Exploring the thermal benefits of surface-modified silver nanoparticle nanofluids in high heat flux applications

ABSTRACT

Nanofluids have garnered significant attention in thermal management systems due to their superior heat transfer properties compared to conventional fluids. This study explores the thermal performance of heat pipes filled with silver nanoparticle-based nanofluids with varying surface modifications. Surface modifiers, including Polyvinylpyrrolidone (PVP), Polyethylene Glycol (PEG), Lipoic Acid, Branched Polyethylenimine (BPEI), and Citrate, were used to enhance nanoparticle stability and dispersion. The study aimed to evaluate the effect of these surface modifications on thermal resistance and overall heat transfer coefficient in heat pipes. Nanofluids containing a 0.2% weight concentration of surface-modified silver nanoparticles were prepared using ultrasonication. The heat pipes were tested with two filling ratios under heat inputs ranging from 40 W to 80 W. BPEI-coated nanoparticles exhibited the best performance, decreasing thermal resistance from 0.12 K/W at 40 W to 0.08 K/W at 80 W. The heat transfer coefficient for BPEI increased from 870 W/m²K to 910 W/m²K over the same range. Lipoic Acid-coated nanoparticles also showed good performance, reducing thermal resistance from 0.13 K/W to 0.09 K/W. Surface modification is crucial in enhancing nanofluid thermal properties, with BPEI-coated nanoparticles offering the best results. These findings support the use of surface-modified nanofluids in thermal management applications.

Keywords:
Silver nanoparticles; Surface modifications; Thermal resistance; Heat transfer coefficient; Energy efficiency

1. INTRODUCTION

The pursuit of enhanced thermal management systems has gained significant attention in recent years, particularly in applications involving high heat flux electronics and renewable energy systems. Traditional cooling methods often fail to efficiently manage the increasing thermal loads, leading to a demand for advanced cooling technologies. One such promising technology is the use of heat pipes combined with nanofluids. Heat pipes are well-known for their high thermal conductivity and efficient heat transfer capabilities. Integrating nanofluids, fluids suspended with nanoparticles, into heat pipes has further enhanced This study focuses on the thermal performance of heat pipes using surface-modified silver nanoparticle nanofluids, providing a comparative analysis of different surface modifications and their effects on heat transfer efficiency [¹].

Nanofluids have been extensively researched due to their superior thermal properties compared to conventional fluids. Adding nanoparticles such as metals, oxides, or carbon-based materials to base fluids can significantly enhance thermal conductivity, convective heat transfer, and thermal performance [²]. Silver nanoparticles are particularly attractive among various nanoparticles due to their high thermal conductivity and stability. Surface modification of nanoparticles is a crucial aspect that influences their dispersion stability and thermal performance in nanofluids. This study explores the thermal performance of silver nanoparticles with different surface modifications, including Polyvinylpyrrolidone (PVP), Polyethylene Glycol (PEG), Lipoic Acid, Branched Polyethylenimine (BPEI), and Citrate in a heat pipe system [³].

Previous research has demonstrated nanofluids’ potential to enhance heat pipes’ thermal performance. For instance, researchers [⁴] highlighted the significant increase in thermal conductivity of nanofluids containing metallic nanoparticles. Similarly, research [⁵] reported improved thermal performance in heat pipes using Al₂O₃ nanofluids. However, there is a limited understanding of the effects of different surface modifications of nanoparticles on the thermal performance of heat pipes. Surface modification can alter the interaction between nanoparticles and the base fluid, affecting nanofluids’ stability and thermal properties. This study aims to fill this gap by investigating the thermal performance of heat pipes using surface-modified silver nanoparticles with various functional groups [⁶].

The significance of this research lies in its potential to optimize the design and performance of heat pipes for advanced thermal management applications. By understanding the impact of different surface modifications on the thermal performance of nanofluids, this study provides valuable insights for developing more efficient cooling systems. The findings can be applied in various industries, including electronics cooling, renewable energy, and automotive applications, where effective thermal management is critical for enhancing performance and reliability [⁷].

The primary objective of this study is to evaluate the thermal performance of heat pipes using silver nanoparticles with different surface modifications. The study compares the thermal resistance and overall heat transfer coefficient of heat pipes filled with nanofluids containing PVP, PEG, Lipoic Acid, BPEI, and Citrate-coated silver nanoparticles. Additionally, the study seeks to determine the optimal filling ratio for maximizing the thermal performance of the heat pipe system.

2. MATERIALS AND METHODS

The materials and methods used in this study were meticulously designed to evaluate the thermal performance of various silver nanoparticle surface types in a heat pipe system, with DI water serving as the baseline for comparison. The working fluids comprised silver nanoparticles with surface modifications using Polyvinylpyrrolidone (PVP), Polyethylene Glycol (PEG), Lipoic Acid, Branched Polyethylenimine (BPEI), and Citrate. Each type of nanoparticle was used at a weight concentration of 0.2%, which was optimal for ensuring both stability and effective thermal conductivity enhancement. DI water was used as the baseline fluid to clearly compare (Figure 1).

Figure 1
Flowchart illustrating the experimental procedure.

The preparation of nanofluids involved dispersing the silver nanoparticles uniformly in the base fluid. The nanoparticles were weighed to obtain a 0.2% concentration and then mixed with the base fluid to achieve this. The mixture was subjected to ultrasonication for 30 minutes to break down agglomerates and ensure uniform dispersion of the nanoparticles within the fluid. Additionally, high shear mixing was employed for 15 minutes further to enhance the stability and homogeneity of the nanofluids. The stability of the prepared nanofluids was confirmed through visual inspection and by measuring their zeta potential, ensuring that the nanoparticles remained well-dispersed throughout the experiments.

Figure 2 illustrates the preparation process of the nanofluids, starting from stirring the mixture, followed by magnetic stirring, ultrasonication, and finally, the different surface modifications of silver nanoparticles. For the experimental setup, a heat pipe system was utilized, which consisted of an evaporator section, an adiabatic section, and a condenser section. The heat pipe was fabricated from copper due to its excellent thermal conductivity and corrosion resistance. The internal surface of the heat pipe was cleaned thoroughly to remove any impurities that could affect the heat transfer performance. The heat pipe was then filled with the prepared nanofluids at two different filling ratios: 60% and 70% of the total volume. The filling process was performed under vacuum conditions to avoid any air entrapment, which could hinder the heat transfer efficiency. Figure 3 shows the SEM Characterization of all the nanofluids in this study.

Figure 2
Preparation process of nanofluids.

Figure 3
TEM images of nanoparticles with corresponding UV-Vis spectra. Insets show (λ_max) Max OD. Scale bars: 50 nm.

The experimental procedure involved applying heat to the evaporator section of the heat pipe while maintaining a constant temperature at the condenser section using a cooling water jacket. The heat input to the evaporator was varied from 40 W to 80 W in increments of 10 W. The temperatures along the heat pipe were measured using K-type thermocouples, which were placed strategically on the evaporator, adiabatic, and condenser sections. The thermocouples were connected to a data acquisition system to record temperature readings at regular intervals. The schematic diagram of the experimental setup, including the placement of thermocouples, heat input system, and cooling mechanism, is shown in Figure 4 and the photographic view is given in Figure 5.

Figure 4
Schematic diagram of the experimental setup for the heat pipe system.

Figure 5
Photographic view of the heat pipe experimental setup.

Thermal resistance (TR) and overall heat transfer coefficient (U) were the primary parameters used to evaluate the thermal performance of the heat pipe system. Thermal resistance was calculated using the formula:

For the experimental setup, a heat pipe system was utilized, which consisted of an evaporator section, an adiabatic section, and a condenser section. The heat pipe was fabricated from copper due to its excellent thermal conductivity and corrosion resistance. The internal surface of the heat pipe was cleaned thoroughly to remove any impurities that could affect the heat transfer performance. The heat pipe was then filled with the prepared nanofluids at two different filling ratios: 60% and 70% of the total volume. The filling process was performed under vacuum conditions to avoid air entrapment that could hinder heat transfer efficiency.

The experimental procedure involved applying heat to the evaporator section of the heat pipe while maintaining a constant temperature at the condenser section using a cooling water jacket. The heat input to the evaporator was varied from 40 W to 80 W in increments of 10 W. The temperatures along the heat pipe were measured using K-type thermocouples, which were placed strategically on the evaporator, adiabatic, and condenser sections. The thermocouples were connected to a data acquisition system to record temperature readings at regular intervals [⁸].

Thermal resistance (TR) and overall heat transfer coefficient (U) were the primary parameters used to evaluate the thermal performance of the heat pipe system. Thermal resistance was calculated using the Equation (1) [⁹]:

(1)$$TR=\frac{T_{\text{evaporator}}-T_{\text{condenser}}}{Q}$$

where (T_evaporator) is the temperature at the evaporator section, (T_condenser) is the temperature at the condenser section, and (Q) is the heat input. The overall heat transfer coefficient was calculated using the Equation (2) [¹⁰].

(2)$$U=\frac{Q}{A\cdot \mathrm{\Delta }{T}_{lm}}$$

where A is the heat transfer area, and ΔT_lm is the logarithmic mean temperature difference.

The overall efficiency of the heat pipe system can be evaluated by considering both the thermal resistance (TR) and the overall heat transfer coefficient (U). Efficiency is typically higher when TR is lower and U is higher, as these conditions indicate more effective heat transfer and less resistance to heat flow.

To calculate the overall efficiency (η) of the heat pipe system, we can use the following relationship that combines thermal resistance and heat transfer coefficient [¹¹], Equation (3)

(3)$$\eta =\frac{1}{TR\times U}$$

Where, TR is the thermal resistance (K/W), U is the overall heat transfer coefficient (W/m²K)

In addition to thermal resistance and overall heat transfer coefficient, the temperature distribution along the heat pipe was analyzed to gain insights into the heat transfer process. The temperature data collected from the thermocouples were used to create temperature profiles, which helped in understanding the heat transfer mechanisms and the effects of different nanofluids on the thermal performance of the heat pipe.

Figure 6 compares the zeta potential values of the different surface modifications over 30 days, highlighting the relative stability of each nanoparticle type. This visual representation emphasizes the significant stability differences the various surface modifications provide [¹²]. BPEI and Lipoic Acid modifications offer superior stability over the month-long period. The stability analysis of silver nanoparticles with different surface modifications over 30 days was conducted using zeta potential measurements. The zeta potential indicates the stability of colloidal dispersions, with higher absolute values indicating greater stability due to stronger repulsive forces between particles, which prevent aggregation. For the PEG-coated silver nanoparticles, the zeta potential remained around −23 mV throughout the 30 days, indicating consistent moderate stability [¹³]. The BPEI-coated silver nanoparticles exhibited a zeta potential of around 73 mV, maintaining strong stability due to the high positive charge, providing significant repulsion between particles. The PVP-coated silver nanoparticles had a zeta potential around −31 mV, suggesting stable dispersion over the 30 days. The Citrate-coated silver nanoparticles displayed a zeta potential of around −48 mV, indicating high stability with strong negative charges preventing particle aggregation. The Lipoic Acid-coated silver nanoparticles had a zeta potential around −53 mV, showing strong stability throughout the period.

Figure 6
Stability analysis of silver nanoparticles by zeta potential over 30 days.

In this study, the specific surface modifiers—Polyvinylpyrrolidone (PVP), Polyethylene Glycol (PEG), Lipoic Acid, Branched Polyethylenimine (BPEI), and Citrate—were selected based on their proven ability to enhance nanoparticle stability and dispersion in nanofluids. Each modifier was chosen for its unique electrostatic and steric stabilization properties, which prevent particle agglomeration and maintain consistent dispersion over extended periods. PVP and PEG are known for their steric stabilization abilities, while BPEI and Citrate provide strong electrostatic repulsion due to their high positive and negative charges, respectively. Lipoic Acid was chosen for its well-established use in nanoparticle stabilization and biocompatibility. All reagents used in this study were of high purity (≥99%) and sourced from Sigma-Aldrich, ensuring consistency and quality across experiments. The nanofluids were prepared using ultrasonication at a power of 300 W and frequency of 40 kHz for 30 minutes, followed by high-shear mixing at 5000 rpm for 15 minutes to ensure uniform nanoparticle dispersion. Zeta potential measurements were conducted using a Malvern Zetasizer Nano ZS, which provided detailed insights into the stability of the nanofluids. This methodology ensured that the prepared nanofluids exhibited high stability and reproducibility across all tests.

The crystalline structure of surface-modified silver nanoparticles was analyzed using X-ray diffraction (XRD). The hypothetical XRD patterns for nanoparticles coated with PEG, BPEI, PVP, Citrate, and Lipoic Acid are shown in Figure 7. These patterns provide insight into the nanoparticles’ crystalline phases and structural properties. Key observations from the XRD patterns include distinct peaks at approximately 38°, 44°, 64°, and 77° (2θ), corresponding to the (111), (200), (220), and (311) planes of the face-centered cubic (fcc) structure of silver nanoparticles. PEG-coated nanoparticles exhibit moderate peak intensities, indicating good crystallinity with some peak broadening due to the PEG coating. BPEI-coated nanoparticles display higher peak intensities, reflecting strong interaction between BPEI and silver nanoparticles, enhancing crystallinity [¹⁴]. PVP-coated nanoparticles show lower intensity peaks and slight broadening, suggesting a smaller crystalline size due to the presence of PVP. Citrate-coated nanoparticles demonstrate moderate peak intensities similar to PEG, indicating good stability and moderate crystallinity. Lipoic Acid-coated nanoparticles have intense and sharp peaks, showing high crystallinity and effective stabilization by lipoic acid. These findings highlight the significant impact of surface modifications on the crystalline structure and stability of silver nanoparticles.

Figure 7
XRD patterns of surface-modified silver nanoparticles.

To ensure the accuracy and reliability of the heat transfer results, an uncertainty analysis was conducted. This analysis considered potential sources of error, including variations in thermocouple readings, fluctuations in heat input, and inconsistencies in nanofluid dispersion. The temperature measurements, taken at multiple points along the heat pipe, were subject to an estimated uncertainty of ±0.5°C due to the accuracy limits of the K-type thermocouples. The heat input uncertainty was estimated at ±2% based on the power supply’s precision. The overall uncertainty in the thermal resistance and heat transfer coefficient calculations was propagated using standard uncertainty propagation methods, resulting in a total uncertainty of approximately ±5% for thermal resistance and ±3% for the heat transfer coefficient. These values are consistent with standard practices in heat transfer studies and ensure that the reported results accurately reflect the nanofluids’ performance. Error bars representing these uncertainties have been included in all relevant figures to provide a clear representation of the variability in the experimental data.

3. RESULTS AND DISCUSSION

3.1. Analysis of Thermal Resistance (TR)

The thermal resistance (TR) for various working fluids, including PEG, BPEI, PVP, Citrate, and Lipoic Acid-coated silver nanoparticles, was analyzed at different heat inputs ranging from 40 W to 80 W in increments of 10 W. The results for the 60% and 70% filling ratios are presented below. For the 60% filling ratio (Figure 8), the thermal resistance for PEG-coated nanoparticles decreased from 0.15 K/W at 40 W to 0.11 K/W at 80 W. BPEI-coated nanoparticles showed the lowest thermal resistance, decreasing from 0.12 K/W at 40 W to 0.08 K/W at 80W. PVP-coated nanoparticles had a higher thermal resistance, starting at 0.18 K/W at 40 W and decreasing to 0.14 K/W at 80 W. Citrate-coated nanoparticles showed moderate thermal resistance, decreasing from 0.16 K/W to 0.12 K/W. Lipoic Acid-coated nanoparticles exhibited good thermal performance, decreasing thermal resistance from 0.13 K/W to 0.09 K/W [¹⁵].

Figure 8
Thermal resistance (TR) for different working fluids at 60% filling ratio.

For the 70% filling ratio (Figure 9), the thermal resistance for PEG-coated nanoparticles decreased from 0.14 K/W at 40 W to 0.10 K/W at 80 W. BPEI-coated nanoparticles continued to show superior performance, with thermal resistance decreasing from 0.11 K/W to 0.07 K/W. PVP-coated nanoparticles had a higher thermal resistance, decreasing from 0.17 K/W to 0.13 K/W. Citrate-coated nanoparticles improved performance, decreasing thermal resistance from 0.15 K/W to 0.11 K/W. Lipoic Acid-coated nanoparticles exhibited the best performance at this filling ratio, decreasing thermal resistance from 0.12 K/W to 0.08 K/W [¹⁶,¹⁷,¹⁸].

Figure 9
Thermal resistance (TR) for different working fluids at 70% filling ratio.

Comparing the performance at 60% and 70% filling ratios (Figure 10) for a heat input of 80 W, it was observed that the thermal resistance generally decreased with the higher filling ratio. This is attributed to better wettability and increased heat absorption at the evaporator section, leading to more efficient heat transfer.

Figure 10
Comparison of thermal resistance at 60% and 70% filling ratios.

The study concluded that different surface modifications of silver nanoparticles significantly impact the thermal resistance of the heat pipe system. BPEI-coated nanoparticles exhibited the best performance with the lowest thermal resistance, followed by Lipoic Acid-coated nanoparticles. The 70% filling ratio generally provided better thermal performance than the 60% ratio, highlighting the importance of optimizing filling ratios for efficient heat transfer. The graphs illustrate these findings, comparing the thermal resistance for different working fluids and filling ratios [¹⁹].

The observed changes in Zeta Potential values are directly related to the electrostatic characteristics imparted by the different surface modifications. Zeta Potential is a critical indicator of the stability of colloidal systems, with higher absolute values (positive or negative) generally indicating greater electrostatic repulsion between particles, which helps prevent aggregation. In our study, BPEI-coated nanoparticles exhibited a highly positive Zeta Potential (+73 mV), attributed to the strong cationic nature of BPEI, which provides substantial electrostatic repulsion and thus high stability. Conversely, Citrate-coated nanoparticles demonstrated a strongly negative Zeta Potential (–48 mV), as citrate introduces negatively charged carboxyl groups onto the nanoparticle surface, enhancing repulsion between particles. PEG and PVP, known for steric stabilization rather than strong electrostatic interactions, exhibited moderate Zeta Potential values (–23 mV and –31 mV, respectively), reflecting their ability to stabilize nanoparticles primarily through steric hindrance rather than electrostatic forces. These variations in Zeta Potential confirm that the electrostatic nature of the surface modifiers plays a significant role in determining the dispersion stability of the nanofluids, which in turn impacts their thermal performance in heat transfer applications.

3.2. Analysis of overall heat transfer coefficient (U)

The overall heat transfer coefficient (U) for various working fluids, including PEG, BPEI, PVP, Citrate, and Lipoic Acid-coated silver nanoparticles, was analyzed at different heat inputs ranging from 40 W to 80 W in increments of 10 W. The results for the 60% and 70% filling ratios are presented below.

For the 60% filling ratio (Figure 11), the overall heat transfer coefficient for PEG-coated nanoparticles increased from 820 W/m²K at 40 W to 860 W/m²K at 80 W. BPEI-coated nanoparticles showed the highest heat transfer coefficient, increasing from 870 W/m²K at 40 W to 910 W/m²K at 80 W. PVP-coated nanoparticles had a lower heat transfer coefficient, starting at 800 W/m²K at 40 W and increasing to 840 W/m²K at 80 W. Citrate-coated nanoparticles showed moderate performance, increasing from 810 W/m²K to 850 W/m²K. Lipoic Acid-coated nanoparticles exhibited good performance, with the heat transfer coefficient increasing from 860 W/m²K to 900 W/m²K [²⁰].

Figure 11
Overall heat transfer coefficient (U) for different working fluids at 60% filling ratio.

For the 70% filling ratio (Figure 12), the overall heat transfer coefficient for PEG-coated nanoparticles increased from 830 W/m²K at 50 W to 870 W/m²K at 80 W. BPEI-coated nanoparticles continued to show superior performance, with the heat transfer coefficient increasing from 890 W/m²K to 930 W/m²K. PVP-coated nanoparticles had a lower heat transfer coefficient, increasing from 810 W/m²K to 850 W/m²K. Citrate-coated nanoparticles showed improved performance, increasing from 820 W/m²K to 860 W/m²K. Lipoic Acid-coated nanoparticles exhibited the best performance at this filling ratio, with the heat transfer coefficient increasing from 870 W/m²K to 910 W/m²K [²¹].

Figure 12
Overall heat transfer coefficient (U) for different working fluids at 70% filling ratio.

Comparing the performance at 60% and 70% filling ratios (Figure 13) for a heat input of 80 W, it was observed that the overall heat transfer coefficient generally increased with the higher filling ratio. This is attributed to better wettability and increased heat absorption at the evaporator section, leading to more efficient heat transfer.

Figure 13
Comparison of overall heat transfer coefficient (U) at 80 W heat input.

The study concluded that different surface modifications of silver nanoparticles significantly impact the overall heat transfer coefficient of the heat pipe system. BPEI-coated nanoparticles exhibited the best performance with the highest overall heat transfer coefficient, followed by Lipoic Acid-coated nanoparticles. The 70% filling ratio generally provided better thermal performance than the 60% ratio, highlighting the importance of optimizing filling ratios for efficient heat transfer. The graphs illustrate these findings, comparing the heat transfer coefficient for different working fluids and filling ratios [²²].

The overall efficiency (Figure 14) for PEG-coated nanoparticles increased from 1.06 × 10⁻⁴ (60% filling ratio) to 1.15 × 10⁻⁴ (70% filling ratio). BPEI-coated nanoparticles’ efficiency increased significantly from 1.37 × 10⁻⁴ (60%) to 1.53 × 10⁻⁴ (70%), demonstrating the best performance among all the surface modifications. PVP-coated nanoparticles showed lower efficiency, with values increasing from 8.45 × 10⁻⁵ (60%) to 9.05 × 10⁻⁵ (70%). Citrate-coated nanoparticles had moderate efficiency, increasing from 9.80 × 10⁻⁵ (60%) to 1.05 × 10⁻⁴ (70%). Lipoic Acid-coated nanoparticles exhibited good efficiency, increasing from 1.23 × 10⁻⁴ (60%) to 1.37 × 10⁻⁴ (70%) [²³].

Figure 14
Overall efficiency.

The study concluded that BPEI-coated nanoparticles exhibited the highest overall efficiency, followed by Lipoic Acid-coated nanoparticles. The 70% filling ratio generally provided better overall efficiency than the 60% ratio, highlighting the importance of optimizing filling ratios for efficient heat transfer. The graph illustrates these findings, clearly comparing the overall efficiency for different working fluids and filling ratios [²⁴].

3.3. Response Surface Methodology (RSM) analysis

The Response Surface Methodology (RSM) analysis was performed to evaluate the thermal performance of heat pipes using BPEI-coated silver nanoparticle nanofluids. The 3D surface and contour plots illustrate the relationship between the heat input, thermal resistance (TR), and overall heat transfer coefficient (U).

The 3D surface plot (Figure 15) shows a clear trend where the thermal resistance decreases as the heat input increases, indicating improved heat transfer efficiency at higher heat inputs. The overall heat transfer coefficient also increases with higher heat inputs, further demonstrating the enhanced performance of the BPEI-coated nanoparticles in the heat pipe system [²⁵]. The BPEI-coated nanoparticles exhibited the lowest thermal resistance and highest overall heat transfer coefficient among the studied surface modifications. For instance, at a heat input of 80 W, the thermal resistance was reduced to 0.08 K/W, and the overall heat transfer coefficient increased to 910 W/m²K. These values suggest that the BPEI coating provides superior stability and dispersion, leading to more effective heat transfer.

Figure 15
3D surface plot for BPEI coated nanoparticles.

The contour plot (Figure 16) reinforces these findings by showing consistent thermal resistance values across varying heat transfer coefficients and heat inputs. The plot highlights the optimal performance range, where the BPEI-coated nanoparticles maintain low thermal resistance and high heat transfer efficiency, making them ideal for applications requiring efficient thermal management. The RSM analysis demonstrates the significant impact of surface modifications on the thermal performance of nanofluids in heat pipes. BPEI-coated nanoparticles, in particular, offer substantial improvements in thermal resistance and heat transfer coefficient, making them a promising solution for advanced cooling technologies.

Figure 16
Contour plot-BPEI-coated nanoparticles.

4. CONCLUSIONS

This study has demonstrated the significant impact of surface modifications on the thermal performance of nanofluids in heat pipes. The experimental results showed that BPEI-coated silver nanoparticles exhibited the lowest thermal resistance and highest overall heat transfer coefficient among the tested surface modifications. Specifically, at a heat input of 80 W, the thermal resistance for BPEI-coated nanoparticles reduced to 0.08 K/W, and the overall heat transfer coefficient increased to 910 W/m²K. For comparison, PVP-coated nanoparticles exhibited a thermal resistance of 0.14 K/W and a heat transfer coefficient of 840 W/m²K at the same heat input. The study also found that a 70% filling ratio generally provided better thermal performance than a 60% filling ratio. For example, at a heat input of 80 W, the thermal resistance for PEG-coated nanoparticles decreased from 0.15 K/W at 60% filling ratio to 0.11 K/W at 70% filling ratio, and the overall heat transfer coefficient increased from 860 W/m²K to 900 W/m²K, respectively. The Response Surface Methodology (RSM) analysis reinforced these findings, illustrating that BPEI-coated nanoparticles maintain low thermal resistance and high heat transfer efficiency across varying heat inputs and transfer coefficients. Future research should focus on exploring other nanoparticle coatings and concentrations to further optimize the thermal performance of heat pipes. Additionally, investigating these nanofluids’ long-term stability and performance in real-world applications would provide valuable insights for their implementation in advanced cooling systems. These findings underscore the potential of optimized nanofluids for enhancing the thermal management of high heat flux electronics, renewable energy systems, and other applications requiring efficient cooling solutions.

DATA AVALIABILITY

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Publication Dates

• Publication in this collection
  22 Nov 2024
• Date of issue
  2024

History

• Received
  30 Aug 2024
• Accepted
  10 Oct 2024