Glycerol and ascorbic acid-assisted synthesis of α-MoO3 nanoparticles as an efficient catalyst for biodiesel production

Glycerol and ascorbic acid-assisted synthesis of α-MoO3 nanoparticles as an efficient catalyst for biodiesel production | 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 Research Article Glycerol and ascorbic acid-assisted synthesis of α-MoO3 nanoparticles as an efficient catalyst for biodiesel production Arefe Moatamed Sabzevar, Mahboube ghahramaninezhad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4820789/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Nov, 2024 Read the published version in Environmental Science and Pollution Research → Version 1 posted 6 You are reading this latest preprint version Abstract This work exhibits a novel method for synthesizing α-MoO 3 nanoparticles (NPs) using a convenient recipe that utilizes glycerol and ascorbic acid as polymerizing and green complexing agents. Different analytical techniques, including XRD, FT-IR, TGA, and scanning electron microscopy (SEM), were employed to identify the as-prepared α-MoO 3 NPs, and it was used as a catalyst in biodiesel production. Moreover, the TPD experiment was performed to determine the catalyst's acidity strength. The α-MoO 3 exhibited high efficiency in producing biodiesel from oleic acid and ethyl alcohol as an oil source and alcohol, respectively. The design of experiments and optimization process were also performed using response surface methodology (RSM) to attain the optimal condition. The influences of several parameters, such as catalyst dosage, reaction time, medium temperature, and alcohol to fatty acid (in molar ratio), were studied. The results demonstrate that at the optimal operating variables of 75°C, 50 min of reaction time, a 30:1 molar ratio of alcohol to oleic acid, and 0.007 g of catalyst, the yield of biodiesel production can approach 85%. Moreover, the obtained results indicated that the catalyst can be efficiently recovered and reused four times without significant loss in its activity. α-MoO3 Ascorbic acid Biodiesel Catalyst Esterification RSM Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction One of the most serious concerns humanity has ever faced is environmental pollution caused by the extensive use of energy for the convenience of modern life (Nomanbhay and Ong, 2017 ). Therefore, the demand to develop and exploit green fuels has grown steadily (Sales et al., 2023 ). In recent years, extensive research has been conducted to identify a viable fuel to replace conventional liquid fuels. As a result, biofuels, particularly biodiesel, have attracted a lot of attention. Biodiesel, compared to fossil fuels, has illustrated technical and environmental benefits (Lin et al., 2006 ). It is a renewable (Farouk et al., 2024 ; Ngomade et al., 2023 ; Pandit et al., 2023 ), biodegradable, non-toxic fuel (Osman et al., 2024 ), and eco-friendly (Abati et al., 2024 ). obtained from sources like vegetable oils and animal fats (Lin et al., 2006 ), and it has great potential as an alternative to fossil fuels (Parida et al., 2024 ). In general, biodiesel can be produced by various methods such as pyrolysis (Zhang et al., 2020 ), and transesterification (Ghaffari and Behzad, 2018 ; Zdujić et al., 2019 ), but the transesterification process in the presence of a sufficient catalyst is the most efficient (Scragg et al., 2003 ). This process involves the reaction of an oil source with an alcohol in the presence of a catalyst. Various types of alcohol are utilized in this procedure, including ethanol, methanol, propanol, and others (Salimi and Hosseini, 2019 ). In this approach, homogeneous and heterogeneous catalysts can be employed; however, separating homogeneous inorganic acid catalysts such as sulfuric acid is challenging and might result in corrosion and contamination of the reaction (Wan et al., 2015 ). Therefore, developing heterogeneous catalyst systems using green, simple, and cost-effective synthesis techniques is critical to address these issues. In recent years, metal oxides, either as active phases or as supports, have been used as catalysts in many chemical reactions such as the synthesis of methanol (Chang et al., 2019 ; Wang et al., 2019 ; Xie et al., 2020 ), oxidation of various alcohols (Kazemnejadi et al., 2019 ; Lu et al., 2019 ; Nasseri et al., 2019 ), oxidative desulfurization of DBT (Alenazi et al., 2020 ; Deng et al., 2020 ; Ghahramaninezhad and Ahmadpour, 2020 , 2022 ), biodiesel production (Huang et al., 2021 ; Khatibi et al., 2021 ; Qu et al., 2021 ) and so on. One type of transition metal oxide is orthorhombic Molybdenum oxide (α-MoO 3 ), which has attracted much attention from scientists because of its non-toxic nature, high stability, and strong Lewis acid sites (Zhu et al., 2019 ). Hence, MoO 3 nanoparticles (NPs) are successfully engaged as an efficient catalyst in many chemical reactions, such as in the oxidation of sulfides (Tosi et al., 2018 ; Wang et al., 2020 ), methanol oxidation (Peña-Bahamonde et al., 2020 ; Puebla et al., 2020 ; Swathi et al., 2020 ), organic photovoltaics (OPVs) (Choi et al., 2019 ; Gong et al., 2020 ), hydrodesulfurization (Li et al., 2020 ; Yang et al., 2020 ), gas sensors (Jiang et al., 2019 ; Zhang et al., 2019 ), and biodiesel production (Gonçalves et al., 2021 ; Silva Lucena de Medeiros et al., 2024; Xie and Zhao, 2014 ). The application of α-MoO 3 nanoparticles as a single-phase bulk active catalyst for biodiesel production is rarely reported (Silva et al., 2022 ), which can be attributed to the lack of a green, convenient, fast, effective, and low-cost method for its synthesis. For this aim, studying influential factors on the production method of metal oxide nanoparticles from green and cost-effective precursors is very significant. To date, molybdenum oxide NPs are mainly fabricated from toxic and costly precursors through high energy-consuming procedures (Shaheen and Ahmad, 2021 ). One of the simple methods for synthesizing metal oxide nanoparticles, including MoO 3, is the sol-gel method (Singh et al., 2018 ), where a complexing agent and a polymerizing agent are used in the synthesis process. Indeed, the complexing agent prevents the accumulation of particles and prevents the growth of nanoparticles by preventing the formation of larger clusters. The polymerizing agent is also used to homogenize the particle distribution. Due to the simultaneous use of these two agents, this method is sometimes called the PC (polymerizing–complexing) method (Habte et al., 2019 ). In this research, for the first time, we are offering a green and novel protocol for the synthesis of α-MoO 3 nanoparticles using two green and non-toxic polymerizing and complexing agents, glycerol and ascorbic acid. The as-made α-MoO 3 nanoparticles will then be employed as an efficient and novel catalyst for biodiesel production. The biodiesel production yield is investigated by reacting between oleic acid as an oil source and ethanol in the presence of α-MoO 3 as a catalyst. In typical, temperature, time of reaction, the ratio of alcohol to fatty acid (A/F molar ratio), and catalyst dosage are the most common factors influencing the biodiesel production yield. The optimal conditions for biodiesel production are experimentally assessed using response surface methodology (RSM). After the reaction, the catalyst is easily separated by centrifugation and reused in the process. Moreover, a supposed mechanism for biodiesel production using the as-prepared α-MoO 3 catalyst is presented. Scheme 1 illustrates a basic diagram of biodiesel production via esterification reaction in the presence of α-MoO 3 NPs. 2. General Experimentation and Methodologies 2.1. Chemicals and instruments All required chemicals and solvents were supplied from SigmaAldrich and used without any additional purification. L-Ascorbic acid (purity > 99%), Glycerol (99%), Ammonium heptamolybdate tetrahydrate (AHM, 99%), Sodium hydroxide, Oleic acid (99%), and ethanol (99.8%) were utilized for α-MoO 3 synthesis. The catalyst's X-ray diffraction (XRD) pattern was attained with a Bruker D8-Advance diffractometer by Cu Kα radiation at room temperature (λ = 0.154 nm). Finally, a scanning electron microscope (SEM, LEO 1450 VP) was used to determine the size and morphology of catalysts. It should be noted that deionized (DI) water is utilized during all experiments. 2.2. Synthesis of α-MoO 3 nanoparticles To synthesize α-MoO 3 NPs, 0.042 mol ascorbic acid, and 0.021 mol glycerol are added to the solution formed by dissolving 0.007 mol AHM in DI-water and agitated for 10 min at 25°C. The resultant solution was then refluxed for 1 h at 100°C. To make a viscose gel, the solution was slowly heated in an open bath at 80°C for 4 h after the reflux. The viscose gel is then immediately heated to transform into a dry gel. After that, the dried gel was calcined for 2 h at 700°C, and α-MoO 3 NPs were prepared (Scheme 2 ). 2.3. General procedure for biodiesel production As shown in Scheme 1 , two reagents (oil and alcohol) are required for biodiesel production. Herein, oleic acid was used as an oil source. Ethanol and methanol are the two most common forms of alcohol used in producing biodiesel processes; however, methanol is usually preferred since it is less expensive and has a higher transesterification reactivity than ethanol. Nevertheless, because the utilization of green resources has been at the top of our priorities for environmental protection, ethanol has been utilized as a green source of alcohol in this study. Hence, a certain volume of concentrated oleic acid was added to a required amount of catalyst, and the resulting mixture was stirred at 25°C for 5 min. Then, a determined volume of ethanol was poured into the above mixture, and the reaction mixture was agitated for 25 min at a specific temperature (Scheme 3 ). The reaction's progress was monitored at regular intervals to determine the yields of the product using the titration method. For this aim, a certain amount of the reaction mixture, around 5 mL, was extracted, and the catalyst was separated by centrifuge. Then, the residual liquid was heated to 150°C for 15 min to remove excess ethanol and water. After that, the titration was fulfilled with a 0.1 N solution of NaOH in the presence of phenolphthalein as an indicator. The endpoint was determined by changing the color from colorless to pink. The yield of biodiesel production is estimated by Eq. (1) (Chang et al., 2013 ; Sabzevar et al., 2021 ): Biodiesel Production Yield (Y%): \(\:(\text{x}-\text{y})\times\:{\text{M}}_{\text{O}\text{A}}\) /[ \(\:\text{x}{\text{M}}_{\text{O}\text{A}}+\text{y}\left({\text{M}}_{\text{E}\text{O}}-{\text{M}}_{\text{O}\text{A}}\right)\) ]100% (1) Where M OA and M EO are the molecular weights of oleic acid and ethyl oleate, respectively, x is the reaction mixture mass after its heating at 150 ºC, and y is the amount of oleic acid (in mass) calculated by NaOH titration (0.1 M) (Chang et al., 2013 ; Sabzevar et al., 2021 ). 2.4. Variable Optimization using RSM Response surface methodology (RSM) is one of the complete sets of statistical techniques for estimating and obtaining the optimal conditions for stochastic models. This instrumentation was reported in the early 1950s by Box and Wilson (Asadi and Zilouei, 2017 ; Şenaras, 2019 ). In this work, experiments utilizing the central composite design (CCD) approach were employed to analyze the main operating variables influencing the yield of the process and to find the optimal operating parameters to achieve the highest yield of biodiesel production. The influence of various variables such as catalyst dosage, alcohol to fatty acid ratio (A/F molar ratio), reaction time, and reaction medium temperature on the process yield was investigated in the presence of α-MoO 3 catalysts. Table 1 shows all variables, their corresponding symbolic names, and the three levels of independent variables evaluated in this study, ranging from − 1 to + 1. Table 1 Biodiesel production variables and levels used for RSM optimization Biodiesel Production Variables Symbols Range and Levels -1 0 + 1 Temperature ( \(\:\text{℃})\) A 25 5075 Reaction time (min) B 5 62.5 120 A/F molar ratio C 10 2030 Catalyst dosage (g) D 0.001 0.0055 0.01 The connection between operating variables and biodiesel production yield obtained from experimental data was investigated using a second-order polynomial Eq. (2) Y = \(\:{\beta\:}\) 0 + \(\:{\beta\:}\) 1 A + \(\:{\beta\:}\) 2 B + \(\:{\beta\:}\) 3 C + \(\:{\beta\:}\) 4 D + \(\:{\beta\:}\) 5 AB + \(\:{\beta\:}\) 6 AC + \(\:{\beta\:}\) 7 AD + \(\:{\beta\:}\) 8 BC + \(\:{\beta\:}\) 9 BD + \(\:{\beta\:}\) 10 CD + \(\:{\beta\:}\) 11 A 2 + \(\:{\beta\:}\) 12 B 2 + \(\:{\beta\:}\) 13 C 2 + \(\:{\beta\:}\) 14 D 2 (2) Y is defined as the response for yield of biodiesel production, and A, B, C, and D are the coded independent variables in this article. β 0 represents the intercept term, and β 1 to β 4 are known as the coefficients indicating the linear effects. The interaction effects of variables are represented by β 5 to β 10 cross-product coefficients, and finally, the squared effects are specified by β 11 , β 12 , β 13 , and β 14 quadratic coefficients (Omar and Amin, 2011 ). 3. Results and discussion 3.1. Characterization of Catalyst To obtain the convenient calcination temperature for the synthesis of α-MoO 3 nanoparticles, thermal analysis was fulfilled from RT to 1000 ºC. As presented in Fig. 1 , there are three distinctive peaks at 335, 700, and about 794 ◦ C. Between 335 ºC to 700 ºC, as shown in Fig. 1 , there was no change in the TGA curve, proposing the creation of stable α-MoO 3 . Above 700 ºC, the sudden weight loss accrues in the TGA curve and shows the melting point of α-MoO 3 . The phase information of the α-MoO 3 nanoparticles was confirmed by XRD. Figure 2 shows the XRD patterns of α-MoO 3 . As presented in Fig. 1 , the x-ray diffraction pattern of α-MoO 3 nanoparticles displays characteristic peaks at 2θ = 12.68 o , 23.40 o , 25.72 o , 35.55 o , 39 o , 45.40 o , 46.20 o , 58.92 o , 64.84 o and 67.80 o , which correspond to the (020), (110), (040), (041), (060), (200), (210), (081), (062) and (0100) planes, respectively. The results agree with standard JCPDS No. 35–0609 (Abboudi et al., 2018 ; Chen et al., 2010 ; Rajagopal et al., 2011 ; Wongkrua et al., 2013 ; Zakharova et al., 2018 ). 3.2. FT-IR characterization The FT-IR spectrum of α-MoO 3 is represented in the 4000–400 cm − 1 region. As shown in Fig. 3 , the α-MoO 3 nanoparticles mainly show three peaks. The peak at 991 cm − 1 corresponds to the terminal oxygen symmetry stretching mode of Mo = O, and the presence peak at 858 cm − 1 is attributed to the symmetry stretching modes of Mo-O-Mo. Moreover, the peak in 571 cm − 1 contributed to the bending vibration of the oxygen atom connected to three metal atoms. The broadband that appeared at 3434 cm − 1 contributed to e O − H stretching vibration; also, the peak at 1608 cm − 1 can be ascribed to the water molecules' vibration due to moisture adsorption (Chen et al., 2010 ; Umbarkar et al., 2006 ; Zakharova et al., 2018 ). The existence of these peaks confirms the successful synthesis of α-MoO 3 nanoparticles. 3.3. SEM analysis The SEM images of the α-MoO 3 are shown in Fig. 4 . As presented, the images exhibited the nanobelts and nanoplates morphology of the preparedα-MoO 3 with 0.3–0.8 µm in width and 2.67–6.35 µm in length. 3.4. Acidity investigation of catalyst by TPD-NH 3 The acidic sites of a catalyst play an essential role in carrying out chemical reactions. An ammonia-TPD experiment quantified the acid strength of the catalyst. The TPD profile related to the desorption of NH 3 from the surface of α-MoO 3 is demonstrated in Fig. 5 . There are three main areas in the NH 3 -TPD profiles: weak, intermediate, and strong acid areas that are located at 100–200, 200–400, and 400–800 ºC, respectively (Umbarkar et al., 2006 ). Notably, the peaks at the strong acidic region (400–650 ºC) indicate that excellent acidic sites exist on the surface of the MoO 3 catalyst, making it a suitable catalyst for biodiesel production. 3.5. RSM's optimization of biodiesel production Changes in the amount of process variables substantially impact the yield of biodiesel production. Optimization of operating variables is required to achieve maximum biodiesel yield with minimal energy loss to save time and energy consumption (Hariram et al., 2019 ). In this study, 29 sets of experiments were designed by RSM, as demonstrated in Table 2 . All the experiments were carried out randomly; their corresponding results are listed in Table 2 . After optimization, a second-order polynomial equation based on process variables for predicting the response (biodiesel production yield) is proposed, as shown in Eq. 3. Predicted biodiesel yield = 66.54 + 5.96 \(\:\times\:\) A+0.64 \(\:\times\:\) B+8.57 \(\:\times\:\) C+1.58 \(\:\times\:\) D-0.24 \(\:\times\:\) AB (3) + 3.89 \(\:\times\:\) AC+1.09 \(\:\times\:\) AD-0.95 \(\:\times\:\) BC+0.3 \(\:\times\:\) BD + 1.4 \(\:\times\:\) CD-0.52 \(\:\times\:\) A 2 -0.73 \(\:\times\:\) B 2 -0.083 \(\:\times\:\) C 2 -3.83 \(\:\times\:\) D 2 ANOVA (analysis of variance) table (Table 3 ) explains the correctness of the predicted model as well as the importance of each variable assessed using a smaller probability value (P value) and a bigger F value. Variables with P values less than 0.05 indicate the significance of the variable (Hariram et al., 2019 ; Renita A et al., 2014). The good fitting between experimental data and the predicted values from the model (Fig. 6 ) was well shown by high values of statistical parameters, including R 2 = 0.975 and R Adj 2 = 0.950. The ANOVA table findings show that the quadratic model chosen for variables optimization is an appropriate and significant method owing to high F-value (F model = 38.31) with a very low probability value (< 0.0001) (Garba et al., 2017 ). Table 2 Design of experiments for esterification of oleic acid using MoO 3 catalyst with observed and predicted the yield of biodiesel production Run A: Temperature ( \(\:\text{℃})\) B: Tim(min) C: A/F molar ratio D: Catalyst dosage (g) Experimental Biodiesel yield (%) 1 50 62.5 20 0.0055 67.34 2 50 62.5 10 0.001 52.645 3 75 120 20 0.0055 70.02 4 25 62.5 30 0.0055 62.15 5 75 62.5 20 0.01 70.09 6 75 62.5 30 0.0055 84.75 7 50 5 10 0.0055 55.41 8 50 62.5 20 0.0055 67.13 9 75 5 20 0.0055 72.15 10 50 120 30 0.0055 75.12 11 50 120 20 0.01 64.31 12 50 62.5 20 0.0055 66.21 13 75 62.5 20 0.001 64.45 14 25 62.5 20 0.01 58.74 15 25 62.5 20 0.001 57.45 16 50 5 30 0.0055 74.144 17 50 5 20 0.01 61.11 18 50 62.5 30 0.001 68.62 19 50 62.5 20 0.0055 65.822 20 25 120 20 0.0055 58.71 21 25 62.5 10 0.0055 54.14 22 50 120 20 0.001 61.49 23 75 62.5 10 0.0055 61.17 24 50 5 20 0.001 59.48 25 50 62.5 30 0.01 75.22 26 50 62.5 20 0.0055 66.21 27 50 62.5 10 0.01 53.64 28 50 120 10 0.0055 60.18 29 25 5 20 0.0055 59.87 Table 3 ANOVA for the production of biodiesel using the RSM method Source Sum of squares df a Mean Square F-Value p-value Model 1518.02 14 108.43 38.31 < 0.0001 significant A-Temperature 426.86 1 426.86 150.80 < 0.0001 B-Time 4.90 1 4.90 1.73 0.2095 C-A/F molar ratio 880.98 1 880.98 311.23 < 0.0001 D-Catalyst amount 30.00 1 30.00 10.60 0.0057 AB 0.24 1 0.24 0.083 0.7774 AC 60.61 1 60.61 21.41 0.0004 AD 4.73 1 4.73 1.67 0.2170 BC 3.60 1 3.60 1.27 0.2785 BD 0.35 1 0.35 0.13 0.7289 CD 7.85 1 7.85 2.77 0.1180 A 2 1.76 1 1.76 0.62 0.4437 B 2 3.48 1 3.48 1.23 0.2860 C 2 0.045 1 0.045 0.016 0.9014 D 2 94.97 1 94.97 33.55 < 0.0001 Residual 39.63 14 2.83 a Degree of Freedom The determination of the optimal values for each variable is usually accomplished using three-dimensional graphs. These plots (7a to 7f) show the effect of A/F molar ratio, dosage of catalyst, time of reaction, and temperature of reaction on the biodiesel yield at central point values of variables, i.e., catalyst dosage: 0.0055 g, A/F molar ratio of 20:1, medium temperature: 50°C, and reaction time: 62.5 min. The conjugate effect of temperature and time of reaction on the yield of biodiesel production is shown in Fig. 7 a. It shows that the yield of the process increases piecemeal by time up to 97 min, but after that, at the longer reaction time, the conversion reaches to a constant value due to the equilibrium nature of the esterification reaction. On the other hand, with increasing temperature, the yield of biodiesel increases as the esterification is known as an endothermic reaction, and the higher temperature will result in a higher conversion (Mohebbi et al., 2020 ). As Fig. 7 a exhibits, at the minimum temperature, the reaction conversion of oleic acid decreases to its lowest value (Asgari et al., 2014 ; Chang et al., 2013 ; Mohebbi et al., 2020 ). Therefore, the highest conversion of fatty acid is observed at around 72% at around 97 min and a temperature of 75°C. A similar trend was also observed in the investigation of the conjugate effect of the A/F molar ratio and reaction time variables on the yield of biodiesel production (see Fig. 7 b). As it can be observed, with increasing the molar ratio, the yield of biodiesel production increases which is attributed to suppressing of the reverse reaction. In other words, expanding the A/F molar ratio boosts fatty acid conversion to ethyl esters while limiting the reverse reaction (Asgari et al., 2014 ; Mohebbi et al., 2020 ). Besides, by increasing the concentration of methanol, the viscosity of the feed mixture decreases, leading to enhanced mass transfer and higher reaction conversion due to better mixing of components (Mohebbi et al., 2020 ). Hence, based on the results presented in Fig. 7 b, the highest percentage of conversion of fatty acid of 74% is obtained after 85 min and at the 30:1 A/F molar ratio. Figure 7 c demonstrates the interaction effect of time and catalyst dosage, indicating that the yield of the process improves with the increase in the amount of catalyst when it varies from 0.001 to 0.007 g. This enhancement is more likely because of an increase in the number of active sites and, consequently, contact between methanol and α-MoO 3 catalyst (Mohebbi et al., 2020 ). Beyond that value, the excessive catalyst will affect the saponification reaction. Hence, it reduces biodiesel yield (Hossain and Mazen, 2010 ). Furthermore, it is believed that the mass transfer resistance and the viscosity of the feed reaction will increase by increasing the amount of catalyst, resulting in conversion reduction at a constant stirring rate (Asgari et al., 2014 ; Mohebbi et al., 2020 ). Similarly, Fig. 7 d illustrates the effect of interaction between the A/F molar ratio and the amount of catalyst. As mentioned earlier, the yield of the process displays a direct relationship with the A/F molar ratio and the amount of catalyst. The maximum conversion value is achieved around 74% at 0.01 g of catalyst and 30:1 A/F molar ratio. On the other hand, Fig. 7 e presents the effect of catalyst dosage and temperature interaction. Like previous performances, the yield of the process has been enhanced when the catalyst dosage and temperature increase. Hence, at a temperature of 75°C and 0.01 g of catalyst, the maximum conversion is achieved at around 70%. Finally, Fig. 7 f unveils the effect of interaction between the temperature of the reaction medium and the A/F molar ratio. The result shows that the conversion increases as the reaction temperature and A/F ratio increase. Therefore, in a molar ratio equal to 30:1 and a temperature of 75°C, the maximum conversion of around 83% is achievable. Specified levels of effective input parameters must be selected after identifying the most effective operating variables affecting biodiesel production yield. The optimal values of each factor for reaching the highest yield of biodiesel production are shown in Fig. 8 . According to these results, by applying the optimal conditions of an amount of adsorbent equal to 0.007 g, a time of reaction of 50 min, temperature medium of 75°C, and alcohol to a fatty acid molar ratio of 30:1, the highest yield value equal to 85% with a desirability amount of 1.00 is obtained. It should be noted that the minimum and maximum production yield of biodiesel is around 52 and 85%, respectively. Furthermore, the predicted model's accuracy was validated by replicating the experiments (3 times) at optimal conditions. The experimental results unveiled an 85 ± 2% yield for biodiesel production, confirming the regression model's good satisfaction. 3.6. The suggested mechanism of esterification of biodiesel production The mechanism recommended in this study is in harmony with the previous articles for producing biodiesel in the presence of metal oxide as a catalyst (Essamlali et al., 2017 ; Rogers and Zheng, 2016 ). As expected, the surface of the α-MoO 3 nanoparticles can act as Lewis’s acid in catalyzing oleic acid esterification. An electron-poor carbon generally results from the excellent interaction between molybdenum trioxide's acidic site and the oleic acid's carbonyl group. The unpaired electron on the oxygen atom in ethanol as a nucleophile center attacked the electron-poor carbon and formed a tetrahedral intermediate. Eventually, by removing water from the tetrahedral intermediate, ethyl oleate was formed (Scheme 4 ). 3.7. Stability and recovery study of the MoO 3 catalyst The recovery of the α-MoO 3 catalyst was simple and efficient from the reaction mixture by centrifuge. Then, the collected catalyst was washed with ethanol, dried under vacuum, and reused directly for the subsequent reaction round without further purification in biodiesel production. As shown in Fig. 9 , it was demonstrated that biodiesel production could be recycled four times without a considerable reduction in the catalytic activity of α-MoO 3 nanoparticles. Table 4 also compares the catalytic activity of α-MoO 3 with other catalysts reported in the literature (Al-Saadi et al., 2020 ; De and Boxi, 2020 ; Essamlali et al., 2017 ; Salimi and Hosseini, 2019 ; Soltani et al., 2020 ; Xie and Wang, 2020 ). The production of biodiesel in less time and at a lower temperature than other catalysts and the use of ethanol as green alcohol has made the MoO 3 catalyst a suitable candidate for biodiesel production. Some previous studies, like Ref (De and Boxi, 2020 ), illustrate milder reaction conditions with almost the same production yield compared to our work. Nonetheless, as mentioned in Table 4 , it is because of the difference in the alcohol type that methanol yields a higher conversion in comparison with ethanol (Sabzevar et al., 2021 ). Table 4 A comparison of the catalytic activity of α-MoO 3 with other catalysts Entry Catalyst Type of alcohol Time (min) Temperature ( \(\:\text{℃})\) Yield (%) Reference 1 SrO-ZnO/Al 2 O 3 Ethanol 180 70 95.1 (71) 2 TiO 2 (10)/NP-800 Methanol 480 150 87 (69) 3 Cu-TiO 2 Methanol 45 5 90.93 (72) 4 ZnO/BiFeO 3 Methanol 360 65 95.43 (73) 5 MoO 3 /B-ZSM-5 Methanol 360 160 98 (65) 6 ZnO-TiO 2 -ICG Methanol 75 100 96.1 (74) 7 Fe 3 O 4 /SiO 2 Methanol 360 120 93.3 (75( 8 α-MoO 3 Ethanol 50 75 85 This work 4. Conclusion This work proposed a novel method for synthesizing α-MoO 3 nanoparticles (NPs) via a convenient recipe using ascorbic acid and glycerol as green complexing and polymerizing agents. Different methodologies, including XRD, FT-IR, SEM, TPD, and TGA, characterized the as-prepared NPs. Then, the as-prepared NPs were used as a biodiesel production catalyst. The obtained results from the design of experiments unveiled that the optimum values of operating variables for achieving the maximum yield of biodiesel production (85%) are 75°C temperature, 50 min of reaction time, ethanol to an oleic acid molar ratio of 30:1, and 0.007 g of α-MoO 3 catalyst. The stability and recyclability of the catalyst were also confirmed, and a slight reduction was observed after four cycles. The results illustrate the as-made α-MoO 3 NPs as a promising nanocatalyst for industrial applications. Declarations Acknowledgements The authors are grateful for the financial of the Iranian National Science Foundation (INSF) under grant number 98017245. Funding This work was supported by the Iranian National Science Foundation (INSF) under grant number 98017245. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Authors Contributions Arefe Moatamed Sabzevar: Conceptualization, Investigation, data collection, Visualization, Resources, and Writing–original draft; Mahboube Ghahramaninezhad: Conceptualization, Methodology, Validation, Writing–review & editing, and Project administration. Ethics declarations Ethical Approval Not applicable. Consent to Participate All named authors declare that they participated in this work and that the manuscript is original and has not been copied from another source. Consent to Publish All authors consent to the publication of this manuscript. Data Availability Statement The data of this study will be available upon reasonable request from the corresponding author. References Abati SM, Bamisaye A, Adaramaja AA, Ige AR, Adegoke KA, Ogunbiyi EO, Idowu MA, Olabintan AB, Saleh TA (2024) Biodiesel production from spent vegetable oil with Al 2 O 3 and Fe 2 O 3 -biobased heterogenous nanocatalysts: Comparative and optimization studies. 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Bioresource technology 297:122480. https://doi.org/10.1016/j.biortech.2019.122480. Zhu Y, Yao Y, Luo Z, Pan C, Yang J, Fang Y, Deng H, Liu C, Tan Q, Liu F (2019) Nanostructured MoO 3 for efficient energy and environmental catalysis. Molecules 25:18. http://doi.org/10.3390/molecules25010018. Schemes Schemes 1 to 4 are available in the Supplementary Files section. Supplementary Files GraphicalAbstract.jpg Graphical abstract Scheme.1.jpg Scheme. 1 A simple schematic of esterification reaction using α-MoO 3 catalyst Scheme2.jpg Scheme. 2 A simple schematic of the synthesis of α-MoO 3 nanoparticles method. Scheme.3.jpg Scheme. 3 Schematic for evaluation of biodiesel production using α-MoO 3 catalyst. Scheme.4.jpg Scheme. 4 Suggested mechanisms for the esterification of biodiesel production using α-MoO 3 catalyst Cite Share Download PDF Status: Published Journal Publication published 22 Nov, 2024 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Major Revision 26 Sep, 2024 Reviewers agreed at journal 02 Sep, 2024 Reviewers invited by journal 02 Sep, 2024 Editor invited by journal 02 Sep, 2024 Editor assigned by journal 01 Aug, 2024 First submitted to journal 30 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4820789","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348567016,"identity":"ed4ede2d-62f7-47a2-b24a-cde1604e5f58","order_by":0,"name":"Arefe Moatamed Sabzevar","email":"","orcid":"","institution":"Ferdowsi University of Mashhad Faculty of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Arefe","middleName":"Moatamed","lastName":"Sabzevar","suffix":""},{"id":348567017,"identity":"43d1230d-d967-4500-bf68-50c265d5f8a6","order_by":1,"name":"Mahboube 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14:31:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11812,"visible":true,"origin":"","legend":"\u003cp\u003eXRD patterns of α-MoO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/514a18486f91d9ceb83c0d25.jpg"},{"id":65854766,"identity":"b08e7178-9477-45bf-a3d4-eac4933352db","added_by":"auto","created_at":"2024-10-03 14:55:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13997,"visible":true,"origin":"","legend":"\u003cp\u003eFTIR spectra of α-MoO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/8c37aabfb6993152325f76f2.jpg"},{"id":65853347,"identity":"262b24d0-d630-47e5-9cd4-204aad3cd396","added_by":"auto","created_at":"2024-10-03 14:39:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51532,"visible":true,"origin":"","legend":"\u003cp\u003eSEM images of the α-MoO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/9df3938b0c820dbfedd428b1.jpg"},{"id":65852466,"identity":"67215309-ecc7-432c-b4c3-de88d66d6a63","added_by":"auto","created_at":"2024-10-03 14:31:50","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":15949,"visible":true,"origin":"","legend":"\u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-TPD profile of α-MoO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/2f16dd87951f771fc06e5ca3.jpg"},{"id":65853342,"identity":"f9c49581-5094-408d-b4dc-2e46620c92af","added_by":"auto","created_at":"2024-10-03 14:39:49","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":13163,"visible":true,"origin":"","legend":"\u003cp\u003eComparing predicted values of the yield of biodiesel production with actual data from experiments\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/af2935f48cb76bb185455c9d.jpg"},{"id":65852465,"identity":"1f3aa86a-52f1-47fb-9627-5e8e4d4f0789","added_by":"auto","created_at":"2024-10-03 14:31:49","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":136419,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface plots of the effects of different variables on the yield of biodiesel production in the presence of α-MoO\u003csub\u003e3\u003c/sub\u003e catalyst under process conditions (T: 50°C, time: 62.5min, ethanol to oleic acid (A/F) molar ration 20:1 and 0.0055g catalyst)\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/4b4a6073e753b877a02fb855.jpg"},{"id":65853758,"identity":"75085506-a449-4494-ba17-366cabf57f31","added_by":"auto","created_at":"2024-10-03 14:47:49","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":30631,"visible":true,"origin":"","legend":"\u003cp\u003eDetermining the optimum condition for biodiesel production using α-MoO\u003csub\u003e3\u003c/sub\u003e catalyst\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/72958a98429be8689729a4b4.jpg"},{"id":65853757,"identity":"f3375bd0-76da-43de-acec-ef452bbc1ba5","added_by":"auto","created_at":"2024-10-03 14:47:49","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":26718,"visible":true,"origin":"","legend":"\u003cp\u003eReusability of the catalyst (reaction condition: T = 75ºC, catalyst dosage = 0.001 g, time = 28 min, and the alcohol to oleic acid molar ratio 30:1)\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/00484a316fbc73702b607e5f.jpg"},{"id":69835209,"identity":"8aeb268d-7502-49c8-b36f-e07b1809405e","added_by":"auto","created_at":"2024-11-25 16:13:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1285381,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/da83783a-061d-444b-9a70-4cc3609ec460.pdf"},{"id":65852450,"identity":"08104e83-f394-4860-ada0-e3847066759d","added_by":"auto","created_at":"2024-10-03 14:31:49","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":43381,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/d0b9a82efd25d368cb5e9409.jpg"},{"id":65853344,"identity":"aa37a29f-658b-4316-980c-82edbd2d3599","added_by":"auto","created_at":"2024-10-03 14:39:49","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScheme. 1\u003c/strong\u003e A simple schematic of esterification reaction using α-MoO\u003csub\u003e3 \u003c/sub\u003ecatalyst\u003c/p\u003e","description":"","filename":"Scheme.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/8319e037d3edc253a6c21888.jpg"},{"id":65852460,"identity":"4fff4670-fa77-461c-af1f-5666e103da69","added_by":"auto","created_at":"2024-10-03 14:31:49","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":51257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScheme. 2\u003c/strong\u003e A simple schematic of the synthesis of α-MoO\u003csub\u003e3 \u003c/sub\u003enanoparticles method.\u003c/p\u003e","description":"","filename":"Scheme2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/ee5f22c78297cb462b3b07db.jpg"},{"id":65852446,"identity":"b5171984-a109-43e4-9de7-fb5ac64eb3cd","added_by":"auto","created_at":"2024-10-03 14:31:49","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":46272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScheme. 3\u003c/strong\u003e Schematic for evaluation of biodiesel production using α-MoO\u003csub\u003e3\u003c/sub\u003ecatalyst.\u003c/p\u003e","description":"","filename":"Scheme.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/cc3fbb60ac74339e73c80abf.jpg"},{"id":65854765,"identity":"43ea034b-7c63-45ba-94ba-55e4c9558ee3","added_by":"auto","created_at":"2024-10-03 14:55:49","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":59418,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScheme. 4\u003c/strong\u003e Suggested mechanisms for the esterification of biodiesel production using α-MoO\u003csub\u003e3\u003c/sub\u003e catalyst\u003c/p\u003e","description":"","filename":"Scheme.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4820789/v1/7f9445453c7a6c160c2bff7e.jpg"}],"financialInterests":"","formattedTitle":"Glycerol and ascorbic acid-assisted synthesis of α-MoO3 nanoparticles as an efficient catalyst for biodiesel production","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOne of the most serious concerns humanity has ever faced is environmental pollution caused by the extensive use of energy for the convenience of modern life (Nomanbhay and Ong, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, the demand to develop and exploit green fuels has grown steadily (Sales et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In recent years, extensive research has been conducted to identify a viable fuel to replace conventional liquid fuels. As a result, biofuels, particularly biodiesel, have attracted a lot of attention. Biodiesel, compared to fossil fuels, has illustrated technical and environmental benefits (Lin et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). It is a renewable (Farouk et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ngomade et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pandit et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), biodegradable, non-toxic fuel (Osman et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and eco-friendly (Abati et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). obtained from sources like vegetable oils and animal fats (Lin et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and it has great potential as an alternative to fossil fuels (Parida et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In general, biodiesel can be produced by various methods such as pyrolysis (Zhang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and transesterification (Ghaffari and Behzad, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zdujić et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), but the transesterification process in the presence of a sufficient catalyst is the most efficient (Scragg et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This process involves the reaction of an oil source with an alcohol in the presence of a catalyst. Various types of alcohol are utilized in this procedure, including ethanol, methanol, propanol, and others (Salimi and Hosseini, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this approach, homogeneous and heterogeneous catalysts can be employed; however, separating homogeneous inorganic acid catalysts such as sulfuric acid is challenging and might result in corrosion and contamination of the reaction (Wan et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, developing heterogeneous catalyst systems using green, simple, and cost-effective synthesis techniques is critical to address these issues. In recent years, metal oxides, either as active phases or as supports, have been used as catalysts in many chemical reactions such as the synthesis of methanol (Chang et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), oxidation of various alcohols (Kazemnejadi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nasseri et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), oxidative desulfurization of DBT (Alenazi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ghahramaninezhad and Ahmadpour, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), biodiesel production (Huang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Khatibi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Qu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and so on. One type of transition metal oxide is orthorhombic Molybdenum oxide (α-MoO\u003csub\u003e3\u003c/sub\u003e), which has attracted much attention from scientists because of its non-toxic nature, high stability, and strong Lewis acid sites (Zhu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hence, MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles (NPs) are successfully engaged as an efficient catalyst in many chemical reactions, such as in the oxidation of sulfides (Tosi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), methanol oxidation (Pe\u0026ntilde;a-Bahamonde et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Puebla et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Swathi et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), organic photovoltaics (OPVs) (Choi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gong et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), hydrodesulfurization (Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), gas sensors (Jiang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and biodiesel production (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Silva Lucena de Medeiros et al., 2024; Xie and Zhao, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The application of α-MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles as a single-phase bulk active catalyst for biodiesel production is rarely reported (Silva et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which can be attributed to the lack of a green, convenient, fast, effective, and low-cost method for its synthesis. For this aim, studying influential factors on the production method of metal oxide nanoparticles from green and cost-effective precursors is very significant.\u003c/p\u003e \u003cp\u003eTo date, molybdenum oxide NPs are mainly fabricated from toxic and costly precursors through high energy-consuming procedures (Shaheen and Ahmad, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). One of the simple methods for synthesizing metal oxide nanoparticles, including MoO\u003csub\u003e3,\u003c/sub\u003e is the sol-gel method (Singh et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), where a complexing agent and a polymerizing agent are used in the synthesis process. Indeed, the complexing agent prevents the accumulation of particles and prevents the growth of nanoparticles by preventing the formation of larger clusters. The polymerizing agent is also used to homogenize the particle distribution. Due to the simultaneous use of these two agents, this method is sometimes called the PC (polymerizing\u0026ndash;complexing) method (Habte et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this research, for the first time, we are offering a green and novel protocol for the synthesis of α-MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles using two green and non-toxic polymerizing and complexing agents, glycerol and ascorbic acid. The as-made α-MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles will then be employed as an efficient and novel catalyst for biodiesel production. The biodiesel production yield is investigated by reacting between oleic acid as an oil source and ethanol in the presence of α-MoO\u003csub\u003e3\u003c/sub\u003e as a catalyst. In typical, temperature, time of reaction, the ratio of alcohol to fatty acid (A/F molar ratio), and catalyst dosage are the most common factors influencing the biodiesel production yield. The optimal conditions for biodiesel production are experimentally assessed using response surface methodology (RSM). After the reaction, the catalyst is easily separated by centrifugation and reused in the process. Moreover, a supposed mechanism for biodiesel production using the as-prepared α-MoO\u003csub\u003e3\u003c/sub\u003e catalyst is presented. Scheme \u003cspan refid=\"Sch1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates a basic diagram of biodiesel production via esterification reaction in the presence of α-MoO\u003csub\u003e3\u003c/sub\u003e NPs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. General Experimentation and Methodologies","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Chemicals and instruments\u003c/h2\u003e \u003cp\u003eAll required chemicals and solvents were supplied from SigmaAldrich and used without any additional purification. L-Ascorbic acid (purity\u0026thinsp;\u0026gt;\u0026thinsp;99%), Glycerol (99%), Ammonium heptamolybdate tetrahydrate (AHM, 99%), Sodium hydroxide, Oleic acid (99%), and ethanol (99.8%) were utilized for α-MoO\u003csub\u003e3\u003c/sub\u003e synthesis. The catalyst's X-ray diffraction (XRD) pattern was attained with a Bruker D8-Advance diffractometer by Cu Kα radiation at room temperature (λ\u0026thinsp;=\u0026thinsp;0.154 nm). Finally, a scanning electron microscope (SEM, LEO 1450 VP) was used to determine the size and morphology of catalysts. It should be noted that deionized (DI) water is utilized during all experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Synthesis of α-MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles\u003c/h2\u003e \u003cp\u003eTo synthesize α-MoO\u003csub\u003e3\u003c/sub\u003e NPs, 0.042 mol ascorbic acid, and 0.021 mol glycerol are added to the solution formed by dissolving 0.007 mol AHM in DI-water and agitated for 10 min at 25\u0026deg;C. The resultant solution was then refluxed for 1 h at 100\u0026deg;C. To make a viscose gel, the solution was slowly heated in an open bath at 80\u0026deg;C for 4 h after the reflux. The viscose gel is then immediately heated to transform into a dry gel. After that, the dried gel was calcined for 2 h at 700\u0026deg;C, and α-MoO\u003csub\u003e3\u003c/sub\u003e NPs were prepared (Scheme \u003cspan refid=\"Sch2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. General procedure for biodiesel production\u003c/h2\u003e \u003cp\u003eAs shown in Scheme \u003cspan refid=\"Sch1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, two reagents (oil and alcohol) are required for biodiesel production. Herein, oleic acid was used as an oil source. Ethanol and methanol are the two most common forms of alcohol used in producing biodiesel processes; however, methanol is usually preferred since it is less expensive and has a higher transesterification reactivity than ethanol. Nevertheless, because the utilization of green resources has been at the top of our priorities for environmental protection, ethanol has been utilized as a green source of alcohol in this study. Hence, a certain volume of concentrated oleic acid was added to a required amount of catalyst, and the resulting mixture was stirred at 25\u0026deg;C for 5 min. Then, a determined volume of ethanol was poured into the above mixture, and the reaction mixture was agitated for 25 min at a specific temperature (Scheme \u003cspan refid=\"Sch3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The reaction's progress was monitored at regular intervals to determine the yields of the product using the titration method. For this aim, a certain amount of the reaction mixture, around 5 mL, was extracted, and the catalyst was separated by centrifuge. Then, the residual liquid was heated to 150\u0026deg;C for 15 min to remove excess ethanol and water. After that, the titration was fulfilled with a 0.1 N solution of NaOH in the presence of phenolphthalein as an indicator. The endpoint was determined by changing the color from colorless to pink. The yield of biodiesel production is estimated by Eq.\u0026nbsp;(1) (Chang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sabzevar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e):\u003c/p\u003e \u003cp\u003eBiodiesel Production Yield (Y%): \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(\\text{x}-\\text{y})\\times\\:{\\text{M}}_{\\text{O}\\text{A}}\\)\u003c/span\u003e\u003c/span\u003e/[\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{x}{\\text{M}}_{\\text{O}\\text{A}}+\\text{y}\\left({\\text{M}}_{\\text{E}\\text{O}}-{\\text{M}}_{\\text{O}\\text{A}}\\right)\\)\u003c/span\u003e\u003c/span\u003e]100% (1)\u003c/p\u003e \u003cp\u003eWhere M\u003csub\u003eOA\u003c/sub\u003e and M\u003csub\u003eEO\u003c/sub\u003e are the molecular weights of oleic acid and ethyl oleate, respectively, x is the reaction mixture mass after its heating at 150 \u0026ordm;C, and y is the amount of oleic acid (in mass) calculated by NaOH titration (0.1 M) (Chang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sabzevar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Variable Optimization using RSM\u003c/h2\u003e \u003cp\u003eResponse surface methodology (RSM) is one of the complete sets of statistical techniques for estimating and obtaining the optimal conditions for stochastic models. This instrumentation was reported in the early 1950s by Box and Wilson (Asadi and Zilouei, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Şenaras, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this work, experiments utilizing the central composite design (CCD) approach were employed to analyze the main operating variables influencing the yield of the process and to find the optimal operating parameters to achieve the highest yield of biodiesel production. The influence of various variables such as catalyst dosage, alcohol to fatty acid ratio (A/F molar ratio), reaction time, and reaction medium temperature on the process yield was investigated in the presence of α-MoO\u003csub\u003e3\u003c/sub\u003e catalysts. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows all variables, their corresponding symbolic names, and the three levels of independent variables evaluated in this study, ranging from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eBiodiesel production variables and levels used for RSM optimization\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiodiesel Production Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymbols\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRange and Levels\u003c/p\u003e \u003cp\u003e-1 0\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{℃})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 5075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReaction time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 62.5 120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA/F molar ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 2030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatalyst dosage (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001 0.0055 0.01\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 connection between operating variables and biodiesel production yield obtained from experimental data was investigated using a second-order polynomial Eq.\u0026nbsp;(2)\u003c/p\u003e \u003cp\u003eY = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e0\u003c/sub\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e1\u003c/sub\u003eA + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e2\u003c/sub\u003eB + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e3\u003c/sub\u003eC + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e4\u003c/sub\u003eD + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e5\u003c/sub\u003eAB + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e6\u003c/sub\u003eAC + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e7\u003c/sub\u003eAD + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e8\u003c/sub\u003eBC + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e9\u003c/sub\u003eBD + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e10\u003c/sub\u003eCD + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e11\u003c/sub\u003eA\u003csup\u003e2\u003c/sup\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e12\u003c/sub\u003eB\u003csup\u003e2\u003c/sup\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e13\u003c/sub\u003eC\u003csup\u003e2\u003c/sup\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e14\u003c/sub\u003eD\u003csup\u003e2\u003c/sup\u003e (2)\u003c/p\u003e \u003cp\u003eY is defined as the response for yield of biodiesel production, and A, B, C, and D are the coded independent variables in this article. β\u003csub\u003e0\u003c/sub\u003e represents the intercept term, and β\u003csub\u003e1\u003c/sub\u003e to β\u003csub\u003e4\u003c/sub\u003e are known as the coefficients indicating the linear effects. The interaction effects of variables are represented by β\u003csub\u003e5\u003c/sub\u003e to β\u003csub\u003e10\u003c/sub\u003e cross-product coefficients, and finally, the squared effects are specified by β\u003csub\u003e11\u003c/sub\u003e, β\u003csub\u003e12\u003c/sub\u003e, β\u003csub\u003e13\u003c/sub\u003e, and β\u003csub\u003e14\u003c/sub\u003e quadratic coefficients (Omar and Amin, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Characterization of Catalyst\u003c/h2\u003e \u003cp\u003eTo obtain the convenient calcination temperature for the synthesis of α-MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles, thermal analysis was fulfilled from RT to 1000 \u0026ordm;C. As presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there are three distinctive peaks at 335, 700, and about 794\u003csup\u003e◦\u003c/sup\u003eC. Between 335 \u0026ordm;C to 700 \u0026ordm;C, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there was no change in the TGA curve, proposing the creation of stable α-MoO\u003csub\u003e3\u003c/sub\u003e. Above 700 \u0026ordm;C, the sudden weight loss accrues in the TGA curve and shows the melting point of α-MoO\u003csub\u003e3\u003c/sub\u003e. The phase information of the α-MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles was confirmed by XRD. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the XRD patterns of α-MoO\u003csub\u003e3\u003c/sub\u003e. As presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the x-ray diffraction pattern of α-MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles displays characteristic peaks at 2θ\u0026thinsp;=\u0026thinsp;12.68\u003csup\u003eo\u003c/sup\u003e, 23.40\u003csup\u003eo\u003c/sup\u003e, 25.72\u003csup\u003eo\u003c/sup\u003e, 35.55 \u003csup\u003eo\u003c/sup\u003e, 39\u003csup\u003eo\u003c/sup\u003e, 45.40\u003csup\u003eo\u003c/sup\u003e, 46.20\u003csup\u003eo\u003c/sup\u003e, 58.92 \u003csup\u003eo\u003c/sup\u003e, 64.84 \u003csup\u003eo\u003c/sup\u003e and 67.80\u003csup\u003eo\u003c/sup\u003e, which correspond to the (020), (110), (040), (041), (060), (200), (210), (081), (062) and (0100) planes, respectively. The results agree with standard JCPDS No. 35\u0026ndash;0609 (Abboudi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rajagopal et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wongkrua et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zakharova et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. FT-IR characterization\u003c/h2\u003e \u003cp\u003eThe FT-IR spectrum of α-MoO\u003csub\u003e3\u003c/sub\u003e is represented in the 4000\u0026ndash;400 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e region. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the α-MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles mainly show three peaks. The peak at 991 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e corresponds to the terminal oxygen symmetry stretching mode of Mo\u0026thinsp;=\u0026thinsp;O, and the presence peak at 858 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e is attributed to the symmetry stretching modes of Mo-O-Mo. Moreover, the peak in 571 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e contributed to the bending vibration of the oxygen atom connected to three metal atoms. The broadband that appeared at 3434 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e contributed to e O\u0026thinsp;\u0026minus;\u0026thinsp;H stretching vibration; also, the peak at 1608 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e can be ascribed to the water molecules' vibration due to moisture adsorption (Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Umbarkar et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zakharova et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The existence of these peaks confirms the successful synthesis of α-MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. SEM analysis\u003c/h2\u003e \u003cp\u003eThe SEM images of the α-MoO\u003csub\u003e3\u003c/sub\u003e are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. As presented, the images exhibited the nanobelts and nanoplates morphology of the preparedα-MoO\u003csub\u003e3\u003c/sub\u003ewith 0.3\u0026ndash;0.8 \u0026micro;m in width and 2.67\u0026ndash;6.35 \u0026micro;m in length.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Acidity investigation of catalyst by TPD-NH\u003csub\u003e3\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eThe acidic sites of a catalyst play an essential role in carrying out chemical reactions. An ammonia-TPD experiment quantified the acid strength of the catalyst. The TPD profile related to the desorption of NH\u003csub\u003e3\u003c/sub\u003e from the surface of α-MoO\u003csub\u003e3\u003c/sub\u003e is demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. There are three main areas in the NH\u003csub\u003e3\u003c/sub\u003e-TPD profiles: weak, intermediate, and strong acid areas that are located at 100\u0026ndash;200, 200\u0026ndash;400, and 400\u0026ndash;800 \u0026ordm;C, respectively (Umbarkar et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Notably, the peaks at the strong acidic region (400\u0026ndash;650 \u0026ordm;C) indicate that excellent acidic sites exist on the surface of the MoO\u003csub\u003e3\u003c/sub\u003e catalyst, making it a suitable catalyst for biodiesel production.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5. RSM's optimization of biodiesel production\u003c/h2\u003e \u003cp\u003eChanges in the amount of process variables substantially impact the yield of biodiesel production. Optimization of operating variables is required to achieve maximum biodiesel yield with minimal energy loss to save time and energy consumption (Hariram et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this study, 29 sets of experiments were designed by RSM, as demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All the experiments were carried out randomly; their corresponding results are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. After optimization, a second-order polynomial equation based on process variables for predicting the response (biodiesel production yield) is proposed, as shown in Eq.\u0026nbsp;3.\u003c/p\u003e \u003cp\u003ePredicted biodiesel yield\u0026thinsp;=\u0026thinsp;66.54\u0026thinsp;+\u0026thinsp;5.96\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eA+0.64\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eB+8.57\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eC+1.58\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eD-0.24\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eAB (3)\u0026thinsp;+\u0026thinsp;3.89\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eAC+1.09\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eAD-0.95\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eBC+0.3\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eBD +\u0026thinsp;1.4\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eCD-0.52\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eA\u003csup\u003e2\u003c/sup\u003e-0.73\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eB\u003csup\u003e2\u003c/sup\u003e-0.083\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eC\u003csup\u003e2\u003c/sup\u003e-3.83\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eD\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eANOVA (analysis of variance) table (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) explains the correctness of the predicted model as well as the importance of each variable assessed using a smaller probability value (P value) and a bigger F value. Variables with P values less than 0.05 indicate the significance of the variable (Hariram et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Renita A et al., 2014). The good fitting between experimental data and the predicted values from the model (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) was well shown by high values of statistical parameters, including R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.975 and R\u003csub\u003eAdj\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e= 0.950.\u003c/p\u003e \u003cp\u003eThe ANOVA table findings show that the quadratic model chosen for variables optimization is an appropriate and significant method owing to high F-value (F\u003csub\u003emodel\u003c/sub\u003e= 38.31) with a very low probability value (\u0026lt;\u0026thinsp;0.0001) (Garba et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDesign of experiments for esterification of oleic acid using MoO\u003csub\u003e3\u003c/sub\u003ecatalyst with observed and predicted the yield of biodiesel production\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA: Temperature (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{℃})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB: Tim(min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC: A/F molar ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD: Catalyst dosage (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003cp\u003eBiodiesel yield (%)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67.34\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.645\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.02\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.09\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67.13\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.31\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.21\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.45\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.74\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.45\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74.144\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61.11\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59.87\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=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA for the production of biodiesel using the RSM method\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1518.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003esignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA-Temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e426.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e426.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eB-Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2095\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\u003eC-A/F molar ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e880.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e880.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e311.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eD-Catalyst amount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0057\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\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7774\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\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0004\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\u003eAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2170\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\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2785\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\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7289\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\u003eCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1180\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\u003eA\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4437\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\u003eB\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2860\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\u003eC\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9014\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\u003eD\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.83\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e Degree of Freedom\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe determination of the optimal values for each variable is usually accomplished using three-dimensional graphs. These plots (7a to 7f) show the effect of A/F molar ratio, dosage of catalyst, time of reaction, and temperature of reaction on the biodiesel yield at central point values of variables, i.e., catalyst dosage: 0.0055 g, A/F molar ratio of 20:1, medium temperature: 50\u0026deg;C, and reaction time: 62.5 min.\u003c/p\u003e \u003cp\u003eThe conjugate effect of temperature and time of reaction on the yield of biodiesel production is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea. It shows that the yield of the process increases piecemeal by time up to 97 min, but after that, at the longer reaction time, the conversion reaches to a constant value due to the equilibrium nature of the esterification reaction. On the other hand, with increasing temperature, the yield of biodiesel increases as the esterification is known as an endothermic reaction, and the higher temperature will result in a higher conversion (Mohebbi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea exhibits, at the minimum temperature, the reaction conversion of oleic acid decreases to its lowest value (Asgari et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mohebbi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, the highest conversion of fatty acid is observed at around 72% at around 97 min and a temperature of 75\u0026deg;C. A similar trend was also observed in the investigation of the conjugate effect of the A/F molar ratio and reaction time variables on the yield of biodiesel production (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). As it can be observed, with increasing the molar ratio, the yield of biodiesel production increases which is attributed to suppressing of the reverse reaction. In other words, expanding the A/F molar ratio boosts fatty acid conversion to ethyl esters while limiting the reverse reaction (Asgari et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mohebbi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Besides, by increasing the concentration of methanol, the viscosity of the feed mixture decreases, leading to enhanced mass transfer and higher reaction conversion due to better mixing of components (Mohebbi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence, based on the results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb, the highest percentage of conversion of fatty acid of 74% is obtained after 85 min and at the 30:1 A/F molar ratio. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec demonstrates the interaction effect of time and catalyst dosage, indicating that the yield of the process improves with the increase in the amount of catalyst when it varies from 0.001 to 0.007 g. This enhancement is more likely because of an increase in the number of active sites and, consequently, contact between methanol and α-MoO\u003csub\u003e3\u003c/sub\u003e catalyst (Mohebbi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Beyond that value, the excessive catalyst will affect the saponification reaction. Hence, it reduces biodiesel yield (Hossain and Mazen, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, it is believed that the mass transfer resistance and the viscosity of the feed reaction will increase by increasing the amount of catalyst, resulting in conversion reduction at a constant stirring rate (Asgari et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mohebbi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed illustrates the effect of interaction between the A/F molar ratio and the amount of catalyst. As mentioned earlier, the yield of the process displays a direct relationship with the A/F molar ratio and the amount of catalyst. The maximum conversion value is achieved around 74% at 0.01 g of catalyst and 30:1 A/F molar ratio. On the other hand, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee presents the effect of catalyst dosage and temperature interaction. Like previous performances, the yield of the process has been enhanced when the catalyst dosage and temperature increase. Hence, at a temperature of 75\u0026deg;C and 0.01 g of catalyst, the maximum conversion is achieved at around 70%. Finally, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef unveils the effect of interaction between the temperature of the reaction medium and the A/F molar ratio. The result shows that the conversion increases as the reaction temperature and A/F ratio increase. Therefore, in a molar ratio equal to 30:1 and a temperature of 75\u0026deg;C, the maximum conversion of around 83% is achievable. Specified levels of effective input parameters must be selected after identifying the most effective operating variables affecting biodiesel production yield. The optimal values of each factor for reaching the highest yield of biodiesel production are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. According to these results, by applying the optimal conditions of an amount of adsorbent equal to 0.007 g, a time of reaction of 50 min, temperature medium of 75\u0026deg;C, and alcohol to a fatty acid molar ratio of 30:1, the highest yield value equal to 85% with a desirability amount of 1.00 is obtained. It should be noted that the minimum and maximum production yield of biodiesel is around 52 and 85%, respectively.\u003c/p\u003e \u003cp\u003eFurthermore, the predicted model's accuracy was validated by replicating the experiments (3 times) at optimal conditions. The experimental results unveiled an 85\u0026thinsp;\u0026plusmn;\u0026thinsp;2% yield for biodiesel production, confirming the regression model's good satisfaction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6. The suggested mechanism of esterification of biodiesel production\u003c/h2\u003e \u003cp\u003eThe mechanism recommended in this study is in harmony with the previous articles for producing biodiesel in the presence of metal oxide as a catalyst (Essamlali et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rogers and Zheng, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As expected, the surface of the α-MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles can act as Lewis\u0026rsquo;s acid in catalyzing oleic acid esterification. An electron-poor carbon generally results from the excellent interaction between molybdenum trioxide's acidic site and the oleic acid's carbonyl group. The unpaired electron on the oxygen atom in ethanol as a nucleophile center attacked the electron-poor carbon and formed a tetrahedral intermediate. Eventually, by removing water from the tetrahedral intermediate, ethyl oleate was formed (Scheme \u003cspan refid=\"Sch4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Stability and recovery study of the MoO\u003csub\u003e3\u003c/sub\u003e catalyst\u003c/h2\u003e \u003cp\u003eThe recovery of the α-MoO\u003csub\u003e3\u003c/sub\u003e catalyst was simple and efficient from the reaction mixture by centrifuge. Then, the collected catalyst was washed with ethanol, dried under vacuum, and reused directly for the subsequent reaction round without further purification in biodiesel production. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, it was demonstrated that biodiesel production could be recycled four times without a considerable reduction in the catalytic activity of α-MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e also compares the catalytic activity of α-MoO\u003csub\u003e3\u003c/sub\u003e with other catalysts reported in the literature (Al-Saadi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; De and Boxi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Essamlali et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Salimi and Hosseini, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Soltani et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xie and Wang, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The production of biodiesel in less time and at a lower temperature than other catalysts and the use of ethanol as green alcohol has made the MoO\u003csub\u003e3\u003c/sub\u003e catalyst a suitable candidate for biodiesel production. Some previous studies, like Ref (De and Boxi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), illustrate milder reaction conditions with almost the same production yield compared to our work. Nonetheless, as mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it is because of the difference in the alcohol type that methanol yields a higher conversion in comparison with ethanol (Sabzevar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA comparison of the catalytic activity of α-MoO\u003csub\u003e3\u003c/sub\u003e with other catalysts\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eEntry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCatalyst\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType of alcohol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTemperature (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{℃})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYield (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReference\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\u003eSrO-ZnO/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEthanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(71)\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\u003eTiO\u003csub\u003e2\u003c/sub\u003e(10)/NP-800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(69)\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\u003eCu-TiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(72)\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\u003eZnO/BiFeO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoO\u003csub\u003e3\u003c/sub\u003e/B-ZSM-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(65)\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\u003eZnO-TiO\u003csub\u003e2\u003c/sub\u003e-ICG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e/SiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(75(\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα-MoO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEthanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThis work\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"},{"header":"4. Conclusion","content":"\u003cp\u003eThis work proposed a novel method for synthesizing α-MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles (NPs) via a convenient recipe using ascorbic acid and glycerol as green complexing and polymerizing agents. Different methodologies, including XRD, FT-IR, SEM, TPD, and TGA, characterized the as-prepared NPs. Then, the as-prepared NPs were used as a biodiesel production catalyst. The obtained results from the design of experiments unveiled that the optimum values of operating variables for achieving the maximum yield of biodiesel production (85%) are 75\u0026deg;C temperature, 50 min of reaction time, ethanol to an oleic acid molar ratio of 30:1, and 0.007 g of α-MoO\u003csub\u003e3\u003c/sub\u003e catalyst. The stability and recyclability of the catalyst were also confirmed, and a slight reduction was observed after four cycles. The results illustrate the as-made α-MoO\u003csub\u003e3\u003c/sub\u003e NPs as a promising nanocatalyst for industrial applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful for the financial of the Iranian National Science Foundation (INSF) under grant number 98017245.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Iranian National Science Foundation (INSF) under grant number 98017245.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArefe Moatamed Sabzevar: Conceptualization, Investigation, \u003cstrong\u003edata collection,\u0026nbsp;\u003c/strong\u003eVisualization, Resources, and Writing\u0026ndash;original draft; Mahboube Ghahramaninezhad: Conceptualization, Methodology, Validation, Writing\u0026ndash;review \u0026amp; editing, and Project administration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll named authors declare that they participated in this work and that the manuscript is original and has not been copied from another source.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent to the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of this study will be available upon reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbati SM, Bamisaye A, Adaramaja AA, Ige AR, Adegoke KA, Ogunbiyi EO, Idowu MA, Olabintan AB, Saleh TA (2024) Biodiesel production from spent vegetable oil with Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e and Fe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e-biobased heterogenous nanocatalysts: Comparative and optimization studies. 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Fuel 304:121463. https://doi.org/10.1016/j.fuel.2021.121463.\u003c/li\u003e\n\u003cli\u003eGong Y, Dong Y, Zhao B, Yu R, Hu S, Tan Za (2020) Diverse applications of MoO\u003csub\u003e3\u003c/sub\u003e for high performance organic photovoltaics: fundamentals, processes and optimization strategies. Journal of Materials Chemistry A 8:978-1009. https://doi.org/10.1039/C9TA12005J.\u003c/li\u003e\n\u003cli\u003eHabte L, Shiferaw, N, Mulatu, D, Thenepalli, T, Chilakala, R, Ahn, JW (2019) Synthesis of nano-calcium oxide from waste eggshell by sol-gel method. Sustainability 11:3196. https://doi.org/10.3390/su11113196.\u003c/li\u003e\n\u003cli\u003eHariram V, Bose A, Seralathan S (2019) Dataset on optimized biodiesel production from seeds of Vitis vinifera using ANN, RSM and ANFIS. 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Molecules 25:18. http://doi.org/10.3390/molecules25010018.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Schemes","content":"\u003cp\u003eSchemes 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"α-MoO3, Ascorbic acid, Biodiesel, Catalyst, Esterification, RSM","lastPublishedDoi":"10.21203/rs.3.rs-4820789/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4820789/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis work exhibits a novel method for synthesizing α-MoO\u003csub\u003e3\u003c/sub\u003e nanoparticles (NPs) using a convenient recipe that utilizes glycerol and ascorbic acid as polymerizing and green complexing agents. Different analytical techniques, including XRD, FT-IR, TGA, and scanning electron microscopy (SEM), were employed to identify the as-prepared α-MoO\u003csub\u003e3\u003c/sub\u003e NPs, and it was used as a catalyst in biodiesel production. Moreover, the TPD experiment was performed to determine the catalyst's acidity strength. The α-MoO\u003csub\u003e3\u003c/sub\u003e exhibited high efficiency in producing biodiesel from oleic acid and ethyl alcohol as an oil source and alcohol, respectively. The design of experiments and optimization process were also performed using response surface methodology (RSM) to attain the optimal condition. The influences of several parameters, such as catalyst dosage, reaction time, medium temperature, and alcohol to fatty acid (in molar ratio), were studied. The results demonstrate that at the optimal operating variables of 75\u0026deg;C, 50 min of reaction time, a 30:1 molar ratio of alcohol to oleic acid, and 0.007 g of catalyst, the yield of biodiesel production can approach 85%. Moreover, the obtained results indicated that the catalyst can be efficiently recovered and reused four times without significant loss in its activity.\u003c/p\u003e","manuscriptTitle":"Glycerol and ascorbic acid-assisted synthesis of α-MoO3 nanoparticles as an efficient catalyst for biodiesel production","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-03 14:31:44","doi":"10.21203/rs.3.rs-4820789/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-09-26T07:00:58+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-09-03T02:37:06+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-02T18:45:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2024-09-02T17:48:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-01T04:44:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2024-07-30T18:01:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"06ec7ee9-782a-4b13-950b-ed985eb42864","owner":[],"postedDate":"October 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T16:07:45+00:00","versionOfRecord":{"articleIdentity":"rs-4820789","link":"https://doi.org/10.1007/s11356-024-35571-1","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2024-11-22 15:58:00","publishedOnDateReadable":"November 22nd, 2024"},"versionCreatedAt":"2024-10-03 14:31:44","video":"","vorDoi":"10.1007/s11356-024-35571-1","vorDoiUrl":"https://doi.org/10.1007/s11356-024-35571-1","workflowStages":[]},"version":"v1","identity":"rs-4820789","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4820789","identity":"rs-4820789","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}