Determination of particle size distribution in a modified hop extract emulsion by electrical resistivity

A procedure has been developed which is suitable for the evaluation of particle size distribution in emulsions of modified hop extracts. A Coulter Counter Model A and a Coulter Counter Model B equipped with a Model M Data Converter were used to assess the weight percent distribution of particles smaller than 10 μm in diameter. The influences of electrolyte concentration, length of analysis, and concentration of the hop extract particles were factors affecting the reproducibility of the study. It was found that identical distribution curves could be obtained with either instrument. The Model B Coulter Counter with the Model M Data Converter is preferred since weight fractions are provided directly and lengthy calculation procedures are eliminated. Coincidence correction on particle counts was unnecessary under the controlled conditions of the analysis.     

Influence of structure and counterions on physicochemical properties of linear alkylbenzene sulfonates

The effects of different inorganic and organic counterions on the physicochemical behavior of three commercial linear alkylbenzene sulfonates (LAS) have been studied. It has been found that the counterion hydration radius of the corresponding commercial linear alkylbenzene sulfonates has great influence on solubility, viscosity, surface tension and critical micelle concentration (CMC). The counterion has no influence on the detergency performance of the finished formulation. The alkyl chain length and the presence of tetralines have an important influence on solubility, viscosity and surface tension.     

Structure formation during spinning of poly(p-phenylenebenzobisthiazole) fiber

The structure formed in the coagulation stage of the spinning process of poly(p-phenylene benzobisthiazole) fiber is studied by electron microscopy. An oriented network of microfibrils with typical fibril diameters of about 80-100Å is observed. We suggest that these microfibrils are the fundamental structural elements of the fiber. Thus, knowledge of the mechanism by which this initial structure is formed may allow for better control of final fiber properties. The relation of structure formation during coagulation to the phase diagram of a rigid polymer solution and to the kinetic mechanism of the phase transition is discussed.     

Parallelised production of fine and calibrated emulsions by coupling flow-focusing technique and partial wetting phenomenon

The capacity of microfluidic technology to fabricate monodisperse emulsion droplets is well established. Parallelisation of droplet production is a prerequisite for using such an approach for making high-quality materials for either fundamental or industrial applications where product quantity matters. Here, we investigate the emulsification efficiency of parallelised drop generators based on a flow-focusing geometry when incorporating the role of partial wetting in order to make emulsion droplets with a diameter below 10 μm. Confinement intrinsically encountered in microsystems intensifies the role played by interfaces between liquids and solids. We thus take advantage of partial wetting to enhance the maximum confinement accessible due to liquid flow focusing. We compare the performances brought by partial wetting to more established routes such as step emulsification. We show that the step configuration and the partial wetting regime are both well suited for being parallelised and thus open the way to the production of fine and calibrated emulsions for further applications. Finally, this new route of emulsification that exploits partial wetting between the fluids and the channel walls opens possibilities to the formation of substantially smaller droplets, as required in many fields of application.     

Distilled Collections from Textual Image Queries

We present a distillation algorithm which operates on a large, unstructured, and noisy collection of internet images returned from an online object query. We introduce the notion of a distilled set, which is a clean, coherent, and structured subset of inlier images. In addition, the object of interest is properly segmented out throughout the distilled set. Our approach is unsupervised, built on a novel clustering scheme, and solves the distillation and object segmentation problems simultaneously. In essence, instead of distilling the collection of images, we distill a collection of loosely cutout foreground “shapes”, which may or may not contain the queried object. Our key observation, which motivated our clustering scheme, is that outlier shapes are expected to be random in nature, whereas, inlier shapes, which do tightly enclose the object of interest, tend to be well supported by similar shapes captured in similar views. We analyze the commonalities among candidate foreground segments, without aiming to analyze their semantics, but simply by clustering similar shapes and considering only the most significant clusters representing non‐trivial shapes. We show that when tuned conservatively, our distillation algorithm is able to extract a near perfect subset of true inliers. Furthermore, we show that our technique scales well in the sense that the precision rate remains high, as the collection grows. We demonstrate the utility of our distillation results with a number of interesting graphics applications.     

Fusing distributional and experiential information for measuring semantic relatedness

Models of semantic relatedness have usually focused on language-based distributional information without taking into account “experiential data” concerning the embodied sensorial source of the represented concepts. In this paper, we present an integrative cognitive model of semantic relatedness. The model – semantic family resemblance – uses a variation of the co-product as a mathematical structure that guides the fusion of distributional and experiential information. Our algorithm provides superior results in a set expansion task and a significant correlation with two benchmarks of human rated word-pair similarity datasets.