Investigating fuel-cell transport limitations using hydrogen limiting current

Reducing mass-transport losses in polymer-electrolyte fuel cells (PEFCs) is essential to increase their power density and reduce overall stack cost. At the same time, cost also motivates the reduction in expensive precious-metal catalysts, which results in higher local transport losses in the catalyst layers. In this paper, we use a hydrogen-pump limiting-current setup to explore the gas-phase transport losses through PEFC catalyst layers and various gas-diffusion and microporous layers. It is shown that the effective diffusivity in the gas-diffusion layers is a strong function of liquid saturation. In addition, it is shown how the catalyst layer unexpectedly contributes significantly to the overall measured transport resistance. This is especially true for low catalyst loadings. It is also shown how the various losses can be separated into different mechanisms including diffusional processes and mass-dependent and independent ones, where the data suggests that a large part of the transport resistance in catalyst layers cannot be attributed to a gas-phase diffusional process. The technique is promising for deconvoluting transport losses in PEFCs.     

Chemical warfare and survival strategies in bacterial range expansions

Dispersal of species is a fundamental ecological process in the evolution and maintenance of biodiversity. Limited control over ecological parameters has hindered progress in understanding of what enables species to colonize new areas, as well as the importance of interspecies interactions. Such control is necessary to construct reliable mathematical models of ecosystems. In our work, we studied dispersal in the context of bacterial range expansions and identified the major determinants of species coexistence for a bacterial model system of three Escherichia coli strains (toxin-producing, sensitive and resistant). Genetic engineering allowed us to tune strain growth rates and to design different ecological scenarios (cyclic and hierarchical). We found that coexistence of all strains depended on three strongly interdependent factors: composition of inoculum, relative strain growth rates and effective toxin range. Robust agreement between our experiments and a thoroughly calibrated computational model enabled us to extrapolate these intricate interdependencies in terms of phenomenological biodiversity laws. Our mathematical analysis also suggested that cyclic dominance between strains is not a prerequisite for coexistence in competitive range expansions. Instead, robust three-strain coexistence required a balance between growth rates and either a reduced initial ratio of the toxin-producing strain, or a sufficiently short toxin range.     

Formic Acid Production Via Methane Peroxide Oxidation Over Oxalic Acid Activated Fe-MFI Catalysts

Topics in Catalysis - The process of synthesis of formic acid via partial peroxide oxidation of methane over Fe-MFI zeolites, as well as the influence of the catalyst activation by oxalic acid on...     

Analog circuit synthesis performing fast Pareto frontier exploration and analysis through 3D graphs

This paper presents a technique for performing analog design synthesis at circuit level providing feedback to the designer through the exploration of the Pareto frontier. A modified simulated annealing which is able to perform crossover with past anchor points when a local minimum is found which is used as the optimization algorithm on the initial synthesis procedure. After all specifications are met, the algorithm searches for the extreme points of the Pareto frontier in order to obtain a non-exhaustive exploration of the Pareto front. Finally, multi-objective particle swarm optimization is used to spread the results and to find a more accurate frontier. Piecewise linear functions are used as single-objective cost functions to produce a smooth and equal convergence of all measurements to the desired specifications during the composition of the aggregate objective function. To verify the presented technique two circuits were designed, which are: a Miller amplifier with 96 dB Voltage gain, 15.48 MHz unity gain frequency, slew rate of 19.2 V/μs with a current supply of 385.15 μA, and a complementary folded cascode with 104.25 dB Voltage gain, 18.15 MHz of unity gain frequency and a slew rate of 13.370 MV/μs. These circuits were synthesized using a 0.35 μm technology. The results show that the method provides a fast approach for good solutions using the modified SA and further good Pareto front exploration through its connection to the particle swarm optimization algorithm.     

Dynamic rough clustering and its applications

Dynamic data mining has gained increasing attention in the last decade. It addresses changing data structures which can be observed in many real-life applications, e.g. buying behavior of customers. As opposed to classical, i.e. static data mining where the challenge is to discover pattern inherent in given data sets, in dynamic data mining the challenge is to understand – and in some cases even predict – how such pattern will change over time. Since changes in general lead to uncertainty, the appropriate approaches for uncertainty modeling are needed in order to capture, model, and predict the respective phenomena considered in dynamic environments. As a consequence, the combination of dynamic data mining and soft computing is a very promising research area. The proposed algorithm consists of a dynamic clustering cycle when the data set will be refreshed from time to time. Within this cycle criteria check if the newly arrived data have structurally changed in comparison to the data already analyzed. If yes, appropriate actions are triggered, in particular an update of the initial settings of the cluster algorithm. As we will show, rough clustering offers strong tools to detect such changing data structures. To evaluate the proposed dynamic rough clustering algorithm it has been applied to synthetic as well as to real-world data sets where it provides new insights regarding the underlying dynamic phenomena.     

Proteome Analysis of Subsarcolemmal Cardiomyocyte Mitochondria: A Comparison of Different Analytical Platforms

Mitochondria are complex organelles that play critical roles in diverse aspects of cellular function. Heart disease and a number of other pathologies are associated with perturbations in the molecular machinery of the mitochondria. Therefore, comprehensive, unbiased examination of the mitochondrial proteome represents a powerful approach toward system-level insights into disease mechanisms. A crucial aspect in proteomics studies is design of bioanalytical strategies that maximize coverage of the complex repertoire of mitochondrial proteins. In this study, we evaluated the performance of gel-based and gel-free multidimensional platforms for profiling of the proteome in subsarcolemmal mitochondria harvested from rat heart. We compared three different multidimensional proteome fractionation platforms: polymeric reversed-phase liquid chromatography at high pH (PLRP), sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and isoelectric focusing (IEF) separations combined with liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS), and bioinformatics for protein identification. Across all three platforms, a total of 1043 proteins were identified. Among the three bioanalytical strategies, SDS-PAGE followed by LC-MS/MS provided the best coverage of the mitochondrial proteome. With this platform, 890 proteins with diverse physicochemical characteristics were identified; the mitochondrial protein panel encompassed proteins with various functional roles including bioenergetics, protein import, and mitochondrial fusion. Taken together, results of this study provide a large-scale view of the proteome in subsarcolemmal mitochondria from the rat heart, and aid in the selection of optimal bioanalytical platforms for differential protein expression profiling of mitochondria in health and disease.     

Der Schau- und Lehrstollen Arzberg/Steiermark: Beispiel für eine intelligente Nachnutzung eines Altbergbaus

Touristic mines are by far not only of public interest. During guided tours the importance of mining and mineral resources for society can be explained in a proper way. Mineral deposits, for long time not accessible and only known from old literature can now be studied in detail by scientists. Old underground mines can be used for different purposes. With respect to Arzberg the different possibilities of utilization of old underground workings are described in detail: A touristic mine serves not only as an attraction, but also as a training centre for underground geological mapping, geochemistry and geophysics research laboratory of universities. For many years the ZAMG (Central Institute for Meteorology and Geodynamics) has been operating a seismic station, and parts of the old mine workings have been used for cheese refinery since 2006. Furthermore, the mine water is utilized for extracting geothermal energy and serves the cooling chambers. The combined use of the old mine workings by different parties permits sharing the substantial maintenance costs.     

Effect of Processing on the Detectability of Pecan Proteins Assessed by Immunological and Proteomic Tools

The present study evaluates the effect of food processing on the antigenicity of pecan proteins as measured by enzyme-linked immunosorbent assay (ELISA). In addition, proteomic tools were used to identify potential pecan markers suitable for confirming the presence of pecan proteins in food and validating new methods developed to detect traces of the commodity. To assess the effects of processing on protein stability and antigenicity, pecan nuts were submitted to heat treatments and extracts were analysed by ELISA, sodium dodecyl sulphate polyacrylamide gel electrophoresis and Western blot. The ELISA method was able to detect pecan traces even after submitting the commodity to rigorous treatments, though these treatments affected the detectability to varying degrees. Proteomic assessment showed that the majority of pecan proteins were matched by homology to walnut proteins, which are more abundantly populated in the protein sequence databases. However, there were a few important exceptions: 7S vicilin, 11S legumin and putative allergen I1, unambiguously identified as pecan in origin. Interestingly, putative allergen I1 offered unique analytical advantages to be used as a pecan marker for validation and confirmation purposes.     

Differential effects of air conditioning type on residential endotoxin levels in a semi‐arid climate

Residential endotoxin exposure is associated with protective and pathogenic health outcomes. Evaporative coolers, an energy‐efficient type of air conditioner used in dry climates, are a potential source of indoor endotoxins; however, this association is largely unstudied. We collected settled dust biannually from four locations in homes with evaporative coolers (n=18) and central air conditioners (n=22) in Utah County, Utah (USA), during winter (Jan‐Apr) and summer (Aug‐Sept), 2014. Dust samples (n=281) were analyzed by the Limulus amebocyte lysate test. Housing factors were measured by survey, and indoor temperature and relative humidity measures were collected during both seasons. Endotoxin concentrations (EU/mg) were significantly higher in homes with evaporative coolers from mattress and bedroom floor samples during both seasons. Endotoxin surface loads (EU/m²) were significantly higher in homes with evaporative coolers from mattress and bedroom floor samples during both seasons and in upholstered furniture during winter. For the nine significant season‐by‐location comparisons, EU/mg and EU/m² were approximately three to six times greater in homes using evaporative coolers. A plausible explanation for these findings is that evaporative coolers serve as a reservoir and distribution system for Gram‐negative bacteria or their cell wall components in homes.     

Optoacoustic imaging using a three-dimensional reconstructionalgorithm

A novel three-dimensional (3-D) computational algorithm to reconstruct 3-D optoacoustic images from two-dimensional (2-D) pressure distributions generated at the sample surface is presented. The 2-D pressure distributions were measured as images at different delay times after the excitation laser pulse. The pressure images were captured with a gated CCD camera as the local pressure induces intensity changes of a reflected probe beam at the surface of the irradiated sample. The illumination time was 10 ns and the resolution of the surface pressure image was 20 μm. The reconstruction algorithm is based on the decomposition into plane waves. The algorithm was tested in the back projection of simulated pressure transients of three sources, and applied to different biological systems. Furthermore the algorithm was compared with the time of flight back projection algorithm. Optoacoustic images with a depth resolution of 15 μm and a lateral resolution of 100 μm are presented