Thermal analysis of nanofluids in microfluidics using an infrared camera

We present the thermal analysis of liquid containing Al(2)O(3) nanoparticles in a microfluidic platform using an infrared camera. The small dimensions of the microchannel along with the low flow rates (less than 120 μl min(-1)) provide very low Reynolds numbers of less than 17.5, reflecting practical parameters for a microfluidic cooling platform. The heat analysis of nanofluids has never been investigated in such a regime, due to the deficiencies of conventional thermal measurement systems. The infrared camera allows non-contact, three dimensional and high resolution capability for temperature profiling. The system was studied at different w/w concentrations of thermally conductive Al(2)O(3) nanoparticles and the experiments were in excellent agreement with the computational fluid dynamics (CFD) simulations.     

Quantum oscillations at a Ga0.47In0.53AsInP heterojunction interface

We report the observation of a two dimensional gas of high mobility electrons at the interface of a Ga_0.47In_0.53AsInP heterojunction grown by MOCVD. The two dimensional electron concentrations and effective mass are determined by Shubnikov-de Haas studies, and compared with theoretical predictions. Evidence of an enhancement of the g-factor is observed. We also report observations of very pronounced quantum Hall steps as seen in GaAs-GaAlAs heterojunctions.     

Stranski–Krastanov growth of InGaN quantum dots emitting in green spectra

Self-assembled InGaN quantum dots (QDs) were grown on GaN templates by metalorganic chemical vapor deposition. 2D–3D growth mode transition through Stranski–Krastanov mode was observed via atomic force microscopy. The critical thickness for In_0.67Ga_0.33N QDs was determined to be four monolayers. The effects of growth temperature, deposition thickness, and V/III ratio on QD formation were examined. The capping of InGaN QDs with GaN was analyzed. Optimized InGaN quantum dots emitted in green spectra at room temperature.     

Modelling investigation on the thermal conductivity of pure liquid,vapour, and supercritical refrigerants and their mixtures by using Heyen EOS

This work presents a literature survey of the available data regarding the thermal conductivity of refrigerants. About 31 pure refrigerants that contain 7127 data points are selected for the temperature range of 91.35–580.00 K, a pressure range of (0.000111-500) bar, and thermal conductivity range of (0.007–0.27) W m^?1 K^?1 containing liquid, vapour, and supercritical phases. Seven binary and three ternary mixtures are also collected both in liquid and vapour phases with an overall of 803 data points. Based on the similarity between the pressure-volume-temperature and Tλ (thermal conductivity) P diagrams, the thermal conductivity model based on Heyen equation of state has been developed for pure refrigerants and their mixtures. The genetic algorithm is used to determine the adjustable parameters of the model. The calculation results prove that this proposed model can reproduce and predict thermal conductivity of refrigerants with good accuracy (overall AAD = 6.85% for pure compounds, AAD = 6.14% for binary mixtures and AAD = 9.32% for ternary mixtures).     

Dynamic drag force based on iterative density mapping: A new numerical tool for three‐dimensional analysis of particle trajectories in a dielectrophoretic system

Dielectrophoresis is a widely used means of manipulating suspended particles within microfluidic systems. In order to efficiently design such systems for a desired application, various numerical methods exist that enable particle trajectory plotting in two or three dimensions based on the interplay of hydrodynamic and dielectrophoretic forces. While various models are described in the literature, few are capable of modeling interactions between particles as well as their surrounding environment as these interactions are complex, multifaceted, and computationally expensive to the point of being prohibitive when considering a large number of particles. In this paper, we present a numerical model designed to enable spatial analysis of the physical effects exerted upon particles within microfluidic systems employing dielectrophoresis. The model presents a means of approximating the effects of the presence of large numbers of particles through dynamically adjusting hydrodynamic drag force based on particle density, thereby introducing a measure of emulated particle–particle and particle–liquid interactions. This model is referred to as “dynamic drag force based on iterative density mapping.” The resultant numerical model is used to simulate and predict particle trajectory and velocity profiles within a microfluidic system incorporating curved dielectrophoretic microelectrodes. The simulated data are compared favorably with experimental data gathered using microparticle image velocimetry, and is contrasted against simulated data generated using traditional “effective moment Stokes‐drag method,” showing more accurate particle velocity profiles for areas of high particle density.