Furfural Oxidation with Hydrogen Peroxide Over ZSM-5 Based Micro-Mesoporous Aluminosilicates

Micro-mesoporous aluminosilicates based on ZSM-5 zeolite, obtained by a dual template method, as well as in the presence of a dual-functional template (i.e. a Gemini-type surfactant), were tested in the oxidation of furfural with hydrogen peroxide. Even substantial changes in acidity and porosity of the catalysts result in minor variations of selectivity towards the desired products. Application of the synthesized zeolite-based materials in the oxidation of furfural with hydrogen peroxide leads to formation of 2(5H)-furanone (yield up to 28.5%) and succinic acid (up to 19.5%) as the main C4 reaction products. The kinetic model developed previously to treat the results for oxidation of furfural over sulfated zirconia was able to describe the data also for micro-mesoporous aluminosilicates.

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Sulfur deactivation and regeneration of Pt/BaO/Al2O3 and Pt/SrO/Al2O3 NO x storage catalysts

Transient experiments were performed to study sulfur deactivation and regeneration of Pt/BaO/Al₂O₃ and Pt/SrO/Al₂O₃ NO _x storage catalysts. It was found that the strontium-based catalysts are more easily regenerated than the barium-based catalysts and that a higher fraction of the NO _x storage sites are regenerated when H₂ is used in combination with CO₂ compared to H₂ only.     

Einfluss der Wechselwirkung von Reaktion und Fluiddynamik auf das Verhalten von Strahlschlaufenreaktoren

Jet loop reactors are used as apparatus to facilitate chemical or biological reactions. This type of apparatus is characterized by an internal circulation flow, essentially driven by the injection of liquid. The nozzle can also be used to inject and disperse gas. The internal fluid dynamics and thus the reactor behavior is significantly determined by the introduced momentum and by the internal gas distribution. To describe the mutual influence of a gas-consuming reaction and the internal fluid dynamics, a simplified model based on a momentum balance and a material balance was used. From the exemplary calculations, a critical range for non-selective reactions and for fluid dynamic stability is given.     

Combination of the Elevated Stearic Acid Trait with Other Fatty Acid Traits in Soybean

Stearic acid is one of five major fatty acids found in soybean oil. It is a fully saturated lipid and is known for neutral or positive effects on LDL cholesterol when consumed by humans. Unfortunately, stearic acid only accounts for about 4% of the total seed oil produced in commodity soybean. Previous work has shown that stearic acid can reach levels as high as 28% of the total oil fraction when the SACPD-C gene, encoding the delta-9-stearoyl-acyl carrier protein desaturase responsible for most of the stearic acid variation in soybean seed, is ablated in combination with other loci. In order to increase stearic acid content and create soybeans with improved utility based on fatty acid composition, we combined mutations in SACPD-C with other mutations in the fatty acid biosynthetic pathway. Soybean plants carrying mutant alleles of both SACPD-C and FAD2-1A produce seed with stearic acid levels from 14% to 21%, and with elevated levels of oleic acid. Soybeans carrying mutations in both SACPD-C and FAD3A or FAD3C have both statistically significantly elevated levels of stearic acid (from 15–21%) and statistically reduced linolenic acid levels. Neither mutant combination appears to affect other agronomic properties such as plant morphology or seed protein levels making this a potentially viable trait.     

Tetranorsesquiterpenoids as Attractants of Yucca Moths to Yucca Flowers

Journal of Chemical Ecology - The obligate pollination mutualism between Yucca and yucca moths is a classical example of coevolution. Oviposition and active pollination by female yucca moths occur...     

A genetic algorithm for the robust resource leveling problem

The resource leveling problem (RLP) involves the determination of a project baseline schedule that specifies the planned activity starting times while satisfying both the precedence constraints and the project deadline constraint under the objective of minimizing the variation in the resource utilization. However, uncertainty is inevitable during project execution. The baseline schedule generated by the deterministic RLP model tends to fail to achieve the desired objective when durations are uncertain. We study the robust resource leveling problem in which the activity durations are stochastic and the objective is to obtain a robust baseline schedule that minimizes the expected positive deviation of both resource utilizations and activity starting times. We present a genetic algorithm for the robust RLP. In order to demonstrate the effectiveness of our genetic algorithm, we conduct extensive computational experiments on a large number of randomly generated test instances and investigate the impact of different factors (the marginal cost of resource usage deviations, the marginal cost of activity starting time deviations, the activity duration variability, the due date, the order strength, the resource factor and the resource constrainedness).     

Shadow Prices in Territory Division

We consider a geographic optimization problem in which we are given a region R, a probability density function f(?) defined on R, and a collection of n utility density functions u _i (?) defined on R. Our objective is to divide R into n sub-regions R _i so as to “balance” the overall utilities on the regions, which are given by the integrals \(\iint _{R_{i}}f(x)u_{i}(x)\, dA\). Using a simple complementary slackness argument, we show that (depending on what we mean precisely by “balancing” the utility functions) the boundary curves between optimal sub-regions are level curves of either the difference function u _i (x) ? u _j (x) or the ratio u _i (x)/u _j (x). This allows us to solve the problem of optimally partitioning the region efficiently by reducing it to a low-dimensional convex optimization problem. This result generalizes, and gives very short and constructive proofs of, several existing results in the literature on equitable partitioning for particular forms of f(?) and u _i (?). We next give two economic applications of our results in which we show how to compute a market-clearing price vector in an aggregate demand system or a variation of the classical Fisher exchange market. Finally, we consider a dynamic problem in which the density function f(?) varies over time (simulating population migration or transport of a resource, for example) and derive a set of partial differential equations that describe the evolution of the optimal sub-regions over time. Numerical simulations for both static and dynamic problems confirm that such partitioning problems become tractable when using our methods.     

Conceptual aspects of geometric quantum computation

Geometric quantum computation is the idea that geometric phases can be used to implement quantum gates, i.e., the basic elements of the Boolean network that forms a quantum computer. Although originally thought to be limited to adiabatic evolution, controlled by slowly changing parameters, this form of quantum computation can as well be realized at high speed by using nonadiabatic schemes. Recent advances in quantum gate technology have allowed for experimental demonstrations of different types of geometric gates in adiabatic and nonadiabatic evolution. Here, we address some conceptual issues that arise in the realizations of geometric gates. We examine the appearance of dynamical phases in quantum evolution and point out that not all dynamical phases need to be compensated for in geometric quantum computation. We delineate the relation between Abelian and non-Abelian geometric gates and find an explicit physical example where the two types of gates coincide. We identify differences and similarities between adiabatic and nonadiabatic realizations of quantum computation based on non-Abelian geometric phases.     

Multigrid gradient vector flow computation on the GPU

Gradient vector flow (GVF) is a feature-preserving spatial diffusion of image gradients. It was introduced to overcome the limited capture range in traditional active contour segmentation. However, the original iterative solver for GVF, using Euler’s method, converges very slowly. Thus, many iterations are needed to achieve the desired capture range. Several groups have investigated the use of graphic processing units (GPUs) to accelerate the GVF computation. Still, this does not reduce the number of iterations needed. Multigrid methods, on the other hand, have been shown to provide a much better capture range using considerable less iterations. However, non-GPU implementations of the multigrid method are not as fast as the Euler method when executed on the GPU. In this paper, a novel GPU implementation of a multigrid solver for GVF written in OpenCL is presented. The results show that this implementation converges and provides a better capture range about 2–5 times faster than the conventional iterative GVF solver on the GPU.