparticles, thereby raising fuel consumption per unit power output. The influence of γ-Al 2 O 3 concentration was more pronounced. The addition of nanoparticles consistently reduced BSFC relative to neat E10, with the lowest values observed at approximately 20 ppm. At this concentration, BSFC decreased by 5.95% at 3000 rpm compared with baseline E10. This reduction is attributed to improved atomization, enhanced catalytic activity, and more complete combustion. This reduction can be ascribed to improved atomization, enhanced catalytic activity, and more complete combustion. At lower engine speeds (1500–2000 rpm), nanoparticle addition at 10 ppm produced only marginal changes in BSFC compared with neat E10, suggesting that the catalytic and atomization effects are less effective under low-turbulence conditions. Beyond the optimal concentration (≈ 30 ppm), BSFC values increased again, likely due to nanoparticle agglomeration or increased viscosity, both of which impair atomization and combustion efficiency. Thus, the most favorable BSFC was achieved at the combination of moderate-to-lower engine speeds (1500–2000 rpm) with 20 ppm γ-Al 2 O 3 , corresponding to the lowest point in the efficiency valley shown in Fig. 2c. The reduction in BSFC can be attributed to the enhanced fuel conversion efficiency promoted by the catalytic properties of γ-Al 2 O 3 nanoparticles. By accelerating oxidation reactions, the nanoparticles enable faster and more thorough combustion, leading to higher in-cylinder pressures and the conversion of a larger portion of chemical energy into productive mechanical work per unit of fuel consumed. Their superior thermal conductivity further enhances heat transfer inside the combustion chamber, contributing to gains in thermal efficiency. At higher nanoparticle concentrations (e.g., 30 ppm), BSFC increases again, likely due to nanoparticle agglomeration and elevated fuel viscosity, which impair dispersion, atomization, and combustion uniformity. This behavior mirrors the trends observed for brake torque and brake power, where peak improvements also occurred near 20 ppm. The consistency across these metrics highlights that γ-Al 2 O 3 addition at the optimal concentration enhances overall engine efficiency by simultaneously improving combustion, power output, and fuel utilization. 3.2. Emissions 3.2.1. Carbon dioxide (CO 2 ) Figure 3 a shows the combined effects of engine speed and γ-Al 2 O 3 nanoparticle concentration on CO 2 emissions, as depicted by the three-dimensional response surface plot. CO 2 emissions consistently increase as engine speed increases across all fuel blends. This behavior is attributed to improved air intake, greater turbulence, and enhanced fuel atomization at higher rpm [ 7 ], all of which support more complete fuel oxidation and optimal energy conversion. Enhanced combustion efficiency is further reflected in the observation that CO 2 emissions increased by up to 5.68% at 20 ppm γ- Al 2 O 3 and 1500 rpm, relative to neat E10 fuel at the same speed. This indicates that the catalytic activity of γ-Al 2 O 3 nanoparticles is especially successful in promoting complete combustion where neat E10 alone may exhibit suboptimal carbon oxidation. The influence of γ- Al 2 O 3 concentration on CO 2 emissions is non-monotonic. Initial nanoparticle additions generally elevate CO 2 emissions, consistent with their catalytic role in facilitating complete combustion. However, at 30 ppm—especially at 3000 rpm— CO 2 emissions show a slight decline (falling below baseline E10 levels at this speed) or remain unchanged at 2000 rpm. This plateau or decrease at higher concentrations and speeds may be due to nanoparticle agglomeration, which can disrupt spray dynamics and atomization, leading to suboptimal fuel-air mixing and localized incomplete combustion. Additionally, excessive nanoparticle loadings could act as thermal barriers, subtly altering the flame structure and oxidation kinetics. At low nanoparticle concentrations (e.g., 10 ppm) and low engine speeds (e.g., 1500 rpm), CO 2 emissions generally increase compared to neat E10 fuel, indicating a positive, though not maximal, catalytic effect under these conditions. A γ-Al 2 O 3 nanoparticle concentration in the range of 10–20 ppm generally promotes higher CO 2 emissions by improving combustion efficiency, particularly at lower engine speeds where the potential for complete oxidation is greater. This optimal concentration range also aligns with improvements in engine performance and BSFC, especially at higher engine speeds, underscoring the overall advantage of nanoparticle-enhanced E10 combustion. The regression model for CO 2 emissions, incorporating engine speed and nanoparticle concentration, is presented in Eq. (6): CO₂ = 10.45 + 2.3 X₁ − 0.386 X₂ − 0.039 X₁X₂ − 0.061 X₁² − 0.202 X₂² − 0.047 X₁²X₂ + 0.041 X₁X₂² − 1.2 X₁³ − 0.305 X₂³ (6) 3.2.2. Carbon monoxide (CO) Figure 3 b illustrates the CO emission trends, which exhibit a complex, non-linear relationship with engine speed for baseline E10 fuel—driven by variations in combustion completeness, temperature, and turbulence across the operating range. The introduction of γ-Al 2 O 3 nanoparticles typically lowered CO emissions. This was achieved through an expansion of the reactive surface area and an enhancement of oxygen availability, which in turn promoted the oxidation of CO into CO₂, a critical step in improving air quality and reducing the environmental impact of the engine. Notably, 10 ppm γ-Al 2 O 3 consistently outperformed 20 ppm in reducing CO emissions at all tested speeds, with the most significant reduction—approximately 14.88% relative to baseline E10—observed at 3000 rpm. This superior performance at 10 ppm is likely due to optimal nanoparticle dispersion at this lower concentration, which maximizes the catalytic surface area for post-combustion CO oxidation without significant agglomeration or adverse physical effects. However, at 30 ppm, CO emissions paradoxically increased above baseline levels, particularly at high speeds, where increases exceeding 5% were observed. This rise is likely attributable to adverse effects at higher concentrations, such as nanoparticle agglomeration that impairs spray dynamics or the formation of thermal barriers, both of which reduce combustion efficiency [ 8 , 10 ]. γ-Al 2 O 3 nanoparticles effectively mitigate CO emissions within an optimal concentration range (typically 10–20 ppm), while excessive concentrations lead to increased emissions. These findings emphasize the importance of precise nanoparticle dosing to achieve both environmental and performance benefits. The regression model developed for CO emissions, incorporating engine speed and nanoparticle concentration as variables, is provided in Eq. (7): CO = 3.023 − 0.638 X₁ + 0.299 X₂ + 0.102 X₁X₂ − 0.017 X₁² + 0.385 X₂² − 0.025 X₁²X₂ + 0.25 X₁X₂² + 0.929 X₁³ − 0.253 X₂³ (7) 3.2.3. Hydrocarbons (HC) As illustrated in the 3D response surface plot (Fig. 3 c), HC emissions exhibit a pronounced inverse relationship with engine speed, decreasing significantly when the engine speed is advanced from 1500 to 3000 rpm. This decline is mainly due to the more complete combustion that occurs at elevated speeds, driven by elevated cylinder wall temperatures, intensified in-cylinder turbulence, and improved fuel vaporization and atomization. These factors collectively enhance air-fuel mixing and support efficient flame propagation, thereby minimizing the formation of unburned hydrocarbons. The E10 fuel blend amplifies this effect further. Due to its lower boiling point and higher octane rating compared to conventional gasoline [ 11 ], E10 enables deeper flame penetration into crevice volumes and quenching zones [ 12 ]. This characteristic improves overall combustion efficiency and contributes to additional reductions in HC emissions. The inclusion of γ-Al 2 O 3 nanoparticles adds a catalytic dimension to HC emission control. At concentrations of 10–20 ppm, these nanoparticles provide active surface sites that lower the activation energy required for hydrocarbon oxidation. This catalytic action facilitates the post-combustion breakdown of carbonaceous deposits and residual unburned fuel through localized temperature enhancement and intensified secondary oxidation processes [ 15 ]. Conversely, the response surface indicates that the highest HC emissions occur at low engine speeds (e.g., 1500 rpm) in the absence of nanoparticles. This outcome reflects incomplete combustion under these conditions, where limited turbulence, lower combustion chamber temperatures, and richer mixtures impede the effective oxidation of unburned hydrocarbons, resulting in elevated HC levels. The lowest HC emissions in this study were recorded at 3000 rpm with a 10 ppm γ-Al 2 O 3 concentration, representing a 6.57% reduction compared to baseline E10 at the same speed. However, exceeding a nanoparticle concentration of 20 ppm resulted in a rise in HC emissions, with increases reaching approximately 4.4% at 3000 rpm. This highlights that there is an ideal nanoparticle concentration for minimizing HC emissions, as excessive loading may induce adverse effects such as thermal insulation or impaired spray homogeneity, ultimately hindering complete combustion. The empirical model for HC emissions, which is a function of both engine speed and nanoparticle concentration, is provided in Eq. (8): HC = 164.883 − 19.252 X₁ + 3.127 X₂ + 1.263 X₁X₂ − 11.884 X₁² + 6.115 X₂² + 0.546 X₁²X₂ + 4.516 X₁X₂² − 5.623 X₁ ³ − 2.814 X₂³ (8) 3.2.4. Nitrogen oxides (NOₓ) The 3D response surface in Fig. 3 d shows that NOₓ emissions increase with engine speed across all nanoparticle concentrations, primarily due to higher combustion temperatures accelerating thermal NO x formation through the Zeldovich mechanism at temperatures exceeding 1600°C [ 34 ]. The highest NO x emissions were recorded at 3000 rpm with 20 ppm γ-Al 2 O 3 nanoparticles, representing a 17.08% increase over the E10 baseline and underscoring the dominant role of engine speed in NO x generation. γ-Al 2 O 3 nanoparticles generally improve combustion efficiency by increasing in-cylinder pressure and enhancing oxygen availability, both of which can raise peak temperatures and promote NO x formation [ 16 ]. For example, at 1500 rpm with 10 ppm nanoparticles, NO x emissions rose compared to baseline E10, reflecting conditions where catalytic effects enhance thermal NO x generation. Increasing the nanoparticle concentration from 20 to 30 ppm consistently reduced NO x emissions across all engine speeds, suggesting a shift in combustion and NO x formation mechanisms. This reduction is likely attributable to thermal insulation effects that lower peak flame temperatures, along with impaired fuel atomization due to increased viscosity or particle agglomeration, leading to slightly less complete combustion. Supporting evidence for this includes the observed increases in CO emissions and BSFC, as well as the decline in brake power at 30 ppm relative to 20 ppm. These findings highlight the dual role of γ-Al 2 O 3 nanoparticles: enhancing oxidation and peak temperatures at lower concentrations while modifying combustion dynamics and limiting temperature extremes at higher concentrations. This underscores the importance of identifying an optimal nanoparticle concentration range for effective emission control. The empirical model describing NO x emissions based on engine speed and nanoparticle concentration is provided in Eq. (9): NOₓ = 887.496 − 6.607 X₁ + 27.535 X₂ − 1.979 X₁X₂ + 7.234 X₁² − 68.421 X₂² + 21.114 X₁²X₂ − 17.285 X₁X₂² + 59.187 X₁³ − 0.0598 X₂³ (9) 3.3. Model adequacy evaluation The predictive capability of the regression models was assessed through a comparison of predicted values to the corresponding experimental measurements, as illustrated in Fig. 4 . A strong linear correlation was observed across all performance and emission responses—including BP, BT, BSFC, and exhaust emissions (CO 2 , CO, HC, NO x ). Coefficients of determination (R²) ranged from 0.99 to 1, indicating excellent agreement between the observed data and the model’s predicted values. These results confirm the adequacy and robustness of the response surface models in capturing the underlying behavior of the system and validating their reliability for optimization and prediction purposes. The validated regression equations provide robust tools for predicting engine responses under untested conditions. Their high R² values (> 0.9) and low CVs (< 3%) support their use in simulation-based fuel design, performance mapping, and emission trade-off analysis. As illustrated in Fig. 5, four key validation metrics—R², adjusted R², predicted R², and CV—were analyzed to assess model robustness. For all responses, both the R² and adjusted R² values were above 0.97, thereby confirming that the fitted models explain more than 97% of the variability in the experimental data. Additionally, a strong concordance was found between the predicted R² and the adjusted R², confirming the model’s generalization capability and predictive reliability beyond the training dataset. Furthermore, the CV for all models remained below 3%, reflecting a high degree of experimental precision and model stability. These validation results affirm the suitability of the developed models as reliable tools for predicting and optimizing trade-offs between engine performance and emissions, as well as for guiding the formulation of bioethanol–gasoline blends containing γ-Al 2 O 3 nanoparticles. Figure 5. Statistical performance metrics for the developed regression models, including R², adjusted R², predicted R², and CV for all performance and emission responses. High R² values (all > 0.9) and low CV values (< 3%) confirm strong model fitting, predictive accuracy, and experimental consistency across engine speed and nanoparticle concentration conditions. 3.4. Model validation and optimization The main goal of the optimization procedure was to identify the best settings for the key variables—engine speed and γ-Al2O3 nanoparticle concentration—that would maximize BP and BT, while simultaneously minimizing BSFC and emissions, including CO 2 , CO, HC, and NOₓ. In the multi-response optimization procedure, the relative importance of each target was weighted, with a higher priority assigned to minimizing NOₓ emissions due to their significant environmental impact. Table 8 outlines the optimization goals and the constraints imposed on each response. Table 9 summarizes the results of the optimization, listing six candidate solutions. Among these, the first solution typically exhibits the most favorable trade-off across all performance and emission criteria, as indicated by the lowest standard error. Table 8 Lower and upper bounds and target objectives for each response variable used in the multi-objective optimization process. Higher importance was assigned to NOx reduction due to its environmental impact. Name Goal Lower Upper Lower Upper Importance Limit Limit Weight Weight Engine speed is in range 1500 3000 1 1 3 Concentration of nanoparticles is in range 0 30 1 1 3 BT maximum 16.56 36.76 1 1 5 BP maximum 106.31 118.12 1 1 5 BSFC minimum 225.6 248.8 1 3 3 CO minimum 2.63 4.12 1 4 4 CO₂ minimum 8.8 11.6 1 2 2 HC minimum 128 182 1 4 4 NOₓ minimum 739 953 1 5 5 Table 9 Optimal operating conditions and predicted responses for engine performance and exhaust emissions determined using RSM. Among the obtained solutions, Solution 1 achieved the highest composite desirability, representing the best trade-off between performance enhancement and emission reduction. Solution X₁ X₂ BT BP BSFC CO₂ CO HC NOₓ Desirability 1 2471.879 5.88 115.485 29.946 243.96 10.864 2.885 159.228 843.082 0.134 (Selected) 2 2507.936 26.47 117.484 30.899 237.955 11.216 3.256 162.633 867.374 0.099 3 1586.82 7.72 109.973 18.182 231.955 8.990 2.907 176.14 823.395 0.074 4 1543.656 30 111.731 17.998 226.639 9.067 2.910 178.641 846.620 0.051 The predicted values associated with Solution 1 were chosen as the most favorable outcome, given that this solution yielded the highest desirability value. These results confirm the effectiveness of RSM in identifying balanced operating conditions that simultaneously enhance performance and mitigate emissions. The developed models are practically valuable, allowing users to predict performance and emission outcomes based on engine speed and nanoparticle concentration, thereby reducing reliance on extensive experimentation. Importantly, the ability of RSM to perform multi-objective optimization underscores its relevance for advancing clean-fuel innovations. 4. Conclusion This study demonstrated that doping E10 fuel with γ-Al 2 O 3 nanoparticles significantly enhances spark-ignition engine performance and emission characteristics through catalytic and thermophysical effects. At the optimal concentration of 20 ppm, BT and BP increased by 7.67% and 6.94%, respectively, while BSFC decreased by 5.95%. These improvements stem from enhanced atomization, higher in-cylinder pressures, and micro-explosion phenomena driven by the nanoparticles’ high surface reactivity and thermal conductivity. Emission analysis revealed that CO and HC were minimized at 10–20 ppm, reflecting improved oxidation kinetics, while CO 2 increased under these conditions, confirming more complete combustion. NO x emissions displayed a dual trend: rising to 20 ppm due to elevated flame temperatures, before declining to 30 ppm as thermal buffering and nanoparticle agglomeration became dominant. Using RSM, the optimal operating point was identified at 2471.87 rpm and a nanoparticle concentration of 5.88 ppm. The regression models demonstrated excellent predictive capability (R² >0.97, CV < 3%), enabling reliable multi-objective optimization of performance and emissions without the need for extensive experimental trials. These findings establish γ-Al 2 O 3 nanoparticles as a viable pathway for advancing thermo-combustion efficiency in ethanol–gasoline blends, supporting sustainable energy targets while mitigating harmful pollutants. Beyond its experimental contributions, this work provides new insights into the underexplored γ-phase of Al 2 O 3 nanoparticles and their role in SI engines. The combination of systematic concentration analysis and statistical modelling provides a robust framework for developing cleaner, more efficient fuel formulations. Future research should validate results under real-world driving conditions, and explore hybrid optimization approaches that integrate RSM with machine learning for adaptive fuel management. Abbreviations Nomenclature Nanoparticle Statistical and Modeling Parameters Symbol Description Symbol Description γ-Al 2 O 3 Gamma-Aluminum Oxide Y Response (dependent) variable Engine and Fuel Parameters Coded value of a variable for analysis BP Brake Power (kW) Actual (measured) value before coding BT Brake Torque (N.m) X 1 Coded value of engine speed BSFC Brake Specific Fuel Consumption (g/kW.hr) X 2 Coded value of nanoparticle concentration E10 Gasoline with 10% bioethanol by volume Sample mean; average of data points Pollutants Intercept (constant term in regression) CO Carbon Monoxide Emissions (ppm) Linear regression coefficients CO 2 Carbon Dioxide Emissions (ppm) Quadratic regression coefficients HC Hydrocarbon Emissions (ppm) Interaction regression coefficients NO x Nitrogen Oxides Emissions (ppm) R² Coefficient of determination Abbreviations Adj. R² Adjusted coefficient of determination SI Spark Ignition Pred. R² Predicted coefficient of determination rpm Revolutions Per Minute ANOVA Analysis of Variance ppm Parts per million CV Coefficient of variation (%) RSM Response Surface Methodology d Absolute difference or standardized effect size NP Nanoparticles D-criterion Design criterion maximizing determinant of information matrix Subscripts SD Standard Deviation i Index of independent variables RSS root sum of squares j Index of independent variables (distinct from i) GUM Guide to the Expression of Uncertainty in Measurement Declarations Author Contribution R.E.B., M.N.K., F.O., and K.M. contributed to the conceptualization, methodology, validation, and writing of the review and editing of the manuscript. R.E.B. and M.N.K. performed the formal analysis, investigation, data curation, and visualization. R.E.B. also handled the software and project administration. F.O. and K.M. provided resources and supervision. F.O. and K.M. were responsible for the project administration. R.E.B. prepared the original draft. All authors read, reviewed, and approved the final manuscript. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Availability The datasets generated and analysed during the current study are fully included within the published article, specifically in Table 5. All data necessary to support the findings of this study are available within the article. Any further inquiries concerning data availability should be directed to the corresponding author, Dr Ramtin Elkaei Behjati. References Tefera, N. T., Nallamothu, R. B. & Alemayehu, G. Analysis of RCCI engine characteristics with n-butanol/gasoline as low reactive fuel and biodiesel blend as high reactive fuel. Sci. Rep. 15 , 26023 (2025). Shaik, F. et al. Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM. Sci. Rep. 15 , 4585 (2025). Yilmaz, E. et al. Investigation of intake air temperature effect on co-combustion characteristics of NH3/gasoline in naturally aspirated high compression ratio engine with sub-chamber. Sci. Rep. 13 , 11649 (2023). Chen, C. W., Yu, W. S., Zheng, Z. X., Cheng, Y. S. & Li, S. Y. Waste valorization through acetone-butanol-ethanol (ABE) fermentation. J. Taiwan. Inst. Chem. Eng. 160 , 105280 (2024). Cesur, I. Effects of water injection strategies on performance and exhaust emissions in an ethanol fueled gasoline engine. Therm. Sci. Eng. Prog . 34 , 101397 (2022). Al-Hasan, M. Effect of ethanol-unleaded gasoline blends on engine performance and exhaust emission. Energy Convers. Manag . 44 , 1547–1561 (2003). Elshenawy, A. A., Razik, A., Gad, M. S. & S. M. & Modeling of combustion and emissions behavior on the effect of ethanol–gasoline blends in a four stroke SI engine. Adv. Mech. Eng. 15 , 1–16 (2023). Ye, Y. et al. Effect of Different Ratios of Gasoline-Ethanol Blend Fuels on Combustion Enhancement and Emission Reduction in Electronic Fuel Injection Engine. Polymers (Basel) 15 , (2023). Walle, M., Yeneneh, K. & Sufe, G. Investigation of spark ignition engine performance in ethanol–petrol blended fuels using artificial neural network. Sci. Rep. 15 , 25516 (2025). Balat, M. & Balat, H. Recent trends in global production and utilization of bio-ethanol fuel. Appl. Energy . 86 , 2273–2282 (2009). Yamada, H., Inomata, S., Tanimoto, H., Hata, H. & Tonokura, K. Estimation of refueling emissions based on theoretical model and effects of E10 fuel on refueling and evaporative emissions from gasoline cars. Sci. Total Environ. 622–623 , 467–473 (2018). Iodice, P., Amoresano, A. & Langella, G. A review on the effects of ethanol/gasoline fuel blends on NOX emissions in spark-ignition engines. Biofuel Res. J. 8 , 1465–1480 (2021). Goyal, R., Pathak, V. K. & Sonawane, C. Optimization of SiO2 based water–diesel emulsified fuel for engine performance and emission characteristics using soft computing approaches. Sci. Rep. 15 , 31685 (2025). Allasi, H. L., Selvam, A. J., Soosaimariyan, M. V. & Arul, S. J. Enhancing diesel engine efficiency with waste cooking oil biodiesel and nano additives for sustainable fuel applications. Sci. Rep. 15 , 31762 (2025). Zamankhan, F., Pirouzfar, V., Ommi, F. & Valihesari, M. Investigating the effect of MgO and CeO2 metal nanoparticle on the gasoline fuel properties: empirical modeling and process optimization by surface methodology. Environ. Sci. Pollut Res. 25 , 22889–22902 (2018). Amirabedi, M., Jafarmadar, S. & Khalilarya, S. Experimental investigation the effect of Mn2O3 nanoparticle on the performance and emission of SI gasoline fueled with mixture of ethanol and gasoline. Appl. Therm. Eng. 149 , 512–519 (2019). Valihesari, M., Pirouzfar, V., Ommi, F. & Zamankhan, F. Investigating the effect of Fe2O3 and TiO2 nanoparticle and engine variables on the gasoline engine performance through statistical analysis. Fuel 254 , 115618 (2019). Manigandan, S. et al. Effect of addition of hydrogen and TiO2 in gasoline engine in various exhaust gas recirculation ratio. Int. J. Hydrogen Energy . 44 , 11205–11218 (2019). Taghavifar, H., Kaleji, B. K. & Kheyrollahi, J. Application of composite TNA nanoparticle with bio-ethanol blend on gasoline fueled SI engine at different lambda ratios. Fuel 277 , 118218 (2020). Pirouzfar, V., Hakami, M., Hassanpour zonoozi, M. & Su, C. H. Improving the performance of gasoline fuels by adding methanol and methyl tertiary-butyl ether along with metal oxides titanium oxide and magnesium oxide. Energy 294 , 130687 (2024). Karimi Abiyazani, N., Pirouzfar, V. & Su, C. H. Enhancing engine power and torque and reducing exhaust emissions of blended fuels derived from gasoline-propanol-nano particles. Energy 241 , 122924 (2022). Nguyen, D. D., Moghaddam, H., Pirouzfar, V., Fayyazbakhsh, A. & Su, C. H. Improving the gasoline properties by blending butanol-Al2O3 to optimize the engine performance and reduce air pollution. Energy 218 , 119442 (2021). Fayyazbakhsh, A. & Pirouzfar, V. Investigating the influence of additives-fuel on diesel engine performance and emissions: Analytical modeling and experimental validation. Fuel 171 , 167–177 (2016). Rossi, S. et al. Exposure to nanoparticles derived from diesel particulate filter equipped engine increases vulnerability to arrhythmia in rat hearts. Environ. Pollut . 284 , 117163 (2021). Fu, J. et al. Emission characteristic, spatial distribution, and health risk of polycyclic aromatic compounds (PAHs, NPAHs, and OPAHs) from light-duty gasoline and diesel vehicles based on on-road measurements. Sci. Total Environ. 941 , 173390 (2024). Yang, J., Wang, H., Zhao, X., Li, Y. L. & Fan, W. L. Correlating the surface structure and hydration of a γ-Al2O3 support with the Run (n = 1–4) cluster adsorption behavior: A density functional theory study. RSC Adv. 6 , 40459–40473 (2016). Atkinson, A. C., Donev, A. N. & Tobias, R. D. Optimum Experimental Designs, with SAS. Optim. Exp. Des. SAS . 10.1093/oso/9780199296590.001.0001 (2007). Ba-Abbad, M. M., Kadhum, A. A. H., Mohamad, A. B., Takriff, M. S. & Sopian, K. Optimization of process parameters using D-optimal design for synthesis of ZnO nanoparticles via sol-gel technique. J. Ind. Eng. Chem. 19 , 99–105 (2013). Xu, W., Wong, W. K., Tan, K. C. & Xu, J. X. Finding high-dimensional D-optimal designs for logistic models via differential evolution. IEEE Access. 7 , 7133–7146 (2019). Sergis, V. & Ouellet-Plamondon, C. M. D-optimal design of experiments applied to 3D high-performance concrete printing mix design. Mater. Des. 218 , 110681 (2022). Chaichan, M. T. Combustion and emission characteristics of E85 and diesel blend in conventional diesel engine operating in PPCI mode. Therm. Sci. Eng. Prog . 7 , 45–53 (2018). He, W., Li, S. & Huang, X. Critical concentration effects of nanoparticle on combustion involved into secondary atomization and micro-explosion for B/JP-10/SP-80 nanofluid fuel. Fuel 358 , 130262 (2024). Venu, H. & Madhavan, V. Effect of Al2O3 nanoparticles in biodiesel-diesel-ethanol blends at various injection strategies: Performance, combustion and emission characteristics. Fuel 186 , 176–189 (2016). Amiri, M., Elkaei Behjati, R., Farmahini Farahani, H. & Mazaheri, K. Experimental study on the effect of cone-shaped bluff body on lean premixed flames in a swirl burner. J. Therm. Anal. Calorim. 149 , 699–710 (2024). Additional Declarations No competing interests reported. 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09:51:31","extension":"html","order_by":53,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":270205,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7703508/v1/5c32dcd12fa71b6d305b4ac5.html"},{"id":96934282,"identity":"5b0983b0-6a2c-4d54-b866-0fa06719282b","added_by":"auto","created_at":"2025-11-27 16:03:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":485339,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental setup used in the study: (a) Prepared bioethanol samples containing γ-Al₂O₃ nanoparticles at concentrations of 10, 20, and 30 ppm; (b) Fuel tank (c) Fuel flow measurments; (d) XU7JP/L3 spark ignition engine; (e) Dynamometer; (f) CAP3200 exhaust gas analyzer (Capelec), equipped with NDIR and electrochemical sensors (g) Control panel; (h) Computer and interface software for system operation and data acquisition.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7703508/v1/a1bd5e08187bde6580ec2561.png"},{"id":97135528,"identity":"4e34d573-60a9-4f49-a0fd-c5ada15f2e7b","added_by":"auto","created_at":"2025-12-01 09:50:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":517521,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface plots showing the combined influence of engine speed and γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticle concentration on (a) BT, (b) BP, and (c) BSFC. The surfaces identify optimal operating regions, with maximum BT and BP and minimum BSFC occurring at approximately 20 ppm nanoparticle concentration and moderate engine speeds. These results highlight the critical role of interaction effects between engine speed and nanoparticle dosage in determining overall engine performance.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7703508/v1/da5d1c19ee969a1d538fa7ac.png"},{"id":97135478,"identity":"697ea849-dd2f-4186-b254-14281000245d","added_by":"auto","created_at":"2025-12-01 09:49:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":519630,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface plots showing the combined effects of engine speed and γ-Al₂O₃ nanoparticle concentration on exhaust emissions: (a) CO\u003csub\u003e2\u003c/sub\u003e, (b) CO, (c) HC, and (d) NO\u003csub\u003ex\u003c/sub\u003e. The surfaces reveal nonlinear, interaction-driven trends. Optimal nanoparticle concentrations (≈10–20 ppm) and moderate engine speeds minimized CO and HC emissions, accompanied by increased CO\u003csub\u003e2\u003c/sub\u003e levels consistent with more complete combustion. NO\u003csub\u003ex\u003c/sub\u003e emissions rose with engine speed and displayed a nonlinear dependence on nanoparticle concentration, peaking at intermediate loadings before declining at higher concentrations.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7703508/v1/c9e3bbbda3b7285641647b58.png"},{"id":96934286,"identity":"8dcad5e8-378e-4271-91e7-07b5f7796773","added_by":"auto","created_at":"2025-11-27 16:03:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":284896,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted versus actual value plots for the developed regression models: (a) BT, (b) BP, (c) BSFC, (d) CO\u003csub\u003e2\u003c/sub\u003e, (e) CO, (f) HC, and (g) NO\u003csub\u003ex\u003c/sub\u003e emissions. The strong linear alignment across all responses indicates excellent agreement between model predictions and experimental observations, supporting the models’ accuracy and predictive capability for nanoparticle-enhanced fuel blends.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7703508/v1/e1041e1005b583410d754dad.png"},{"id":96934283,"identity":"943bb319-ea71-418d-b2f6-bdd454e6f874","added_by":"auto","created_at":"2025-11-27 16:03:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":93424,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical performance metrics for the developed regression models, including R², adjusted R², predicted R², and CV for all performance and emission responses. High R² values (all \u0026gt;0.9) and low CV values (\u0026lt;3%) confirm strong model fitting, predictive accuracy, and experimental consistency across engine speed and nanoparticle concentration conditions.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7703508/v1/1f58eb157ab9ef4be28e4cc8.png"},{"id":97252443,"identity":"4ac635e4-b643-4e6a-be25-7db61e85c3c4","added_by":"auto","created_at":"2025-12-02 13:21:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3305353,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7703508/v1/86994a82-1227-49c2-833c-bcaeda006bfe.pdf"},{"id":97136715,"identity":"6b0b59a9-5064-431e-9c28-64e523ec13c5","added_by":"auto","created_at":"2025-12-01 09:56:55","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":14967,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.1A.2A.3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7703508/v1/eed18eb240de6455e3e08549.docx"},{"id":97136472,"identity":"7508e1cb-f3c0-4912-95bf-d01843f48b8e","added_by":"auto","created_at":"2025-12-01 09:56:37","extension":"jpg","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":1195214,"visible":true,"origin":"","legend":"","description":"","filename":"AbstractGraphic.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7703508/v1/2e3b94bb8283d1caa37f0c9e.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePerformance and Emission Multi-Objective Optimization of γ-Al\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e Nanoparticle-Enhanced E10 Bioethanol–Gasoline Combustion in Spark-Ignition Engines\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003e\u0026gamma;-Al₂O₃ nanoparticles improved E10 fuel performance in SI engines and lowered CO, HC.\u003c/li\u003e\n \u003cli\u003eIncreased CO₂ emissions indicated more complete combustion despite higher NOₓ.\u003c/li\u003e\n \u003cli\u003eOptimal 20 ppm concentration enhanced brake torque (+7.67%) and brake power (+6.94%).\u003c/li\u003e\n \u003cli\u003eBrake specific fuel consumption decreased by 5.95% at the optimal nanoparticle level.\u003c/li\u003e\n \u003cli\u003eRSM models (R\u0026sup2; \u0026gt; 0.97) enabled multi-objective optimization of performance and emissions.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eThe global demand for sustainable energy has accelerated research into clean and efficient alternatives to conventional fossil fuels. Spark-ignition (SI) engines, which are still predominantly fueled by gasoline, remain a significant source of greenhouse gas emissions. Accordingly, improved fuel formulations that enhance combustion efficiency while reducing harmful exhaust emissions are of growing scientific and industrial interest [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAmong liquid biofuels, ethanol is widely recognized as a renewable, oxygenated additive that improves combustion efficiency and reduces pollutant formation [\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Ethanol–gasoline blends containing 3–10% ethanol are now mandated or encouraged in many countries [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In particular, E10 (10% ethanol, 90% gasoline) has demonstrated higher octane ratings, improved oxygen content, and reduced regulated emissions compared with E5 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, ethanol levels above 10% often reduce brake thermal efficiency due to ethanol’s lower heating value, leading to increased fuel consumption and diminished engine performance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite drawbacks such as higher Reid vapor pressure [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], E10 is considered the most practical compromise between efficiency and compatibility, without requiring significant engine modifications [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent research has explored the addition of nanoparticles as fuel additives further to improve the thermo-physical and catalytic properties of biofuel blends [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Nanoparticles such as MgO, CeO₂, Mn₂O₃, Fe₂O₃, and TiO₂ have been reported to improve combustion efficiency, reduce unburned hydrocarbons and CO emissions, and in some cases enhance power output [\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, most prior studies suffer from two limitations: (i) they focused on limited nanoparticle–fuel combinations (often gasoline with butanol, methanol, or higher ethanol blends), and (ii) they did not systematically optimize nanoparticle concentration across a range of engine operating conditions. Without rigorous optimization, the comparative significance of the results is difficult to establish.\u003c/p\u003e\u003cp\u003eFor example, Zamakhan et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] studied MgO and CeO\u003csub\u003e2\u003c/sub\u003e nanoparticles in E10, reporting improved performance but without concentration optimization. Amirabadi et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] found that Mn₂O₃ nanoparticles improved brake power by nearly 20% but at the expense of increased CO₂ emissions. Valihesari et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] examined Fe₂O₃ and TiO₂ in methanol–gasoline blends, while Taghayifar et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] investigated TiO₂-based nanocomposites in E20. Other studies incorporated hydrogen, exhaust gas recirculation, or oxygenates such as MTBE alongside nanoparticles [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], complicating direct comparison. Although some works employed RSM to identify optimal concentrations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], most focused on different fuels (butanol, methanol, or propanol) and on non-γ phases of Al₂O₃.\u003c/p\u003e\u003cp\u003eAlthough Al₂O₃ has been studied in different alcohol–gasoline blends [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], the specific γ-phase remains underexplored. The γ-phase of Al₂O₃ offers unique advantages, including high surface area, superior thermal stability, and moisture adsorption capacity, which make it especially suitable for stabilizing ethanol-containing fuels and promoting efficient combustion. At the same time, excessive nanoparticle concentrations may induce agglomeration, raise viscosity, and pose health or environmental concerns [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e–\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], underlining the need to identify precise optimal levels.\u003c/p\u003e\u003cp\u003eIn this context, the present study systematically investigates the effect of low concentrations of γ-Al₂O₃ nanoparticles (10, 20, and 30 ppm) in E10 on engine performance and emissions. Using a D-optimal design under Response Surface Methodology (RSM), the combined influence of nanoparticle concentration and engine speed (1500, 2000, 2500, and 3000 rpm) was analyzed for brake torque (BT), brake power (BP), brake-specific fuel consumption (BSFC), and regulated emissions (CO₂, CO, HC, and NOₓ). The objective is to develop robust predictive models and identify optimal trade-offs between efficiency gains and emission reductions. By focusing on the underexplored γ-phase of Al₂O₃, systematically varying concentrations across multiple engine speeds, and applying a rigorous optimization framework, this study provides new insights into nanoparticle-assisted biofuel combustion and establishes a methodology for clean fuel innovation.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Materials and Sample Preparation","content":"\u003cp\u003eThe gasoline used in this study represents commercially available fuel commonly distributed at local petrol stations, ensuring both scientific relevance and industrial applicability, and bioethanol with a volumetric purity of 99.8% was also used. The key physicochemical properties of gasoline and bioethanol are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles (purity: 99.5%) employed in this work were selected on account of their high surface area, robust thermal stability, and capacity for moisture adsorption (Table\u0026nbsp;2). These properties make γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e particularly suitable for stabilizing ethanol-containing fuels, as they help mitigate degradation phenomena typically associated with ethanol blends. In addition, their catalytic activity and heat transfer characteristics enhance combustion efficiency under practical engine operating conditions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe physical and chemical characteristics of the gasoline and bioethanol used in this study.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecifications\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGasoline\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBioethanol\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemical symbol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC\u003csub\u003ex\u003c/sub\u003eH\u003csub\u003ey\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003csub\u003e2\u003c/sub\u003eH\u003csub\u003e5\u003c/sub\u003eOH\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg/mol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDensity at 16°C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e747.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e785\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecific Gravity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg/ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOctane Index (MON)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOctane Index (RON)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOctane Index (AKI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlash point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e̊C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower heating value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMJ/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLatent heat of vaporization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekJ/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e854\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eTable\u0026nbsp;2\u003c/p\u003e\u003cp\u003ePhysicochemical properties of γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecifications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eValues\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003estructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eGamma (γ)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003ePowder\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage particle size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMorphology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eAlmost spherical\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e3.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecific surface area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003em\u003csup\u003e2\u003c/sup\u003e/g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e85.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emelting point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e̊C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eboiling point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e̊C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2980\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003e2.2. Engine and Experimental Set-up\u003c/h2\u003e\u003cp\u003eThe experiment was carried out on a spark-ignition (SI) engine (model XU7JP/L3) with a displacement of 1,761 cm³, a compression ratio of 9.25:1, four cylinders, and eight poppet valves. The engine is rated for a maximum power output of 74.57 kW and a peak torque of 153 N.m.\u003c/p\u003e\u003cp\u003eTo maintain stable operation, a cooling tower was incorporated into the system, and an oil temperature sensor was installed to ensure that the lubricant temperature remained below 95°C. Engine torque and power were measured using a 130 kW eddy current dynamometer, which was mechanically coupled to the engine. The dynamometer system was controlled through a panel and computer interface, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFuel consumption was monitored using a precision system that included a fuel pump, a return fuel cooling fan, and a high-accuracy fuel flow sensor positioned in the lower section of the fuel tank. This system is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Exhaust gas emissions were assessed with a CAP 3200 gas analyser (Capelec), equipped with a non-dispersive infrared (NDIR) sensor for quantifying CO\u003csub\u003e2\u003c/sub\u003e, CO, and HC. NO\u003csub\u003ex\u003c/sub\u003e emissions were determined using electrochemical detection methods.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003e2.3. Fuel Preparation and Experimental Procedure\u003c/h2\u003e\u003cp\u003eAll experimental procedures were performed under ambient laboratory conditions at approximately 25°C. The base fuel blend was prepared by combining 10% (v/v) bioethanol with 90% commercial gasoline. To ensure accurate dosing, the displacement caused by solid nanoparticles was taken into account when preparing each batch. Fuel blends were prepared in 1 L volumes due to limitations of the ultrasonic homogenizer.\u003c/p\u003e\u003cp\u003eγ- Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles were incorporated at concentrations of 10, 20, and 30 ppm, corresponding to 10, 20, and 30 mg of nanoparticles per liter of fuel, respectively. The preparation process began by ultrasonically dispersing γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e into the bioethanol fraction using a probe ultrasonic homogenizer (Bandelin SONOPULS HD 3200). The stability of the nanoparticle–bioethanol suspension was confirmed before blending with gasoline. Homogenization of the complete mixture was homogenized with a magnetic stirrer (IKA C-MAG HS 7), which ensured a uniform distribution of nanoparticles. The resulting blends were stored for 24 hours under static conditions and visually inspected for sedimentation. No visible sedimentation was observed, confirming adequate colloidal stability of the nanoparticle-enhanced fuels.\u003c/p\u003e\u003cp\u003eFor engine testing, the prepared batches were combined to achieve the total required fuel volume. Trials were conducted under wide-open throttle conditions at four engine speeds: 1,500, 2,000, 2,500, and 3,000 rpm. After steady-state operation was achieved, key performance parameters—BT, BP, and BSFC—along with exhaust gas emissions, were recorded for subsequent analysis.\u003c/p\u003e\u003ch2\u003e2.4. Statistical analysis and model construction\u003c/h2\u003e\u003cp\u003eA D-optimal experimental design was created with the aid of Design-Expert® software (version 11.0). This design strategy was selected for its ability to maximize information gained from a limited number of experimental runs due to a minimized determinant of the covariance matrix of estimated parameters (D-criterion). Compared to conventional factorial or central composite designs, the D-optimal approach provides more precise parameter estimates while reducing the total number of required experiments [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It is particularly suitable for irregular experimental domains and bounded factor ranges, making it well aligned with this study, where the independent variables—engine speed and nanoparticle concentration—were constrained within specific limits [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Thus, the design enabled significant reductions in experimental time and cost while maintaining high model accuracy and predictive reliability [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo facilitate regression analysis, the actual experimental variables (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{X}}_{\\text{a}\\text{c}\\text{t}\\text{u}\\text{a}\\text{l}}\\)\u003c/span\u003e\u003c/span\u003e) were transformed into dimensionless coded values (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{X}}_{\\text{C}\\text{o}\\text{d}\\text{e}\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e) according to Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{X}_{Coded}\\:=\\:\\frac{{X}_{actual}-\\stackrel{-}{X}}{d}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere \u003cem\u003ex̅\u003c/em\u003e denotes the average of the actual variable, and \u003cem\u003ed\u003c/em\u003e is defined as the step size (i.e., the absolute difference between levels). The response variable \u003cem\u003eY\u003c/em\u003e was modeled as a function of the coded variables using multiple regression and the least squares method, which was based on the second-order polynomial shown in Eq.\u0026nbsp;(2):\u003c/p\u003e\u003cp\u003e\u003cem\u003eY\u003c/em\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e+\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\sum\\:_{i=1}^{n}{\\beta\\:}_{i}{X}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=1}^{n-1}{\\sum\\:}_{j=i+1}^{n}{\\beta\\:}_{ij}{X}_{i}{X}_{j}\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=1}^{n}{\\beta\\:}_{ii}{{X}_{i}}^{2}\\)\u003c/span\u003e\u003c/span\u003e(2)\u003c/p\u003e\u003cp\u003eIn this equation, \u003cem\u003eY\u003c/em\u003e represents the predicted response, \u003cem\u003en\u003c/em\u003e denotes the number of factors, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei​\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​\u003c/em\u003e are the independent (coded) variables, while the coefficients (\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e​, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e​, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003eii\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​\u003c/em\u003e, and \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​\u003c/em\u003e) correspond to the intercept, linear, quadratic, and interaction effects, respectively. In this study, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e correspond to engine speed (rpm) and nanoparticle concentration (ppm).\u003c/p\u003e\u003cp\u003eThe adequacy of the regression models was evaluated using statistical indicators, including p-values (with model terms considered significant at p \u0026lt; 0.05), lack-of-fit tests to verify model assumptions, and diagnostic metrics such as the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), adjusted R², predicted R², and the coefficient of variation (CV). These statistical measures were applied to select the most appropriate polynomial models and validate their predictive reliability. The factor levels and variable coding applied in the design are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe independent variables used for response surface modeling, showing both their actual and coded values.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eLevel\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eActual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoded\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEngine speed (rpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e− 0.333\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003econcentration of nanoparticles (ppm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e− 0.333\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003e2.5. Error analysis\u003c/h2\u003e\u003cp\u003eAn uncertainty analysis was carried out to verify the accuracy and reliability of the experimental findings. This process quantifies the potential deviation between measured and actual values by considering both instrument precision and measurement resolution. The measurement accuracies and associated uncertainties for all parameters are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, based on the manufacturers’ specifications. The measurement accuracies and associated uncertainties for all parameters are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, according to the manufacturers’ specifications. Each directly measured parameter was recorded in triplicate to assess repeatability. The standard deviation (SD) of these repeated measurements was incorporated into the combined uncertainty, determined via the root sum of squares (RSS) method. This approach integrates both the instrument accuracy and the variability of repeated tests.\u003c/p\u003e\u003cp\u003eFor derived parameters such as BT, BP, and BSFC, uncertainties were calculated by propagating the input measurement errors according to the Guide to the Expression of Uncertainty in Measurement (GUM) framework. For example, the uncertainty in BSFC was determined by propagating the uncertainties in the fuel flow rate and brake power. Full details of the uncertainty propagation procedure are provided in Appendix A.1, A.2, and A.3.\u003c/p\u003e\u003cp\u003eThe final dataset, incorporating these uncertainty estimates, was processed using Design-Expert® software to develop regression models and identify the optimal operating conditions of the nanoparticle-enhanced fuel blends.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasurement parameters, their operating ranges, instrument accuracies, and associated percentage uncertainties used in the experimental setup.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasurements\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOperating Range\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInstrument Accuracy \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(\\mp\\:\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRelative Uncertainty (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEngine speed (rpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0-7000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEngine load (N.m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0-600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFuel flow measurement (g/hr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1000–20000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime (s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature (°C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO (% vol.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0–15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO₂ (% vol.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0–20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHC (ppm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0-10000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNOₓ (ppm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0-5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBT (N.m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBP (kW)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBSFC (g/kW.hr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eBefore each experimental run, the fuel system was thoroughly flushed to remove any residual blend from previous tests and eliminate the risk of cross-contamination. All experiments were conducted in triplicate under identical operating conditions, and the reported values in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e represent the arithmetic mean of the three trials.\u003c/p\u003e\u003cp\u003eThe experimental dataset was analyzed using Design-Expert software to develop regression models that describe how engine speed and nanoparticle concentration affect performance and emissions. These models were subsequently employed to identify the optimal operating conditions for the γ-Al2O3\u0026ndash;E10 blends. The following subsections present the statistical evaluation of model adequacy, analysis of variance (ANOVA), and the detailed discussion of engine performance and emission characteristics.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExperimental conditions and corresponding results for engine performance and exhaust emissions. Each value represents the average of three repeated trials conducted under steady-state operation at wide-open throttle.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c3\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c10\" namest=\"c4\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eEngine Performance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003eEmissions\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpeed (rpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConcentration (ppm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBT\u003c/p\u003e\u003cp\u003e(N.m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBP\u003c/p\u003e\u003cp\u003e(kW)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBSFC\u003c/p\u003e\u003cp\u003e(g/kW.hr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e (%Vol.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCO\u003c/p\u003e\u003cp\u003e(%Vol.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHC (ppm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNO\u003csub\u003ex\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e(ppm)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e232.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e739\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e231.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e743\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e248.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e814\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd 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colname=\"c4\"\u003e\u003cp\u003e115.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e242.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e922\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c4\"\u003e\u003cp\u003e118.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e236.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e889\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e117.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e234.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e953\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e226.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e843\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e841\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e233.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e857\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e116.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e241.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e840\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e238.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e911\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e238.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e907\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. The engine performance results\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1. Brake Torque (BT)\u003c/h2\u003e\u003cp\u003eThe response surface equation for BT, obtained through regression analysis of the experimental dataset, is expressed in Eq.\u0026nbsp;(3):\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBT\u003c/em\u003e\u0026thinsp;=\u0026thinsp;115.7\u0026thinsp;+\u0026thinsp;8.735 \u003cem\u003eX₁\u003c/em\u003e + 1.218 \u003cem\u003eX₂\u003c/em\u003e \u0026minus; 0.031 \u003cem\u003eX₁X₂\u003c/em\u003e \u0026minus; 1.113 \u003cem\u003eX₁\u0026sup2;\u003c/em\u003e \u0026minus; 3.782 \u003cem\u003eX₂\u0026sup2;\u003c/em\u003e + 0.323 \u003cem\u003eX₁\u0026sup2;X₂\u003c/em\u003e \u0026minus; 0.806 \u003cem\u003eX₁X₂\u0026sup2;\u003c/em\u003e \u0026minus; 6.462 \u003cem\u003eX₁\u0026sup3;\u003c/em\u003e + 1.333 \u003cem\u003eX₂\u0026sup3;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eX₁\u003c/em\u003e and \u003cem\u003eX₂\u003c/em\u003e represent the coded variables for engine speed and Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticle concentration, respectively.\u003c/p\u003e\u003cp\u003eThe regression coefficients indicate that BT decreases with increasing engine speed (negative coefficient for \u003cem\u003eX₁\u003c/em\u003e), whereas nanoparticle addition initially enhances BT (positive coefficient for \u003cem\u003eX₂\u003c/em\u003e). The presence of quadratic, cubic, and interaction terms confirms the nonlinear relationship between these parameters, consistent with the observed performance trends.\u003c/p\u003e\u003cp\u003eThe three-dimensional response surface plot in Fig.\u0026nbsp;2a illustrates this interaction. BT exhibits a distinct maximum at an engine speed of approximately 2500 rpm and a nanoparticle concentration near 20 ppm. At this point, torque for both the baseline E10 fuel and its nanoparticle-enhanced blends reaches a peak before declining at higher speeds and concentrations. This peak reflects the balance between improved combustion and volumetric efficiency at moderate speeds versus reduced cylinder filling time, increased friction, and shorter combustion duration at elevated speeds. The results demonstrate that γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles enhance BT up to an optimal concentration (\u0026asymp;\u0026thinsp;20 ppm), beyond which performance declines, reinforcing the importance of careful dosage optimization.\u003c/p\u003e\u003cp\u003eExamination of the nanoparticle concentration axis in Fig.\u0026nbsp;2a shows that the addition of γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles consistently enhanced BT across all engine speeds. Torque increased progressively from the baseline E10 fuel to 10 ppm and further to 20 ppm, reaching a maximum of 118.12 N\u0026middot;m at 2500 rpm, which corresponds to a relative increase of 7.67%. At 30 ppm, however, BT exhibited a slight decline or plateau, indicating that the optimal nanoparticle concentration was approximately 20 ppm. The decrease at higher concentrations can be attributed to nanoparticle agglomeration or an increase in fuel viscosity, both of which can impair atomization and reduce combustion efficiency.\u003c/p\u003e\u003cp\u003eSeveral mechanisms can explain the observed improvements in torque. First, Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles likely act as combustion catalysts, accelerating oxidation reactions and increasing in-cylinder pressures, thereby promoting higher torque generation. Second, their high thermal conductivity enhances heat transfer within the combustion chamber, contributing to improved thermal efficiency. Ultimately, the formation of tribological boundary layers on engine surfaces may reduce internal friction and parasitic losses, resulting in more efficient power delivery.\u003c/p\u003e\u003cp\u003eThese findings are consistent with previous investigations that have reported improved performance in nanoparticle-fuel blends [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The statistical robustness of the regression analysis was confirmed by the model selection criteria summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The estimated parameters and goodness-of-fit metrics obtained via the least-squares method are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The same modeling framework was subsequently applied to other engine performance parameters, ensuring consistency and reliability across the analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of variance (ANOVA) results for the response surface model predicting BT as a function of engine speed and \u0026thinsp;\u0026minus;\u0026thinsp;Al2O3 ​nanoparticle concentration.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eγ\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum of squares\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDegree of freedom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean vs. total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.276E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.276E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLinear vs. mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2FI vs. linear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuadratic vs. 2FI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCubic vs. quadratic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e240.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001 (Suggested)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartic vs Cubic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1465\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.278E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12657.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLeast-squares regression coefficients and statistical parameters for the response surface model of BT as a function of engine speed and -Al2O3 nanoparticle concentration.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eγ\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum of squares\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDegree of freedom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e213.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e968.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001 (significant)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX₁\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1584.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX₂\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX₁X₂\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5939\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX₁\u0026sup2;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e160.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX₂\u0026sup2;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1855.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX₁\u0026sup2;X₂\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0177\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX₁X₂\u0026sup2;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX₁\u0026sup3;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e908.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eX₂\u0026sup3;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCor Total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e214.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2. Brake Power (BP)\u003c/h2\u003e\u003cp\u003eBioethanol has a higher latent heat of vaporization than gasoline, which typically lowers combustion chamber temperatures and can reduce thermal efficiency in spark-ignition engines [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The addition of γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles helps counteract this drawback through several synergistic mechanisms. First, γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles adsorb residual moisture in bioethanol, mitigating its adverse effects on combustion stability and reducing the risk of cylinder corrosion. Second, the nanoparticles promote micro-explosions within the combustion chamber, which enhance localized ignition and accelerate flame propagation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Third, they lower fuel viscosity, producing finer droplets and improving atomization [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which in turn promotes more complete combustion and higher energy release.\u003c/p\u003e\u003cp\u003eThe experimental results confirm these effects. As illustrated in Fig.\u0026nbsp;2b, the baseline E10 fuel (10% bioethanol without nanoparticles) delivered a maximum BP of 34.36 kW at 3000 rpm. With the addition of γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles at the optimal concentration of 20 ppm, BP increased to 36.76 kW, representing a 6.94% improvement. At concentrations above 20 ppm, BP declined slightly, suggesting that excessive nanoparticle loading may promote agglomeration or alter flame characteristics, thereby limiting combustion efficiency.\u003c/p\u003e\u003cp\u003eAcross all tested conditions, BP showed a strong positive correlation with engine speed, primarily due to enhanced turbulence and air\u0026ndash;fuel mixing at higher rotational speeds. The influence of γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles was more pronounced at higher speeds, where the benefits of improved atomization and micro-explosion effects were maximized. At lower engine speeds, these enhancements were less significant. This highlights the importance of the interactive effects of engine speed and nanoparticle concentration in determining brake power. The observed trends were accurately captured by the cubic polynomial regression model expressed in Eq.\u0026nbsp;(4), which incorporates both nonlinear and interaction terms of the independent variables.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27.367\u0026thinsp;+\u0026thinsp;11.228 \u003cem\u003eX₁\u003c/em\u003e + 0.289 \u003cem\u003eX₂\u003c/em\u003e + 0.186 \u003cem\u003eX₁X₂\u003c/em\u003e \u0026minus; 0.227 \u003cem\u003eX₁\u0026sup2;\u003c/em\u003e \u0026minus; 0.942 \u003cem\u003eX₂\u0026sup2;\u003c/em\u003e + 0.089 \u003cem\u003eX₁\u0026sup2;X₂\u003c/em\u003e \u0026minus; 0.466 \u003cem\u003eX₁X₂\u0026sup2;\u003c/em\u003e \u0026minus; 1.698 \u003cem\u003eX₁\u0026sup3;\u003c/em\u003e + 0.298 \u003cem\u003eX₂\u0026sup3;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe regression model expressed in Eq.\u0026nbsp;(4) defines a cubic relationship between BP and the independent variables, capturing both nonlinear and interaction effects. The inclusion of interaction terms indicates that the influence of each factor\u0026mdash;engine speed and nanoparticle concentration\u0026mdash;varies depending on the level of the other. In particular, the negative coefficients of the higher-order speed terms highlight the dominant role of engine speed, with gains in BP diminishing at elevated rpm. These findings emphasize the necessity of simultaneously optimizing engine speed and nanoparticle concentration to achieve maximum BP.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3. Brake Specific Fuel Consumption (BSFC)\u003c/h2\u003e\u003cp\u003eBSFC represents the ratio of the fuel mass flow rate to the engine's BP output, and it is a key metric for assessing fuel efficiency. A lower BSFC value signifies a more efficient conversion of the chemical energy \"A lower BSFC value signifies a more efficient conversion of the chemical energy of the fuel into usable mechanical output. The following cubic polynomial expresses the empirical model representing BSFC as a function of \u003cem\u003eX₁\u003c/em\u003e and \u003cem\u003eX₂\u003c/em\u003e in Eq.\u0026nbsp;(5):\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBSFC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;235.865\u0026thinsp;+\u0026thinsp;11.279 \u003cem\u003eX₁\u003c/em\u003e \u0026minus; 8.015 \u003cem\u003eX₂\u003c/em\u003e \u0026minus; 1.335 \u003cem\u003eX₁X₂\u003c/em\u003e \u0026minus; 2.869 \u003cem\u003eX₁\u0026sup2;\u003c/em\u003e + 3.289 \u003cem\u003eX₂\u0026sup2;\u003c/em\u003e \u0026minus; 2.971 \u003cem\u003eX₁\u0026sup2;X₂\u003c/em\u003e + 2.432 \u003cem\u003eX₁X₂\u0026sup2;\u003c/em\u003e \u0026minus; 6.363 \u003cem\u003eX₁\u0026sup3;\u003c/em\u003e + 7.02 \u003cem\u003eX₂\u0026sup3;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe regression model highlights the nonlinear influence of nanoparticle concentration (\u003cem\u003eX₂\u003c/em\u003e)​ on BSFC. The positive cubic term (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e₂\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e, coefficient: +7.02) indicates that moderate nanoparticle additions initially reduce BSFC, but exceeding the optimal threshold causes a sharp rise, reflecting diminishing returns. This behavior is illustrated in the three-dimensional response surface plot shown in Fig.\u0026nbsp;2c, which displays a characteristic valley representing the region of minimum BSFC.\u003c/p\u003e\u003cp\u003eBSFC tended to increase with engine speed across all nanoparticle concentrations, primarily due to greater mechanical losses (e.g., friction) and reduced volumetric efficiency at higher rpm [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These effects offset the combustion improvements provided by ethanol and nanoparticles, thereby raising fuel consumption per unit power output.\u003c/p\u003e\u003cp\u003eThe influence of γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e concentration was more pronounced. The addition of nanoparticles consistently reduced BSFC relative to neat E10, with the lowest values observed at approximately 20 ppm. At this concentration, BSFC decreased by 5.95% at 3000 rpm compared with baseline E10. This reduction is attributed to improved atomization, enhanced catalytic activity, and more complete combustion. This reduction can be ascribed to improved atomization, enhanced catalytic activity, and more complete combustion.\u003c/p\u003e\u003cp\u003eAt lower engine speeds (1500\u0026ndash;2000 rpm), nanoparticle addition at 10 ppm produced only marginal changes in BSFC compared with neat E10, suggesting that the catalytic and atomization effects are less effective under low-turbulence conditions. Beyond the optimal concentration (\u0026asymp;\u0026thinsp;30 ppm), BSFC values increased again, likely due to nanoparticle agglomeration or increased viscosity, both of which impair atomization and combustion efficiency. Thus, the most favorable BSFC was achieved at the combination of moderate-to-lower engine speeds (1500\u0026ndash;2000 rpm) with 20 ppm γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, corresponding to the lowest point in the efficiency valley shown in Fig.\u0026nbsp;2c.\u003c/p\u003e\u003cp\u003eThe reduction in BSFC can be attributed to the enhanced fuel conversion efficiency promoted by the catalytic properties of γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles. By accelerating oxidation reactions, the nanoparticles enable faster and more thorough combustion, leading to higher in-cylinder pressures and the conversion of a larger portion of chemical energy into productive mechanical work per unit of fuel consumed. Their superior thermal conductivity further enhances heat transfer inside the combustion chamber, contributing to gains in thermal efficiency.\u003c/p\u003e\u003cp\u003eAt higher nanoparticle concentrations (e.g., 30 ppm), BSFC increases again, likely due to nanoparticle agglomeration and elevated fuel viscosity, which impair dispersion, atomization, and combustion uniformity. This behavior mirrors the trends observed for brake torque and brake power, where peak improvements also occurred near 20 ppm. The consistency across these metrics highlights that γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e addition at the optimal concentration enhances overall engine efficiency by simultaneously improving combustion, power output, and fuel utilization.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Emissions\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e)\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea shows the combined effects of engine speed and γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticle concentration on CO\u003csub\u003e2\u003c/sub\u003e emissions, as depicted by the three-dimensional response surface plot. CO\u003csub\u003e2\u003c/sub\u003e emissions consistently increase as engine speed increases across all fuel blends. This behavior is attributed to improved air intake, greater turbulence, and enhanced fuel atomization at higher rpm [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], all of which support more complete fuel oxidation and optimal energy conversion.\u003c/p\u003e\u003cp\u003eEnhanced combustion efficiency is further reflected in the observation that CO\u003csub\u003e2\u003c/sub\u003e emissions increased by up to 5.68% at 20 ppm γ- Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e and 1500 rpm, relative to neat E10 fuel at the same speed. This indicates that the catalytic activity of γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles is especially successful in promoting complete combustion where neat E10 alone may exhibit suboptimal carbon oxidation.\u003c/p\u003e\u003cp\u003eThe influence of γ- Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e concentration on CO\u003csub\u003e2\u003c/sub\u003e emissions is non-monotonic. Initial nanoparticle additions generally elevate CO\u003csub\u003e2\u003c/sub\u003e emissions, consistent with their catalytic role in facilitating complete combustion. However, at 30 ppm\u0026mdash;especially at 3000 rpm\u0026mdash; CO\u003csub\u003e2\u003c/sub\u003e emissions show a slight decline (falling below baseline E10 levels at this speed) or remain unchanged at 2000 rpm. This plateau or decrease at higher concentrations and speeds may be due to nanoparticle agglomeration, which can disrupt spray dynamics and atomization, leading to suboptimal fuel-air mixing and localized incomplete combustion. Additionally, excessive nanoparticle loadings could act as thermal barriers, subtly altering the flame structure and oxidation kinetics.\u003c/p\u003e\u003cp\u003eAt low nanoparticle concentrations (e.g., 10 ppm) and low engine speeds (e.g., 1500 rpm), CO\u003csub\u003e2\u003c/sub\u003e emissions generally increase compared to neat E10 fuel, indicating a positive, though not maximal, catalytic effect under these conditions.\u003c/p\u003e\u003cp\u003eA γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticle concentration in the range of 10\u0026ndash;20 ppm generally promotes higher CO\u003csub\u003e2\u003c/sub\u003e emissions by improving combustion efficiency, particularly at lower engine speeds where the potential for complete oxidation is greater. This optimal concentration range also aligns with improvements in engine performance and BSFC, especially at higher engine speeds, underscoring the overall advantage of nanoparticle-enhanced E10 combustion. The regression model for CO\u003csub\u003e2\u003c/sub\u003e emissions, incorporating engine speed and nanoparticle concentration, is presented in Eq.\u0026nbsp;(6):\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCO₂\u003c/em\u003e = 10.45\u0026thinsp;+\u0026thinsp;2.3 \u003cem\u003eX₁\u003c/em\u003e \u0026minus; 0.386 \u003cem\u003eX₂\u003c/em\u003e \u0026minus; 0.039 \u003cem\u003eX₁X₂\u003c/em\u003e \u0026minus; 0.061 \u003cem\u003eX₁\u0026sup2;\u003c/em\u003e \u0026minus; 0.202 \u003cem\u003eX₂\u0026sup2;\u003c/em\u003e \u0026minus; 0.047 \u003cem\u003eX₁\u0026sup2;X₂\u003c/em\u003e + 0.041 \u003cem\u003eX₁X₂\u0026sup2;\u003c/em\u003e \u0026minus; 1.2 \u003cem\u003eX₁\u0026sup3;\u003c/em\u003e \u0026minus; 0.305 \u003cem\u003eX₂\u0026sup3;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2. Carbon monoxide (CO)\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb illustrates the CO emission trends, which exhibit a complex, non-linear relationship with engine speed for baseline E10 fuel\u0026mdash;driven by variations in combustion completeness, temperature, and turbulence across the operating range. The introduction of γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles typically lowered CO emissions. This was achieved through an expansion of the reactive surface area and an enhancement of oxygen availability, which in turn promoted the oxidation of CO into CO₂, a critical step in improving air quality and reducing the environmental impact of the engine. Notably, 10 ppm γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e consistently outperformed 20 ppm in reducing CO emissions at all tested speeds, with the most significant reduction\u0026mdash;approximately 14.88% relative to baseline E10\u0026mdash;observed at 3000 rpm. This superior performance at 10 ppm is likely due to optimal nanoparticle dispersion at this lower concentration, which maximizes the catalytic surface area for post-combustion CO oxidation without significant agglomeration or adverse physical effects.\u003c/p\u003e\u003cp\u003eHowever, at 30 ppm, CO emissions paradoxically increased above baseline levels, particularly at high speeds, where increases exceeding 5% were observed. This rise is likely attributable to adverse effects at higher concentrations, such as nanoparticle agglomeration that impairs spray dynamics or the formation of thermal barriers, both of which reduce combustion efficiency [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eγ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles effectively mitigate CO emissions within an optimal concentration range (typically 10\u0026ndash;20 ppm), while excessive concentrations lead to increased emissions. These findings emphasize the importance of precise nanoparticle dosing to achieve both environmental and performance benefits. The regression model developed for CO emissions, incorporating engine speed and nanoparticle concentration as variables, is provided in Eq.\u0026nbsp;(7):\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCO\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.023\u0026thinsp;\u0026minus;\u0026thinsp;0.638 \u003cem\u003eX₁\u003c/em\u003e + 0.299 \u003cem\u003eX₂\u003c/em\u003e + 0.102 \u003cem\u003eX₁X₂\u003c/em\u003e \u0026minus; 0.017 \u003cem\u003eX₁\u0026sup2;\u003c/em\u003e + 0.385 \u003cem\u003eX₂\u0026sup2;\u003c/em\u003e \u0026minus; 0.025 \u003cem\u003eX₁\u0026sup2;X₂\u003c/em\u003e + 0.25 \u003cem\u003eX₁X₂\u0026sup2;\u003c/em\u003e + 0.929 \u003cem\u003eX₁\u0026sup3;\u003c/em\u003e \u0026minus; 0.253\u003cem\u003eX₂\u0026sup3;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3. Hydrocarbons (HC)\u003c/h2\u003e\u003cp\u003eAs illustrated in the 3D response surface plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), HC emissions exhibit a pronounced inverse relationship with engine speed, decreasing significantly when the engine speed is advanced from 1500 to 3000 rpm. This decline is mainly due to the more complete combustion that occurs at elevated speeds, driven by elevated cylinder wall temperatures, intensified in-cylinder turbulence, and improved fuel vaporization and atomization. These factors collectively enhance air-fuel mixing and support efficient flame propagation, thereby minimizing the formation of unburned hydrocarbons.\u003c/p\u003e\u003cp\u003eThe E10 fuel blend amplifies this effect further. Due to its lower boiling point and higher octane rating compared to conventional gasoline [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], E10 enables deeper flame penetration into crevice volumes and quenching zones [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This characteristic improves overall combustion efficiency and contributes to additional reductions in HC emissions.\u003c/p\u003e\u003cp\u003eThe inclusion of γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles adds a catalytic dimension to HC emission control. At concentrations of 10\u0026ndash;20 ppm, these nanoparticles provide active surface sites that lower the activation energy required for hydrocarbon oxidation. This catalytic action facilitates the post-combustion breakdown of carbonaceous deposits and residual unburned fuel through localized temperature enhancement and intensified secondary oxidation processes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConversely, the response surface indicates that the highest HC emissions occur at low engine speeds (e.g., 1500 rpm) in the absence of nanoparticles. This outcome reflects incomplete combustion under these conditions, where limited turbulence, lower combustion chamber temperatures, and richer mixtures impede the effective oxidation of unburned hydrocarbons, resulting in elevated HC levels.\u003c/p\u003e\u003cp\u003eThe lowest HC emissions in this study were recorded at 3000 rpm with a 10 ppm γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e concentration, representing a 6.57% reduction compared to baseline E10 at the same speed. However, exceeding a nanoparticle concentration of 20 ppm resulted in a rise in HC emissions, with increases reaching approximately 4.4% at 3000 rpm. This highlights that there is an ideal nanoparticle concentration for minimizing HC emissions, as excessive loading may induce adverse effects such as thermal insulation or impaired spray homogeneity, ultimately hindering complete combustion. The empirical model for HC emissions, which is a function of both engine speed and nanoparticle concentration, is provided in Eq.\u0026nbsp;(8):\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabh\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;164.883\u0026thinsp;\u0026minus;\u0026thinsp;19.252 \u003cem\u003eX₁\u003c/em\u003e + 3.127 \u003cem\u003eX₂\u003c/em\u003e + 1.263 \u003cem\u003eX₁X₂\u003c/em\u003e \u0026minus; 11.884 \u003cem\u003eX₁\u0026sup2;\u003c/em\u003e + 6.115 \u003cem\u003eX₂\u0026sup2;\u003c/em\u003e + 0.546 \u003cem\u003eX₁\u0026sup2;X₂\u003c/em\u003e + 4.516 \u003cem\u003eX₁X₂\u0026sup2;\u003c/em\u003e \u0026minus; 5.623 \u003cem\u003eX₁\u003c/em\u003e\u0026sup3; \u0026minus; 2.814 \u003cem\u003eX₂\u0026sup3;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.2.4. Nitrogen oxides (NOₓ)\u003c/h2\u003e\u003cp\u003eThe 3D response surface in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ed shows that NOₓ emissions increase with engine speed across all nanoparticle concentrations, primarily due to higher combustion temperatures accelerating thermal NO\u003csub\u003ex\u003c/sub\u003e formation through the Zeldovich mechanism at temperatures exceeding 1600\u0026deg;C [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The highest NO\u003csub\u003ex\u003c/sub\u003e emissions were recorded at 3000 rpm with 20 ppm γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles, representing a 17.08% increase over the E10 baseline and underscoring the dominant role of engine speed in NO\u003csub\u003ex\u003c/sub\u003e generation.\u003c/p\u003e\u003cp\u003eγ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles generally improve combustion efficiency by increasing in-cylinder pressure and enhancing oxygen availability, both of which can raise peak temperatures and promote NO\u003csub\u003ex\u003c/sub\u003e formation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For example, at 1500 rpm with 10 ppm nanoparticles, NO\u003csub\u003ex\u003c/sub\u003e emissions rose compared to baseline E10, reflecting conditions where catalytic effects enhance thermal NO\u003csub\u003ex\u003c/sub\u003e generation.\u003c/p\u003e\u003cp\u003eIncreasing the nanoparticle concentration from 20 to 30 ppm consistently reduced NO\u003csub\u003ex\u003c/sub\u003e emissions across all engine speeds, suggesting a shift in combustion and NO\u003csub\u003ex\u003c/sub\u003e formation mechanisms. This reduction is likely attributable to thermal insulation effects that lower peak flame temperatures, along with impaired fuel atomization due to increased viscosity or particle agglomeration, leading to slightly less complete combustion. Supporting evidence for this includes the observed increases in CO emissions and BSFC, as well as the decline in brake power at 30 ppm relative to 20 ppm. These findings highlight the dual role of γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles: enhancing oxidation and peak temperatures at lower concentrations while modifying combustion dynamics and limiting temperature extremes at higher concentrations. This underscores the importance of identifying an optimal nanoparticle concentration range for effective emission control. The empirical model describing NO\u003csub\u003ex\u003c/sub\u003e emissions based on engine speed and nanoparticle concentration is provided in Eq.\u0026nbsp;(9):\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabi\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNOₓ\u003c/em\u003e = 887.496\u0026thinsp;\u0026minus;\u0026thinsp;6.607 \u003cem\u003eX₁\u003c/em\u003e + 27.535 \u003cem\u003eX₂\u003c/em\u003e \u0026minus; 1.979 \u003cem\u003eX₁X₂\u003c/em\u003e + 7.234 \u003cem\u003eX₁\u0026sup2;\u003c/em\u003e \u0026minus; 68.421 \u003cem\u003eX₂\u0026sup2;\u003c/em\u003e + 21.114 \u003cem\u003eX₁\u0026sup2;X₂\u003c/em\u003e \u0026minus; 17.285 \u003cem\u003eX₁X₂\u0026sup2;\u003c/em\u003e + 59.187 \u003cem\u003eX₁\u0026sup3;\u003c/em\u003e \u0026minus; 0.0598 \u003cem\u003eX₂\u0026sup3;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Model adequacy evaluation\u003c/h2\u003e\u003cp\u003eThe predictive capability of the regression models was assessed through a comparison of predicted values to the corresponding experimental measurements, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. A strong linear correlation was observed across all performance and emission responses\u0026mdash;including BP, BT, BSFC, and exhaust emissions (CO\u003csub\u003e2\u003c/sub\u003e, CO, HC, NO\u003csub\u003ex\u003c/sub\u003e). Coefficients of determination (R\u0026sup2;) ranged from 0.99 to 1, indicating excellent agreement between the observed data and the model\u0026rsquo;s predicted values. These results confirm the adequacy and robustness of the response surface models in capturing the underlying behavior of the system and validating their reliability for optimization and prediction purposes. The validated regression equations provide robust tools for predicting engine responses under untested conditions. Their high R\u0026sup2; values (\u0026gt;\u0026thinsp;0.9) and low CVs (\u0026lt;\u0026thinsp;3%) support their use in simulation-based fuel design, performance mapping, and emission trade-off analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;5, four key validation metrics\u0026mdash;R\u0026sup2;, adjusted R\u0026sup2;, predicted R\u0026sup2;, and CV\u0026mdash;were analyzed to assess model robustness. For all responses, both the R\u0026sup2; and adjusted R\u0026sup2; values were above 0.97, thereby confirming that the fitted models explain more than 97% of the variability in the experimental data. Additionally, a strong concordance was found between the predicted R\u0026sup2; and the adjusted R\u0026sup2;, confirming the model\u0026rsquo;s generalization capability and predictive reliability beyond the training dataset. Furthermore, the CV for all models remained below 3%, reflecting a high degree of experimental precision and model stability.\u003c/p\u003e\u003cp\u003eThese validation results affirm the suitability of the developed models as reliable tools for predicting and optimizing trade-offs between engine performance and emissions, as well as for guiding the formulation of bioethanol\u0026ndash;gasoline blends containing γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;5.\u003c/b\u003e Statistical performance metrics for the developed regression models, including R\u0026sup2;, adjusted R\u0026sup2;, predicted R\u0026sup2;, and CV for all performance and emission responses. High R\u0026sup2; values (all \u0026gt;\u0026thinsp;0.9) and low CV values (\u0026lt;\u0026thinsp;3%) confirm strong model fitting, predictive accuracy, and experimental consistency across engine speed and nanoparticle concentration conditions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Model validation and optimization\u003c/h2\u003e\u003cp\u003eThe main goal of the optimization procedure was to identify the best settings for the key variables\u0026mdash;engine speed and γ-Al2O3 nanoparticle concentration\u0026mdash;that would maximize BP and BT, while simultaneously minimizing BSFC and emissions, including CO\u003csub\u003e2\u003c/sub\u003e, CO, HC, and NOₓ. In the multi-response optimization procedure, the relative importance of each target was weighted, with a higher priority assigned to minimizing NOₓ emissions due to their significant environmental impact. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e outlines the optimization goals and the constraints imposed on each response. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e9\u003c/span\u003e summarizes the results of the optimization, listing six candidate solutions. Among these, the first solution typically exhibits the most favorable trade-off across all performance and emission criteria, as indicated by the lowest standard error.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLower and upper bounds and target objectives for each response variable used in the multi-objective optimization process. Higher importance was assigned to NOx reduction due to its environmental impact.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGoal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eImportance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLimit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLimit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEngine speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eis in range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConcentration of nanoparticles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eis in range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emaximum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emaximum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBSFC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eminimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e225.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e248.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eminimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO₂\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eminimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eminimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNOₓ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eminimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOptimal operating conditions and predicted responses for engine performance and exhaust emissions determined using RSM. Among the obtained solutions, Solution 1 achieved the highest composite desirability, representing the best trade-off between performance enhancement and emission reduction.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eX₁\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eX₂\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBSFC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCO₂\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNOₓ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eDesirability\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2471.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e243.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e159.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e843.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.134 (Selected)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2507.936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e117.484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e237.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e162.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e867.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1586.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e231.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e176.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e823.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1543.656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e111.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e226.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e178.641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e846.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe predicted values associated with Solution 1 were chosen as the most favorable outcome, given that this solution yielded the highest desirability value. These results confirm the effectiveness of RSM in identifying balanced operating conditions that simultaneously enhance performance and mitigate emissions. The developed models are practically valuable, allowing users to predict performance and emission outcomes based on engine speed and nanoparticle concentration, thereby reducing reliance on extensive experimentation. Importantly, the ability of RSM to perform multi-objective optimization underscores its relevance for advancing clean-fuel innovations.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study demonstrated that doping E10 fuel with γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles significantly enhances spark-ignition engine performance and emission characteristics through catalytic and thermophysical effects. At the optimal concentration of 20 ppm, BT and BP increased by 7.67% and 6.94%, respectively, while BSFC decreased by 5.95%. These improvements stem from enhanced atomization, higher in-cylinder pressures, and micro-explosion phenomena driven by the nanoparticles\u0026rsquo; high surface reactivity and thermal conductivity.\u003c/p\u003e\u003cp\u003eEmission analysis revealed that CO and HC were minimized at 10\u0026ndash;20 ppm, reflecting improved oxidation kinetics, while CO\u003csub\u003e2\u003c/sub\u003e increased under these conditions, confirming more complete combustion. NO\u003csub\u003ex\u003c/sub\u003e emissions displayed a dual trend: rising to 20 ppm due to elevated flame temperatures, before declining to 30 ppm as thermal buffering and nanoparticle agglomeration became dominant.\u003c/p\u003e\u003cp\u003eUsing RSM, the optimal operating point was identified at 2471.87 rpm and a nanoparticle concentration of 5.88 ppm. The regression models demonstrated excellent predictive capability (R\u0026sup2; \u0026gt;0.97, CV\u0026thinsp;\u0026lt;\u0026thinsp;3%), enabling reliable multi-objective optimization of performance and emissions without the need for extensive experimental trials. These findings establish γ-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles as a viable pathway for advancing thermo-combustion efficiency in ethanol\u0026ndash;gasoline blends, supporting sustainable energy targets while mitigating harmful pollutants.\u003c/p\u003e\u003cp\u003eBeyond its experimental contributions, this work provides new insights into the underexplored γ-phase of Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e nanoparticles and their role in SI engines. The combination of systematic concentration analysis and statistical modelling provides a robust framework for developing cleaner, more efficient fuel formulations. Future research should validate results under real-world driving conditions, and explore hybrid optimization approaches that integrate RSM with machine learning for adaptive fuel management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"106%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003cstrong\u003eNomenclature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Nanoparticle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical and Modeling Parameters \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSymbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSymbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026gamma;-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eGamma-Aluminum Oxide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eResponse (dependent) variable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEngine and Fuel Parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cimg width=\"37\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eCoded value of a variable for analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eBrake Power (kW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cimg width=\"36\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eActual (measured) value before coding\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eBT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eBrake Torque (N.m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003csub\u003e1\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003eCoded value of engine speed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eBSFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eBrake Specific Fuel Consumption (g/kW.hr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eCoded value of nanoparticle\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003econcentration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eE10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eGasoline with 10% bioethanol by volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cimg width=\"9\" height=\"17\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA0AAAAaCAMAAABb5iZtAAAAAXNSR0IArs4c6QAAAFFQTFRFAAAAAAAAAAA6AABmADo6ADpmADqQAGaQAGa2OgAAOgA6OpDbZgAAZjoAZrb/kDoAkNv/tmYAttv/tv//25A627Zm2////7Zm/9uQ//+2///bKoWoJAAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAdElEQVQoU52Q0Q6AIAhFwbLSLDMttf//0NAs32rrPrDBDvcyAP4rKrw0fHgEkZiEZ9Jjv6faUQU4dBqaqwFwjQXHbXEMYvQ0KDp0K5ea5XCujWeinhHkYp61qGYKK2icRqBL7ugMGZZNNTKCNoF8/f+Tt80TJ8sEdVDb5EsAAAAASUVORK5CYII=\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eSample mean; average of data points\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Pollutants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cimg width=\"13\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eIntercept (constant term in regression)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eCO\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eCarbon Monoxide Emissions (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cimg width=\"68\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003eLinear regression coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eCarbon Dioxide Emissions (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cimg width=\"82\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eQuadratic regression coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eHydrocarbon Emissions (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cimg width=\"83\" height=\"19\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eInteraction regression coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eNO\u003csub\u003ex\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eNitrogen Oxides Emissions (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eCoefficient of determination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Abbreviations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eAdj. R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eAdjusted coefficient of determination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eSpark Ignition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003ePred. R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003ePredicted coefficient of determination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003erpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eRevolutions Per Minute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eAnalysis of Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eppm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eParts per million\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eCoefficient of variation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eRSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eResponse Surface Methodology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003ed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eAbsolute difference or standardized effect size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eNanoparticles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eD-criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eDesign criterion maximizing determinant of information matrix\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Subscripts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eSD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eStandard Deviation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003ei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eIndex of independent variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eRSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eroot sum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003ej\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eIndex of independent variables (distinct from i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eGUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eGuide to the Expression of Uncertainty in Measurement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.E.B., M.N.K., F.O., and K.M. contributed to the conceptualization, methodology, validation, and writing of the review and editing of the manuscript. R.E.B. and M.N.K. performed the formal analysis, investigation, data curation, and visualization. R.E.B. also handled the software and project administration. F.O. and K.M. provided resources and supervision. F.O. and K.M. were responsible for the project administration. R.E.B. prepared the original draft. All authors read, reviewed, and approved the final manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are fully included within the published article, specifically in Table 5.\u003c/p\u003e\n\u003cp\u003eAll data necessary to support the findings of this study are available within the article. Any further inquiries concerning data availability should be directed to the corresponding author, Dr Ramtin Elkaei Behjati.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTefera, N. T., Nallamothu, R. B. \u0026amp; Alemayehu, G. Analysis of RCCI engine characteristics with n-butanol/gasoline as low reactive fuel and biodiesel blend as high reactive fuel. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 26023 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShaik, F. et al. Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 4585 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYilmaz, E. et al. Investigation of intake air temperature effect on co-combustion characteristics of NH3/gasoline in naturally aspirated high compression ratio engine with sub-chamber. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 11649 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, C. W., Yu, W. S., Zheng, Z. X., Cheng, Y. S. \u0026amp; Li, S. Y. Waste valorization through acetone-butanol-ethanol (ABE) fermentation. \u003cem\u003eJ. Taiwan. Inst. Chem. Eng.\u003c/em\u003e \u003cb\u003e160\u003c/b\u003e, 105280 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCesur, I. Effects of water injection strategies on performance and exhaust emissions in an ethanol fueled gasoline engine. \u003cem\u003eTherm. Sci. Eng. Prog\u003c/em\u003e. \u003cb\u003e34\u003c/b\u003e, 101397 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl-Hasan, M. Effect of ethanol-unleaded gasoline blends on engine performance and exhaust emission. \u003cem\u003eEnergy Convers. Manag\u003c/em\u003e. \u003cb\u003e44\u003c/b\u003e, 1547\u0026ndash;1561 (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElshenawy, A. A., Razik, A., Gad, M. S. \u0026amp; S. M. \u0026amp; Modeling of combustion and emissions behavior on the effect of ethanol\u0026ndash;gasoline blends in a four stroke SI engine. \u003cem\u003eAdv. Mech. Eng.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1\u0026ndash;16 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYe, Y. et al. Effect of Different Ratios of Gasoline-Ethanol Blend Fuels on Combustion Enhancement and Emission Reduction in Electronic Fuel Injection Engine. \u003cem\u003ePolymers (Basel)\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalle, M., Yeneneh, K. \u0026amp; Sufe, G. Investigation of spark ignition engine performance in ethanol\u0026ndash;petrol blended fuels using artificial neural network. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 25516 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalat, M. \u0026amp; Balat, H. Recent trends in global production and utilization of bio-ethanol fuel. \u003cem\u003eAppl. Energy\u003c/em\u003e. \u003cb\u003e86\u003c/b\u003e, 2273\u0026ndash;2282 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYamada, H., Inomata, S., Tanimoto, H., Hata, H. \u0026amp; Tonokura, K. Estimation of refueling emissions based on theoretical model and effects of E10 fuel on refueling and evaporative emissions from gasoline cars. \u003cem\u003eSci. Total Environ.\u003c/em\u003e \u003cb\u003e622\u0026ndash;623\u003c/b\u003e, 467\u0026ndash;473 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIodice, P., Amoresano, A. \u0026amp; Langella, G. A review on the effects of ethanol/gasoline fuel blends on NOX emissions in spark-ignition engines. \u003cem\u003eBiofuel Res. J.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 1465\u0026ndash;1480 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoyal, R., Pathak, V. K. \u0026amp; Sonawane, C. Optimization of SiO2 based water\u0026ndash;diesel emulsified fuel for engine performance and emission characteristics using soft computing approaches. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 31685 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllasi, H. L., Selvam, A. J., Soosaimariyan, M. V. \u0026amp; Arul, S. J. Enhancing diesel engine efficiency with waste cooking oil biodiesel and nano additives for sustainable fuel applications. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 31762 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZamankhan, F., Pirouzfar, V., Ommi, F. \u0026amp; Valihesari, M. Investigating the effect of MgO and CeO2 metal nanoparticle on the gasoline fuel properties: empirical modeling and process optimization by surface methodology. \u003cem\u003eEnviron. Sci. Pollut Res.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 22889\u0026ndash;22902 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmirabedi, M., Jafarmadar, S. \u0026amp; Khalilarya, S. Experimental investigation the effect of Mn2O3 nanoparticle on the performance and emission of SI gasoline fueled with mixture of ethanol and gasoline. \u003cem\u003eAppl. Therm. Eng.\u003c/em\u003e \u003cb\u003e149\u003c/b\u003e, 512\u0026ndash;519 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValihesari, M., Pirouzfar, V., Ommi, F. \u0026amp; Zamankhan, F. Investigating the effect of Fe2O3 and TiO2 nanoparticle and engine variables on the gasoline engine performance through statistical analysis. \u003cem\u003eFuel\u003c/em\u003e \u003cb\u003e254\u003c/b\u003e, 115618 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManigandan, S. et al. Effect of addition of hydrogen and TiO2 in gasoline engine in various exhaust gas recirculation ratio. \u003cem\u003eInt. J. Hydrogen Energy\u003c/em\u003e. \u003cb\u003e44\u003c/b\u003e, 11205\u0026ndash;11218 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaghavifar, H., Kaleji, B. K. \u0026amp; Kheyrollahi, J. Application of composite TNA nanoparticle with bio-ethanol blend on gasoline fueled SI engine at different lambda ratios. \u003cem\u003eFuel\u003c/em\u003e \u003cb\u003e277\u003c/b\u003e, 118218 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePirouzfar, V., Hakami, M., Hassanpour zonoozi, M. \u0026amp; Su, C. H. Improving the performance of gasoline fuels by adding methanol and methyl tertiary-butyl ether along with metal oxides titanium oxide and magnesium oxide. \u003cem\u003eEnergy\u003c/em\u003e \u003cb\u003e294\u003c/b\u003e, 130687 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarimi Abiyazani, N., Pirouzfar, V. \u0026amp; Su, C. H. Enhancing engine power and torque and reducing exhaust emissions of blended fuels derived from gasoline-propanol-nano particles. \u003cem\u003eEnergy\u003c/em\u003e \u003cb\u003e241\u003c/b\u003e, 122924 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen, D. D., Moghaddam, H., Pirouzfar, V., Fayyazbakhsh, A. \u0026amp; Su, C. H. Improving the gasoline properties by blending butanol-Al2O3 to optimize the engine performance and reduce air pollution. \u003cem\u003eEnergy\u003c/em\u003e \u003cb\u003e218\u003c/b\u003e, 119442 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFayyazbakhsh, A. \u0026amp; Pirouzfar, V. Investigating the influence of additives-fuel on diesel engine performance and emissions: Analytical modeling and experimental validation. \u003cem\u003eFuel\u003c/em\u003e \u003cb\u003e171\u003c/b\u003e, 167\u0026ndash;177 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRossi, S. et al. Exposure to nanoparticles derived from diesel particulate filter equipped engine increases vulnerability to arrhythmia in rat hearts. \u003cem\u003eEnviron. Pollut\u003c/em\u003e. \u003cb\u003e284\u003c/b\u003e, 117163 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFu, J. et al. Emission characteristic, spatial distribution, and health risk of polycyclic aromatic compounds (PAHs, NPAHs, and OPAHs) from light-duty gasoline and diesel vehicles based on on-road measurements. \u003cem\u003eSci. Total Environ.\u003c/em\u003e \u003cb\u003e941\u003c/b\u003e, 173390 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, J., Wang, H., Zhao, X., Li, Y. L. \u0026amp; Fan, W. L. Correlating the surface structure and hydration of a γ-Al2O3 support with the Run (n\u0026thinsp;=\u0026thinsp;1\u0026ndash;4) cluster adsorption behavior: A density functional theory study. \u003cem\u003eRSC Adv.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 40459\u0026ndash;40473 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAtkinson, A. C., Donev, A. N. \u0026amp; Tobias, R. D. Optimum Experimental Designs, with SAS. \u003cem\u003eOptim. Exp. Des. SAS\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/oso/9780199296590.001.0001\u003c/span\u003e\u003cspan address=\"10.1093/oso/9780199296590.001.0001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBa-Abbad, M. M., Kadhum, A. A. H., Mohamad, A. B., Takriff, M. S. \u0026amp; Sopian, K. Optimization of process parameters using D-optimal design for synthesis of ZnO nanoparticles via sol-gel technique. \u003cem\u003eJ. Ind. Eng. Chem.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 99\u0026ndash;105 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu, W., Wong, W. K., Tan, K. C. \u0026amp; Xu, J. X. Finding high-dimensional D-optimal designs for logistic models via differential evolution. \u003cem\u003eIEEE Access.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 7133\u0026ndash;7146 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSergis, V. \u0026amp; Ouellet-Plamondon, C. M. D-optimal design of experiments applied to 3D high-performance concrete printing mix design. \u003cem\u003eMater. Des.\u003c/em\u003e \u003cb\u003e218\u003c/b\u003e, 110681 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChaichan, M. T. Combustion and emission characteristics of E85 and diesel blend in conventional diesel engine operating in PPCI mode. \u003cem\u003eTherm. Sci. Eng. Prog\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, 45\u0026ndash;53 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe, W., Li, S. \u0026amp; Huang, X. Critical concentration effects of nanoparticle on combustion involved into secondary atomization and micro-explosion for B/JP-10/SP-80 nanofluid fuel. \u003cem\u003eFuel\u003c/em\u003e \u003cb\u003e358\u003c/b\u003e, 130262 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVenu, H. \u0026amp; Madhavan, V. Effect of Al2O3 nanoparticles in biodiesel-diesel-ethanol blends at various injection strategies: Performance, combustion and emission characteristics. \u003cem\u003eFuel\u003c/em\u003e \u003cb\u003e186\u003c/b\u003e, 176\u0026ndash;189 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmiri, M., Elkaei Behjati, R., Farmahini Farahani, H. \u0026amp; Mazaheri, K. Experimental study on the effect of cone-shaped bluff body on lean premixed flames in a swirl burner. \u003cem\u003eJ. Therm. Anal. Calorim.\u003c/em\u003e \u003cb\u003e149\u003c/b\u003e, 699\u0026ndash;710 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gasoline, bioethanol, γ- Al2O3 nanoparticles, emissions, spark ignition engine, Multi-objective Optimization","lastPublishedDoi":"10.21203/rs.3.rs-7703508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7703508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the increasing demand for green energy, bioethanol is widely recognized as a alternative to fossil fuels in spark-ignition (SI) engines. While aluminum oxide (Al\u003csub\u003e2​\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e) nanoparticles have been studied with other fuels, this research is the first to investigate the impact of γ-Al\u003csub\u003e2\u003c/sub\u003e​O\u003csub\u003e3\u003c/sub\u003e​ nanoparticles on E10 blends at concentrations of 10, 20, and 30 ppm. Experimental tests utilized Response Surface Methodology (RSM) with a D-optimal design to model and optimize engine performance and emissions. Multi-objective optimization identified 2471.87 rpm and 5.88 ppm as the most balanced operating conditions. Results showed that at 20 ppm γ-Al\u003csub\u003e2\u003c/sub\u003e​O\u003csub\u003e3\u003c/sub\u003e​, brake torque (BT) and brake power (BP) increased by 7.67% and 6.94%, respectively, while brake specific fuel consumption (BSFC) decreased by 5.91%. Emissions analysis revealed reductions of 14.88% in carbon monoxide (CO) and 5.84% in hydrocarbon (HC) emissions, accompanied by a 5.68% increase in carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e​). Nitrogen Oxides (NO\u003csub\u003ex\u003c/sub\u003e​) emissions increased by 17.08% at higher engine speeds, consistent with elevated flame temperatures. Exceeding the optimal nanoparticle threshold resulted in a decline in performance and increased pollutant emissions. The findings confirm the potential of alumina nanoparticles to boost combustion efficiency and emission characteristics in SI engines, providing a data-driven framework for optimizing these fuel blends.\u003c/p\u003e","manuscriptTitle":"Performance and Emission Multi-Objective Optimization of γ-Al2O3 Nanoparticle-Enhanced E10 Bioethanol–Gasoline Combustion in Spark-Ignition Engines","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 16:03:01","doi":"10.21203/rs.3.rs-7703508/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-21T06:29:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-20T14:56:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-15T11:32:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199896377197619264417798294786493273869","date":"2025-11-30T06:41:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320704017924982312510626200743940901220","date":"2025-11-23T07:40:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260625147225125614330507969814215240691","date":"2025-11-20T18:22:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-20T17:00:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T12:04:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-20T09:55:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-26T16:54:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-26T16:49:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f25898e-d893-4c6e-9a5a-cafa49963955","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":58726321,"name":"Physical sciences/Chemistry"},{"id":58726322,"name":"Physical sciences/Energy science and technology"},{"id":58726323,"name":"Physical sciences/Engineering"},{"id":58726324,"name":"Earth and environmental sciences/Environmental sciences"},{"id":58726325,"name":"Physical sciences/Materials science"}],"tags":[],"updatedAt":"2026-05-18T13:53:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-27 16:03:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7703508","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7703508","identity":"rs-7703508","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}