The thermal conductivity of alumina nanoparticles dispersed in ethylene glycol

The thermal conductivities of several nanofluids (dispersions of alumina nanoparticles in ethylene glycol) were measured at temperatures ranging from 298 to 411 K using a liquid metal transient hot wire apparatus. Our measurements span the widest range of temperatures that have been investigated to date for any nanofluid. A maximum in the thermal conductivity versus temperature behavior was observed at all mass fractions of nanoparticles, closely following the behavior of the base fluid (ethylene glycol). Our results confirm that additional temperature contributions inherent in Brownian motion models are not necessary to describe the temperature dependence of the thermal conductivity of nanofluids. Our results also show that the effect of mass or volume fraction of nanoparticles on the thermal conductivity of nanofluids can be correlated using the Hamilton and Crosser or Yu and Choi models with one adjustable parameter (the shape factor in the Hamilton and Crosser model, or the ordered liquid layer thickness in the Yu and Choi model).     

Applying different types of artificial neural network for modeling thermal conductivity of nanofluids containing silica particles

Journal of Thermal Analysis and Calorimetry - Nanofluids are widely applicable in thermal devices with porous structures. Silica nanoparticles have been dispersed in different heat transfer fluids...     

Forecasting the thermal conductivity of a nanofluid using artificial neural networks

Journal of Thermal Analysis and Calorimetry - In this study, the influence of incorporating MWCNT on the thermal conductivity of paraffin was evaluated numerically. Input variables including mass...     

ANN modelling and experimental investigation on effective thermal conductivity of ethylene glycol:water nanofluids

Journal of Thermal Analysis and Calorimetry - In this study, ethylene glycol (EG)–water (35:65 %v)-based nanofluids have been prepared to study enhancement in thermal conductivity....     

Surface modification of nanoporous alumina surfaces with poly(ethylene glycol)

Nanoporous alumina surfaces have a variety of applications in biosensors, biofiltration, and targeted drug delivery. However, the fabrication route to create these nanopores in alumina results in surface defects in the crystal lattice. This results in inherent charge on the porous surface causing biofouling, that is, nonspecific adsorption of biomolecules. Poly(ethylene glycol) (PEG) is known to form biocompatible nonfouling films on silicon surfaces. However, its application to alumina surfaces is very limited and has not been well investigated. In this study, we have covalently attached PEG to nanoporous alumina surfaces to improve their nonfouling properties. A PEG-silane coupling technique was used to modify the surface. Different concentrations of PEG for different immobilization times were used to form PEG films of various grafting densities. X-ray photoelectron spectroscopy (XPS) was used to verify the presence of PEG moieties on the alumina surface. High-resolution C1s spectra show that with an increase in concentration and immobilization time, the grafting density of PEG also increases. Further, a standard overlayer model was used to calculate the thickness of PEG films formed using the XPS intensities of the Al2p peaks. The films formed by this technique are less than 2.5 nm thick, suggesting that such films will not clog the pores which are in the range of 70-80 nm.     

Enhancement of thermal conductivity and tensile strength of liquid silicone rubber by three-dimensional alumina network

ABSTRACT

Rapidly increasing demands for higher integration density and stability of electronic devices embrace higher requirements for thermally conductive silicone rubber, which is promisingly used in ultra-thin components. In this work, alumina whiskers (AWs) and alumina flakes (AFs) are used to modify liquid silicone rubber (LSR) by fabricating binary (AFs/LSR) or ternary (AWs/AFs/LSR) composites. The thermal conductivity and mechanical strength of the binary and ternary composites were investigated. Thermal conductivity of the binary AFs/LSR composite (25AFs/LSR) was 0.1990 W m^?1 K^?1, while the thermal conductivity of the ternary AFs/AWs/LSR composite (20AFs/5AWs/LSR) was 0.2655 W m^?1 K^?1. Furthermore, the tensile strength of the ternary AWs/AFs/LSR composites increased by 180.9% as compared with the binary system, increased to 7.81 MPa from 2.78 MPa due to the introduction of 1 wt% AWs. As a reason, a significant synergistic effect of AWs and AFs in the enhancement of both thermal and mechanical properties of the LSR was proved. Furthermore, the dielectric property measurements demonstrated that the ternary composites exhibited a lower dielectric constant and dielectric loss, indicating that the AWs/AFs/LSR composites were qualified to be applied in the field of electronic devices.     

Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network

The application of nanofluids in energy systems is developing day by day. Before using a nanofluid in an energy system, it is necessary to measure the properties of nanofluids. In this paper, first the results of experiments on the thermal conductivity of MgO/ethylene glycol (EG) nanofluids in a temperature range of 25–55 °C and volume concentrations up to 5 % are presented. Different sizes of MgO nanoparticles are selected to disperse in EG, including 20, 40, 50, and 60 nm. Based on the results, an empirical correlation is presented as a function of temperature, volume fraction, and nanoparticle size. Next, the model of thermal conductivity enhancement in terms of volume fraction, particle size, and temperature was developed via neural network based on the measured data. It is observed that neural network can be used as a powerful tool to predict the thermal conductivity of nanofluids.     

Thermal conductivity prediction of nanofluids containing CuO nanoparticles by using correlation and artificial neural network

Journal of Thermal Analysis and Calorimetry - Nanofluids are employed in different thermal devices due to their enhanced thermophysical features which lead to noticeable heat transfer augmentation....     

Thermal conductivity modeling of nanofluids with ZnO particles by using approaches based on artificial neural network and MARS

Journal of Thermal Analysis and Calorimetry - Nanofluids are attractive alternatives for the current heat transfer fluids due to their remarkably higher thermal conductivity which leads to the...     

Constructing a force interaction model for thermal conductivity computation using molecular dynamics simulation: ethylene glycol as an example

This study aims to construct a force interaction model for thermal conductivity computation and to analyze the liquid properties in atomic level for liquid ethylene glycol (EG) using molecular dynamic simulation. The microscopic details of the molecular system and the macroscopic properties of experimental interest are connected by Green-Kubo relations. In addition, the major contributions of heat transfer modes for thermal conductivity due to convection, interaction, and torque are obtained quantitatively. This study reveals that the intramolecular interaction force fields result in different conformations of the EG in the liquid and thus the molecular shapes. The trans∕gauche ratio for EG's O-Me-Me-O torsional angle and the number of intermolecular∕intramolecular H-bonds are found to be important parameters affecting the thermal conductivity.     

Quantum-chemical study on thermal transformations of urea in ethylene glycol

Thermal decomposition of urea in ethylene glycol with formation of isocyanic acid and ammonia was studied at the B3LYP/6-311++G(df,p) level of theory. The decomposition process is efficiently catalyzed by monomeric and dimeric forms of ethylene glycol. Ethylene glycol dimer formed via intermolecular hydrogen bonding is a stronger acid than the monomeric species, which is responsible for the higher catalytic activity of the former. Ethylene glycol associates efficiently catalyze addition of ammonium to isocyanic acid in the synthesis of ethylene carbonate.     

A comparative study of heat transfer analysis on ethylene glycol or engine oil as base fluid with gold nanoparticle in presence of thermal radiation

Journal of Thermal Analysis and Calorimetry - The present work addresses to analyse the heat transfer enhancement of unsteady laminar incompressible MHD flow of couple stress nanofluid through a...     

Effect of copper nanoparticle aggregation on the thermal conductivity of nanofluids

The thermal conductivity of water and glycerol is investigated via the transient hot wire method by adding small amounts of copper nanoparticles to solutions. At a 0.2% copper nanoparticle concentration, the thermal conductivity coefficient rises to 25% for the Cu + glycerol system, and to 35% for Cu + water system. A mechanism and mathematical model for describing the nanoparticle aggregation effect on the thermal properties of nanofluids are proposed, based on an analysis of the accumulated experimental data. It is shown that the enhancement of nanofluid thermal conductivity at low nanoparticle concentrations is directly proportional to their volume fraction and thermal conductivity coefficient, and (in accordance with the literature data) is inversely proportional to the radius and the aggregation ratio. The proposed model describes the existing experimental data quite well. The results from this work can be applied to the rapid cooling of electronic components, in the power engineering for ensuring the rapid and effective transfer of thermal energy in a nuclear reactor, and in the oil industry for thermal stimulation.     

Forecasting of water thermal conductivity enhancement by adding nano-sized alumina particles

Operating fluids play an important role in heat transfer equipment. Water is inexpensive popular operating fluid with extensive applications, but its thermophysical properties are not good enough, especially for high temperature processes. Therefore, modification of its inherent characteristics by adding nano-sized solid particles found high popularities. Thermal conductivity is one of the most important thermophysical properties of an operating fluid in relatively all energy-based processes. Variation of thermal conductivity of nanofluids with different operating conditions is required to be understood in such processes. Therefore, the focus of this study is concentrated on modeling of thermal conductivity of water-alumina nanofluids using four different smart paradigms. Multilayer perceptron, radial basis function, cascade feedforward, and generalized regression neural networks are employed for the considered task. The best structure of these paradigms is determined, and then, their accuracies are compared using different statistical indices. Accuracy analyses confirmed that the generalized regression neural network outperforms other considered smart methodologies. It predicted more than 280 experimental datasets with excellent absolute average relative deviation?=?0.71%, mean square error?=?0.0006, root mean square error?=?0.023 and regression coefficient (R²)?=?0.9675. In the final stage, the proposed paradigm is used for investigation of the effect of influential parameters on the thermal conductivity of water-alumina nanofluids. This type of accurate and straightforward paradigm can broaden our insight about thermal behavior of homogeneous suspension of nano-size alumina particles in water.

     

温控聚乙二醇两相体系中纳米钯催化肉桂醛选择性加氢反应

将具有“高温混溶、室温分相”功能的聚乙二醇4000(PEG₄₀₀₀)与甲苯-正庚烷组成的两相体系用于纳米钯催化的肉桂醛选择性加氢反应中.在优化的反应条件下,肉桂醛转化率和氢化肉桂醛选择性分别为99%和98%.钯纳米催化剂经简单分相即可与产物分离,且循环使用8次,其活性和选择性基本保持不变.     

Artificial neural network applied to solid state thermal decomposition

A multi-layer neural network is constructed to describe the thermal decomposition of rhodium acetate. Critical analysis of the residual, trained, interpolated and extrapolated errors, with the number of neurons, indicates the efficiency of the present approach. It was possible, within this framework, to improve the A _n model, with a better correlation between the results. A new value of the activation energy, E _a, and frequency factor, Z, are calculated for the decomposition process. Since the neural network is more precise than a particular model, the calculated values for these quantities are believed to be more precise. The computed values are E _a=194.0 kJ mol^-1 and Z=5.23·1016 s^-1. The neural network eliminates the step to decide, among the available models, the one that best fit the data. An agreement up to four significant figures can be achieved even for data not used in the training process, both in the interpolated and extrapolated regions. This method suggests, therefore, an important alternative tool for the experimentalists. The present approach can also be adapted to other systems and to data in two dimensions. This revised version was published online in July 2006 with corrections to the Cover Date.