Superparamagnetic particle dynamics and mixing in a rotating capillary tube with a stationary magnetic field

The dynamics of superparamagnetic particles subject to competing magnetic and viscous drag forces have been examined with a uniform, stationary, external magnetic field. In this approach, competing drag and magnetic forces were created in a fluid suspension of superparamagnetic particles that was confined in a capillary tube; competing viscous drag and magnetic forces were established by rotating the tube. A critical Mason number was determined for conditions under which the rotation of the capillary prevents the formation of chains from individual particles. The statistics of chain length was investigated by image analysis while varying parameters such as the rotation speed and the viscosity of the liquid. The measurements showed that the rate of particle chain formation was decreased with increased viscosity and rotation speed; the particle dynamics could be quantified by the same dimensionless Mason number that has been demonstrated for rotating magnetic fields. The potential for enhancement of mixing in a bioassay was assessed using a fast chemical reaction that was diffusion-limited. Reducing the Mason number below the critical value, so that chains were formed in the fluid, gave rise to a modest improvement in the time to completion of the reaction.     

Large-Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds

Active learning and crowdsourcing are promising ways to efficiently build up training sets for object recognition, but thus far techniques are tested in artificially controlled settings. Typically the vision researcher has already determined the dataset’s scope, the labels “actively” obtained are in fact already known, and/or the crowd-sourced collection process is iteratively fine-tuned. We present an approach for live learning of object detectors, in which the system autonomously refines its models by actively requesting crowd-sourced annotations on images crawled from the Web. To address the technical issues such a large-scale system entails, we introduce a novel part-based detector amenable to linear classifiers, and show how to identify its most uncertain instances in sub-linear time with a hashing-based solution. We demonstrate the approach with experiments of unprecedented scale and autonomy, and show it successfully improves the state-of-the-art for the most challenging objects in the PASCAL VOC benchmark. In addition, we show our detector competes well with popular nonlinear classifiers that are much more expensive to train.     

Fabrication and high-temperature structural characterization study of porous anodic alumina membranes

Porous anodic alumina (PAA) membranes with highly ordered array of nanopores were prepared by two-step anodization process. Studies on structural and thermal properties and the thermal stability of the prepared PAA membranes were carried out. Investigation using scanning electron microscopy, atomic force microscopy, X-ray diffraction, thermal analysis and infrared spectroscopy was performed on the prepared PAA membranes at room temperature and in the temperature range 600?C1,400?°C. The as-prepared PAA membranes revealed the amorphous nature. Polycrystalline PAA membranes were obtained by annealing carried out at different temperatures. Annealing study confirmed that the heat treatment transformed the amorphous PAA membranes to their crystalline phases, namely, ??-alumina at about 870?°C and then to ??-alumina around 1,250?°C.     

Simulation of nanoparticle synthesis in an aerosol flame reactor using a coupled flame dynamics–monodisperse population balance model

Flame aerosol synthesis is one of the commonly employed techniques for producing ultra fine particles of commodity chemicals such as titanium dioxide, silicon dioxide and carbon black. Large volumes of these materials are produced in industrial flame reactors. Particle size distribution of product powder is the most important variable and it depends strongly on flame dynamics inside the reactor, which in turn is a function of input process variables such as reactant flow rate and concentration, flow rates of air, fuel and the carrier gas and the burner geometry. A coupled flame dynamics–monodisperse population balance model for nanoparticle synthesis in an aerosol flame reactor is presented here. The flame dynamics was simulated using the commercial computational fluid dynamics software CFX and the particle population dynamics was represented using a monodisperse population balance model for continuous processes that predicts the evolution of particle number concentration, particle volume and surface area. The model was tested with published experimental data for synthesis of silica nanoparticles using different burner configurations and with different reactor operating conditions. The model predictions for radial flame temperature profiles and for the effects of process variables like precursor concentration and oxygen flow rate on particle specific surface area and mean diameter are in close agreement with published experimental data.     

Upconversion in NaYF(4):Yb, Er nanoparticles amplified by metal nanostructures

Upconversion (UC) fluorescence in NaYF(4):Yb, Er nanoparticles amplified by metal nanostructures was compared in two nanostructure geometries: gold nanoshells surrounding nanoparticles and silver nanostructures adjacent to the nanoparticles, both placed on a dielectric silica surface. Enhanced UC luminescence signals and modified lifetimes induced by these two metals were observed in our study. The UC luminescence intensities of green and red emissions were enhanced by Ag nanostructures by a factor of approximately 4.4 and 3.5, respectively. The corresponding UC lifetimes were reduced ～ 1.7-fold and ～ 2.4-fold. In NaYF(4):Yb, Er nanoparticles encapsulated in gold nanoshells, higher luminescence enhancement factors were obtained (～9.1-fold for the green emission and ～ 6.7-fold for the red emission). However, the Au shell coating extended the red emission by a factor of 1.5 and did not obviously change the lifetime of green emission. The responsible mechanisms such as plasmonic enhancement and surface effects are discussed.     

Silica fibre-composites obtained using long-chain carboxylic acid as templates

Fibres of silica-composites having different long-chain carboxylic acids were prepared. The morphology and the composition of these different inorganic-organic composites, thus obtained, were similar owing to their common mechanism of formation. Their morphology was observed to be sensitive to the water content.     

Modeling and simulation of carbon black synthesis in an aerosol flame reactor

Carbon black has diverse industrial applications such as a reinforcing agent in tires and as a black pigment in printing inks. Flame aerosol synthesis is the commonly employed route for large scale production of carbon black. A coupled flame dynamics – monodisperse population balance model for the synthesis of carbon black in an aerosol flame reactor is presented here. The population balance model was incorporated into the commercial computational fluid dynamics software CFX to simulate the effect of flame dynamics on particle synthesis. The model was tested with published experimental data for acetylene black synthesis through oxidative thermal decomposition of acetylene with oxygen in a premixed flame reactor. The predicted axial temperature profiles at different equivalence ratios (actual acetylene/oxidizer ratio divided by stoichiometric acetylene/oxidizer ratio) were reasonably close to experimental data. The effect of the equivalence ratio on process parameters like maximum temperature of the flame, specific surface area of particles and flame structure was investigated. It was observed that the maximum temperature of the flame and specific surface area of carbon black particles decrease upon increasing the equivalence ratio which is in agreement with published experimental studies. Simulation results are also presented for carbon black synthesis in a diffusion flame reactor with different burner designs.     

A study of polyaniline-carbon nanotube composites

Composites of polyaniline (PANI) have been prepared with pristine multiwalled and single-walled nanotubes as well as nanotubes subjected to acid treatment and subsequent reaction with thionyl chloride. The composites have been characterized by various techniques, including X-ray diffraction, electron microscopy, as well as infrared and Raman spectroscopy. Electrical resistivities of the PANI-nanotube composites have been measured and compared with those of the nanotubes and PANI.     

Cost-Sensitive Active Visual Category Learning

We present an active learning framework that predicts the tradeoff between the effort and information gain associated with a candidate image annotation, thereby ranking unlabeled and partially labeled images according to their expected ??net worth?? to an object recognition system. We develop a multi-label multiple-instance approach that accommodates realistic images containing multiple objects and allows the category-learner to strategically choose what annotations it receives from a mixture of strong and weak labels. Since the annotation cost can vary depending on an image??s complexity, we show how to improve the active selection by directly predicting the time required to segment an unlabeled image. Our approach accounts for the fact that the optimal use of manual effort may call for a combination of labels at multiple levels of granularity, as well as accurate prediction of manual effort. As a result, it is possible to learn more accurate category models with a lower total expenditure of annotation effort. Given a small initial pool of labeled data, the proposed method actively improves the category models with minimal manual intervention.