Improved formic acid oxidation using electrodeposited Pd–Cd electrocatalysts in sulfuric acid solution

In the present research, nanostructured Pd–Cd alloy electrocatalysts with different compositions were produced using the electrodeposition process. The morphology of the samples was studied by scanning electron microscopy analysis. Also, the elemental composition of the samples was determined by energy-dispersive X-ray spectroscopy and elemental mapping tests. Tafel polarization and electrochemical impedance spectroscopy methods were employed to determine the electrochemical corrosion properties of the synthesized samples in a solution containing 0.5 M sulfuric acid and 0.1 M formic acid. The linear sweep voltammetry, cyclic voltammetry, and chronoamperometry techniques were also employed to evaluate the electrocatalytic activity of prepared samples toward the oxidation of formic acid. In this respect, the influence of some factors such as formic acid and sulfuric acid concentrations and also potential scan rate was investigated. Compared to the pure Pd sample, the Pd–Cd samples were more reactive for the oxidation of formic acid. Besides, the sample with a lower amount of Pd (Pd_1·3Cd) demonstrated much higher electrocatalytic activity than the Pd_7·1Cd and Pd_2·1Cd samples. The observed high mass activity of 15.06 A mg^?1_Pd for the Pd_1·3Cd sample which is 21.1 times higher than Pd/C is an interesting result of this study.     

Study of the effect of BDMA catalyst in the epoxy novolac curing process by isothermal DSC

Epoxy novolac/anhydride cure kinetics has been studied by differential scanning calorimetry under isothermal conditions. The system used in this study was an epoxy novolac resin (DEN431), with nadic methyl anhydride as hardener and benzyldimethylamine as accelerator. Kinetic parameters including the reaction order, activation energy and kinetic rate constants, were investigated. The cure reaction was described with the catalyst concentration, and a normalized kinetic model developed for it. It is shown that the cure reaction is dependent on the cure temperature and catalyst concentration, and that it proceeds through an autocatalytic kinetic mechanism. The curing kinetic constants and the cure activation energies were obtained using the Arrhenius kinetic model. A suggested kinetic model with a diffusion term was successfully used to describe and predict the cure kinetics of epoxy novolac resin compositions as a function of the catalyst content and temperature. Copyright © 2003 Society of Chemical Industry     

Truthful Approximation Mechanisms for Scheduling Selfish Related Machines

We consider the problem of scheduling jobs on related machines owned by selfish agents. We provide a 5-approximation deterministic truthful mechanism, the first deterministic truthful result for the problem. Previously, Archer and Tardos showed a 2-approximation randomized mechanism which is truthful in expectation only (a weaker notion of truthfulness). In case the number of machines is constant, we provide a deterministic Fully Polynomial-Time Approximation Scheme (FPTAS) and a suitable payment scheme that yields a truthful mechanism for the problem. This result, which is based on converting FPTAS to monotone FPTAS, improves a previous result of Auletta et al., who showed a (4 + ε)-approximation truthful mechanism.     

The marginal cost of CO2 emissions

Haraden's model for estimating the economic cost of global warming is analysed. We change his method of discounting and some of his input parameters in a manner consistent with physical and economic theory as well as empirical data. We then find much higher costs than Haraden found. These costs are compared to the cost of reducing CO₂ emissions and we find that deep cuts of the emissions of CO₂ are preferable. A check of the sensitivity of our results with respect to some crucial parameter values does not alter that conclusion.     

Performance analysis of support vector machines classifiers in breast cancer mammography recognition

Support vector machine (SVM) is a supervised machine learning approach that was recognized as a statistical learning apotheosis for the small-sample database. SVM has shown its excellent learning and generalization ability and has been extensively employed in many areas. This paper presents a performance analysis of six types of SVMs for the diagnosis of the classical Wisconsin breast cancer problem from a statistical point of view. The classification performance of standard SVM (St-SVM) is analyzed and compared with those of the other modified classifiers such as proximal support vector machine (PSVM) classifiers, Lagrangian support vector machines (LSVM), finite Newton method for Lagrangian support vector machine (NSVM), Linear programming support vector machines (LPSVM), and smooth support vector machine (SSVM). The experimental results reveal that these SVM classifiers achieve very fast, simple, and efficient breast cancer diagnosis. The training results indicated that LSVM has the lowest accuracy of 95.6107 %, while St-SVM performed better than other methods for all performance indices (accuracy = 97.71 %) and is closely followed by LPSVM (accuracy = 97.3282). However, in the validation phase, the overall accuracies of LPSVM achieved 97.1429 %, which was superior to LSVM (95.4286 %), SSVM (96.5714 %), PSVM (96 %), NSVM (96.5714 %), and St-SVM (94.86 %). Value of ROC and MCC for LPSVM achieved 0.9938 and 0.9369, respectively, which outperformed other classifiers. The results strongly suggest that LPSVM can aid in the diagnosis of breast cancer.     

Buffer Management for Colored Packets with Deadlines

We consider buffer management of unit packets with deadlines for a multi-port device with reconfiguration overhead. The goal is to maximize the throughput of the device, i.e., the number of packets delivered by their deadline. For a single port or with free reconfiguration, the problem reduces to the well-known packets scheduling problem, where the celebrated earliest-deadline-first (EDF) strategy is optimal 1-competitive. However, EDF is not 1-competitive when there is a reconfiguration overhead. We design an online algorithm that achieves a competitive ratio of 1−o(1) when the ratio between the minimum laxity of the packets and the number of ports tends to infinity. This is one of the rare cases where one can design an almost 1-competitive algorithm. One ingredient of our analysis, which may be interesting on its own right, is a perturbation theorem on EDF for the classical packets scheduling problem. Specifically, we show that a small perturbation in the release and deadline times cannot significantly degrade the optimal throughput. This implies that EDF is robust in the sense that its throughput is close to the optimum even when the deadlines are not precisely known.     

A Preemptive Algorithm for Maximizing Disjoint Paths on Trees

We consider the on-line version of the maximum vertex disjoint path problem when the underlying network is a tree. In this problem, a sequence of requests arrives in an on-line fashion, where every request is a path in the tree. The on-line algorithm may accept a request only if it does not share a vertex with a previously accepted request. The goal is to maximize the number of accepted requests. It is known that no on-line algorithm can have a competitive ratio better than Ω(log n) for this problem, even if the algorithm is randomized and the tree is simply a line. Obviously, it is desirable to beat the logarithmic lower bound. Adler and Azar (Proc. of the 10th ACM-SIAM Symposium on Discrete Algorithm, pp. 1–10, 1999) showed that if preemption is allowed (namely, previously accepted requests may be discarded, but once a request is discarded it can no longer be accepted), then there is a randomized on-line algorithm that achieves constant competitive ratio on the line. In the current work we present a randomized on-line algorithm with preemption that has constant competitive ratio on any tree. Our results carry over to the related problem of maximizing the number of accepted paths subject to a capacity constraint on vertices (in the disjoint path problem this capacity is 1). Moreover, if the available capacity is at least 4, randomization is not needed and our on-line algorithm becomes deterministic.     

Advanced SPICE modeling of large power IGBT modules

An enhanced insulated gate bipolar transistor (IGBT) model based on the Kraus model with new derivations based on an extra parameter accounting for p-i-n injection was developed to allow simulation of both trench and DMOS IGBT structures. Temperature dependence was also implemented in the model. The model was validated against steady-state and transient measurements done on an 800-A 1.7-kV Dynex IGBT module at 25/spl deg/C and 125/spl deg/C. The Spice model has also shown excellent agreement with mixed mode MEDICI simulations. The Spice model also takes into account for the first time the parasitic thyristor effect allowing the dc and dynamic temperature-dependent latchup modeling of power modules as well as their temperature-dependent safe operating area.     

Soft Matter Technology at KIT: Chemical Perspective from Nanoarchitectures to Microstructures

Bioinspiration has emerged as an important design principle in the rapidly growing field of materials science and especially its subarea, soft matter science. For example, biological cells form hierarchically organized tissues that not only are optimized and designed for durability, but also have to adapt to their external environment, undergo self‐repair, and perform many highly complex functions. Being able to create artificial soft materials that mimic those highly complex functions will enable future materials applications. Herein, soft matter technologies that are used to realize bioinspired material structures are described, and potential pathways to integrate these into a comprehensive soft matter research environment are addressed. Solutions become available because soft matter technologies are benefitting from the synergies between organic synthesis, polymer chemistry, and materials science.     

Plant oil bioconversion into increase biological activity through lipases derived from wastes

The aim of this study was to show that abundant and inexpensive plant oils can be biotransformed to increase biological activity (antioxidant, antimicrobial and cytotoxicity) through hydrolysis reaction catalysed by lipases. We tested homemade and commercial lipases through the biotransformation of nine different plant oils in forty different combinations. First, the chemical composition of the samples was investigated. Thereafter, biological tests were conducted to assess the antioxidant and antimicrobial activity of the sampled biotransformation products, as well as analyzed their influence on the viability of healthy and cancer cells. Summarising, sunflower, corn and olive oils modified by orange waste-obtained lipases presented the most promising results, reaching up to 90% of antioxidant activity increase and significant growth inhibition of bacteria colonies belonging to genera Escherichia, Listeria, Staphylococcus, Pseudomonas and Salmonella. In addition, those compounds affected human oral squamous carcinoma cells. The bioconversion of plant oils through lipases improves their biological properties and might be an option for biotechnological application.