Structural and optical properties were studied as a function of Nano membrane after prepared, for tests. Nano membrane was deposited by the spray coating method on substrates (glass) of thickness 100 mm. The X-ray diffraction spectra of (CNTs, WO3) were studied. AFM tests are good information about the roughness, It had been designed electrolysis cell and fuel cell. Studies have been performed on electrochemical parameters.
In this paper the manufacture of an alkaline fuel cell electrodes made upfrom a Nano mesh (Pt:NiO) catalyst has been studying , made from a Nano mesh (Pt:NiO ) catalyst. The general morphology of the samples is were imaged by using with the an Atomic Force Microscope (AFM) to determine the roughness of the prepared surface, it constructed from nanostructure with dimensions in order of 35 nm. The Structural characteristics were studied through the analysis of X-ray diffraction (XRD) of the prepared nanomaterial for determining the yielding phase;1. 72 volt was also obtained at 0.02 A/cm2 current density for an alkaline fuel cell.
In this paper had been studied the characterization of the nanocatalyst (NiO) Mesh electrodes. For fuel cell. The catalyst is prepared and also the electrodes The structural were studied through the analysis of X-ray diffraction of the prepared nanocatalyst for determining the yielding phase and atomic force microscope to identify the roughness of prepared catalyst surface, Use has been nanocatalyst led to optimization of cell voltage, current densities & power for a fuel cell.
Study of the development of an activated carbon nanotube catalyst for alkaline fuel cell technology. Through the prepared carbon nanotubes catalyst by an electrochemical deposition technique. Different analytical approaches such as X-ray diffraction (XRD) to determine the structural properties and Scanning Electron Microscope (SEM), were used to characterize, Mesh stainless steel catalyst substrate had an envelope structure and a large surface area. Voltages were also obtained at 1.83 V and current at 3.2 A of alkaline fuel cell. In addition, study the characterization of the electrochemical parameters.
In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de
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