Electrosynthesis of hierarchical Cu2O–Cu(OH)2 nanodendrites supported on carbon nanofibers/poly(para-phenylenediamine) nanocomposite as high-efficiency catalysts for methanol electrooxidation

The development of highly efficient catalysts using inexpensive and earth-abundant metals is a crucial factor in a large-scale commercialization of direct methanol fuel cells (DMFCs). In this study, we explored a new catalyst based on copper nanodendrites (CuNDs) supported on carbon nanofibers/poly (para-phenylenediamine) (CNF/PpPD) nanocomposite for methanol oxidation reaction (MOR). The catalyst support was prepared on a carbon paste electrode by electropolymerization of para-phenylenediamine monomer on a drop-cast carbon nanofibers network. Afterwards, CuNDs were electrodeposited on the nanocomposite through a potentiostatic method. The morphology and the structure of the prepared nanomaterials were characterized by transmission electron microscope, scanning electron microscope, energy dispersive X-ray, X-ray diffraction, and X-ray photoelectron spectroscope. The results suggested that a three-dimensional nanodendritic structure consisting of Cu₂O and Cu(OH)₂ formed on the hybrid CNF/PpPD nanocomposite. The catalytic performance of CuNDs supported on CNF, PpPD and CNF/PpPD was evaluated for MOR under alkaline conditions. The CNF/PpPD/CuNDs exhibits a highest activity (50 mA cm^?2) and stability toward MOR over 6 h, with respect to CNF/CuNDs (40 mA cm^?2) and PpPD/CuNDs (36 mA cm^?2). This inexpensive catalyst with high catalytic activity and stability is a promising anode catalyst for alkaline DMFC applications.     

Study of the Microstructure and Phase Composition of Crude Niobium Electron-Beam Remelting Sublimates

Metallurgist - crude niobium. The phase composition of sublimates formed during the first remelting of crude niobium is studied by scanning electron microscopy and X-ray microanalysis. Sublimates...     

An experimental study for determination of the effects of machining parameters on surface roughness in electrical discharge machining (EDM)

Electrical discharge machining (EDM) is a non-traditional production method that has been widely used in the production of dies throughout the world in recent years. The most important performance measure in EDM is the surface roughness; among other measures material removal and tool wear rates could be listed. In this study, experiments were performed to determine parameters effecting surface roughness. The data obtained for performance measures have been analyzed using the design of experiments methods. A considerably profound equation is obtained for the surface roughness using power, pulse time, and spark time parameters. The results are discussed.     

A State-of-the-art Elliptic Curve Cryptographic Processor Operating in the Frequency Domain

We propose a novel area/time efficient elliptic curve cryptography (ECC) processor architecture which performs all finite field arithmetic operations in the discrete Fourier domain. The proposed architecture utilizes a class of optimal extension fields (OEF) GF(q ^m ) where the field characteristic is a Mersenne prime q = 2 ⁿ  − 1 and m = n. The main advantage of our architecture is that it achieves extension field modular multiplication in the discrete Fourier domain with only a linear number of base field GF(q) multiplications in addition to a quadratic number of simpler operations such as addition and bitwise rotation. We achieve an area between 25k and 50k equivalent gates for the implementations over OEFs of size 169, 289 and 361 bits. With its low area and high speed, the proposed architecture is well suited for ECC in small device environments such as sensor networks. The work at hand presents the first hardware implementation of a frequency domain multiplier suitable for ECC and the first hardware implementation of ECC in the frequency domain.

Design of neural networks model for transmission angle of a modified mechanism

This paper discusses Neural Networks as predictor for analyzing of transmission angle of slider-crank mechanism. There are different types of neural network algorithms obtained by using chain rules. The neural network is a feedforward neural network. On the other hand, the slider-crank mechanism is a modified mechanism by using an additional link between connecting rod and crank pin. Through extensive simulations, these neural network models are shown to be effective for prediction and analyzing of a modified slider-crank mechanism’s transmission angle.     

Structure and phase composition of a V-Al-N master alloy

The structure and phase composition of the V-Al-N master alloys produced using out-of-furnace aluminothermic melting of charges containing nitrided vanadium filings or powdered electrolytic vanadium are studied. It has been found that the nitrogen-containing phase of these alloys is represented by aluminum nitride. The master alloy ingots melted using the nitriding agents are characterized by identical values of the integral parameters of nitride phases. Based on metallography results, certain similarity is found between inclusions and the nitriding agent added to the charge for melting of a V-Al-N master alloy.