506 research outputs found

    The Effect of Iron Impurities on Transition Metal Catalysts for the Oxygen Evolution Reaction in Alkaline Environment: Activity Mediators or Active Sites?

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    There is an ongoing debate on elucidating the actual role of Fe impurities in alkaline water electrolysis, acting either as reactivity mediators or as co-catalysts through synergistic interaction with the main catalyst material. This perspective summarizes the most prominent oxygen evolution reaction (OER) mechanisms mostly for Ni-based oxides as model transition metal catalysts and highlights the effect of Fe incorporation on the catalyst surface in the form of impurities originating from the electrolyte or co-precipitated in the catalyst lattice, in modulating the OER reaction kinetics, mechanism and stabilit

    Electrocatalysis Beyond 2020: How to Tune the Preexponential Frequency Factor

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    After a century of research on electrocatalytic reactions, a universal theory of electrocatalysis is still not established due to limited understanding of complex energy conversion processes at electrified electrode-electrolyte interfaces. Most of the research efforts directed toward the acceleration of important electrocatalytic reactions (e. g. hydrogen evolution reaction) were in the direction of minimizing activation energy by tuning the adsorption energies of key intermediates. This kind of approach is well-established and, importantly, in some cases it was valuable by predicting the design of electrocatalysts with advanced properties. However, in some very important research endeavors, advancement in performance of newly designed electrocatalysts could not be attributed to altered/minimized activation energy. Important to note is that modern electrocatalysis almost completely disregards influence of the preexponential factor on reaction rate. In this work, we open some important questions relevant for future of electrocatalysis and electrochemical energy conversion, with special focus on preexponential factor as major contributor to electrocatalytic reaction rate

    Electrochemical evaluation of the de-/re-activation of oxygen evolving Ir oxide

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    Understanding the influence of dynamic and stationary polarization on the deactivation of state-of-the-art IrOx catalysts is imperative for the design and operation of robust and efficient proton exchange membrane water electrolyzers. In this work, the deactivation and activity regeneration of a commercial IrOx catalyst investigated under potentiodynamic and potentiostatic conditions in acidic media by means of rotating disk electrode and electrogravimetry. Systematic electrochemical protocols were designed to decouple reversible from irreversible activity losses. Cyclic voltammetry provided a metric of the active surface area and traced the charge growth under different oxygen evolution reaction conditions. A direct logt dependent charge growth is observed, accompanied by the same fractional kinetic activity decay under potentiodynamic conditions. The loss is essentially recoverable after electrochemical reductive treatment, however at the expense of mild material dissolution. In contrast, extended potentiostatic operation induced irreversible intrinsic degradation after a critical time (0.5-1 h), accompanied by stability enhancement. This irreversible deactivation attributed to a gradual transformation of the hydrated IrOx to a dehydrated condensed oxide. Our results suggest that Ir dissolution during the regenerative treatment is not prohibitive, as long as the low potential modulations are not frequent

    Update on the Direct Detection of Supersymmetric Dark Matter

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    We compare updated predictions for the elastic scattering of supersymmetric neutralino dark matter with the improved experimental upper limit recently published by CDMS II. We take into account the possibility that the \pi-nucleon \Sigma term may be somewhat larger than was previously considered plausible, as may be supported by the masses of exotic baryons reported recently. We also incorporate the new central value of m_t, which affects indirectly constraints on the supersymmetric parameter space, for example via calculations of the relic density. Even if a large value of \Sigma is assumed, the CDMS II data currently exclude only small parts of the parameter space in the constrained MSSM (CMSSM) with universal soft supersymmetry-breaking Higgs, squark and slepton masses. None of the previously-proposed CMSSM benchmark scenarios is excluded for any value of \Sigma, and the CDMS II data do not impinge on the domains of the CMSSM parameter space favoured at the 90 % confidence level in a recent likelihood analysis. However, some models with non-universal Higgs, squark and slepton masses and neutralino masses \lappeq 700 GeV are excluded by the CDMS II data.Comment: 25 pages, 28 eps figure

    Neutrino Fluxes from CMSSM LSP Annihilations in the Sun

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    We evaluate the neutrino fluxes to be expected from neutralino LSP annihilations inside the Sun, within the minimal supersymmetric extension of the Standard Model with supersymmetry-breaking scalar and gaugino masses constrained to be universal at the GUT scale (the CMSSM). We find that there are large regions of typical CMSSM (m1/2,m0)(m_{1/2}, m_0) planes where the LSP density inside the Sun is not in equilibrium, so that the annihilation rate may be far below the capture rate. We show that neutrino fluxes are dependent on the solar model at the 20% level, and adopt the AGSS09 model of Serenelli et al. for our detailed studies. We find that there are large regions of the CMSSM (m1/2,m0)(m_{1/2}, m_0) planes where the capture rate is not dominated by spin-dependent LSP-proton scattering, e.g., at large m1/2m_{1/2} along the CMSSM coannihilation strip. We calculate neutrino fluxes above various threshold energies for points along the coannihilation/rapid-annihilation and focus-point strips where the CMSSM yields the correct cosmological relic density for tan(beta) = 10 and 55 for ÎĽ\mu > 0, exploring their sensitivities to uncertainties in the spin-dependent and -independent scattering matrix elements. We also present detailed neutrino spectra for four benchmark models that illustrate generic possibilities within the CMSSM. Scanning the cosmologically-favored parts of the parameter space of the CMSSM, we find that the IceCube/DeepCore detector can probe at best only parts of this parameter space, notably the focus-point region and possibly also at the low-mass tip of the coannihilation strip.Comment: 32 pages, 13 figures. v2: updated/expanded discussion of IceCube/DeepCor

    The effects of increasing dietary levels of soy protein concentrate (SPC) on the immune responses and disease resistance (furunculosis) of vaccinated and non-vaccinated Atlantic salmon (Salmo salar L.) parr

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    Juvenile salmon, with an initial weight of 9g, were fed three experimental diets, formulated to replace 35 (SPC35), 58 (SPC58) and 80 (SPC80) of high quality fishmeal (FM) with soy protein concentrate (SPC) in quadruplicate tanks. Higher dietary SPC inclusion was combined with increased supplementation of methionine, lysine, threonine and phosphorus. The experiment was carried out for 177 days. On day 92 salmon in each tank were bulk weighed. Post weighing eighty salmon from each tank were redistributed in two sets of 12 tanks. Salmon from the first set of tanks were vaccinated, while the second group was injected with phosphate buffer saline (PBS). Salmon were sampled on day 92 (pre-vaccination), day 94 (2 days post vaccination [dpv]/PBS injection [dpPBSinj]) and day 154 (62 dpv/dpPBSinj) of the trial for the assessment of their immune responses, prior to the performance of salmon bulk weights for each tank. On day 154, fish from each tank were again bulk weighed and then seventeen salmon per tank were redistributed in two sets of twelve tanks and intra-peritoneally infected with Aeromonas salmonicida. At Day 154, SPC80 demonstrated lower performance (weight gain, specific growth rate and thermal growth coefficient and feed conversion ratio) compared to SPC35 salmon. Reduced classical and total complement activities for salmon fed diets with over 58% of protein from SPC, were demonstrated prior to vaccination. Reduced alternative complement activity was detected for both SPC58 and SPC80 salmon at 2 dpv and for the SPC80 group at 62 dpv. Total and classical complement activities demonstrated no differences among the dietary groups after vaccination. Numerical increases in classical complement activity were apparent upon increased dietary SPC levels. Increased phagocytic activity (% phagocytosis and phagocytic index) was exhibited for the SPC58 group compared to SPC35 salmon at 62 dpPBSinj. No differences in serum lysozyme activity, total IgM, specific antibodies, protein, glucose and HKM respiratory burst were detected among the dietary groups at any timepoint or state. Mortalities as a result of the experimental infection only occurred in PBS-injected fish. No differences in mortality levels were demonstrated among the dietary groups. SPC58 diet supported both good growth and health in juvenile Atlantic salmon while SPC80 diet did not compromise salmon’ immunity or resistance to intraperitoneally inflicted furunculosis

    Reconstruction of Hydraulic Data by Machine Learning

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    Numerical simulation models associated with hydraulic engineering take a wide array of data into account to produce predictions: rainfall contribution to the drainage basin (characterized by soil nature, infiltration capacity and moisture), current water height in the river, topography, nature and geometry of the river bed, etc. This data is tainted with uncertainties related to an imperfect knowledge of the field, measurement errors on the physical parameters calibrating the equations of physics, an approximation of the latter, etc. These uncertainties can lead the model to overestimate or underestimate the flow and height of the river. Moreover, complex assimilation models often require numerous evaluations of physical solvers to evaluate these uncertainties, limiting their use for some real-time operational applications. In this study, we explore the possibility of building a predictor for river height at an observation point based on drainage basin time series data. An array of data-driven techniques is assessed for this task, including statistical models, machine learning techniques and deep neural network approaches. These are assessed on several metrics, offering an overview of the possibilities related to hydraulic time-series. An important finding is that for the same hydraulic quantity, the best predictors vary depending on whether the data is produced using a physical model or real observations.Comment: Submitted to SimHydro 201
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