Autoxidation of Hydrocarbons: From Chemistry to Catalysis

This contribution summarizes our recent efforts to unravel the radical chemistry of autoxidations. Abstraction of the weakly bonded αH-atom of the primary hydroperoxide product by chain carrying peroxyl radicals is identified as the source of major end products such as alcohol and ketone/aldehyde. In the case of cyclohexane autoxidation, this reaction is even responsible for the majority of ring-opened by-products. In a second part, different approaches to autoxidation catalysis are discussed, ranging from transition metal ion catalysis to organocatalysis with immobilized N-hydroxyphthalimide.     

Taste recognition threshold concentrations of styrene in oil-in-water emulsions and yoghurts

Taste recognition threshold concentrations (TRTC) of styrene were determined in samples of oil-in-water emulsions (30–300 g kg^?1 oil) and yoghurts (1–30 g kg^?1 fat), spiked with styrene. The observed TRTC increased linearly with increasing fat content and ranged from 0–3 to 2–1 mg kg^?1 for the emulsions and from 36 to 171 g kg^?1 for the yoghurts. Styrene equilibrium partition coefficients between emulsions and their respective vapour phases were determined. The concentrations of styrene in the continuous aqueous phase of the emulsions and yoghurts were calculated at the TRTC. The styrene concentrations in the continuous phase had constant values of about 15 g kg^?1 indicating that perception of styrene for oil-in–water emulsions is determined by the aqueous phase of the emulsion. The concentrations of styrene in the vapor phases above the emulsions and yoghurts were also calculated and were found to be constant at the TRTC. This relationship probably resulted from the equilibrium of distribution of styrene between the respective phases. Commercial yoghurt packed in polystyrene beakers contained styrene levels in the range 2–11 g kg^?1, much lower then the TRTC reported.     

Cross-property connections for copper–graphite composites

Copper–graphite composite materials in the range of 0–10 vol% of carbon phase were prepared from the mixture of copper and graphite powders by hot isostatic pressing. The microstructure, mechanical (tensile strength, elongation to fracture) and physical (electrical and thermal conductivity) properties of composite samples were investigated, and the cross-property connections were calculated. It was shown that electrical and thermal conductivity cross-property (Lorenz number) is almost constant and increases only slightly (no more than 10 % increase was observed). This implies that in the investigated composition range the Lorenz number of a copper–graphite composite system behaves according to Franz–Wiedemann law for pure metals at constant temperature. On the contrary, the conductivity to tensile strength cross-property connections showed significant linear increase (over 200 % in the investigated composition range) for both electrical conductivity and thermal conductivity of composite materials. The cross-property connections of conductivity to the elongation to fracture exhibit a nonlinear dependence on the volume fraction of graphite.     

Deterministic convergence of conjugate gradient method for feedforward neural networks

Conjugate gradient methods have many advantages in real numerical experiments, such as fast convergence and low memory requirements. This paper considers a class of conjugate gradient learning methods for backpropagation neural networks with three layers. We propose a new learning algorithm for almost cyclic learning of neural networks based on PRP conjugate gradient method. We then establish the deterministic convergence properties for three different learning modes, i.e., batch mode, cyclic and almost cyclic learning. The two deterministic convergence properties are weak and strong convergence that indicate that the gradient of the error function goes to zero and the weight sequence goes to a fixed point, respectively. It is shown that the deterministic convergence results are based on different learning modes and dependent on different selection strategies of learning rate. Illustrative numerical examples are given to support the theoretical analysis.     

Slow and fast diffusion effects in image processing

A mathematical model for a nonlinear image multiscale analysis is studied. Processing of an image is based on a solution of the strongly nonlinear parabolic partial differential equation, which can degenerate depending on values of the greylevel intensity function. The governing PDE is a generalization of the regularized (in the sense of Catté, Lions, Morel and Coll) Perona-Malik anisotropic diffusion equation. We present numerical techniques for solving the suggested initial-boundary value problem and also existence and convergence results. Numerical experiments are discussed. Received: 6 May 1998 / Accepted: 27 July 2000     

Knockdown of the Halloween Genes spook,shadow and shade Influences Oocyte Development,Egg Shape,Oviposition and Hatching in the Desert Locust

Ecdysteroids are widely investigated for their role during the molting cascade in insects; however, they are also involved in the development of the female reproductive system. Ecdysteroids are synthesized from cholesterol, which is further converted via a series of enzymatic steps into the main molting hormone, 20-hydoxyecdysone. Most of these biosynthetic conversion steps involve the activity of cytochrome P450 (CYP) hydroxylases, which are encoded by the Halloween genes. Three of these genes, spook (spo), phantom (phm) and shade (shd), were previously characterized in the desert locust, Schistocerca gregaria. Based on recent sequencing data, we have now identified the sequences of disembodied (dib) and shadow (sad), for which we also analyzed spatiotemporal expression profiles using qRT-PCR. Furthermore, we investigated the possible role(s) of five different Halloween genes in the oogenesis process by means of RNA interference mediated knockdown experiments. Our results showed that depleting the expression of SchgrSpo, SchgrSad and SchgrShd had a significant impact on oocyte development, oviposition and hatching of the eggs. Moreover, the shape of the growing oocytes, as well as the deposited eggs, was very drastically altered by the experimental treatments. Consequently, it can be proposed that these three enzymes play an important role in oogenesis.     

Effects of Desolvating Agent Types,Ratios, and Temperature on Size and Nanostructure of Nanoparticles from α‐Lactalbumin and Ovalbumin

In this study, we compare the preparation of ovalbumin (OVA) and α‐lactalbumin (α‐LA) nanoparticles using different desolvating agents (ethanol, acetone, and methanol) and water: desolvating agent volume ratios (1:3, 1:4, 1:5, 1:10, and 1:20). Also the effects of protein solution temperature (25, 50, and 80 ℃) on the size of nanoparticles and the stability of crosslinked nanoparticles for 30 d were studied. OVA and α‐LA were shown to be good candidates for nanoparticulation and nanoparticles in the range of 60 to 230 nm were obtained. The comparison between the 2 proteins offers guidance to optimize OVA and α‐LA nanoparticle fabrication and to efficiently obtain nanoparticles with desired characteristics. The particle sizes of OVA nanoparticles were found to be in the range of 60 to 160 nm, and the particle sizes of α‐LA were between 150 and 230 nm. The sizes varied with different desolvating agents: for OVA, ethanol, and methanol both produced nanoparticles smaller than 100 nm; for α‐LA, methanol produced the smallest nanoparticles. Water: desolvating agent ratios, in the studied range, did not show a significant effect on the particle sizes for both OVA and α‐LA nanoparticles. The size and morphology of the nanoparticles were found to change when the protein solutions were heated up to 50 and 80 ℃ and cooled down before nanoparticulation and most nanoparticles had a smaller diameter.     

Nonlinear blind source separation using a radial basis functionnetwork

This paper proposes a novel neural-network approach to blind source separation in nonlinear mixture. The approach utilizes a radial basis function (RBF) neural-network to approximate the inverse of the nonlinear mixing mapping which is assumed to exist and able to be approximated using an RBF network. A contrast function which consists of the mutual information and partial moments of the outputs of the separation system, is defined to separate the nonlinear mixture. The minimization of the contrast function results in the independence of the outputs with desirable moments such that the original sources are separated properly. Two learning algorithms for the parametric RBF network are developed by using the stochastic gradient descent method and an unsupervised clustering method. By virtue of the RBF neural network, this proposed approach takes advantage of high learning convergence rate of weights in the hidden layer and output layer, natural unsupervised learning characteristics, modular structure, and universal approximation capability. Simulation results are presented to demonstrate the feasibility, robustness, and computability of the proposed method.     

Study of a new energy efficient process for French fries production

A new, energy efficient production process for French fries was developed and evaluated. Superheated steam (SHS) was used for evaporation of water instead of pre-drying with air and par-frying with oil. The product was frozen by vacuum cooling. Unfortunately, with this process it was not possible to reach the quality of conventional French fries. Sensory analysis indicated that the main quality defect was a tough crust with a fatty appearance. Confocal Scanning Laser Microscopy showed that this was caused by skin formation on the surface during both SHS drying and vacuum cooling. A frying step was necessary to obtain a porous crust. A satisfactory product quality was feasible after drying with SHS instead of air. Due to the concessions made for the product quality, the final energy reduction was limited. Nevertheless, this study has gained more insight into how processing affects potato tissue on micro-scale and it has shown that a porous structure is essential for good quality French fries.