Graphene Oxide Nanofluids Research Articles

Energy and exergy analysis of photovoltaic systems integrated with pulsating heat pipe and hybrid nanofluids

Solar energy is harnessed from solar radiation and converted into various forms of energy, primarily electricity. It is a crucial part of the renewable energy landscape due to its abundance, sustainability, and potential to reduce greenhouse gas emissions. Photovoltaic (PV) panels have become a critical component in the transition toward sustainable energy; however, the efficiency and longevity of PV systems are significantly influenced by their operating temperature. Excessive heat can reduce the efficiency of PV cells and accelerate material degradation. Pulsating heat pipes (PHPs), with their excellent thermal management capabilities, present a promising solution to these challenges. This paper comprehensively investigates the effect of incorporating a three-dimensional PHP into PV systems, using working fluids with varying thermal properties, namely water, graphene oxide (GO) nanofluid with three different concentrations (0.2, 0.4, and 0.8 g/L) and their mixture with nano-encapsulated phase change material (Nano PCM), forming a hybrid nanofluid, at a concentration of 5 g/L. The collected data are analyzed from energy and exergy perspectives. The findings show that using Nano PCM in the nanofluid-based PV system (at the highest concentration) produces 22.3 Wh/day, increasing the system’s electrical power output by approximately 12 % compared to a PV panel with no cooling system. It outperforms the other systems studied, with the highest overall exergy efficiency of 35.4 %. The results also indicate an average improvement in first-law efficiency. Additionally, the study shows varying levelized costs of energy (LCOE) for the system cooled with different coolants.

Thermophysical & photothermal characteristics of surfactant free graphene oxide, functionalized graphene oxide and reduced graphene oxide nanofluids

Graphene nanostructures-based dispersions, owing to their improved thermophysical and optical characteristics are being actively explored for use in thermal energy conversion and management. Here, thermophysical and photothermal characteristics of graphene oxide (GO), functionalized graphene oxide (f-GO), and reduced graphene oxide (rGO) based nanofluids (without surfactants) of various concentrations (0.05–0.23 wt.%) are evaluated. The structural, morphology and stability of the nanofluids are determined by XRD analysis, Transmission electron microscopy, UV- visible spectroscopy and zeta potential analysis. Subsequently, thermal conductivity and contact angle measurements are performed. Photothermal behaviour is investigated experimentally. Overall, it is found that among the prepared nanofluids, the f-GO nanofluids performed better compared to GO and rGO nanofluids. Maximum stability of f-GO nanofluids is obtained at 0.05 wt.% while the thermal conductivity and photothermal response is best obtained at 0.23 wt.% concentration.

Development of Graphene Oxide Nanofluids From Recycled Graphite: Part I—Materials Characterization

Development of Graphene Oxide Nanofluids From Recycled Graphite: Part I—Materials Characterization

Stability, thermophysical properties, forced convective heat transfer, entropy minimization and exergy performance of a novel hybrid nanofluid: Experimental study

This research explores how incorporating graphene oxide (GO) into red mud (RM) creates a superior nanomaterial for heat transfer applications. RM's inherent stability and thermal conductivity (TC), stemming from its metal oxide composition, are further amplified by this hybrid nano-composite. The study primarily investigates the thermal performance of water-based RM mono nanofluid and hybrid RM + GO (50:50) nanofluids (HNFs) at nanoparticle concentrations of 0.1–0.75 vol%. An experimental setup consisting of a copper tube under turbulent flow conditions with a constant heat flux and a bulk fluid temperature of 60 °C was used to test the performance of HNF. Various techniques are used to characterize the nanoparticles (NPs), and evaluate the thermophysical properties of nanofluids. The experimental data reveal that the heat transfer coefficient (HTC) increases with higher inlet fluid velocity and nanoparticle concentrations. Notably, the HNF demonstrates a significant improvement of 47.2 % in Nusselt number (Nu) and a 13.6 % rise in pressure drop (Δp) compared to base fluid, at 0.75 vol%. The least entropy generation number (EGN) is obtained for the HNF (0.0049) compared to the RM NF (0.00583) at a concentration of 0.75 vol%. The exergy efficiency improves with an increase in concentration and Reynolds number (Re). Additionally, the study identifies the performance index (PI) of NFs and modelled correlations for estimating the Nu and friction factor (f) within the investigated concentration range.

Prediction of heat exchanger efficiency using laminar heat transfer in swirling flow of radiated graphene oxide with nano fluid additives using machine learning technique

Nanotechnology has recently led to new possibilities for enhancing heat transfer in heat exchangers. The remarkable thermal characteristics of graphene oxide (GO)-based nanofluids with nanoscale additions have garnered significant attention in particular. When nanofluids and intricate flow patterns are involved, traditional analytical models frequently fail to appropriately forecast the efficiency of heat exchangers. Analyzed the phenomenon of laminar heat transfer in a heat exchanger that has a swirling flow of fluid dynamics, and pressure drop, and predicted the condensation heat transfer coefficient (HTC) of LHT. The functionalized radiated GO was chosen as nanomaterials in the present for the Pre-processing of Data Methods for managing outliers by machine learning (ML) models, like the CLAHE algorithm. The logarithmic mean temperature difference (LMTD) can be used to determine the temperature driving power for heat transfer within a heat exchanger. The Dynamic Smagorinsky Model (DSLM) and Wall-Adapting Local Eddy-viscosity (WALE) Model are turbulence models primarily designed for capturing turbulent behavior in fluid flows. The Kern technique and Hagen–Poiseuille equation are used for the pressure drop and pumping power needed in a shell and tube in laminar flow through a microtube based on Levenberg–Marquardt and Momentum Algorithm to predict Nusselt number and pressure drop in heat exchangers. The prediction sensitivity of the HTC by an LHT-trained ML model is used to evaluate the performance. The ML methods may be effectively used to forecast and maximize heat exchanger performance in the setting of laminar heat transfer in radiated GO nanofluid swirling flows. The accuracy score of 99%, demonstrating its exceptional predictive capabilities and results has great potential to improve energy efficiency, save operating costs, and advance sustainable practices in various industrial applications.

Enhancing Combustion Efficiency: Utilizing Graphene Oxide Nanofluids as Fuel Additives with Tomato Oil Methyl Ester in CI Engines

The research aims to conduct a thorough examination of the combustion, injection, performance, and emission characteristics of a diesel engine below different engine loads, as well as the synthesis of graphene oxide (GO) nano fuels and their utilization in combination with Tomato Oil methyl ester (TOME) and diesel fuel blend. The (GO), plays a vital role in the pre-mixed combustion phase of diesel engines. Addition of GO nano fuels enhances the high-pressure combustion stage, resulting in increased maximum pressure and heat release rate (HRR). TOME (B20GO75) and TOME (B20GO100) exhibit comparable HRR to diesel due to improved fuel characteristics and quicker ignition delay duration. While TOME (B20) shows a slight decrease in (BTE) compared to diesel, the addition of GO improves BTE, with TOME (B20GO50) displaying the highest BTE at full load, indicating enhanced combustion efficiency. Moreover, GO addition leads to a reduction in carbon monoxide (CO) and hydrocarbon (HC) emissions, with emissions decreasing as the dosage of GO increases. However, NOx emissions initially decrease with TOME (B20) compared to diesel but increase with higher GO dosages. Smoke emissions increase with TOME (B20) but decrease with higher GO dosages. Overall, the incorporation of GO nano fuels into TOME biodiesel fuel blends demonstrates potential for improving engine performance and reducing emissions.

Heat Transfer Enhancement in Industrial Heat Exchangers Using Graphene Oxide Nanofluids

Heat Transfer Enhancement in Industrial Heat Exchangers Using Graphene Oxide Nanofluids

A hybrid MCDM optimization for utilization of novel set of biosynthesized nanofluids on thermal performance for solar thermal collectors

Recent advancements in solar technology have spurred researchers to develop a precise and cost-effective method for monitoring solar radiation across diverse environmental conditions. This study focuses on optimizing performance parameters by replacing conventional nanofluids, produced through chemical and physical processes, with biosynthesized counterparts. Biosynthesized nanoparticles, derived from biological sources like microorganisms or plants, offer a promising avenue for enhancing solar panel efficiency. Through the construction and testing of an integrated photovoltaic thermal system, varying operating parameters have been explored to maximize photovoltaic efficiency. The highest overall efficiency, reaching approximately 62 %, was achieved with biosynthesized Graphene oxide nanofluid under higher Direct Normal Irradiance (DNI) levels, while the lowest was observed with Aluminum oxide nanofluid at lower DNI values. Employing Multi-Criteria Decision methods such as Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), nanofluids were ranked based on criteria including Exergy loss, Surface temperature, Overall efficiency, and Electrical efficiency, with weights of 49.58 %, 28.43 %, 13.45 %, and 8.54 % respectively. TOPSIS prioritized Graphene Oxide as the most favorable nanofluid, followed by Copper-Oxide and Cerium-Oxide, while Aluminum-Oxide received the lowest priority in optimizing PV panel performance.

Experimental investigation of photothermal performance in nanofluid-based direct absorption solar collection for solar-driven water desalination

As the demand for energy continues to rise unabated, an examination of the constraints posed by finite fossil fuel reserves becomes crucial. There is need to explore and underscore the imperative shift towards renewable energy resources, where, solar energy is widely available and can readily be used in different applications, especially in water desalination applications. In the present research, photothermal performance of water based nanofluids containing graphene oxide (GO), zinc oxide (ZnO), iron oxide (FeO), and their composites (GO-ZnO and GO-FeO) have been studied under the natural sun to determine the potential for water desalination applications. All type of the nanofluid are prepared through a two-step method and are characterized morphologically using a scanning electron microscope (SEM). UV–visible spectroscopy is used to assess the optical absorbance of prepared nanofluids. The outcomes of this study show that pure GO nanofluids exhibited excellent absorption in the visible and near-infrared regions as compared to other nanofluids used in this research study, which makes them efficient nanofluids in converting solar energy into heat. For more assessment, a direct or volumetric solar thermal collector was used to evaluate the photothermal efficiency of water based nanofluids GO, ZnO, FeO, and their binary composites of GO-ZnO, and GO-FeO under natural solar flux at weight concentrations (0.01 wt%). The sensible heating efficiency due to the rise in temperature and latent heat of evaporation efficiency due to mass loss of nanofluid samples contributed towards photothermal efficiency. Pure GO nanofluids showed the highest photothermal efficiency, which is 71 % among all the nanofluids used in this study due to their carbon-based structure, highest optical absorption peak, excellent dispersion stability, and highest thermal conductivity. This experimental work also revealed that binary nanofluids containing composites of (GO-ZnO) and (GO-FeO) showed higher photothermal efficiency as compared to individual nanofluids ZnO and FeO except graphene oxide GO. According to the results and observation of this experimental work, pure GO-based nanofluids are potential candidates for direct absorption solar collection used in water desalination applications.

Design and tribological performance of graphene nanofluids dispersed by dragging effect of bicomponent gelator

Nanofluids have excellent lubrication and high thermal conductivity. However, the agglomeration and sedimentation produced by the large surface energy of nanoparticles in base liquid threaten the long-term dispersion stability and impact the wide application of nanofluid. In this work, based on the self-assemble behavior and continuous network structure formed by low molecular weight organic gelator, the uniform clusters were formed through regulating the kinetics behavior in the gelling process. The dragging effect was demonstrated by oleic acid - sodium dodecyl sulfate (OA-SDS) bicomponent gelator and graphene oxide (GO) nanosheets. The results showed that GO nanofluids dispersed by OA-SDS were stable for more than 12 months. The well-dispersed GO nanofluid exhibited better anti-friction and anti-wear properties under both immersion and electrostatic minimum quantity lubrication conditions. Moreover, the lower contact angle, surface tension and droplet size of nanofluids after charging improved the wettability on the frictional interface. The GO adsorption film formed on the friction interface protected the tribochemical reaction film of iron oxide and prevented the occurrence of sintering of base oil.

Effect of Variable Viscosity on Entropy Generation Analysis Due to Graphene Oxide Nanofluid Convective Flow in Concentric Cylinders

Aggregated studies of graphene nanoparticles is important for the effective utilization of their striking thermophysical properties and extensive industrial applications. This investigation is one such computational study to explore the flow of graphene oxide nanofluids with temperature dependant viscosity between two concentric cylinders. Buongiorno model is used to develop the flow of graphene nanofluids including the impacts of Soret and Dufour effects and the effects of nanoparticle characteristics such as thermophoresis and Brownian motion. The modelled equations are transformed and are numerically solved using linearization method together with Chebyshev’s spectral collocation method under convective conditions. The impacts of embedded parameters on temperature, concentration and skin friction profiles of the chosen nanofluid and their consequent impacts on the predominant cause for the generated entropy are studied. From the tabulated values of Nusselt number and Sherwood number, it is observed that convective heat transfer can be enhanced by thermal Biot number whereas Soret number enhances diffusive mass transfer and variable viscosity parameter preferably reduces the skin friction. A comparison table is presented and it shows that the values obtained from the present method are in good agreement with existing literature. Also, the obtained results are depicted and interpreted in detail. Furthermore, entropy generation is analysed and its irreversibilty is calculated.

Design optimization of solar collectors with hybrid nanofluids: An integrated ansys and machine learning study

The current study discussed the integration between two computational approaches to evaluate the hydrothermal properties, such as pressure drop (ΔP), energy efficiency (ηeng), and absorbed energy (Qabs) of solar collectors using hybrid nanofluids. The physical problem was solved through a 3D model using Ansys 2021R1. After that, the three outputs were predicted through MATLAB R2022b using Optimize a Boosted Regression Ensemble by implementing Least-Squares Boosting (LSBoost) with Bayesian optimization (BO). Various working fluids were tested: distilled water (DW), Graphene oxide (GO-DW) nanofluids, and hybrid graphene oxide/silicon dioxide (GO/SiO2-DW) nanofluids in the mixing ratio (50:50), in the concentration range 0.01-1 vol%. The simulations covered a range of Reynolds numbers (300 ≤ Re ≤ 1500) and inlet temperatures (30 °C, 40 °C, 50 °C, and 60 °C). The results indicated that, the maximum variation compared to the first and second validations for mass flow rates and different nanofluid concentrations was between 3.96-4.71% and 4.32–6.45%. At an inlet temperature of 30 °C, GO-DW-1% exhibited the highest ΔP, ηeng, and Qabs, while GO-SiO2-DW-1% closely followed with slightly lower energy values. The maximum errors between computational fluid dynamics and machine learning predictions for (ΔP), (ηeng), and (Qabs) ranged from 2.40% to 6.32%, depending on the type of nanofluids (single or hybrid) and the volume concentration. To conclude, the current approach showed a promising result in the photothermal devices.

Thermal Performance Analysis of an Indirect Solar Cooker Using a Graphene Oxide Nanofluid

Solar energy has become an energy source for a wide range of uses, including in solar cookers, due to its availability, cleanliness, environmental friendliness, and sustainability. In this study, an indirect solar cooker was investigated by measuring its thermal performance using a Graphene Oxide (GO) nanofluid. Water, GO (250 ppm)-water, and GO (500 ppm)-water were used as heat transfer fluids. The experimental set-up consisted of the cooking part and a solar collector, which are the two essential elements in indirect solar cookers. The cooking part was a wooden box solar cooker, and the parabolic trough solar collector was a polished stainless steel structure. The solar cooker was assessed using the stagnation test and load test as well as energy and exergy measurements. According to the test results, the averaged F1 was 0.1 for the base fluid water, 0.11 for GO (250 ppm)-water, and 0.13 for GO (500 ppm)-water. In addition, using a GO nanofluid instead of water in the solar cooker, the system’s thermal performance, energy, and exergy efficiency were improved. The use of the GO (250 ppm)-water and GO (500 ppm)-water nanofluids instead of water in the system improved the overall energy efficiency of the system by 3.3 and 4.2%. Moreover, using GO (500 ppm)-water allowed for achieving superior performance.

Thermo-electro-rheological properties of graphene oxide and MXene hybrid nanofluid for vanadium redox flow battery: Application of explainable ensemble machine learning with hyperparameter optimization

Recent research has extensively focused on 2D materials such as graphene oxide (GO) and MXene due to their intriguing properties, significantly advancing nanotechnology and materials research. This experimental study explores the use of a vanadium electrolyte-based hybrid nanofluid (HNF) composed of GO and MXene (90:10) to enhance vanadium redox flow batteries (VRFBs). The synthesis and characterization of GO and Mxene nanoparticles (NPs) were conducted using various techniques. The HNF, produced at different weight concentrations, underwent analysis for stability, rheology, thermal conductivity (TC), and electrical conductivity (EC) within a temperature range of 10–45 °C. The results indicate that the HNF exhibits favorable stability and Newtonian behavior in the specified temperature range. At 45 °C, the HNF achieves a maximum enhancement of 20.5 % in EC and 6.81 % in TC for 0.1 wt% compared to the vanadium electrolyte. Subsequently, a prognostic model was developed using an explainable ensemble LSBoost-based machine learning approach, employing a test dataset and applying 5-fold cross-validation to prevent overfitting. Hyperparameter optimization was achieved using the Bayesian technique. The LSBoost-based prognostic models created for TC, EC, and viscosity (VST) demonstrated high effectiveness, with R2 values of 0.9981, 0.99, and 0.9954, respectively. The prediction errors were minimal, with RMSE values of 0.00089255, 5.553, and 0.09391 for the TC, EC, and VST models, respectively. Similarly, the MAE values were low, at 0.00068948, 4.0919, and 0.06129.

Ethylene Glycol-water Based Graphene Oxide Nanofluid as Corrosion Inhibitor in Automotive Radiator

A rapid cooling process is essential to maintain an optimal working temperature in a vehicle, which directly impacts its efficiency. Corrosion is a persistent and inevitable damage in cooling systems that use water-based fluids. The current challenge is to explore water-based fluids that not only exhibit excellent corrosion resistance but also possess superior heat conduction properties to improve vehicle efficiency. This study investigated the incorporation of Graphene Oxide, renowned for its corrosion inhibition properties, into ethylene glycol/water solution to assess its protective efficacy on Al6061 material. A series of analytical methods, including Optical Emission Spectroscopy (OES), pH, conductivity, Fourier-Transform Infrared Spectroscopy (FTIR), and polarization techniques, are used to evaluate the corrosion inhibition performance of graphene oxide at various concentrations and under different ambient temperatures. The results showed a decrease in pH value and conductivity with increasing concentration of graphene oxide. FTIR analysis confirmed the formation of a protective layer on the surface of Al6061. Corrosion rate assessment was performed on Al6061 samples immersed in ethylene glycol/water mixture with graphene oxide concentrations of 0, 0.03%, 0.05%, and 0.10%. There was a significant decrease in corrosion rate with the addition of graphene oxide to the cooling system: at 30°C, the rate decreased to 4.620, 3.308, 2.565, and 1.006 mpy; at 40°C, up to 4,728, 2,541, 1,503, and 1,270 mpy; and at 50°C, up to 5.629, 1.146, 2.947, and 1.441 mpy, corresponding graphene oxide concentrations of 0.03%, 0.05%, and 0.1%, respectively. Experimental data confirmed that graphene oxide effectively reduces the corrosion rate of Al6061 in ethylene glycol/water mixtures. The study concluded that the use of graphene oxide as a corrosion inhibitor markedly improved the resistance and performance of Al6061 in ethylene glycol/water, with graphene oxide contributing to this protective mechanism through the process of physisorption.

Thermal Analysis of Radiator Using Sustainable Graphene oxide Nanofluid Mixture of Ethylene Glycol and Water

The purpose of the research is to determine if adding grapheme oxide (GO) fluids combined with EG (ethylene glycol) or water might boost the transfer of heat in automobile radiators. Radiators are essential parts of car cooling systems; they dissipate extra heat that the engine produces. The capacity of conventional coolants to transport temperature is limited, including Glycol and water. The ability to conduct heat may be improved with the use of nanoparticles fluids, which are basically solutions of particles in a base liquidize. This technique uses ethylene glycol and water to create a nanoparticles fluid by dispersing GO particles. Using experiments, the resilience or thermal features of the nanoparticle fluids are described. Next, utilizing an early version radiators arrangement, many heat transfer tests are carried out. In comparison to traditional coolants, the radiator’s ability to dissipate heat in various functioning circumstances has been assessed while utilizing the GO nanoparticles fluids together. Comparing the radiator’s heat transfer efficiency with plain ethylene glycol (or water, initial results indicate the addition with GO nanoparticles fluids improves it. Increased thermal conductivity in the nanoparticles fluids combination results in more efficient heat dissipation. For the purpose of to ensure the efficient utilization of the nanoparticles fluids on car cooling mechanisms, it is further evaluated for durability during extended exposure to elevated temperatures. The continued attempts to provide cutting-edge cooling systems for automotive applications are aided by this study. The results indicate that the use of GO nanoparticles fluids in conjunction with conventional coolants has a chance to improve car radiator thermal transfer or general efficiency. It is advised to carry out greater refinement and calibration research to fully realize the potential advantages of this unique coolant composition.

Experimental and ensemble machine learning analyses of heat transfer, friction factor and thermal performance factor of rGO/water nanofluids in a tube

This paper explains about the experimental evaluation of thermophysical properties, heat transfer coefficient, Nusselt number, friction factor, and thermal performance factor of water-based reduced graphene oxide (rGO) nanofluids passes through a tube under turbulent flow conditions. The rGO nanoparticles in this study was prepared based on the Hummer's procedure. The thermophysical properties were estimated at different volume loadings from 0.5 % to 2.0 % and at different temperatures from 20 °C to 60 °C, respectively, similarly, the heat transfer and friction factor experiments were conducted in the Reynolds number ranging from 1800 to 21,000. The obtained data of heat transfer coefficient, Nusselt number, friction factor and thermal performance factor was predicted based on the advanced machine learning algorithms of boosted regression tree (BRT), and extreme gradient boosting (XGB), respectively. Results indicated that, at 2.0 % vol. of rGO/water nanofluid the thermal conductivity is raised by 26.95 % at a temperature of 60 °C, viscosity is raised by 63.29 % at a temperature of 20 °C, over base fluid. Moreover, the results are further shown that, at 2.0 % vol. of nanofluid the Nusselt number, heat transfer coefficient, factor in friction, drop in pressure, and pumping power raise of 34.80 %, 44.19 %, 16.73 %, 10.48 %, and 4.55 % at a Reynolds number of 13,705, against water. The correlation coefficient (R2) results predicted from BRT and XGB models for Nusselt number data is 0.9831 and 0.994, respectively. It is understood that the XBG algorithm predicts very accurate values compared to BRT algorithm with a less mean square error. The overall performance of the system was raised by 1.28 -folds than water. Using the experimental data, new Nusselt number and friction factor correlations were proposed.

Enhancing Thermal Performance of Evacuated Tube Solar Collector using Novel Graphene Oxide Nanofluid

The research article investigates the thermal performance of heat pipe-based evacuated tube solar collector (ETSC) experimentally using graphene oxide (GO) and deionised (DI) water as working fluid with a mass flow rate of 0.5 lit/min, 0.75 lit/min, and 1 lit/min. The different volumetric concentrations of 0.001%, 0.002%, and 0.003% graphene oxide nanofluid samples were prepared in the deionised water. The X-ray diffraction (XRD) was used to determine the structural properties of graphene oxide and the morphology of graphene oxide was studied by scanning electron microscope (SEM). To evaluate the stability of nanofluid samples, the Zeta potential analysis was carried out which showed that prepared nanofluid samples remain stable for up to 30 days. The effect of different nanofluid concentrations on various thermo physical properties of nanofluid was studied and discussed. The thermal performance of ETSC was investigated by considering the effect of volumetric concentrations of nanofluid and mass flow rates. According to the findings, there is a significant increment in temperature difference and energy gain by using nanofluid samples, and the maximum thermal efficiency of ETSC was found to be 37.1% for a volumetric concentration of 0.003% at 1 lit/min mass flow rate as compared to water.

Experimental analysis of stability, thermal conductivity and photothermal behaviour of amine functionalised graphene oxide nanofluid

Experimental analysis of stability, thermal conductivity and photothermal behaviour of amine functionalised graphene oxide nanofluid

Fabrication and thermal performance of a solar-driven heat pipe filled with reduced graphene oxide nanofluids

Nowadays, the solar-thermal technology, converting solar energy into useful thermal energy, was regarded as a direct and facile method to harvest solar irradiation for alleviating the energy crisis and environmental issues. In this work, we reported a transparent solar-driven heat pipe filled with rGO nanofluids, which combined volumetric solar-thermal harvesting and heat pipe technology to realize efficient solar capture while fast transporting of the collected thermal energy to the application ends simultaneously. The rGO nanofluids not only were used as solar volumetric absorbers to absorb solar radiation directly, but also completed the whole thermal cycle as the working fluid. We investigated the impact of filling ratio, inclination angle, working fluids, and solar power density on the thermal property of the heat pipe experimentally. It was found that 20% was the optimal filling ratio for the heat pipe with a lowest thermal resistance of 0.25 K/W at the solar power density of 17.5 W/cm2. Under the vertical operation mode, the heat transfer performance of the solar-driven heat pipe was better than that under the horizontal operation mode. Finally, this developed solar-driven heat pipe had been successfully applied for solar water heating systems, showing excellent heat transfer performance.