Thermal conductivity measurement of liquids in a microfluidic device

A new microfluidic-based approach to measuring liquid thermal conductivity is developed to address the requirement in many practical applications for measurements using small (microlitre) sample size and integration into a compact device. The approach also gives the possibility of high-throughput testing. A resistance heater and temperature sensor are incorporated into a glass microfluidic chip to allow transmission and detection of a planar thermal wave crossing a thin layer of the sample. The device is designed so that heat transfer is locally one-dimensional during a short initial time period. This allows the detected temperature transient to be separated into two distinct components: a short-time, purely one-dimensional part from which sample thermal conductivity can be determined and a remaining long-time part containing the effects of three-dimensionality and of the finite size of surrounding thermal reservoirs. Identification of the one-dimensional component yields a steady temperature difference from which sample thermal conductivity can be determined. Calibration is required to give correct representation of changing heater resistance, system layer thicknesses and solid material thermal conductivities with temperature. In this preliminary study, methanol/water mixtures are measured at atmospheric pressure over the temperature range 30-50A degrees C. The results show that the device has produced a measurement accuracy of within 2.5% over the range of thermal conductivity and temperature of the tests. A relation between measurement uncertainty and the geometric and thermal properties of the system is derived and this is used to identify ways that error could be further reduced.

Thermal conductivity of building materials: an overview of its determination

A range of instruments are available to measure thermal conductivity of building materials. Some of these tools are heat-flow meter, hot plate, hot box and heat transfer analyzer. Thermal conductivity data derived by using different instruments can be different from each other. Implication of these variations in thermal conductivity is significant in terms of commercial profile of the insulations and also in terms of calculating energy saving in large scale use of that specific insulation. Thus it is important to know which of the measuring instrument for thermal conductivity can produce relatively accurate and representative result. This paper firstly looks at the methods and instrument for measuring thermal conductivity of building materials and secondly compares and analyses the results of testing thermal conductivity of fibrous insulations using a heat analyzer and a hot plate.

The effect of thermal conductivity of RIM moulds in kinetics cure

Reaction Injection Moulding (RIM) is a moulding technology used for the production of large size and complex plastic parts. The RIM process is characterized essentially by the injection of a highly reactive chemical system (usually polyurethane) and fast cure, in a mould properly closed and thermally controlled. Several studies show that rapid manufacturing moulds obtained in epoxy resins for Thermoplastic Injection Moulding (TIM) affect the moulding process and the final properties of parts. The cycle time and mechanical properties of final parts are reduced, due to a low thermal conductivity of epoxy materials. In contrast, the low conductivity of materials usually applied for the rapid manufacturing of RIM moulds, increase the mechanical properties of final injected parts and reduce the cycle time. This study shows the effect of the rapid manufacturing moulds material during the RIM process. Several materials have been tested for rapid manufacturing of RIM moulds and the analysis of both, temperature profile of moulded parts during injection and the cure data experimentally obtained in a mixing and reaction cell, allow to determine and model the real effect of the mould material on the RIM process.

The role of copper in the thermal conductivity of thermoelectric oxychalcogenides: do lone pairs matter?

Understanding the underlying mechanisms that suppress thermal conduction in solids is of paramount importance for the targeted design of materials for thermal management and thermoelectric energy conversion applications. Bismuth copper oxychalcogenides, BiOCuQ (Q = Se, Te), are highly crystalline thermoelectric materials with an unusually low lattice thermal conductivity of approx. 0.5 Wm-1K-1, a value normally found in amorphous materials. Here we unveil the origin of the unusual thermal transport properties of these phases. First principles calculations of the vibrational properties combined with analysis of in-situ neutron diffraction data, demonstrate that weak bonding of copper atoms within the structure leads to an unexpected vibrational mode at low frequencies, which is likely to be a major contributor to the low thermal conductivity of these materials. In addition, we show that anharmonicity and the large Grüneisen parameter in these oxychalcogenides are mainly related to the low frequency copper vibrations, rather than to the Bi3+ lone pairs.

Thermal conductivity studies on wool fabrics with conductive coatings

Methods of improving the thermal conductivity of wool fabrics have been investigated. Thermal conductivity measurement techniques, influence of synthesis parameters on the thermal conductivity of polypyrrole (PPy)-coated wool fabrics, and the relationship between electrical conductivity and thermal conductivity of PPy-coated wool fabrics are presented. An improvement in thermal conductivity was observed when fabrics were coated with the PPy. The thermal conductivity increased with the increase of pyrrole concentration and synthesis time. Anthraquinone-2-sulfonic acid and ferric chloride showed an optimal concentration for their influence on the thermal conductivity of the coated fabric. The improvement of thermal conductivity of wool fabrics is also investigated by Physical Vapor Deposition technique.

Thermal conductivity of sand and its effect on the temperature of Loggerhead Sea Turtle (Caretta Caretta) nests

The conductivity of sand at a depth of 30–50 cm was measured at 15 sites on the beach at Captiva Island in south-west Florida which is used by nesting loggerhead turtles (Caretta caretta). The mean daily temperature of the sand was correlated with conductivity at the same depth measured the same day (r=0·611). When day to day variation was removed the correlation between nest temperature and conductivity increased to 0·694. The sand was highly variable in its grain structure. The dominant variability (80·6%) was redescribed by the first two principal components of a Principal Components Analysis (PCA). These two components were influenced mostly by percentages of large (> 1 mm) and small (< 500 μm) grains respectively. Conductivity was strongly correlated with the grain structure of the sand. The first three principal components describing sand grain structure, explained 84·1% of the variation in conductivity. Moisture content of the sand (always < 5%) was not an important factor. Sites dominated by larger grains generally had poorer conductivity and were cooler. Comparisons of eight nests to seven adjacent random sites revealed no strong evidence for directional selection in nest placement relative to sand conductivity. The variance in conductivities recorded at nests was also not significantly different from the variance at random sites.

Improving thermal conductivity of cotton fabrics using composite coatings containing graphene, multiwall carbon nanotube or boron nitride fine particles

Graphene, multi-wall carbon nanotube (MWCNT) and fine boron nitride (BN) particles were separately applied with a resin onto a cotton fabric, and the effect of the thin composite coatings on the thermal conductive property, air permeability, wettability and color appearance of the cotton fabric was examined. The existence of the fillers within the coating layer increased the thermal conductivity of the coated cotton fabric. At the same coating content, the increase in fabric thermal conductivity was in the order of graphene > BN > MWCNT, ranging from 132 % to 842 % (based on pure cotton fabric). The coating led to 73 %, 69 % and 64 % reduction in air permeability when it respectively contained 50.0 wt% graphene, BN and MWCNTs. The graphene and MWCNT treated fabrics had a black appearance, but the coating had almost no influence on the fabric hydrophilicity. The BN coating made cotton fabric surface hydrophobic, with little change in fabric color.

Effect of maltodextrin on the freezing point and thermal conductivity of uvaia pulp (Eugenia piriformis Cambess)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Influence of fluid concentration on freezing point depression and thermal conductivity of frozen orange juice

The freezing point depression (FPD) of orange juice at different concentrations was measured by using a simple apparatus. Results showed that the initial freezing point decreased approximately 90% with the increase of juice concentration between 46degrees and 66degrees Brix (water content respectively between 52.8 and 32.8% w/w). The thermal conductivity of orange juice as a function of fluid concentration was also investigated by using a coaxial dual-cylinder apparatus. Below the freezing point, the thermal conductivity was strongly affected by both the orange juice concentration and temperature. Simple equations in terms of water content and temperature could be adjusted to experimental data of FPD and thermal conductivity.

Modeling thermal conductivity, specific heat, and density of milk: A neural network approach

The accurate determination of thermophysical properties of milk is very important for design, simulation, optimization, and control of food processing such as evaporation, heat exchanging, spray drying, and so forth. Generally, polynomial methods are used for prediction of these properties based on empirical correlation to experimental data. Artificial neural networks are better Suited for processing noisy and extensive knowledge indexing. This article proposed the application of neural networks for prediction of specific heat, thermal conductivity, and density of milk with temperature ranged from 2.0 to 71.0degreesC, 72.0 to 92.0% of water content (w/w), and 1.350 to 7.822% of fat content (w/w). Artificial neural networks presented a better prediction capability of specific heat, thermal conductivity, and density of milk than polynomial modeling. It showed a reasonable alternative to empirical modeling for thermophysical properties of foods.

Thermal conductivity features of ZnO-based varistors using the laser-pulse method

The thermal conductivity of several commercial ZnO-based varistor systems was determined based on the laser-pulse method, a technique that proved extremely useful and easy to apply. Using this technique, the thermal conductivity was found to be dependent on the microstructural features of the devices, involving the mean grain size and phase composition. Among the phases existing in commercial ZnO-based varistors, ZniSb2O12 and Bi2O3 were found to contribute strongly to the thermal conductivity of the devices. (C) 2003 Elsevier B.V. All rights reserved.