Application of SAXS to the study of particle-size-dependent thermal conductivity in silica nanofluids

Knowledge of the size and distribution of nanoparticles in solution is critical to understanding the observed enhancements in thermal conductivity and heat transfer of nanofluids. We have applied small-angle X-ray scattering (SAXS) to the characterization of SiO₂ nanoparticles (10–30 nm) uniformly dispersed in a water-based fluid using the Advanced Photon Source at Argonne National Laboratory. Size distributions for the suspended nanoparticles were derived by fitting experimental data to an established model. Thermal conductivity of the SiO₂ nanofluids was also measured, and the relation between the average particle size and the thermal conductivity enhancement was established. The experimental data contradict models based on fluid interfacial layers or Brownian motion but support the concept of thermal resistance at the liquid–particle interface.     

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.     

Thermal conductivity of nanofluids and size distribution of nanoparticles by Monte Carlo simulations

Nanofluids, a class of solid–liquid suspensions, have received an increasing attention and studied intensively because of their anomalously high thermal conductivites at low nanoparticle concentration. Based on the fractal character of nanoparticles in nanofluids, the probability model for nanoparticle’s sizes and the effective thermal conductivity model are derived, in which the effect of the microconvection due to the Brownian motion of nanoparticles in the fluids is taken into account. The proposed model is expressed as a function of the thermal conductivities of the base fluid and the nanoparticles, the volume fraction, fractal dimension for particles, the size of nanoparticles, and the temperature, as well as random number. This model has the characters of both analytical and numerical solutions. The Monte Carlo simulations combined with the fractal geometry theory are performed. The predictions by the present Monte Carlo simulations are shown in good accord with the existing experimental data.     

Investigation of thermal transport in colloidal silica dispersions (nanofluids)

Thermal conductivity enhancement in colloidal silica dispersions (nanofluids) is investigated experimentally using a novel optical technique. The effects of nanoparticle size, concentration, and state of aggregation are examined. New data on well dispersed systems are compared to published data obtained using the more conventional transient hot-wire technique and good agreement was found. Experimental results are also compared with model predictions for relative thermal conductivity based on effective medium theory. For systems composed of larger diameter nanoparticles (~30 nm), good agreement was found between the measured thermal conductivity enhancement and that predicted by the classical Maxwell-Garnett model. For systems composed of smaller nanoparticles (∼10 and 20 nm), thermal conductivity enhancement was reduced by as much as 10%, presumably because interfacial thermal resistance effects become important. Measurements on two systems that were induced to form gels exhibited an increase in thermal conductivity of approximately 5% relative to the well-dispersed systems. The observed increase in thermal conductivity is larger than that predicted by a recently proposed model for aggregated nanofluids.     

Eavesdropping and Improvement to Multiparty Quantum Secret Sharing with Collective Eavesdropping-Check

Because quantum devices are expensive, quantum secret sharing protocols with collective eavesdropping-check are more efficient and easier to realize than protocols employing step-by-step detection. In a recent paper (Lin et al. in Opt. Commun. 282:4455, 2009), put forward a quantum secret sharing protocol with collective eavesdropping-check. However, Gao found the four-party protocol of Lin et al. is insecure in the sense that two dishonest agents may collaborate to eavesdrop half of Alice’s secret without introducing any error (Gao in Opt. Commun. 283:2997, 2010). We point that there is a grievous mistake in Gao’s attack strategy and the two agents can only get one eighth of, not half of, Alice’s secret. In this paper, we study the properties of entanglement swapping and improve Gao’s eavesdropping strategy so that two dishonest agents can get all of Alice’s secret. Also we improve Lin et al.’s quantum secret sharing protocol against such attack.     

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.     

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.     

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     

Rheological behaviour of ethylene glycol-titanate nanotube nanofluids

Experimental work has been performed on the rheological behaviour of ethylene glycol based nanofluids containing titanate nanotubes over 20–60 °C and a particle mass concentration of 0–8%. It is found that the nanofluids show shear-thinning behaviour particularly at particle concentrations in excess of ~2%. Temperature imposes a very strong effect on the rheological behaviour of the nanofluids with higher temperatures giving stronger shear thinning. For a given particle concentration, there exists a certain shear rate below which the viscosity increases with increasing temperature, whereas the reverse occurs above such a shear rate. The normalised high-shear viscosity with respect to the base liquid viscosity, however, is independent of temperature. Further analyses suggest that the temperature effects are due to the shear-dependence of the relative contributions to the viscosity of the Brownian diffusion and convection. The analyses also suggest that a combination of particle aggregation and particle shape effects is the mechanism for the observed high-shear rheological behaviour, which is also supported by the thermal conductivity measurements and analyses.     

Predicting the thermal conductivity enhancement of nanofluids using computational intelligence

Nanofluids, composed of nanoparticles in base liquids, have drawn increasing attention in heat transfer applications due to their anomalously increased thermal conductivity. Pertinent parameters, including the base liquid thermal conductivity, particle thermal conductivity, particle size, particle volume fraction, and temperature, have been shown to have significant but complex effects on thermal performance of nanofluids, which is commonly characterized by the thermal conductivity enhancement, E%. In this work, machine learning is used to develop the Gaussian process regression model to find statistical correlations between E% and aforementioned physical parameters among various types of nanofluids. Nearly 300 nanofluid samples, dispersions of metal and ceramic nanoparticles in water, ethylene glycol, and transformer oil, are explored for this purpose. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of E%.     

A model for the thermal conductivity of nanofluids – the effect of interfacial layer

A model for predicting the effective thermal conductivity of nanofluids is proposed. It has been documented that the interfacial layer at the solid (particle)/liquid interface and particle size is one of the major mechanisms for enhancing the thermal conductivity of nanofluids. Comparing with other classical models, the proposed model takes into account some additional effects including volume fraction, thickness, thermal conductivity of the interfacial layer and particle size. The proposed model is found to be better than the existing models since the predicted effective thermal conductivity of different types of nanofluids are closer to the experimental results.     

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.     

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.     

A response to Murshed et al., J Nanopart Res (2010) 12:2007–2010

This brief communication provides a response to Murshed et al. (J Nanopart Res 12:2007–2010, 2010). We acknowledge that three of the equations in our original article (Doroodchi et al. J Nanopart Res 11:1501–1507, 2009) contained minor typographical errors. However, we confirm that these misprinted equations have no bearing on the results presented within that article. In addition, we would like to clarify that we do not challenge the methodology of Leong et al. (J Nanopart Res 8:245–254, 2006). Instead, we repeated their analysis using a more general form for the temperature field with continuity imposed across the particle–nanolayer–liquid interfaces and found that the solution reduces to the Renovated-Maxwell model.     

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.     

Adsorption kinetics of alkanethiol-capped gold nanoparticles at the hexane–water interface

The pendant drop technique was used to characterize the adsorption behavior of n-dodecane-1-thiol and n-hexane-1-thiol-capped gold nanoparticles at the hexane–water interface. The adsorption process was studied by analyzing the dynamic interfacial tension versus nanoparticle concentration, both at early times and at later stages (i.e., immediately after the interface between the fluids is made and once equilibrium has been established). A series of gold colloids were made using nanoparticles ranging in size from 1.60 to 2.85 nm dissolved in hexane for the interfacial tension analysis. Following free diffusion of nanoparticles from the bulk hexane phase, adsorption leads to ordering and rearrangement of the nanoparticles at the interface and formation of a dense monolayer. With increasing interfacial coverage, the diffusion-controlled adsorption for the nanoparticles at the interface was found to change to an interaction-controlled assembly and the presence of an adsorption barrier was experimentally verified. At the same bulk concentration, different sizes of n-dodecane-1-thiol nanoparticles showed different absorption behavior at the interface, in agreement with the findings of Kutuzov et al. (Phys Chem Chem Phys 9:6351–6358, 2007). The experiments additionally demonstrated the important role played by the capping agent. At the same concentration, gold nanoparticles stabilized by n-hexane-1-thiol exhibited greater surface activity than gold nanoparticles of the same size stabilized by n-dodecane-1-thiol. These findings contribute to the design of useful supra-colloidal structures by the self-assembly of alkane-thiol-capped gold nanoparticles at liquid–liquid interfaces.     

Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks

In this paper, the thermal conductivity ratio of MgO-MWCNTs/EG hybrid nanofluids has been predicted by an optimal artificial neural network at solid volume fractions of 0.05%, 0.1%, 0.15%, 0.2%, 0.4% and 0.6% in the temperature range of 25–50 °C. In this way, at the first, thirty six experimental data was presented to determine the thermal conductivity ratio of the hybrid nanofluid. Then, four optimal artificial neural networks with 6, 8, 10 and 12 neurons in hidden layer were designed to predict the thermal conductivity ratio of the nanofluid. The comparison between four optimal ANN results and experimental showed that the ANN with 12 neurons in hidden layer was the best model. Moreover, the results obtained from the best ANN indicated the maximum deviation margin of 0.8%.     

A response to ‘Comments on the effect of liquid layering on the thermal conductivity of nanofluids’, E. Doroodchi, T. M. Evans & B. Moghtaderi, 2009. J Nanopart Res 11(6):1501–1507

This article provides a response to a recent brief communication ‘Comments on the effect of liquid layering on the thermal conductivity of nanofluids’ by Doroodchi et al. in J Nanopart Res 11(6):1501–1507, 2009. It provides an opportunity for us to clarify the fundamental differences between the models of Yu and Choi (2003) and Leong et al. (2006) mentioned in the communication, followed by an explanation of the development of Leong et al.’s model. While we re-affirm that the model of Leong et al. (2006) was developed based on the right methodology, appropriate boundary conditions and mathematical basis and is therefore valid, there are at least three incorrect equations in Doroodchi et al.’s communication which raise serious doubts on their results calculated from the above models. Hence, the comments by Doroodchi et al. (2009) about the model of Leong et al. (2006) are not well-justified.     

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.     

Rare Event Simulation for T-cell Activation

The problem of statistical recognition is considered, as it arises in immunobiology, namely, the discrimination of foreign antigens against a background of the body’s own molecules. The precise mechanism of this foreign-self-distinction, though one of the major tasks of the immune system, continues to be a fundamental puzzle. Recent progress has been made by van den Berg, Rand, and Burroughs (J. Theor. Biol. 209:465–486, 2001), who modelled the probabilistic nature of the interaction between the relevant cell types, namely, T-cells and antigen-presenting cells (APCs). Here, the stochasticity is due to the random sample of antigens present on the surface of every APC, and to the random receptor type that characterises individual T-cells. It has been shown previously (van den Berg et al. in J. Theor. Biol. 209:465–486, 2001; Zint et al. in J. Math. Biol. 57:841–861, 2008) that this model, though highly idealised, is capable of reproducing important aspects of the recognition phenomenon, and of explaining them on the basis of stochastic rare events. These results were obtained with the help of a refined large deviation theorem and were thus asymptotic in nature. Simulations have, so far, been restricted to the straightforward simple sampling approach, which does not allow for sample sizes large enough to address more detailed questions. Building on the available large deviation results, we develop an importance sampling technique that allows for a convenient exploration of the relevant tail events by means of simulation. With its help, we investigate the mechanism of statistical recognition in some depth. In particular, we illustrate how a foreign antigen can stand out against the self background if it is present in sufficiently many copies, although no a priori difference between self and nonself is built into the model.