Photocatalytic degradation of a phenylurea, chlortoluron, in water using an industrial titanium dioxide coated media

The degradation of an herbicide, chlortoluron, by UV/TiO₂ photocatalysis in water using industrial titanium dioxide coated non-woven paper was studied. The influence of parameters such as adsorption capacity, initial concentration and TiO₂ implementation (coated or in suspension) was investigated. The results emphasize the importance of operational conditions and reactor geometry on the kinetic degradation rate. The analysis of the first organic by-products suggests that a hydroxyl radical attack occurs on the phenyl ring and methyl groups of the chlortoluron before the opening of the aromatic ring. The fate of hetero-atoms has been investigated. The fates of the two nitrogen atoms in the molecule are different, not only did it depend on their initial oxidation degree, as both had the same oxidation degree, but also on the initial oxidation state of neighboring carbons. The chlorine atoms were completely released as chloride ions. The study of the influence of the oxygen concentration showed the importance of the oxygen mass transfer when designing an industrial photocatalytic reactor. An original calculation process was carried out to evaluate the adsorption constant of oxygen over the whole photocatalysis period and not only in the initial conditions as previously provided. This work points out the necessity of extending the understanding of the efficiency of chlortoluron removal or of the removal of various complex pesticide mixtures found in agricultural wastewater, using alternative, more industrially realistic, reactors.     

Fourier methods for quasi‐periodic oscillations

Quasi‐periodic oscillations and invariant tori play an important role in the study of forced or coupled oscillators. This paper presents two new numerical methods for the investigation of quasi‐periodic oscillations. Both algorithms can be regarded as generalizations of the averaging and the harmonic (spectral) balance methods. The algorithms are easy to implement and require only minimal a priori knowledge of the system. Most importantly, the methods do not depend on an a priori co‐ordinate transformation. The methods are applied to a number of illustrative examples from non‐linear electrical engineering and the results show that the methods are efficient and reliable. In addition, these examples show that the presented algorithms can also continue through regions of sub‐harmonic (phase‐locked) resonance even though they are designed only for the quasi‐periodic case. Copyright © 2006 John Wiley & Sons, Ltd.     

Teaching meshes, subdivision and multiresolution techniques

In recent years, geometry processing algorithms that directly operate on polygonal meshes have become an indispensable tool in computer graphics, CAD/CAM applications, numerical simulations, and medical imaging. Because the demand for people that are specialized in these techniques increases steadily the topic is finding its way into the standard curricula of related lectures on computer graphics and geometric modeling and is often the subject of seminars and presentations. In this article we suggest a toolbox to educators who are planning to set up a lecture or talk about geometry processing for a specific audience. For this we propose a set of teaching blocks, each of which covers a specific subtopic. These teaching blocks can be assembled so as to fit different occasions like lectures, courses, seminars and talks and different audiences like students and industrial practitioners. We also provide examples that can be used to deepen the subject matter and give references to the most relevant work.     

Generalized notions of mind change complexity

Gold introduced the notion of learning in the limit where a class S is learnable iff there is a recursive machine M which reads the course of values of a function f and converges to a program for f whenever f is in S. An important measure for the speed of convergence in this model is the quantity of mind changes before the onset of convergence. The oldest model is to consider a constant bound on the number of mind changes M makes on any input function; such a bound is referred here as type 1. Later this was generalized to a bound of type 2 where a counter ranges over constructive ordinals and is counted down at every mind change. Although ordinal bounds permit the inference of richer concept classes than constant bounds, they still are a severe restriction. Therefore the present work introduces two more general approaches to bounding mind changes. These are based on counting by going down in a linearly ordered set (type 3) and on counting by going down in a partially ordered set (type 4). In both cases the set must not contain infinite descending recursive sequences. These four types of mind changes yield a hierarchy and there are identifiable classes that cannot be learned with the most general mind change bound of type 4. It is shown that existence of type 2 bound is equivalent to the existence of a learning algorithm which converges on every (also nonrecursive) input function and the existence of type 4 is shown to be equivalent to the existence of a learning algorithm which converges on every recursive function. A partial characterization of type 3 yields a result of independent interest in recursion theory. The interplay between mind change complexity and choice of hypothesis space is investigated. It is established that for certain concept classes, a more expressive hypothesis space can sometimes reduce mind change complexity of learning these classes. The notion of mind change bound for behaviourally correct learning is indirectly addressed by employing the above four types to restrict the number of predictive errors of commission in finite error next value learning (NV′′)—a model equivalent to behaviourally correct learning. Again, natural characterizations for type 2 and type 4 bounds are derived. Their naturalness is further illustrated by characterizing them in terms of branches of uniformly recursive families of binary trees.     

Search for Superconductivity in Ultra-dense Deuterium D(?1) at Room Temperature: Depletion of D(?1) at Field Strength >0.05 T

Ultra-dense deuterium D(?1) is expected to be both a superfluid and a superconductor as shown by recent theoretical research. Condensed D(?1) can be deposited on surfaces by a source which produces a stream of clusters. A magnetic field strongly influences the type of material formed. Very little of D(?1) and of the form D(1), which is strongly coupled to D(?1), exists on the magnet surface or within several mm from the magnet surface. Even the formation of D(?1) on the source emitter is strongly influenced by a magnetic field, with a critical field strength in the range 0.03?C0.07 T. Higher excitation levels D(2) and D(3) dominate in a magnetic field. The excitation level D(2) is now observed for the first time. The removal of D(?1) and D(1) in strong magnetic fields is proposed to be due to a Meissner effect in long D(?1) clusters by large-orbit electron motion. The lifting of long D(?1) clusters above the magnet surface is slightly larger than expected, possibly due to the coupling to D(1). The previously reported oscillation between D(?1) and D(1) in an electric field is proposed to be due to destruction of D(?1).     

Correcting for contact geometry in Seebeck coefficient measurements of thin film devices

Driven by promising recent results, there has been a revived interest in the thermoelectric properties of organic (semi)conductors. Concomitantly, there is a need to probe the Seebeck coefficient S of modestly conducting materials in thin film geometry. Here we show that geometries that seem desirable from a signal-to-noise perspective may induce systematic errors in the measured value of S, S_m, by a factor 3 or more. The enhancement of S_m by the device geometry is related to competing conduction paths outside the region between the electrodes. We derive a universal scaling curve that allows correcting for this and show that structuring the semiconductor is not needed for the optimal electrode configuration, being a set of narrow, parallel strips.     

Performance Enhancement of Polycondensate‐Based Superplasticizers by Encapsulation

A cost‐effective and robust encapsulation system for chemical admixtures with delayed release mechanisms for construction materials like dry mix mortars is presented. Based on supplementary cementitious materials, a superplasticizer was encapsulated in matrix‐based encapsulations. Subsequently, the particle characteristics of the agglomerates and the release behavior of the superplasticizer were examined. The main objective of this research was to prove the functionality of such encapsulations for the future use in construction materials. The results obtained indicate that the performance of construction chemicals could be improved by encapsulation and controlled release for the future development of new construction materials. Furthermore, material characteristics of the supplementary cementitious material like the incline to excessive dusting or the materials density were improved by encapsulation.     

Temperature and pump power dependent photoluminescence characterization of MBE grown GaAsBi on GaAs

Bulk and quantum well GaAs_1?xBi_x/GaAs layers with Bi mole fractions from 0.02 to 0.10 are grown by molecular-beam epitaxy at temperatures ranging from 280 to 320?°C. The samples are characterized using temperature and pump-power dependent photoluminescence measurements covering 8–300?K and 1–250?mW (7–1,800?W/cm²), respectively. The results indicate that there is strong reduction in bandgap energy with the incorporation of small amounts of Bi and that GaAsBi most likely forms a weak type-I band alignment with GaAs.