Thermal Conductivity Enhancement of Ag/MCM-41 Hybrid Nanofluid for Solar Photothermal Applications

Thermal Conductivity Enhancement of Ag/MCM-41 Hybrid Nanofluid for Solar Photothermal Applications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Thermal Conductivity Enhancement of Ag/MCM-41 Hybrid Nanofluid for Solar Photothermal Applications Reza Afsharianzadeh, Mohammad Behbahani, Rashid Pourrajab, Saman Bagheri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7502565/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We studied thermal properties of silver nanoparticle and mesoporous silica (MCM-41) nanofluid in aqueous solution and evaluates its potential for enhancing solar thermal system performance. FE-SEM, EDS, and Zeta-potential confirmed the successful preparation of the hybrid nanofluid. Experimental conditions were optimized via Box-Behnken design and thermal conductivities were experimentally measured using a KD2 Pro device (from 30°C to 50°C). Under optimal conditions (79.11 ppm Ag, 746.08 ppm MCM-41 at 50°C), results demonstrated a 13.44% improvement compared to water. Further theoretical calculations and TRNSYS simulations assessed the nanofluid's performance in a solar collector model. Data suggest an increase of 1.26% and 6.05% (vs. water) in the solar collector's thermal efficiency and the convection heat transfer coefficient, respectively. Conventional fluids are less satisfactory with low thermal conductivity and instability at high temperatures. Here, Ag/MCM-41 hybrid nanofluid is a promising medium for improving heat transfer in solar thermal systems and potentially other industrial applications. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Nanoscience and technology Silver hybrid-nanofluid silver Silica thermal conductivity Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction For the last decade, we have seen a growing demand for stablishing platforms and alternatives for sufficient energy consumption. Among these, managing and minimizing the loss of thermal energy has been studied in many industrial and renewable energy systems. In heat transfer systems, such as solar thermal systems, water or ethylene glycol is often a standard choice. They are environmentally safe and industrially cheap. However, they suffer from inherently low thermal conductivity (TC) and limited overall efficiency 1 , 2 . Nanofluids have recently emerged as alternatives and can more efficiently improve heat transfer. Nanofluids are comprised of active materials with high TC (like metals and carbon-based materials) 3 , 4 . More recently, hybrid nanofluids, where a combination of nanomaterials are used, have attracted attention for their potential to synergistically enhance TC beyond what single component nanofluids can achieve 5 – 7 . Despite these advancements, challenges such as nanoparticle aggregation, sedimentation, increased viscosity, and stability issues hinder the practical application of nanofluids. Addressing these limitations is critical to translating laboratory-scale improvements into durable, real-world heat transfer solutions. Mesoporous materials provide high surface area and can enhance nanoparticle dispersion and stability, offering structural benefits that improve suspension stability 8 , 9 . Metals and metal oxide nanoparticles, by contrast, are interesting for their high TC. Combining these materials in a hybrid nanofluid can thus deliver both high conductivity and improved stability, which is crucial for practical thermal applications. However, such hybrid systems remain underexplored in solar thermal and photovoltaic (PV) applications. When designing a nanofluid system, three factors are particularly important: (i) a green and environmentally friendly base fluid, (ii) a stable and easily dispersible porous material, and (iii) a highly conductive nanoparticle. Among available base fluids, water is the most environmentally friendly choice 6 . In addition to its eco-friendliness, water has several advantageous properties: (i) a relatively high specific heat capacity for efficient heat absorption and storage, (ii) reasonable TC, (iii) wide availability and low cost, (iv) well-characterized thermophysical properties, and (v) excellent solvent and dispersing capabilities for many nanoparticles 10 . The choice of dispersing material depends on several criteria: it should be chemically stable, have an extended shelf-life, allow high nanoparticle loading, exhibit low viscosity, and be environmentally safe in both use and synthesis. Candidate materials include MXenes, metal-organic frameworks (MOFs), porous carbons, graphene-based materials, and mesoporous silica materials (MSMs) 11 . MXenes offer high nanoparticle loading and long shelf-life but generally suffer from low chemical stability in water and environmentally challenging synthesis 12 . MOFs are highly porous, but their dispersity and water stability vary widely depending on the metal-ligand combination 13 . Porous carbons often display poor dispersity in water. MSMs, however, meet all the criteria for an effective dispersing material in TC experiments: they are highly dispersible in water due to their uniform SiO 2 framework and abundant hydroxyl groups, water-stable, cost-effective, environmentally friendly, possess slightly better TC than MOFs, exhibit high affinity for metal nanoparticle loading, and maintain low viscosity 14 , 15 . Finally, the thermal conductor must possess intrinsic high conductivity, small particle size (nanoparticles), low tendency to aggregate, low toxicity, and scalability for industrial applications 1 . Carbon nanotubes and graphene oxide/rGO are excellent thermal conductors 16 but suffer from poor water stability and aggregation issues. Metal oxides are inexpensive, chemically stable, and widely used in water-based nanofluids but have lower TC 17 , 18 . Metals, in contrast, are far superior thermal conductors 11 , 19 but tend to aggregate, making a dispersing agent like MSM essential 18 . Among metals, silver nanoparticles are particularly attractive as they are thermally conductive, relative abundant, and stability 20 , 21 . While many studies have investigated individual nanoparticles such as silver or silica for enhancing TC, limited research has addressed their combined effects in hybrid nanofluids, particularly for photovoltaic (PV) systems. Previous theoretical and experimental studies show that incorporating nanofluids into solar thermal systems can significantly improve both thermal and electrical performance. For instance, PV/T systems using Al 2 O 3 , SiC, or TiO 2 nanofluids have demonstrated thermal efficiency increases exceeding 100% compared to water. However, these enhancements strongly depend on nanofluid formulation, stability, and operating conditions 17 . This study addresses these gaps by investigating the thermal properties of silver and MCM-41 hybrid nanofluid. TC measurements were conducted, and the Box–Behnken design (BBD) method was employed to optimize nanoparticle concentrations and operating temperature for maximum solar collector performance. The optimized nanofluid was further evaluated in a simulated PV/T collector using TRNSYS to assess its potential for improving system efficiency. Temperature and nanoparticle concentration influence TC and viscosity of nanofluids 22 . Most prior studies have examined these variables individually, limiting insight into their combined effects. The multi-factor experimental design provided by BBD allows systematic evaluation of interactions between temperature, concentration, and material properties. BBD is a statistical design method that reduces experimental runs compared to full factorial designs by strategically selecting combinations of factor levels, allowing efficient modeling and identification of optimal conditions. It is widely used in engineering and materials science to improve process performance and accurately predict outcomes 23 , 24 . By integrating experimental characterization, TC evaluation, and predictive modeling with BBD, along with system-level simulation through TRNSYS, this work demonstrates a scalable, stable, and efficient hybrid nanofluid formulation suitable for solar thermal and energy-efficient systems. The approach also considers sustainability and economic aspects: while silver nanoparticles are costly, their combination with mesoporous silica can optimize material usage and enhance energy efficiency, potentially offsetting costs through improved energy savings and reduced emissions. The research is structured around five core objectives: (1) hybrid nanofluid preparation and characterization, (2) evaluation of TC under varying temperature and concentration conditions, (3) development of a predictive model using response surface methodology, (4) validation of theoretical predictions with experimental data, and (5) assessment of the nanofluid’s impact on solar collector performance using TRNSYS simulation. By combining experimental and system-level analysis, this study provides a comprehensive framework for designing stable and efficient hybrid nanofluids for future solar thermal applications. 2. Materials and methodology 2.1. Box-Behnken design BBD 23 optimized the TC experiments and investigate the interactions among key influencing factors. We carried out a series of randomized experiments based on this design and analyzed the results to evaluate three main aspects: (1) significance, (2) accuracy, and (3) adequacy of the proposed model. The significance and accuracy were assessed through data presented in the ANOVA (Analysis of Variance) table, while the adequacy was evaluated using diagnostic plots. These plots, including residual and normal probability charts, help verify the model’s reliability and overall quality. Starting with statistical significance, this measures the likelihood that observed effects are not due to random chance, based on P-values. Essentially, it indicates the probability of error in assuming a relationship between factors. Table S1 (SI) summarizes the ANOVA results, breaking down the variability in photodegradation percentage into contributions from each factor. To confirm significance, the mean square values were compared against the experimental error. For TC experiments involving thirteen factors (including the concentrations of silver and MCM-41 nanoparticles, as well as the system temperature), P-values were below 0.05, demonstrating statistically significant effects at the 95% confidence level. Silver concentrations were varied between 1 ppm and 100 ppm, while MCM-41 concentrations ranged from 1 ppm to 1000 ppm. The experimental temperature was controlled within the range of 30 to 50 ºC. Next, model accuracy was examined through several statistical parameters: lack-of-fit P-value and F-value, model P-value, and coefficients of determination. The lack-of-fit test evaluates whether the model fits the experimental data by comparing residual variability. Here, a lack-of-fit P-value of 0.6379 (greater than 0.05) and an F-value of 0.7869 indicate that the model fits well. Furthermore, model P-values below 0.005 reinforce its accuracy. The R², adjusted R², and predicted R² values were 0.9997, 0.9994, and 0.9985 respectively, showing strong agreement and confirming the model’s predictive power. Additionally, the adequacy precision ratio was 174.2148, far exceeding the desired threshold of 4, which suggests a robust signal capable of effectively navigating the design space. Finally, model adequacy was confirmed using several diagnostic plots: normal probability plot of residuals, Box-Cox transformation plot, and Cook’s distance. These plots, illustrated in Fig. 2 , support the model’s validity and reliability. 2.2. Thermal conductivity setup The KD2 Pro was used to measure thermal properties (up to about 50°C). A Memmert water bath was used to regulate the temperature. An OHAUS Adventurer balance was used to weigh the materials. UP400S ultrasonic probe was used to disperse the nanomaterials into the base fluid. 2.3. Preparation of Ag/MCM-41 nanofluid and thermal conductivity measurement Initially, a 100 ppm solution of silver nanoparticle and a 1000 ppm solution of MCM-41 was prepared. Based on the parameters suggest by BBD, hybrid nanofluid samples were prepared (30 mL, 9 different concentrations). Before testing, all samples underwent ultrasonication, with the duration and intensity adjusted according to their concentration and volume. Typically, ultrasonication was performed at 50% amplitude for 10 minutes. Samples were sonicated before proceeding to further experimental steps. 15 mL of the hybrid nanofluids or water was poured into a vial, then was fully immersed in a water bath. KS-1 probe was cleaned with ethanol before each measurement. After reaching thermal equilibrium, KS-1 probe was placed in the center of the vial. All experiments were performed in triplicates. The TC of hybrid nanofluids were performed against distilled water as the references. Results with a 1.5% margin of error measurement were discarded. To avoid thermal gradients, experiments were performed with a 15 minutes wait between each measurements. Precautions were taken to create a static environment to minimize potential sources of interference, such as eliminating air currents as well as reducing acoustic disturbances caused by personnel and laboratory equipment. All samples were weighed using “Adventurer pro” balance, mixed by “IKA RW20 digital” mixer and ultrasonicated by “UP400s” device. 2.4. Characterization TESCAN MIRA3 (TESCAN, Czech Republic) was used to collect field emission scanning electron microscopy (FESEM) images. Zeta-potential and dynamic light scattering (DLS) were performed using HORIBA SZ-100 (Japan). 2.5. Characteristics of the PVT panel The dataset of a PVT collector in an experimental study was employed for the data validation of this study. This collector was made of three vertical tubes, each 4.5 feet long, with middle-to-middle spacing of 8.75 inches. Two pipes connected the vertical tubes while acting as input and output of the collector simultaneously. A heat plate was applied to the back of the collector to distribute the heat through all of the pipes. The plate was in the shape of a roll, 18 inches wide. In addition to that, a rigid thermal insulation board, 1.5 inches, was cut to fit the photovoltaic frame, with grooves made to house the thermal panel tubes. The experimental test was conducted with a flow rate of 0.03 kg/s 25 . 2.6. Design of the Solar Power Plant in TRNSYS In this study TRNSYS18 was used to model PVT power plants with time step of 0.125 hours. Accordingly, conducting nanofluids in solar power plants was used to asses Ag/MCM-41 hybrid nanofluid in the same system. The results of simulation system in Trnsys were validated against experimental data. The calculated errors were 6.80% and − 13.43% for the electrical and the thermal parts, respectively 18 . 3. Results and discussion 3.1. Choice of material When selecting materials for TC enhancement, the challenge lies not only in identifying constituents with intrinsically high TC but also in ensuring that these materials can be stably dispersed in the base fluid under realistic operating conditions. A purely “high-conductivity” approach, such as using silver nanoparticles, risks overlooking the fact that exceptional intrinsic conductivity is of little practical value if the nanomaterial aggregates rapidly, sediments, or fails to maintain a uniform dispersion, all of which undermine long-term thermal performance. On the hand, materials such as mesoporous silica (such as MCM-41) exhibit outstanding dispersibility and stability in water (a green and environmentally friendly base fluid) but possess limited intrinsic TC, making them less optimal as sole additives for heat transfer enhancement. Scheme 1 (a, b) exhibits an overview of our study, where a nanofluid based on MCM-41 mesoporous silica and silver nanoparticles is used as a thermal conductor. MCM-41 is a silica-based mesoporous material commonly synthesized by reacting Pluronic acid copolymer with tetraethyl orthosilicate (TEOS) in either acidic or basic media at elevated temperatures. Depending on the synthesis conditions, MCM-41 can exhibit various morphologies, including spherical, tubular, rod-shaped, and nanoparticulate forms. Silver nanoparticles are a class of materials with particle sizes in the range of sub-100 nm with various physical and chemical properties. These include antimicrobial activity, surface plasmon resonance, high electrical conductivity, as well as high TC. Both MCM-41 and silver nanoparticles have found applications in many industries. Our hybrid nanofluid design directly addresses this trade-off by pairing the high TC of silver nanoparticles with the superior dispersion stability of MCM-41. In doing so, it exploits complementary properties: the mesoporous silica acts as a stabilizing scaffold, physically separating silver nanoparticles to mitigate aggregation while also providing high surface area for effective interfacial contact with the base fluid. The result is a synergistic system in which the stability and wettability of MCM-41 enable the high-conductivity silver phase to function effectively over time, rather than being compromised by sedimentation. This strategy underscores a broader principle in TC material selection: optimal performance emerges not from maximizing a single property, but from engineering a balanced combination of thermal transport capability, stability, interfacial compatibility, and environmental sustainability. Scheme 1 (c) presents the solar collector diagram and the TRNSYS design. In order to supply a portion of the thermal and electrical load, we designed a solar power plant. Solar energy translates to thermal and electrical energy through the PVT collector. By cooling down the photovoltaic panel (via a fluid flowing through the tubes), we can significantly enhance the electrical efficiency. The absorbed thermal energy is transferred to a heat exchanger in later stages, enabling its use in the building’s heating system. This study focuses on Ahvaz, a city in the south of Iran. To optimize the collector’s tilt angle for year-round performance, it is generally recommended to set it close to the city’s latitude of 31.3°. 3.2. Material characterization The MCM-41 nanoparticles were purchased from Sigma-Aldrich (Milwaukee, USA). An SEM image of the Ag/MCM-41 hybrid nanofluid confirms the spherical morphology and an average particle size of ~50 nm (Figure 1 (a)). The Ag nanoparticles, which are only a few nanometers in size, are not visible in the SEM image but are well dispersed within the MCM-41 matrix, as evidenced by the EDX mapping shown in Figure 1 (b). Silicon (Si), oxygen (O), and silver (Ag) are displayed separately and as an overlay. The EDX analysis indicates a silver content of 5.69 wt.% in the final sample (Figure 1 (c)). Furthermore, MCM-41 has a high surface area of 930 m 2 g -1 , which is an important parameter in TC measurements. This high surface area leads to significantly better dispersion, improved micro-convection within the nanofluid, increased interfacial thermal layer formation, and larger exposed interfaces that allow for better interaction between the nanoparticles and the base fluid 26,27 . Figure 1 (d, e) shows the Zeta potential and DLS data for the prepared nanofluid. A high zeta potential value of -29.1 mV indicates relatively strong repulsion among the nanoparticles, resulting in a stable and well-dispersed sample with minimal aggregation. This is corroborated by the DLS analysis, which shows a Z-average particle size of 66.1 nm. These properties and a high BET surface-area, contribute to improved heat transfer and reduced thermal resistance 28 . 3.3. Box-Behnken Design and thermal conductivity of Ag/MCM-41 Using the Box–Behnken Design, we optimized the TC measurements and examined how key factors influence each other. Three main aspects were evaluated: (i) significance, (ii) accuracy, and (iii) adequacy. Significance and accuracy were confirmed using the ANOVA (Analysis of Variance) table, while adequacy was assessed through diagnostic plots. The experimental data were entered into Design Expert software to check the accuracy of the results and to propose a model to calculate TC under different conditions. The obtained model shows a predicted R 2 value of 0.9988, which is close to the adjusted R 2 of 0.9785, which means the model fits the data well. The model’s signal-to-noise ratio, called adequate precision, is 159.644, which is much higher than the minimum desired value of 4, showing the model is reliable. The proposed model is shown in Equation 1: TC enhance = -18.9948 + (0.125982 * A) + (0.00505482 * B) + (0.960652 * C) - ((2.48734×10 -5 ) * AB + (0.00168687 * AC) + (0.000161161 * BC) – (0.0012111 * A 2 ) – (7.46492*10 -6 ) * B 2 – (0.011525 * C 2 ) Equation 1 Where TC enhance , A, B, and C are TC improvement, Silver and MCM-41concentrations, and temperature, respectivley. Error! Reference source not found. Figure 2 (a) shows the normal plot where each residual is plotted against its expected value assuming normality 23 . The normalization plot in Figure 2 (a) was used to determine whether the fitted model accurately represents the actual system. A significant deviation from linearity indicates inadequacy; however, the straight-line distribution in Figure 2 (a) shows the model is adequate. Error! Reference source not found. Figure 2 (b) presents the Box-Cox plot for the model. The Box-Cox plot in Figure 2 (b) helps identify whether a data transformation is needed to achieve normality. Three lines are shown: red (low/high confidence limits), green (current confidence interval point), and blue (model value). The overlap of blue and green lines within the red boundaries indicates normality, so no transformation was required. Figure 2 (c) shows Cook’s distance, used to detect influential outliers. In this diagram, the run numbers are plotted against Cook’s distance, which ranges from 0 to +1. Any data points exceeding the value of +1 are regarded as influential outliers that could negatively impact the system. The results displayed here indicate no evidence of such negative effects under the proposed conditions, and the model is therefore considered adequate. Overall, results from the ANOVA table (Table S1) and diagnostic plots confirm that the model is precise, accurate, and adequate. Figure 3 presents the response surface plots and thermal conductivity results of the prepared nanofluids as a function of three variables: silver nanoparticle concentration, MCM-41 nanoparticle concentration, and system temperature. The 3D plots illustrate the relationships between thermal conductivity and (i) silver concentration versus MCM-41 concentration, (ii) temperature versus silver concentration, and (iii) temperature versus MCM-41 concentration (Figure 3 (a-c)). In Figure 3 (a), an increase in silver nanoparticle concentration enhances thermal conductivity even when MCM-41 content is relatively low. While, high MCM-41 concentrations can yield peak thermal conductivity at moderate silver levels. Although higher concentrations generally improve thermal conductivity, excessive loading reduces nanofluid stability, which can decrease conductivity enhancement in certain regions. A similar pattern is observed in Figure 3 (b), where we can see the correlation between temperature and concentration of Ag nanoparticles. At low silver concentrations, thermal conductivity improvements remain moderate even at the upper end of the temperature range. In contrast, MCM-41 demonstrates the ability to enhance conductivity at moderate concentrations, likely due to its dispersive properties, which improve silver nanoparticle distribution and promote the Brownian motion effect (Figure 3 (c)). Overall, Temperature plays a significant role in nanoparticle dispersion and interaction within the fluid. At lower temperatures, conductivity improvements are modest, while increasing the temperature to an optimal range substantially boosts performance. However, excessively high temperatures lead to slight declines, potentially due to changes in nanoparticle structure or dispersion (Figure 3 (b, c)). Based on the experimental data, concentration optimization was performed to identify the maximum thermal conductivity improvement. Among 17 proposed points by BBD, the nanofluid with 79.11 ppm of Ag, 746.08 ppm of MCM-41, and a temperature of 50 °C showed the highest enhancement of 13.44%, with a desirability score of 0.98. KD2 Pro device recorded the TC of the hybrid nanofluids, and the improvement compared to distilled water was calculated for each sample. Although the experiments were performed at temperature range of 30-50 °C, the correlation suggested by Design Expert was used to predict the nanofluid’s performance over a wider temperature range (20 to 55 °C). The comparison between predicted and experimental results is shown in Figure 3 (d), confirming that the prepared hybrid nanofluid consistently outperforms distilled water in thermal conductivity 3.4. Theoretical studies and simulation Due to hardware limitations, some properties, such as the heat capacity of the prepared nanofluids, could not be measured directly. In such cases, theoretical calculations provide valuable estimates for properties of our Ag/MCM-41 nanofluid. Using Equation 1, the heat capacity was calculated based on the nanoparticle concentration and their individual heat capacities, resulting in a value of 4142 J/kg·K for the hybrid nanofluid. Additionally, Equation 2 was used to determine the viscosity, yielding a viscosity coefficient of 1.00035. Figure 4 (a) compares the collector’s thermal output over the entire year, plotting thermal output against solar radiation using both experimental measurements and TRNSYS simulation data. The simulation error was calculated as -13.43%. The simulations assumed the working fluid in the collector was the nanofluid. Figures 4 (b) and (c) illustrate the thermal output from January to June and July to September, respectively, while Figures 4 (d) and (e) show the electrical output for the same periods. In all figures, the x-axis represents the number of hours elapsed from January 1 to December 31. Furthermore, the collector generated its highest output between June and August. An efficient controller ensured the pump only ran when needed, keeping the collector’s thermal output at zero or above throughout the year. This controller prevented the fluid from circulating if the collector’s temperature was lower than the temperature of the tank, avoiding heat loss from the tank to the cooler collector. Figures 4 (f) and (g) display the temperature of the hybrid nanofluid at the collector outlet from January to June and July to September, respectively, with the highest temperatures observed between May and September. 3.5. Influence of hybrid nanofluids on various performance parameters To better evaluate the collector’s performance in this power plant, several important parameters were analyzed at 50 °C. These included the panel’s thermal efficiency, convection heat transfer coefficient, and dimensionless numbers like Nusselt (Nu), Reynolds (Re), and Prandtl (Pr). The temperature of 50 °C was chosen because the fluid showed its highest thermal conductivity at this point. Table 1 shows how the hybrid nanofluid affected different parts of the solar system. The 13.44% increase in the nanofluid’s TC led to higher convection heat transfer and improved the panel’s thermal efficiency. However, the increase in viscosity had a negative effect on the Nu, Re, and Pr numbers, causing their values to decrease. Table 1. Impact of hybrid-nanofluid Calculated amount (%) Unit Parameter Name +1.26 - Thermal Efficiency Panel +6.05 W/m 2 .K Convection heat transfer coefficient­ -2.86 - Nu -0.03 - Re -9.13 - Pr 3.6. Comparison studies To contextualize the performance of the Ag/MCM-41 hybrid-nanofluid, its TC was compared with previously reported nanofluids based on porous silica and other common materials. Prior research highlights that single-component nanofluids, such as silver, silica, or metal oxides, can enhance thermal conductivity, but often face limitations in stability, dispersibility, or scalability. For instance, Hao et al. 29 reported a maximum TC enhancement of 18.3% for silver nanoparticles in a green base fluid, while Nyamgoudar et al. 30 showed that silver nanorods could achieve improvements up to 78.4% at elevated temperatures. Similarly, studies on cobalt/silica 31 and silicon dioxide 32 nanofluids demonstrated moderate enhancements, strongly dependent on nanoparticle concentration, size, and preparation methods. In PV/T applications, the choice of nanofluid strongly influences both thermal and electrical efficiency. Zamen et al. 30 recorded a 126.7% increase in thermal efficiency using 0.5 wt% aluminum oxide in water nanofluid. These studies collectively indicate that thermal conductivity gains are dependent to nanoparticle properties such as type, size, and shape, as well as their concentration, and base fluid characteristics. The present study builds on these insights by combining silver nanoparticles, known for their high intrinsic thermal conductivity, with mesoporous silica (MCM-41), which provides excellent water dispersibility and long-term stability. Table 2 compares the TC of the Ag/MCM-41 hybrid nanofluid with several other porous silica-based nanofluids. The hybrid nanofluid outperforms single-component nanofluids due to the synergistic effect of highly conductive silver and the dispersive, stabilizing properties of MCM-41. For example, compared to Methoxy-Polyethylene Glycol-Silane (MPEG-Silane) systems, the Ag/MCM-41 nanofluid exhibits higher thermal conductivity at similar concentrations, demonstrating that effective material pairing can significantly enhance heat transfer performance. This comparison not only validates the choice of hybrid design but also underscores the importance of optimizing both nanoparticle concentration and system temperature. By integrating the high thermal conductivity of metals with the stability and dispersibility provided by mesoporous materials, hybrid nanofluids such as Ag/MCM-41 can achieve superior performance in solar thermal and PV/T systems. These results reinforce the rationale for subsequent Box–Behnken design optimization and TRNSYS-based system-level evaluation, bridging lab-scale characterization with practical energy applications. Table 2. Comparison of various nanofluids. Nanofluid Base Fluid Nanoparticle Concentration Temperature (°C) TC* Enhancement (%) Reference Ag/MCM-41 Water Ag MCM-41 79.1 ppm Ag, 746 ppm MCM-41 50 13.44 This study AgNRs Water Silver nanorods 0.01–0.1 wt.% 50 78.4 30 Co/SiO2 Glycerol–Water Co SiO2 0.18 / 0.675 70 31.5 / 16.4 31 SiO2 Water SiO2 0.01–1 % 25–50 0.5–6.8 32 Al2O3 Water Al2O3 0.5 wt.% 25–50 126.7% thermal efficiency 17 AgNRs Water Silver nanorods 0.01–0.1 wt.% 323 K 78.4 30 SBA-15 60:40% Glycerol:Water SiO2 1-5 wt.% 10-60 22 15 MSiO2 Glycerol SiO2 2-4wt.% 20-50 9.24 15 Ag/MSiO2 Glycerol Ag/SiO2 2.98 wt.% 25 10.95 24 4. Conclusion In this study, a hybrid nanofluid composed of silver (Ag) and porous nano-silica (MCM-41) was prepared. Thermal conductivity enhancements were measured across 15 experimental runs, conducted between 30 and 50°C, using the KD2 Pro device. Material characterization through SEM, EDX, DLS, and Zeta potential confirmed the successful formation of the nanomaterials. System optimization was performed using Box-Behnken Design, and optimum conditions were as follow: 79.11 ppm Ag and 746.08 ppm MCM-41 at 50°C. under these conditions, we observed a 13.44% increase in thermal conductivity. To evaluate the practical application of the Ag-MCM-41 nanofluid, its performance was tested in solar collector tubes. Results showed a 6.05% increase in the convection heat transfer coefficient and a 1.26% improvement in the thermal efficiency of the panel. These findings suggest that the Ag/MCM-41 hybrid-nanofluid can enhance the efficiency of solar thermal systems and other industrial applications. However, further studies are needed to assess its economic viability in such systems. Declarations Declaration of competing interest The authors declare no competing interests. AI and AI-assisted technologies ChatGPT was used to improve the language. The following prompt was used: Identify grammatical and lexical issues. Also identify where the text is not coherent and lacks fluency. No prompt was used to generate text or discussions regarding any of the presented data. Content of the article was reviewed and edited and authors are responsible for this work. Author Contribution M.B. and R.A. Conceived the idea of this study and prepared samples. M.B. and R.P. supervised the project. M.B. R.A. and R.P. analyzed the experimental results. R.A., M.B., R.P., and S.B. wrote the manuscript. S.B. and M.B. edited the manuscript. All authors have approved the final version of the manuscript. Acknowledgement The authors thank Shahid Chamran University of Ahvaz for their support. There is no funding for this work. Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Younes, H. et al. Nanofluids: Key parameters to enhance thermal conductivity and its applications. 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The effects of graphene oxide nanosheets and ultrasonic oscillation on the supercooling and nucleation behavior of nanofluids PCMs. Microfluidics and Nanofluidics 18 , 81-89, doi:10.1007/s10404-014-1411-1 (2015). Hao, N. V. et al. High thermal conductivity of green nanofluid containing Ag nanoparticles prepared by using solution plasma process with Paramignya trimera extract. Journal of Thermal Analysis and Calorimetry 148 , 7579-7590, doi:10.1007/s10973-023-12266-2 (2023). Nyamgoudar, S. M. et al. Analysis of shape dependency of thermal conductivity of silver-based nanofluids. Journal of Thermal Analysis and Calorimetry 147 , 14031-14038, doi:10.1007/s10973-022-11604-0 (2022). Prasad, T. R., Krishna, K. R., Sharma, K. V. & Mantravadi, N. Viscosity and Thermal Conductivity of Cobalt and Silica Nanofluid in an Optimum Mixture of Glycerol and Water. Colloid Journal 84 , 208-221, doi:10.1134/S1061933X22020090 (2022). Milyani, A. H. et al. Artificial intelligence optimization and experimental procedure for the effect of silicon dioxide particle size in silicon dioxide/deionized water nanofluid: Preparation, stability measurement and estimate the thermal conductivity of produced mixture. Journal of Materials Research and Technology 26 , 2575-2586, doi:https://doi.org/10.1016/j.jmrt.2023.08.074 (2023). Additional Declarations No competing interests reported. Supplementary Files SI.docx floatimage1.jpeg Graphical abstract floatimage2.jpeg Scheme 1. Summary of the study. (a) step-wise preparation of hybrid-nanofluid. (b) Thermal conductivity measurement. (c) solar collector and the TRNSYS design. Cite Share Download PDF Status: Posted Version 1 posted 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. 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1","display":"","copyAsset":false,"role":"figure","size":5614105,"visible":true,"origin":"","legend":"\u003cp\u003eMaterial Characterization. (a) SEM image of Ag/MCM-41 hybrid nanofluid. (b) EDX mappings of Si, O, Ag, and overlayed of all elements. (c) Weight percent of atomic distributions in final Ag/MCM-41 hybrid nanofluid. (d) Zeta potential. (e) DLS.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7502565/v1/62d3d5c11a5dd5b5b9c9b5cc.jpeg"},{"id":91818977,"identity":"2327180c-8271-4361-8da9-3e29beff175f","added_by":"auto","created_at":"2025-09-22 07:05:43","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1318738,"visible":true,"origin":"","legend":"\u003cp\u003eBox-Behnken Design experiments. (a) Normal Plot of Residuals. (b) The Box-Cox power transformation plot. (c) Cook’s distance.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7502565/v1/1003252b38241b78c28f222f.jpeg"},{"id":91818755,"identity":"727de930-4be8-4607-998e-fff71856a415","added_by":"auto","created_at":"2025-09-22 07:05:12","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4541793,"visible":true,"origin":"","legend":"\u003cp\u003eBox-Behnken Design experiments. (a-c) 3D surface model graphs of thermal conductivity enhancement correlating the concentration of MCM-41 NP, Ag NP, and temperature. (d) Thermal conductivity of water and Ag/MCM-41 nanofluid\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7502565/v1/fd4bc762c64975dfeefd3ae3.jpeg"},{"id":91819147,"identity":"b2df4a63-3ef4-40fe-b552-1e1c5f16027b","added_by":"auto","created_at":"2025-09-22 07:05:58","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8427544,"visible":true,"origin":"","legend":"\u003cp\u003eSimulation study. (a) Comparison of thermal output of collectors. (b) Thermal output of collector from January to June and (c) Thermal output of collector from July to September. (d) Electrical output of collector from January to June and (e) Electrical output of collector from July to September. (f) Outlet temperature of collector from January to June and (g) outlet temperature of collector from July to September.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7502565/v1/22b77c60c61b6b0a1fe6836d.jpeg"},{"id":97896338,"identity":"6044e0dd-d41b-4cf3-b392-6db8187b4e62","added_by":"auto","created_at":"2025-12-10 15:36:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":20791327,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7502565/v1/ebd960c7-7531-49a9-b494-83e329863c7e.pdf"},{"id":91818866,"identity":"0a5f176b-e47b-4ac9-aa7b-a137e916956b","added_by":"auto","created_at":"2025-09-22 07:05:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30817,"visible":true,"origin":"","legend":"","description":"","filename":"SI.docx","url":"https://assets-eu.researchsquare.com/files/rs-7502565/v1/d0ebd9cc251d4bc2c158a181.docx"},{"id":91818804,"identity":"7d980e7a-c9be-4c64-8153-b80b0287ed14","added_by":"auto","created_at":"2025-09-22 07:05:13","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":536065,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7502565/v1/e56aedca1d70b26b0d62fb67.jpeg"},{"id":91818824,"identity":"7b31848d-6f7b-4d2a-a2c2-5470af9e32df","added_by":"auto","created_at":"2025-09-22 07:05:18","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3307043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScheme 1\u003c/strong\u003e. Summary of the study. (a) step-wise preparation of hybrid-nanofluid. (b) Thermal conductivity measurement. (c) solar collector and the TRNSYS design.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7502565/v1/ccb4b16257de4ab3a38695d1.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Thermal Conductivity Enhancement of Ag/MCM-41 Hybrid Nanofluid for Solar Photothermal Applications","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFor the last decade, we have seen a growing demand for stablishing platforms and alternatives for sufficient energy consumption. Among these, managing and minimizing the loss of thermal energy has been studied in many industrial and renewable energy systems. In heat transfer systems, such as solar thermal systems, water or ethylene glycol is often a standard choice. They are environmentally safe and industrially cheap. However, they suffer from inherently low thermal conductivity (TC) and limited overall efficiency \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Nanofluids have recently emerged as alternatives and can more efficiently improve heat transfer. Nanofluids are comprised of active materials with high TC (like metals and carbon-based materials) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. More recently, hybrid nanofluids, where a combination of nanomaterials are used, have attracted attention for their potential to synergistically enhance TC beyond what single component nanofluids can achieve \u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Despite these advancements, challenges such as nanoparticle aggregation, sedimentation, increased viscosity, and stability issues hinder the practical application of nanofluids.\u003c/p\u003e\u003cp\u003eAddressing these limitations is critical to translating laboratory-scale improvements into durable, real-world heat transfer solutions. Mesoporous materials provide high surface area and can enhance nanoparticle dispersion and stability, offering structural benefits that improve suspension stability \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Metals and metal oxide nanoparticles, by contrast, are interesting for their high TC. Combining these materials in a hybrid nanofluid can thus deliver both high conductivity and improved stability, which is crucial for practical thermal applications. However, such hybrid systems remain underexplored in solar thermal and photovoltaic (PV) applications.\u003c/p\u003e\u003cp\u003eWhen designing a nanofluid system, three factors are particularly important: (i) a green and environmentally friendly base fluid, (ii) a stable and easily dispersible porous material, and (iii) a highly conductive nanoparticle. Among available base fluids, water is the most environmentally friendly choice \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In addition to its eco-friendliness, water has several advantageous properties: (i) a relatively high specific heat capacity for efficient heat absorption and storage, (ii) reasonable TC, (iii) wide availability and low cost, (iv) well-characterized thermophysical properties, and (v) excellent solvent and dispersing capabilities for many nanoparticles \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe choice of dispersing material depends on several criteria: it should be chemically stable, have an extended shelf-life, allow high nanoparticle loading, exhibit low viscosity, and be environmentally safe in both use and synthesis. Candidate materials include MXenes, metal-organic frameworks (MOFs), porous carbons, graphene-based materials, and mesoporous silica materials (MSMs) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. MXenes offer high nanoparticle loading and long shelf-life but generally suffer from low chemical stability in water and environmentally challenging synthesis \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. MOFs are highly porous, but their dispersity and water stability vary widely depending on the metal-ligand combination \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Porous carbons often display poor dispersity in water. MSMs, however, meet all the criteria for an effective dispersing material in TC experiments: they are highly dispersible in water due to their uniform SiO\u003csub\u003e2\u003c/sub\u003e framework and abundant hydroxyl groups, water-stable, cost-effective, environmentally friendly, possess slightly better TC than MOFs, exhibit high affinity for metal nanoparticle loading, and maintain low viscosity \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFinally, the thermal conductor must possess intrinsic high conductivity, small particle size (nanoparticles), low tendency to aggregate, low toxicity, and scalability for industrial applications \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Carbon nanotubes and graphene oxide/rGO are excellent thermal conductors \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e but suffer from poor water stability and aggregation issues. Metal oxides are inexpensive, chemically stable, and widely used in water-based nanofluids but have lower TC \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Metals, in contrast, are far superior thermal conductors \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e but tend to aggregate, making a dispersing agent like MSM essential \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Among metals, silver nanoparticles are particularly attractive as they are thermally conductive, relative abundant, and stability \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile many studies have investigated individual nanoparticles such as silver or silica for enhancing TC, limited research has addressed their combined effects in hybrid nanofluids, particularly for photovoltaic (PV) systems. Previous theoretical and experimental studies show that incorporating nanofluids into solar thermal systems can significantly improve both thermal and electrical performance. For instance, PV/T systems using Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, SiC, or TiO\u003csub\u003e2\u003c/sub\u003e nanofluids have demonstrated thermal efficiency increases exceeding 100% compared to water. However, these enhancements strongly depend on nanofluid formulation, stability, and operating conditions \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study addresses these gaps by investigating the thermal properties of silver and MCM-41 hybrid nanofluid. TC measurements were conducted, and the Box\u0026ndash;Behnken design (BBD) method was employed to optimize nanoparticle concentrations and operating temperature for maximum solar collector performance. The optimized nanofluid was further evaluated in a simulated PV/T collector using TRNSYS to assess its potential for improving system efficiency.\u003c/p\u003e\u003cp\u003eTemperature and nanoparticle concentration influence TC and viscosity of nanofluids \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Most prior studies have examined these variables individually, limiting insight into their combined effects. The multi-factor experimental design provided by BBD allows systematic evaluation of interactions between temperature, concentration, and material properties. BBD is a statistical design method that reduces experimental runs compared to full factorial designs by strategically selecting combinations of factor levels, allowing efficient modeling and identification of optimal conditions. It is widely used in engineering and materials science to improve process performance and accurately predict outcomes \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBy integrating experimental characterization, TC evaluation, and predictive modeling with BBD, along with system-level simulation through TRNSYS, this work demonstrates a scalable, stable, and efficient hybrid nanofluid formulation suitable for solar thermal and energy-efficient systems. The approach also considers sustainability and economic aspects: while silver nanoparticles are costly, their combination with mesoporous silica can optimize material usage and enhance energy efficiency, potentially offsetting costs through improved energy savings and reduced emissions.\u003c/p\u003e\u003cp\u003eThe research is structured around five core objectives: (1) hybrid nanofluid preparation and characterization, (2) evaluation of TC under varying temperature and concentration conditions, (3) development of a predictive model using response surface methodology, (4) validation of theoretical predictions with experimental data, and (5) assessment of the nanofluid\u0026rsquo;s impact on solar collector performance using TRNSYS simulation. By combining experimental and system-level analysis, this study provides a comprehensive framework for designing stable and efficient hybrid nanofluids for future solar thermal applications.\u003c/p\u003e"},{"header":"2. Materials and methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Box-Behnken design\u003c/h2\u003e\u003cp\u003eBBD \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e optimized the TC experiments and investigate the interactions among key influencing factors. We carried out a series of randomized experiments based on this design and analyzed the results to evaluate three main aspects: (1) significance, (2) accuracy, and (3) adequacy of the proposed model. The significance and accuracy were assessed through data presented in the ANOVA (Analysis of Variance) table, while the adequacy was evaluated using diagnostic plots. These plots, including residual and normal probability charts, help verify the model\u0026rsquo;s reliability and overall quality.\u003c/p\u003e\u003cp\u003eStarting with statistical significance, this measures the likelihood that observed effects are not due to random chance, based on P-values. Essentially, it indicates the probability of error in assuming a relationship between factors. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (SI) summarizes the ANOVA results, breaking down the variability in photodegradation percentage into contributions from each factor. To confirm significance, the mean square values were compared against the experimental error. For TC experiments involving thirteen factors (including the concentrations of silver and MCM-41 nanoparticles, as well as the system temperature), P-values were below 0.05, demonstrating statistically significant effects at the 95% confidence level. Silver concentrations were varied between 1 ppm and 100 ppm, while MCM-41 concentrations ranged from 1 ppm to 1000 ppm. The experimental temperature was controlled within the range of 30 to 50 \u0026ordm;C.\u003c/p\u003e\u003cp\u003eNext, model accuracy was examined through several statistical parameters: lack-of-fit P-value and F-value, model P-value, and coefficients of determination. The lack-of-fit test evaluates whether the model fits the experimental data by comparing residual variability. Here, a lack-of-fit P-value of 0.6379 (greater than 0.05) and an F-value of 0.7869 indicate that the model fits well. Furthermore, model P-values below 0.005 reinforce its accuracy. The R\u0026sup2;, adjusted R\u0026sup2;, and predicted R\u0026sup2; values were 0.9997, 0.9994, and 0.9985 respectively, showing strong agreement and confirming the model\u0026rsquo;s predictive power. Additionally, the adequacy precision ratio was 174.2148, far exceeding the desired threshold of 4, which suggests a robust signal capable of effectively navigating the design space.\u003c/p\u003e\u003cp\u003eFinally, model adequacy was confirmed using several diagnostic plots: normal probability plot of residuals, Box-Cox transformation plot, and Cook\u0026rsquo;s distance. These plots, illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, support the model\u0026rsquo;s validity and reliability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Thermal conductivity setup\u003c/h2\u003e\u003cp\u003eThe KD2 Pro was used to measure thermal properties (up to about 50\u0026deg;C). A Memmert water bath was used to regulate the temperature. An OHAUS Adventurer balance was used to weigh the materials. UP400S ultrasonic probe was used to disperse the nanomaterials into the base fluid.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Preparation of Ag/MCM-41 nanofluid and thermal conductivity measurement\u003c/h2\u003e\u003cp\u003eInitially, a 100 ppm solution of silver nanoparticle and a 1000 ppm solution of MCM-41 was prepared. Based on the parameters suggest by BBD, hybrid nanofluid samples were prepared (30 mL, 9 different concentrations). Before testing, all samples underwent ultrasonication, with the duration and intensity adjusted according to their concentration and volume. Typically, ultrasonication was performed at 50% amplitude for 10 minutes. Samples were sonicated before proceeding to further experimental steps.\u003c/p\u003e\u003cp\u003e15 mL of the hybrid nanofluids or water was poured into a vial, then was fully immersed in a water bath. KS-1 probe was cleaned with ethanol before each measurement. After reaching thermal equilibrium, KS-1 probe was placed in the center of the vial. All experiments were performed in triplicates. The TC of hybrid nanofluids were performed against distilled water as the references. Results with a 1.5% margin of error measurement were discarded. To avoid thermal gradients, experiments were performed with a 15 minutes wait between each measurements. Precautions were taken to create a static environment to minimize potential sources of interference, such as eliminating air currents as well as reducing acoustic disturbances caused by personnel and laboratory equipment.\u003c/p\u003e\u003cp\u003eAll samples were weighed using \u0026ldquo;Adventurer pro\u0026rdquo; balance, mixed by \u0026ldquo;IKA RW20 digital\u0026rdquo; mixer and ultrasonicated by \u0026ldquo;UP400s\u0026rdquo; device.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Characterization\u003c/h2\u003e\u003cp\u003eTESCAN MIRA3 (TESCAN, Czech Republic) was used to collect field emission scanning electron microscopy (FESEM) images. Zeta-potential and dynamic light scattering (DLS) were performed using HORIBA SZ-100 (Japan).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Characteristics of the PVT panel\u003c/h2\u003e\u003cp\u003eThe dataset of a PVT collector in an experimental study was employed for the data validation of this study. This collector was made of three vertical tubes, each 4.5 feet long, with middle-to-middle spacing of 8.75 inches. Two pipes connected the vertical tubes while acting as input and output of the collector simultaneously. A heat plate was applied to the back of the collector to distribute the heat through all of the pipes. The plate was in the shape of a roll, 18 inches wide. In addition to that, a rigid thermal insulation board, 1.5 inches, was cut to fit the photovoltaic frame, with grooves made to house the thermal panel tubes. The experimental test was conducted with a flow rate of 0.03 kg/s \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Design of the Solar Power Plant in TRNSYS\u003c/h2\u003e\u003cp\u003eIn this study TRNSYS18 was used to model PVT power plants with time step of 0.125 hours. Accordingly, conducting nanofluids in solar power plants was used to asses Ag/MCM-41 hybrid nanofluid in the same system. The results of simulation system in Trnsys were validated against experimental data. The calculated errors were 6.80% and \u0026minus;\u0026thinsp;13.43% for the electrical and the thermal parts, respectively \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1. Choice of material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen selecting materials for TC enhancement, the challenge lies not only in identifying constituents with intrinsically high TC but also in ensuring that these materials can be stably dispersed in the base fluid under realistic operating conditions. A purely \u0026ldquo;high-conductivity\u0026rdquo; approach, such as using silver nanoparticles, risks overlooking the fact that exceptional intrinsic conductivity is of little practical value if the nanomaterial aggregates rapidly, sediments, or fails to maintain a uniform dispersion, all of which undermine long-term thermal performance. On the hand, materials such as mesoporous silica (such as MCM-41) exhibit outstanding dispersibility and stability in water (a green and environmentally friendly base fluid) but possess limited intrinsic TC, making them less optimal as sole additives for heat transfer enhancement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScheme 1\u003c/strong\u003e (a, b) exhibits an overview of our study, where a nanofluid based on MCM-41 mesoporous silica and silver nanoparticles is used as a thermal conductor. MCM-41 is a silica-based mesoporous material commonly synthesized by reacting Pluronic acid copolymer with tetraethyl orthosilicate (TEOS) in either acidic or basic media at elevated temperatures. Depending on the synthesis conditions, MCM-41 can exhibit various morphologies, including spherical, tubular, rod-shaped, and nanoparticulate forms. Silver nanoparticles are a class of materials with particle sizes in the range of sub-100 nm with various physical and chemical properties. These include antimicrobial activity, surface plasmon resonance, high electrical conductivity, as well as high TC. Both MCM-41 and silver nanoparticles have found applications in many industries. Our hybrid nanofluid design directly addresses this trade-off by pairing the high TC of silver nanoparticles with the superior dispersion stability of MCM-41. In doing so, it exploits complementary properties: the mesoporous silica acts as a stabilizing scaffold, physically separating silver nanoparticles to mitigate aggregation while also providing high surface area for effective interfacial contact with the base fluid. The result is a synergistic system in which the stability and wettability of MCM-41 enable the high-conductivity silver phase to function effectively over time, rather than being compromised by sedimentation. This strategy underscores a broader principle in TC material selection: optimal performance emerges not from maximizing a single property, but from engineering a balanced combination of thermal transport capability, stability, interfacial compatibility, and environmental sustainability.\u003c/p\u003e\n\u003cp\u003eScheme 1 (c) presents the solar collector diagram and the TRNSYS design. In order to supply a portion of the thermal and electrical load, we designed a solar power plant. Solar energy translates to thermal and electrical energy through the PVT collector. By cooling down the photovoltaic panel (via a fluid flowing through the tubes), we can significantly enhance the electrical efficiency. The absorbed thermal energy is transferred to a heat exchanger in later stages, enabling its use in the building\u0026rsquo;s heating system. This study focuses on Ahvaz, a city in the south of Iran. To optimize the collector\u0026rsquo;s tilt angle for year-round performance, it is generally recommended to set it close to the city\u0026rsquo;s latitude of 31.3\u0026deg;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Material characterization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MCM-41 nanoparticles were purchased from Sigma-Aldrich (Milwaukee, USA). An SEM image of the Ag/MCM-41 hybrid nanofluid confirms the spherical morphology and an average particle size of ~50 nm (Figure 1 (a)). The Ag nanoparticles, which are only a few nanometers in size, are not visible in the SEM image but are well dispersed within the MCM-41 matrix, as evidenced by the EDX mapping shown in Figure 1 (b). Silicon (Si), oxygen (O), and silver (Ag) are displayed separately and as an overlay. The EDX analysis indicates a silver content of 5.69 wt.% in the final sample (Figure 1 (c)). Furthermore, MCM-41 has a high surface area of 930 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e, which is an important parameter in TC measurements. This high surface area leads to significantly better dispersion, improved micro-convection within the nanofluid, increased interfacial thermal layer formation, and larger exposed interfaces that allow for better interaction between the nanoparticles and the base fluid \u003csup\u003e26,27\u003c/sup\u003e. Figure 1 (d, e) shows the Zeta potential and DLS data for the prepared nanofluid. A high zeta potential value of -29.1 mV indicates relatively strong repulsion among the nanoparticles, resulting in a stable and well-dispersed sample with minimal aggregation. This is corroborated by the DLS analysis, which shows a Z-average particle size of 66.1 nm. These properties and a high BET surface-area, contribute to improved heat transfer and reduced thermal resistance \u003csup\u003e28\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Box-Behnken Design and thermal conductivity of Ag/MCM-41\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the Box\u0026ndash;Behnken Design, we optimized the TC measurements and examined how key factors influence each other. Three main aspects were evaluated: (i) significance, (ii) accuracy, and (iii) adequacy. Significance and accuracy were confirmed using the ANOVA (Analysis of Variance) table, while adequacy was assessed through diagnostic plots. The experimental data were entered into Design Expert software to check the accuracy of the results and to propose a model to calculate TC under different conditions. The obtained model shows a predicted R\u003csup\u003e2\u003c/sup\u003e value of 0.9988, which is close to the adjusted R\u003csup\u003e2\u003c/sup\u003e of 0.9785, which means the model fits the data well. The model\u0026rsquo;s signal-to-noise ratio, called adequate precision, is 159.644, which is much higher than the minimum desired value of 4, showing the model is reliable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe proposed model is shown in Equation 1:\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"610\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 610px;\"\u003e\n \u003cp\u003eTC enhance = -18.9948 + (0.125982 * A) + (0.00505482 * B) + (0.960652 * C) - ((2.48734\u0026times;10\u003csup\u003e-5\u003c/sup\u003e) * AB + (0.00168687 * AC) + (0.000161161 * BC) \u0026ndash; (0.0012111 * A\u003csup\u003e2\u003c/sup\u003e) \u0026ndash; (7.46492*10\u003csup\u003e-6\u003c/sup\u003e) * B\u003csup\u003e2\u003c/sup\u003e \u0026ndash; (0.011525 * C\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eEquation 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWhere TC\u003csub\u003eenhance\u003c/sub\u003e, A, B, and C are TC improvement, Silver and MCM-41concentrations, and temperature, respectivley.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eError! Reference source not found.\u003c/strong\u003eFigure 2 (a) shows the normal plot \u0026nbsp;where each residual is plotted against its expected value assuming normality \u003csup\u003e23\u003c/sup\u003e. The normalization plot in Figure 2 (a) was used to determine whether the fitted model accurately represents the actual system. A significant deviation from linearity indicates inadequacy; however, the straight-line distribution in Figure 2 (a) shows the model is adequate. \u003cstrong\u003eError! Reference source not found.\u003c/strong\u003eFigure 2 (b) presents the Box-Cox plot for the model. The Box-Cox plot in Figure 2 (b) helps identify whether a data transformation is needed to achieve normality. Three lines are shown: red (low/high confidence limits), green (current confidence interval point), and blue (model value). The overlap of blue and green lines within the red boundaries indicates normality, so no transformation was required. Figure 2 (c) shows Cook\u0026rsquo;s distance, used to detect influential outliers. In this diagram, the run numbers are plotted against Cook\u0026rsquo;s distance, which ranges from 0 to +1. Any data points exceeding the value of +1 are regarded as influential outliers that could negatively impact the system. The results displayed here indicate no evidence of such negative effects under the proposed conditions, and the model is therefore considered adequate. Overall, results from the ANOVA table (Table S1) and diagnostic plots confirm that the model is precise, accurate, and adequate.\u003c/p\u003e\n\u003cp\u003eFigure 3 presents the response surface plots and thermal conductivity results of the prepared nanofluids as a function of three variables: silver nanoparticle concentration, MCM-41 nanoparticle concentration, and system temperature. The 3D plots illustrate the relationships between thermal conductivity and (i) silver concentration versus MCM-41 concentration, (ii) temperature versus silver concentration, and (iii) temperature versus MCM-41 concentration (Figure 3 (a-c)).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Figure 3 (a), an increase in silver nanoparticle concentration enhances thermal conductivity even when MCM-41 content is relatively low. While, high MCM-41 concentrations can yield peak thermal conductivity at moderate silver levels. Although higher concentrations generally improve thermal conductivity, excessive loading reduces nanofluid stability, which can decrease conductivity enhancement in certain regions. A similar pattern is observed in Figure 3 (b), where we can see the correlation between temperature and concentration of Ag nanoparticles. At low silver concentrations, thermal conductivity improvements remain moderate even at the upper end of the temperature range. In contrast, MCM-41 demonstrates the ability to enhance conductivity at moderate concentrations, likely due to its dispersive properties, which improve silver nanoparticle distribution and promote the Brownian motion effect (Figure 3 (c)). Overall, Temperature plays a significant role in nanoparticle dispersion and interaction within the fluid. At lower temperatures, conductivity improvements are modest, while increasing the temperature to an optimal range substantially boosts performance. However, excessively high temperatures lead to slight declines, potentially due to changes in nanoparticle structure or dispersion (Figure 3 (b, c)).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the experimental data, concentration optimization was performed to identify the maximum thermal conductivity improvement. Among 17 proposed points by BBD, the nanofluid with 79.11 ppm of Ag, 746.08 ppm of MCM-41, and a temperature of 50 \u0026deg;C showed the highest enhancement of 13.44%, with a desirability score of 0.98. KD2 Pro device recorded the TC of the hybrid nanofluids, and the improvement compared to distilled water was calculated for each sample. Although the experiments were performed at temperature range of 30-50 \u0026deg;C, the correlation suggested by Design Expert was used to predict the nanofluid\u0026rsquo;s performance over a wider temperature range (20 to 55 \u0026deg;C). The comparison between predicted and experimental results is shown in Figure 3 (d), confirming that the prepared hybrid nanofluid consistently outperforms distilled water in thermal conductivity\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e3.4. Theoretical studies and simulation\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eDue to hardware limitations, some properties, such as the heat capacity of the prepared nanofluids, could not be measured directly. In such cases, theoretical calculations provide valuable estimates for properties of our Ag/MCM-41 nanofluid. Using Equation 1, the heat capacity was calculated based on the nanoparticle concentration and their individual heat capacities, resulting in a value of 4142 J/kg\u0026middot;K for the hybrid nanofluid. Additionally, Equation 2 was used to determine the viscosity, yielding a viscosity coefficient of 1.00035.\u003c/p\u003e\n\u003cp\u003eFigure 4 (a) compares the collector\u0026rsquo;s thermal output over the entire year, plotting thermal output against solar radiation using both experimental measurements and TRNSYS simulation data. The simulation error was calculated as -13.43%. The simulations assumed the working fluid in the collector was the nanofluid. Figures 4 (b) and (c) illustrate the thermal output from January to June and July to September, respectively, while Figures 4 (d) and (e) show the electrical output for the same periods. In all figures, the x-axis represents the number of hours elapsed from January 1 to December 31.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the collector generated its highest output between June and August. An efficient controller ensured the pump only ran when needed, keeping the collector\u0026rsquo;s thermal output at zero or above throughout the year. This controller prevented the fluid from circulating if the collector\u0026rsquo;s temperature was lower than the temperature of the tank, avoiding heat loss from the tank to the cooler collector. Figures 4 (f) and (g) display the temperature of the hybrid nanofluid at the collector outlet from January to June and July to September, respectively, with the highest temperatures observed between May and September.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e3.5. Influence of hybrid nanofluids on various performance parameters\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTo better evaluate the collector\u0026rsquo;s performance in this power plant, several important parameters were analyzed at 50 \u0026deg;C. These included the panel\u0026rsquo;s thermal efficiency, convection heat transfer coefficient, and dimensionless numbers like Nusselt (Nu), Reynolds (Re), and Prandtl (Pr). The temperature of 50 \u0026deg;C was chosen because the fluid showed its highest thermal conductivity at this point. Table 1 shows how the hybrid nanofluid affected different parts of the solar system. The 13.44% increase in the nanofluid\u0026rsquo;s TC led to higher convection heat transfer and improved the panel\u0026rsquo;s thermal efficiency. However, the increase in viscosity had a negative effect on the Nu, Re, and Pr numbers, causing their values to decrease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Impact of hybrid-nanofluid\u003c/p\u003e\n\u003ctable dir=\"rtl\" border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eCalculated amount (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eParameter Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e+1.26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eThermal Efficiency Panel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e+6.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eW/m\u003csup\u003e2\u003c/sup\u003e.K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eConvection heat transfer coefficient\u0026shy;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e-2.86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eNu\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e-0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eRe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e-9.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003ePr\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cstrong\u003e3.6. Comparison studies\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTo contextualize the performance of the Ag/MCM-41 hybrid-nanofluid, its TC was compared with previously reported nanofluids based on porous silica and other common materials. Prior research highlights that single-component nanofluids, such as silver, silica, or metal oxides, can enhance thermal conductivity, but often face limitations in stability, dispersibility, or scalability. For instance, Hao et al. \u003csup\u003e29\u003c/sup\u003e reported a maximum TC enhancement of 18.3% for silver nanoparticles in a green base fluid, while Nyamgoudar et al. \u003csup\u003e30\u003c/sup\u003e showed that silver nanorods could achieve improvements up to 78.4% at elevated temperatures. Similarly, studies on cobalt/silica \u003csup\u003e31\u003c/sup\u003e and silicon dioxide \u003csup\u003e32\u003c/sup\u003e nanofluids demonstrated moderate enhancements, strongly dependent on nanoparticle concentration, size, and preparation methods.\u003c/p\u003e\n\u003cp\u003eIn PV/T applications, the choice of nanofluid strongly influences both thermal and electrical efficiency. Zamen et al. \u003csup\u003e30\u003c/sup\u003e recorded a 126.7% increase in thermal efficiency using 0.5 wt% aluminum oxide in water nanofluid. These studies collectively indicate that thermal conductivity gains are dependent to nanoparticle properties such as type, size, and shape, as well as their concentration, and base fluid characteristics.\u003c/p\u003e\n\u003cp\u003eThe present study builds on these insights by combining silver nanoparticles, known for their high intrinsic thermal conductivity, with mesoporous silica (MCM-41), which provides excellent water dispersibility and long-term stability. Table 2 compares the TC of the Ag/MCM-41 hybrid nanofluid with several other porous silica-based nanofluids. The hybrid nanofluid outperforms single-component nanofluids due to the synergistic effect of highly conductive silver and the dispersive, stabilizing properties of MCM-41. For example, compared to Methoxy-Polyethylene Glycol-Silane (MPEG-Silane) systems, the Ag/MCM-41 nanofluid exhibits higher thermal conductivity at similar concentrations, demonstrating that effective material pairing can significantly enhance heat transfer performance.\u003c/p\u003e\n\u003cp\u003eThis comparison not only validates the choice of hybrid design but also underscores the importance of optimizing both nanoparticle concentration and system temperature. By integrating the high thermal conductivity of metals with the stability and dispersibility provided by mesoporous materials, hybrid nanofluids such as Ag/MCM-41 can achieve superior performance in solar thermal and PV/T systems. These results reinforce the rationale for subsequent Box\u0026ndash;Behnken design optimization and TRNSYS-based system-level evaluation, bridging lab-scale characterization with practical energy applications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Comparison of various nanofluids.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNanofluid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBase\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFluid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNanoparticle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConcentration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemperature (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTC*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEnhancement (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eAg/MCM-41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eAg\u003c/p\u003e\n \u003cp\u003eMCM-41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e79.1 ppm Ag, 746 ppm MCM-41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e13.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eAgNRs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eSilver\u003c/p\u003e\n \u003cp\u003enanorods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.01\u0026ndash;0.1 wt.%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e78.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eCo/SiO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eGlycerol\u0026ndash;Water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003cp\u003eSiO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.18 / 0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e31.5 / 16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eSiO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eSiO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.01\u0026ndash;1 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e25\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.5\u0026ndash;6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003csup\u003e32\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eAl2O3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eAl2O3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.5 wt.%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e25\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e126.7% thermal efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eAgNRs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eSilver nanorods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.01\u0026ndash;0.1 wt.%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e323 K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e78.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eSBA-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e60:40% Glycerol:Water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eSiO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e1-5 wt.%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e10-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eMSiO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eGlycerol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eSiO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e2-4wt.%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e20-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e9.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eAg/MSiO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eGlycerol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eAg/SiO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e2.98 wt.%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e10.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn this study, a hybrid nanofluid composed of silver (Ag) and porous nano-silica (MCM-41) was prepared. Thermal conductivity enhancements were measured across 15 experimental runs, conducted between 30 and 50\u0026deg;C, using the KD2 Pro device. Material characterization through SEM, EDX, DLS, and Zeta potential confirmed the successful formation of the nanomaterials. System optimization was performed using Box-Behnken Design, and optimum conditions were as follow: 79.11 ppm Ag and 746.08 ppm MCM-41 at 50\u0026deg;C. under these conditions, we observed a 13.44% increase in thermal conductivity. To evaluate the practical application of the Ag-MCM-41 nanofluid, its performance was tested in solar collector tubes. Results showed a 6.05% increase in the convection heat transfer coefficient and a 1.26% improvement in the thermal efficiency of the panel. These findings suggest that the Ag/MCM-41 hybrid-nanofluid can enhance the efficiency of solar thermal systems and other industrial applications. However, further studies are needed to assess its economic viability in such systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eAI and AI-assisted technologies\u003c/h2\u003e\u003cp\u003eChatGPT was used to improve the language. The following prompt was used: Identify grammatical and lexical issues. Also identify where the text is not coherent and lacks fluency. No prompt was used to generate text or discussions regarding any of the presented data. Content of the article was reviewed and edited and authors are responsible for this work.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.B. and R.A. Conceived the idea of this study and prepared samples. M.B. and R.P. supervised the project. M.B. R.A. and R.P. analyzed the experimental results. R.A., M.B., R.P., and S.B. wrote the manuscript. S.B. and M.B. edited the manuscript. All authors have approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Shahid Chamran University of Ahvaz for their support. There is no funding for this work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYounes, H.\u003cem\u003e et al.\u003c/em\u003e Nanofluids: Key parameters to enhance thermal conductivity and its applications. \u003cem\u003eApplied Thermal Engineering\u003c/em\u003e \u003cstrong\u003e207\u003c/strong\u003e, 118202, doi:https://doi.org/10.1016/j.applthermaleng.2022.118202 (2022).\u003c/li\u003e\n\u003cli\u003eHasan, N.\u003cem\u003e et al.\u003c/em\u003e Ethylene glycol-based nanofluids: machine learning predictions for improved solar thermal performance. \u003cem\u003eJ. Therm. Anal. 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H.\u003cem\u003e et al.\u003c/em\u003e Artificial intelligence optimization and experimental procedure for the effect of silicon dioxide particle size in silicon dioxide/deionized water nanofluid: Preparation, stability measurement and estimate the thermal conductivity of produced mixture. \u003cem\u003eJournal of Materials Research and Technology\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 2575-2586, doi:https://doi.org/10.1016/j.jmrt.2023.08.074 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Silver hybrid-nanofluid, silver, Silica, thermal conductivity","lastPublishedDoi":"10.21203/rs.3.rs-7502565/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7502565/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe studied thermal properties of silver nanoparticle and mesoporous silica (MCM-41) nanofluid in aqueous solution and evaluates its potential for enhancing solar thermal system performance. FE-SEM, EDS, and Zeta-potential confirmed the successful preparation of the hybrid nanofluid. Experimental conditions were optimized via Box-Behnken design and thermal conductivities were experimentally measured using a KD2 Pro device (from 30°C to 50°C). Under optimal conditions (79.11 ppm Ag, 746.08 ppm MCM-41 at 50°C), results demonstrated a 13.44% improvement compared to water. Further theoretical calculations and TRNSYS simulations assessed the nanofluid's performance in a solar collector model. Data suggest an increase of 1.26% and 6.05% (vs. water) in the solar collector's thermal efficiency and the convection heat transfer coefficient, respectively. Conventional fluids are less satisfactory with low thermal conductivity and instability at high temperatures. Here, Ag/MCM-41 hybrid nanofluid is a promising medium for improving heat transfer in solar thermal systems and potentially other industrial applications.\u003c/p\u003e","manuscriptTitle":"Thermal Conductivity Enhancement of Ag/MCM-41 Hybrid Nanofluid for Solar Photothermal Applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 06:56:51","doi":"10.21203/rs.3.rs-7502565/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8d0dcc67-cf95-445e-8904-81c1d2745219","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54690511,"name":"Physical sciences/Energy science and technology"},{"id":54690512,"name":"Physical sciences/Engineering"},{"id":54690513,"name":"Physical sciences/Mathematics and computing"},{"id":54690514,"name":"Physical sciences/Nanoscience and technology"}],"tags":[],"updatedAt":"2025-12-09T18:38:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 06:56:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7502565","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7502565","identity":"rs-7502565","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}