Dispersion stability and thermal conductivity of propylene glycol-based nanofluids

The dispersion stability and thermal conductivity of propylene glycol-based nanofluids containing Al₂O₃ and TiO₂ nanoparticles were studied in the temperature range of 20–80 °C. Nanofluids with different concentrations of nanoparticles were formulated by the two-step method and no dispersant was used. In contrast to the common belief, the average particle size of nanofluids was observed to decrease with increasing temperature, and nanofluids showed an excellent stability over the temperature range of interest. Thermal conductivity enhancement for both studied nanofluids was a nonlinear function of concentration and was temperature independent. Theoretical analyses were also performed using existing models compared with experimental results. The model based on the aggregation theory appears to be the best.     

The effect of particle size on the thermal conductivity of alumina nanofluids

We present new data for the thermal conductivity enhancement in seven nanofluids containing 8–282 nm diameter alumina nanoparticles in water or ethylene glycol. Our results show that the thermal conductivity enhancement in these nanofluids decreases as the particle size decreases below about 50 nm. This finding is consistent with a decrease in the thermal conductivity of alumina nanoparticles with decreasing particle size, which can be attributed to phonon scattering at the solid–liquid interface. The limiting value of the enhancement for nanofluids containing large particles is greater than that predicted by the Maxwell equation, but is predicted well by the volume fraction weighted geometric mean of the bulk thermal conductivities of the solid and liquid. This observation was used to develop a simple relationship for the thermal conductivity of alumina nanofluids in both water and ethylene glycol.     

Computational analysis of factors influencing thermal conductivity of nanofluids

Numerical investigations are conducted to study the effect of factors such as particle clustering and interfacial layer thickness on thermal conductivity of nanofluids. Based on this, parameters including Kapitza radius and fractal and chemical dimension which have received little attention by previous research are rigorously investigated. The degree of thermal enhancement is analyzed for increasing aggregate size, particle concentration, interfacial thermal resistance, and fractal and chemical dimensions. This analysis is conducted for water-based nanofluids of Alumina (Al₂O₃), CuO, and Titania (TiO₂) nanoparticles where the particle concentrations are varied up to 4 vol%. Results from the numerical work are validated using available experimental data. For the case of aggregate size, particle concentration, and interfacial thermal resistance, the aspect ratio (ratio of radius of gyration of aggregate to radius of primary particle, R _g/a) is varied from 2 to 60. It was found that the enhancement decreases with interfacial layer thickness. Also the rate of decrease is more significant after a given aggregate size. For a given interfacial resistance, the enhancement is mostly sensitive to R _g/a < 20 indicated by the steep gradients of data plots. Predicted and experimental data for thermal conductivity enhancement are in good agreement. On the influence of fractal and chemical dimensions (d _l and d _f) of Alumina–water nanofluid, the R _g/a was varied from 2 to 8, d _l from 1.2 to 1.8, and d _f from 1.75 to 2.5. For a given concentration, the enhancement increased with the reduction of d _l or d _f. It appears a distinctive sensitivity of the enhancement to d _f, in particular, in the range 2–2.25, for all values of R _g/a. However, the sensitivity of d _l was largely depended on the value of R _g/a. The information gathered from this study on the sensitivity of thermal conductivity enhancement to aggregate size, particle concentration, interfacial resistance, and fractal and chemical dimensions will be useful in manufacturing highly thermally conductive nanofluids. Further research on the refine cluster evolution dynamics as a function of particle-scale properties is underway.     

Thermal conductivity and thermal diffusivity of SiO<Subscript>2</Subscript> nanopowder

A 3ω approach for the simultaneous determination of the effective thermal conductivity and thermal diffusivity of nanopowder materials was developed. A 3ω experimental system was established, and the thermal properties of water and alcohol were measured to validate and estimate the accuracy of the current experimental system. The effective thermal conductivity and thermal diffusivity of the SiO₂ nanopowder with 375, 475, and 575 nm diameters were measured at 290–490 K and at different densities. At room temperature, the effective thermal conductivity and thermal diffusivity of the SiO₂ nanopowder increased with temperature; however, both values decreased as the particle diameter was reduced. An optimum SiO₂ powder density that decreased with decreasing diameter was also observed within the measurement range. The minimum effective thermal conductivity and maximum effective thermal diffusivity were obtained at 85 × 10⁻³ kg/L, when the particle diameter was 575 nm. The optimum densities of the particles with 375 and 475 nm diameters were less than 50.23 × 10⁻³ and 64.82 × 10⁻³ kg/L, respectively.     

Dispersion behavior and thermal conductivity characteristics of Al2O3–H2O nanofluids

Nanofluid is a kind of new engineering material consisting of solid nanoparticles with sizes typically of 1–100 nm suspended in base fluids. In this study, Al₂O₃–H₂O nanofluids were synthesized, their dispersion behaviors and thermal conductivity in water were investigated under different pH values and different sodium dodecylbenzenesulfonate (SDBS) concentration. The sedimentation kinetics was determined by examining the absorbency of particle in solution. The zeta potential and particle size of the particles were measured and the Derjaguin–Landau–Verwey–Overbeek (DLVO) theory was used to calculate attractive and repulsive potentials. The thermal conductivity was measured by a hot disk thermal constants analyser. The results showed that the stability and thermal conductivity enhancements of Al₂O₃–H₂O nanofluids are highly dependent on pH values and different SDBS dispersant concentration of nano-suspensions, with an optimal pH value and SDBS concentration for the best dispersion behavior and the highest thermal conductivity. The absolute value of zeta potential and the absorbency of nano-Al₂O₃ suspensions with SDBS dispersant are higher at pH 8.0. The calculated DLVO interparticle interaction potentials verified the experimental results of the pH effect on the stability behavior. The Al₂O₃–H₂O nanofluids with an ounce of Al₂O₃ have noticeably higher thermal conductivity than the base fluid without nanoparticles, for Al₂O₃ nanoparticles at a weight fraction of 0.0015 (0.15 wt%), thermal conductivity was enhanced by up to 10.1%.     

A thermal conductivity model for nanofluids including effect of the temperature-dependent interfacial layer

The interfacial layer of nanoparticles has been recently shown to have an effect on the thermal conductivity of nanofluids. There is, however, still no thermal conductivity model that includes the effects of temperature and nanoparticle size variations on the thickness and consequently on the thermal conductivity of the interfacial layer. In the present work, the stationary model developed by Leong et al. (J Nanopart Res 8:245–254, 2006) is initially modified to include the thermal dispersion effect due to the Brownian motion of nanoparticles. This model is called the ‘Leong et al.’s dynamic model’. However, the Leong et al.’s dynamic model over-predicts the thermal conductivity of nanofluids in the case of the flowing fluid. This suggests that the enhancement in the thermal conductivity of the flowing nanofluids due to the increase in temperature does not come from the thermal dispersion effect. It is more likely that the enhancement in heat transfer of the flowing nanofluids comes from the temperature-dependent interfacial layer effect. Therefore, the Leong et al.’s stationary model is again modified to include the effect of temperature variation on the thermal conductivity of the interfacial layer for different sizes of nanoparticles. This present model is then evaluated and compared with the other thermal conductivity models for the turbulent convective heat transfer in nanofluids along a uniformly heated tube. The results show that the present model is more general than the other models in the sense that it can predict both the temperature and the volume fraction dependence of the thermal conductivity of nanofluids for both non-flowing and flowing fluids. Also, it is found to be more accurate than the other models due to the inclusion of the effect of the temperature-dependent interfacial layer. In conclusion, the present model can accurately predict the changes in thermal conductivity of nanofluids due to the changes in volume fraction and temperature for various nanoparticle sizes.     

Investigation on characteristics of thermal conductivity enhancement of nanofluids

It has been shown that a nanofluid consisting of nanoparticles dispersed in base fluid has much higher effective thermal conductivity than pure fluid. In this study, four kinds of nanofluids such as multiwalled carbon nanotube (MWCNT) in water, CuO in water, SiO₂ in water, and CuO in ethylene glycol, are produced. Their thermal conductivities are measured by a transient hot-wire method. The thermal conductivity enhancement of water-based MWCNT nanofluid is increased up to 11.3% at a volume fraction of 0.01. The measured thermal conductivities of MWCNT nanofluids are higher than those calculated with Hamilton–Crosser model due to neglecting solid–liquid interaction at the interface. The results show that the thermal conductivity enhancement of nanofluids depends on the thermal conductivities of both particles and the base fluid.     

Prediction of thermal conductivity and viscosity of nanofluids by molecular dynamics simulation

Limitations of conventional heat transfer fluids in different industries because of their poor thermal conductivity made heat transfer improvement in working fluids was performing, as a new method of advanced heat transfer. Therefore, the dispersion solid particle idea in fluids, which has been started with mili- and micrometer particles, completed by using nanoparticles and today nanofluids have been found to provide a considerable heat transfer and viscosity enhancement in comparison to conventional fluids such as water, ethylene glycol, and engine oil. In this study, molecular dynamics simulation was used to predict thermal conductivity and viscosity of nanofluids. Water was used as a base fluid. The simple point charge-extended (SPC/E) model was used for simulation of water and Ewald sum method for electrostatic interactions. Lennard–Jones potential for Van der Waals interactions, KTS potential for water and SiO₂ and Spor and Heinzinger correlation for water and Pt were used. The results were compared with experimental data. For investigation of the effect of temperature, simulation was done for three temperatures of 20, 30, and 50?C. The results showed that the ratio of thermal conductivity of nanofluid to base fluid and viscosity will decrease as the temperature increases. The effect of the concentration of nanoparticle was studied for three different concentrations, namely, 0.45, 1.85, and 4%. The thermal conductivity of nanofluid increases with increasing the concentration. Moreover, the effect of two nanoparticle sizes (i.e., 5 and 7 nm) on the thermal conductivity of nanofluid was investigated. It was shown that an increase in the size causes a decrease in the thermal conductivity. Finally, by replacing the SiO₂nanoparticle with a Pt nanoparticle in the nanofluid, it was observed that the kind of nanoparticle had not a considerable effect on increasing the thermal conductivity of nanofluid.     

Novel nanofluids based on mesoporous silica for enhanced heat transfer

Nanofluids, which are liquids with engineered nanometer-sized particles suspensions, have drawn remarkable attraction from the researchers because of their enormous potential to enhance the efficiency in heat-transfer fluids. In the present study, water-based calcined mesoporous silica nanofluids were prepared and characterized. The commercial mesoporous silica (MPSiO₂) nanoparticles were dispersed in deionized water by means of pH adjustment and ultrasonic agitation. MPSiO₂ nanoparticles were observed to have an average particle size of 350 ± 100 nm by SEM analysis. The concentration of MPSiO₂ was varied between 1 and 6 wt%. The physicochemical properties of nanofluids were characterized using various techniques, such as particle size analyzer, zeta-potential meter, TEM, and FT-IR. The thermal conductivity was measured by Transient Plane Source (TPS) method, and nanofluids showed a higher thermal conductivity than the base liquid for all the tested concentrations.     

Fabrication, characterization and measurement of thermal conductivity of Fe3O4 nanofluids

Magnetite Fe₃O₄ nanoparticles were synthesized by a co-precipitation method at different pH values. The products were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, and transmission electronic microscopy. Their magnetic properties were evaluated on a vibrating sample magnetometer. The results show that the shape of the particles is cubic and they are superparamagnetic at room temperature. Magnetic nanofluids were prepared by dispersing the Fe₃O₄ nanoparticles in water as a base fluid in the presence of tetramethyl ammonium hydroxide as a dispersant. The thermal conductivity of the nanofluids was measured as a function of volume fraction and temperature. The results show that the thermal conductivity ratio of the nanofluids increases with increase in temperature and volume fraction. The highest enhancement of thermal conductivity was 11.5% in the nanofluid of 3 vol% of nanoparticles at 40 °C. The experimental results were also compared with the theoretical models.     

Influence of the particle size distribution on the thermal conductivity of nanofluids

In a previous study, we have obtained an equation to predict the thermal conductivity of nanofluids containing nanoparticles with conductive interface. The model is maximal particle packing dependent. In this study, the maximal packing is obtained as a function of the particle size distribution, which is the Gamma distribution. The thermal conductivity enhancement depends on the averaged particle size. Discussion concerning the influence of the suspension pH on the particle packing is made. The proposed model is evaluated using number of sets from the published experimental data to the thermal conductivity enhancement for different nanofluids.     

Particle agglomeration and properties of nanofluids

The present study demonstrates the importance of actual agglomerated particle size in the nanofluid and its effect on the fluid properties. The current work deals with 5 to 100 nm nanoparticles dispersed in fluids that resulted in 200 to 800 nm agglomerates. Particle size distributions for a range of nanofluids are measured by dynamic light scattering (DLS). Wet scanning electron microscopy method is used to visualize agglomerated particles in the dispersed state and to confirm particle size measurements by DLS. Our results show that a combination of base fluid chemistry and nanoparticle type is very important to create stable nanofluids. Several nanofluids resulted in stable state without any stabilizers, but in the long term had agglomerations of 250 % over a 2 month period. The effects of agglomeration on the thermal and rheological properties are presented for several types of nanoparticle and base fluid chemistries. Despite using nanodiamond particles with high thermal conductivity and a very sensitive laser flash thermal conductivity measurement technique, no anomalous increases of thermal conductivity was measured. The thermal conductivity increases of nanofluid with the particle concentration are as those predicted by Maxwell and Bruggeman models. The level of agglomeration of nanoparticles hardly influenced the thermal conductivity of the nanofluid. The viscosity of nanofluids increased strongly as the concentration of particle is increased; it displays shear thinning and is a strong function of the level of agglomeration. The viscosity increase is significantly above of that predicted by the Einstein model even for very small concentration of nanoparticles.     

Heat transfer enhancement of laminar nanofluids flow in a triangular duct using vortex generator

In this work, two dimensional laminar flow of different nanofluids flow inside a triangular duct with the existence of vortex generator is numerically investigated. The governing equations of mass, momentum and energy were solved using the finite volume method (FVM). The effects of type of the nanoparticles, particle concentrations, and Reynolds number on the heat transfer coefficient and pressure drop of nanofluids are examined. Reynolds number is ranged from 100 to 800. A constant surface temperature is assumed to be the thermal condition for the upper and lower heated walls. In the present work, three nanofluids are examined which are Al₂O₃, CuO and SiO₂ suspended in the base fluid of ethylene glycol with nanoparticles concentrations ranged from 1 to 6%. The results show that for the case of SiO₂–EG, at ? = 6% and Re = 800, it is found that the average Nusselt number is about 50.0% higher than the case of Re = 100.     

A new thermal conductivity model for nanofluids

In a quiescent suspension, nanoparticles move randomly and thereby carry relatively large volumes of surrounding liquid with them. This micro-scale interaction may occur between hot and cold regions, resulting in a lower local temperature gradient for a given heat flux compared with the pure liquid case. Thus, as a result of Brownian motion, the effective thermal conductivity, k_eff, which is composed of the particles conventional static part and the Brownian motion part, increases to result in a lower temperature gradient for a given heat flux. To capture these transport phenomena, a new thermal conductivity model for nanofluids has been developed, which takes the effects of particle size, particle volume fraction and temperature dependence as well as properties of base liquid and particle phase into consideration by considering surrounding liquid traveling with randomly moving nanoparticles.The strong dependence of the effective thermal conductivity on temperature and material properties of both particle and carrier fluid was attributed to the long impact range of the interparticle potential, which influences the particle motion. In the new model, the impact of Brownian motion is more effective at higher temperatures, as also observed experimentally. Specifically, the new model was tested with simple thermal conduction cases, and demonstrated that for a given heat flux, the temperature gradient changes significantly due to a variable thermal conductivity which mainly depends on particle volume fraction, particle size, particle material and temperature. To improve the accuracy and versatility of the k_effmodel, more experimental data sets are needed.     

Whole-body distribution of <Superscript>14</Superscript>C-labeled silica nanoparticles and submicron particles after intravenous injection into Mice

We analyzed the whole-body distribution of ¹⁴C–ADP-labeled silica nanoparticles (¹⁴C–ADP–SiO₂ nanoparticles) and submicron particles (¹⁴C–ADP–SiO₂ submicron particles) after intravenous injection into ICR mice. The ¹⁴C–ADP–SiO₂ nanoparticles and submicron particles were synthesized before the injection and the particle size was 19.6 and 130 nm, respectively. Similarly, the shape was spherical and the crystallinity was amorphous. After the synthesis, we injected mice with the ¹⁴C–ADP–SiO₂ nanoparticles or the ¹⁴C–ADP–SiO₂ submicron particles and dissected tissues after 1, 2, 4, 8 and 24 h. The radioactivity in the tissues was measured with a liquid scintillation counter. As a result, the retention percentage in bone, skin, lymph nodes, and the digestive mixture was at least twofold higher in the ¹⁴C–ADP–SiO₂ nanoparticles-exposed mice, whereas the retention percentage in the kidney was statistically higher in the ¹⁴C–ADP–SiO₂ submicron particles-exposed mice. Both types of ¹⁴C–ADP–SiO₂ particles mainly translocated to the muscle, bone, skin, and liver, but hardly translocated to the brain and olfactory bulb. Furthermore, the ¹⁴C–ADP–SiO₂ nanoparticles had a higher retention percentage (62.4 %) in the entire body at 24-h post-injection than did the ¹⁴C–ADP–SiO₂ submicron particles (50.7 %). Therefore, we suggested that the ¹⁴C–ADP–SiO₂ nanoparticles might be more likely than the ¹⁴C–ADP–SiO₂ submicron particles to be retained in the body, and consequently they might be gradually accumulated by chronic exposure.     

Comparison between Experimental and Theoretical Thermal Conductivity of Nanofluids Containing Multi-walled Carbon Nanotubes Decorated with TiO2 Nanoparticles

This article reports the thermal conductivity modeling of nanofluids containing decorated multi-walled carbon nanotubes with TiO₂ nanoparticles. TiO₂ nanoparticles and decorated multi-walled carbon nanotubes are synthesized with different amounts of TiO₂ nanoparticles. The experimental results show that the measured thermal conductivities of TiO₂ nanofluids and multi-walled carbon nanotube nanofluids are higher than the predicted values by theoretical models. The comparison results of multi-walled carbon nanotube nanofluids and multi-walled carbon nanotube–TiO₂ nanofluids reveal that the predicted values by the Xue model are closer to the measured values. In addition, the results show that the thermal conductivity of nanofluids containing multi-walled carbon nanotube–TiO₂ increases with respect to TiO₂ content of hybrid.     

Modeling of thermal conductivity of nanofluids by modifying Maxwell’s equation using cell model approach

Nanofluid is an innovative heat transfer fluid with superior potential for enhancing the heat transfer performance of conventional fluids. Though many attempts have been made to investigate the abnormal high thermal conductivity of nanofluids, the existing models cannot precisely predict the same. An attempt has been made to develop a model for predicting the thermal conductivity of different types of nanofluids. The model presented here is derived based on the fact that thermal conductivity of nanofluids depends on thermal conductivity of particle and fluid as well as micro-convective heat transfer due to Brownian motion of nanoparticles. Novelty of the article lies in giving a unique equation which predicts thermal conductivity of nanofluids for different concentrations and particle sizes which also correctly predicts the trends observed in experimental data over a wide range of particle sizes, temperatures, and particle concentrations.     

Brownian dynamic simulation for the prediction of effective thermal conductivity of nanofluid

Nanofluid is a colloidal solution of nanosized solid particles in liquids. Nanofluids show anomalously high thermal conductivity in comparison to the base fluid, a fact that has drawn the interest of lots of research groups. Thermal conductivity of nanofluids depends on factors such as the nature of base fluid and nanoparticle, particle concentration, temperature of the fluid and size of the particles. Also, the nanofluids show significant change in properties such as viscosity and specific heat in comparison to the base fluid. Hence, a theoretical model becomes important in order to optimize the nanofluid dispersion (with respect to particle size, volume fraction, temperature, etc.) for its performance. As molecular dynamic simulation is computationally expensive, here the technique of Brownian dynamic simulation coupled with the Green Kubo model has been used in order to compute the thermal conductivity of nanofluids. The simulations were performed for different concentration ranging from 0.5 to 3 vol%, particle size ranging from 15 to 150 nm and temperature ranging from 290 to 320 K. The results were compared with the available experimental data, and they were found to be in close agreement. The model also brings to light important physical aspect like the role of Brownian motion in the thermal conductivity enhancement of nanofluids.     

Effective thermal conductivity of condensed polymeric nanofluids (nanosolids) controlled by diffusion and interfacial scattering

Thermal properties of polymeric nanosolids, obtained by condensing the corresponding nanofluids, are investigated using photothermal techniques. The heat transport properties of two sets of polyvinyl alcohol (PVA) based nanosolids, TiO₂/PVA and Cu/PVA, prepared by condensing the respective nanofluids, which are prepared by dispersing nanoparticles of TiO₂ and metallic copper in liquid PVA, are reported. Two photothermal techniques, the photoacoustic and the photopyroelectric techniques, have been employed for measuring thermal diffusivity, thermal conductivity and specific heat capacity of these nanosolids. The experimental results indicate that thermal conduction in these polymer composites is controlled by heat diffusion through the embedded particles and interfacial scattering at matrix–particle boundaries. These two mechanisms are combined to arrive at an expression for their effective thermal conductivity. Analysis of the results reveals the possibility to tune the thermal conductivity of such nanosolids over a wide range using the right types of nanoparticles and right concentration.     

水基ZnO纳米流体电导和热导性能研究

利用水热法生成了形状规则、粒径均匀的球形ZnO纳米颗粒, 并超声分散于水中, 制备得到稳定的水基ZnO纳米流体. 实验测量水基ZnO纳米流体在体积分数和温度变化时的电导率, 并测试室温下水基ZnO纳米流体在不同体积分数下的热导率. 实验结果表明, ZnO纳米颗粒的添加较大地提高了基液(纯水)的热导率和电导率, 水基ZnO纳米流体的电导率随纳米颗粒体积分数增加呈非线性增加关系, 而电导率随温度变化呈现出拟线性关系; 纳米流体的热导率与纳米颗粒体积分数增加呈近似线性增加关系. 本文在经典Maxwell热导模型和布朗动力学理论的基础上, 同时考虑了吸附层、团聚体和布朗运动等因素对热导率的影响, 提出了热导率修正模型.将修正模型预测值与实验值对比, 结果表明修正模型可以较为准确地计算出纳米流体的热导率. 关键词： 水热法 电导率 热导率 热导模型