The Influence and Interpretation of Surface Parameters for Wetting Transitions in Ternary Mixtures

Starting from a microscopic Hamiltonian defined on a semi-infinite cubic lattice, and employing a mean-field approximation, the surface parameters relevant for wetting in confined ternary mixtures are derived. These are found in terms of the microscopic coupling constants, and yield a physical interpretation of their origins. In comparison with the standard expression for the surface free-energy density, several new terms arising from the derivation are identified. The influence of the surface parameters on a predicted unbinding transition in a mixture of oil, water, and amphiphile demonstrate that existing results are robust to the addition of the extra surface terms.     

Modeling aviation baggage screening security systems: a case study

Aviation security protects vital national interests, as well as passengers and aircraft. Key components of an aviation security system include baggage and passenger screening devices and operations. Determining how and where to assign (deploy) such devices can be quite challenging. Moreover, even after such systems are in place, it can be difficult to measure their effectiveness. This paper describes how discrete optimization models can be used to address these questions, based on three performance measures that quantify the effectiveness of airport baggage screening security device systems. These models are used to solve for optimal airport baggage screening security device deployments considering the number of passengers on a set of flights who have not been cleared using a security risk assessment system in use by the Federal Aviation Administration (i.e., passengers whose baggage is subjected to screening), the number of flights in this set, and the size of the aircraft for such flights. Several examples are provided to illustrate these results, including an example that uses data available from the Official Airline Guide.     

Gamma-irradiation-induced Ag/SiO2 composite films and their optical absorption properties

Small Ag particles or clusters dispersed mesoporous SiO₂ composite films were prepared by a new method: First the matrix SiO₂ films were prepared by sol-gel process combined with the dip-coating technique, then they were soaked in AgNO₃ solutions followed by irradiation of γ-ray at room temperature and in ambient pressure. The structures of these films were examined by X-ray diffraction (XRD), high-resolution transmission electron microscope (HRTEM), and optical absorption spectroscopy. It has been shown that the Ag particles grown within the porous SiO₂ films are very small, and they are isolated and dispersed from each other with very narrow size distributions. With increasing the soaking concentration and an additional annealing, an opposite peakshift effect of the surface plasmon resonance (SPR) was observed in the optical absorption measurements.     

Bio-layer management in anaerobic membrane bioreactors for wastewater treatment.

Membrane separation technology represents an alternative way to achieve biomass retention in anaerobic bioreactors for wastewater treatment. Due to high biomass concentrations of anaerobic reactors, cake formation is likely to represent a major cause of flux decline. In the presented research, experiments are performed on the effect of biomass concentration and level of gas sparging on the hydraulic capacity of a submerged anaerobic membrane bioreactor. Both parameters significantly affected the hydraulic capacity, with biomass exerting the most pronounced effect. After 50 days of continuous operation the critical flux remained virtually unchanged, despite an increase in membrane resistance, suggesting that biomass characteristics and hydraulic conditions determine the bio-layer formation rather than the membrane's fouling level. The concept of bio-layer management is introduced to describe the programmed combination of actions performed in order to control the formation of biomass layer over membranes.     

Segmented polymer networks containing amino‐dendrimers

Polymer networks in which poly(propylene imine) dendrimers (Astramol?) are connected to each other by linear polytetrahydrofuran (polyTHF) segments, were prepared by two methods. The first method was a one‐step procedure in which bifunctionally living polyTHF, obtained by initiation of the THF polymerization with trifluoromethane sulfonic anhydride (triflic anhydride), was reacted with an amino‐dendrimer. This reaction was very fast but did not allow formation of the end products. The second method was a two‐step procedure. In a first step, living polyTHF, prepared with acryloyloxybutyl triflate as initiator, was grafted on an amino‐dendrimer, to form a star‐like, acrylate‐terminated polyTHF multi‐macromonomer with the dendrimer as core. In a second step, networks were obtained by Michael addition between the acrylate end‐groups and unreacted amino‐groups of the dendrimer. This cross‐linking reaction occurred spontaneously upon heating of the solution of the multi‐macromonomer with gelation times varying from a few minutes to a few hours, depending on the temperature and the composition of the prepolymers. With this method it was possible to prepare networks in the form of coatings or films. © 2003 Society of Chemical Industry     

Low Temperature Physics at the Laboratoire National des Champs Magnétiques Pulsés in Toulouse

An overview over past and present activities and future developments at the Toulouse pulsed magnetic field facility is given, both as far as technical developments of the infrastructure, as well as low temperature physics performed at the LNCMP are concerned.     

Volumetric Change and Surface Properties of Temperature-Sensitive Polyvinylalcohol (PVA) Hydrogel

Polyvinylalcohol (PVA) polymer gel is a temperature sensitive polymeric gel, with a critical transition temperature (with H₂O) of 310 K. At higher than 310 K, this temperature sensitive polymer gel shrinks because of discharging water, whereas at lower temperatures, the gel swelled because of absorbing water. The reversibility of the gel's volume change was confirmed by temperature swing. The adsorption behavior of an organic compound onto the PVA polymer gel in water was tested at various temperatures. The amount of adsorbed organic compound increased remarkably at temperatures higher than about 310 K. Then, it was confirmed that the organic compound in PVA polymer gel could be reversibly adsorbed and desorbed by a temperature change between 293 and 323 K. The mechanism of adsorption of the organic compound onto the PVA polymer gel could be explained by hydration and dehydration of polymer gel.     

Classification of audio radar signals using radial basis function neural networks

Radial basis function (RBF) neural networks are used to classify real-life audio radar signals that are collected by a ground surveillance radar mounted on a tank. Currently, a human operator is required to operate the radar system to discern among signals bouncing off tanks, vehicles, planes, and so on. The objective of this project is to investigate the possibility of using a neural network to perform this target recognition task, with the aim of reducing the number of personnel required in a tank. Different signal classification methods in the neural net literature are considered. The first method employs a linear autoregressive (AR) model to extract linear features of the audio data, and then perform classification on these features, i.e, the AR coefficients. AR coefficient estimations based on least squares and higher order statistics are considered in this study. The second approach uses nonlinear predictors to model the audio data and then classifies the signals according to the prediction errors. The real-life audio radar data set used here was collected by an AN/PPS-15 ground surveillance radar and consists of 13 different target classes, which include men marching, a man walking, airplanes, a man crawling, and boats, etc. It is found that each classification method has some classes which are difficult to classify. Overall, the AR feature extraction approach is most effective and has a correct classification rate of 88% for the training data and 67% for data not used for training.