Poly(2,5-bibenzimidazole) Membranes: Physico-Chemical Characterization Focused on Fuel Cell Applications

Membranes of Poly(2,5-benzimidazole) (ABPBI), prepared by polycondensation in polyphosphoric acid, were characterized from the fuel cell application point of view: mechanical properties of the membranes for different acid doping levels, thermal stability, permeability for the different gases/vapors susceptible of use in the cell (hydrogen, oxygen, methanol and ethanol), electro-osmotic water drag coefficient, oxidation stability to hydroxyl radicals, phosphoric acid leaching rate and, finally, in-plane membrane conductivity. ABPBI membranes presented an excellent thermal stability, above 500 degrees C in oxygen, suitable mechanical properties for high phosphoric acid doping levels, a low methanol and ethanol limiting permeation currents, and oxygen permeability compared to Nafion membranes, and a low phosphoric acid leaching rate when exposed to water vapor. On the contrary, hydrogen permeation current was higher than that of Nafion, and the chemical stability was very limited. Membrane conductivity achieved 0.07 S cm(-1) after equilibration with a humid environment. Fuel cell tests showed reasonable good performances, with a maximum power peak of 170 mW cm(-2) for H-2/air at 170 degrees C operating under a humidified hydrogen stream, 39.9 mW cm(-2) for CH3OH/O-2 at 200 degrees C for a methanol/water weight ratio of 1: 2, and 31.5 mW cm(-2) for CH3CH2OH/O-2 at the same conditions than for methanol. (C) 2012 The Electrochemical Society. [DOI: 10.1149/2.014207jes] All rights reserved.

Investigation of a Pt3Sn/C Electro-Catalyst in a Direct Ethanol Fuel Cell Operating at Low Temperatures for Portable Applications

A 20% Pt3Sn/C catalyst was prepared by reduction with formic acid and used in a direct ethanol fuel cell at low temperatures. The electro-catalytic activity of this bimetallic catalyst was compared to that of a commercial 20% Pt/C catalyst. The PtSn catalyst showed better results in the investigated temperature range (30 degrees-70 degrees C). Generally, Sn promotes ethanol oxidation by adsorption of OH species at considerably lower potentials compared to Pt, allowing the occurrence of a bifunctional mechanism. The bimetallic catalyst was physico-chemically characterized by X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) analyses. The presence of SnO2 in the bulk and surface of the catalyst was observed. It appears that SnO2 can enhance the ethanol electro-oxidation activity at low potentials due to the supply of oxygen-containing species for the oxidative removal of CO and CH3CO species adsorbed on adjacent Pt active sites.

Structural and textural studies of NiO impregnation in mesoporous ZrO2-CeO2 for catalysis applications.

The synthesis of zirconia-based ordered mesoporous structures for catalytic applications is a research area under development. These systems are also potential candidates as anodes in intermediate temperature solid oxide fuel cells (it-SOFC) due to an enhancement on their surface area [1-4]. The structural features of mesoporous zirconia-ceria materials in combination with oxygen storage/release capacity (OSC) are crucial for various catalytic reactions. The direct use of hydrocarbons as fuel for the SOFC (instead of pure H2), without the necessity of reforming and purification reactors can improve global efficiency of these systems [4]. The X-ray diffraction data showed that ZrO2-x%CeO2 samples with x>50 are formed by a larger fraction of the cubic phase (spatial group Fm3m), while for x<50 the major crystalline structure is the tetragonal phase (spatial group P42/nmc). The crystallite size of the cubic phase increases with increase in ceria content. The tetragonal crystallite size decreases when ceria content increases. After impregnation, the Rietveld analysis showed a NiO content around 60wt.% for all samples. The lattice parameters for the ZrO2 tetragonal phase are lower for higher ZrO2 contents, while for all samples the cubic NiO and CeO2 parameters do not present changes. The calculated densities are higher for higher ceria content, as expected. The crystallite size of NiO are similar (~20nm) for all samples and 55nm for the NiO standard. Nitrogen adsorption experiments revealed a broader particle size distribution for higher CeO2 content. The superficial area values were around 35m2/g for all samples, the average pore diameter and pore volumes were higher when increasing ceria content. After NiO impregnation the particle size distribution was the same for all samples, with two pore sizes, the first around 3nm and a broader peak around 10nm. The superficial area increased to approximately 45m2/g for all samples, and the pore volume was also higher after impregnation and increased when ceria content increased. These results point up that the impregnation of NiO improves the textural characteristics of the pristine material. The complementary TEM/EDS images present a homogeneous coating of NiO particles over the ZrO2-x%CeO2 support, showing that these samples are excellent for catalysis applications. [1] D. Y. Zhao, J. Feng, Q. Huo, N. Melosh, G. H. Fredrickson, B. F. Chmelka, G. D. Stucky, Science 279, 548-552 (1998). [2] C. Yu, Y. Yu, D. Zhao, Chem. Comm. 575-576 (2000). [3] A. Trovarelli, M. Boaro, E. Rocchini, C. de Leitenburg, G. Dolcetti, J. Alloys Compd. 323-324 (2001) 584-591. [4] S. Larrondo, M. A. Vidal, B. Irigoyen, A. F. Craievich, D. G. Lamas, I. O. Fábregas, et al. Catal. Today 107–108 (2005) 53-59.

A system for classification of time-series data from industrial non-destructive device

This work proposes a system for classification of industrial steel pieces by means of magnetic nondestructive device. The proposed classification system presents two main stages, online system stage and off-line system stage. In online stage, the system classifies inputs and saves misclassification information in order to perform posterior analyses. In the off-line optimization stage, the topology of a Probabilistic Neural Network is optimized by a Feature Selection algorithm combined with the Probabilistic Neural Network to increase the classification rate. The proposed Feature Selection algorithm searches for the signal spectrogram by combining three basic elements: a Sequential Forward Selection algorithm, a Feature Cluster Grow algorithm with classification rate gradient analysis and a Sequential Backward Selection. Also, a trash-data recycling algorithm is proposed to obtain the optimal feedback samples selected from the misclassified ones.