Ab-initio study of the relation between electric polarization and electric field gradients in ferroelectrics

The hyperfine interaction between the quadrupole moment of atomic nuclei and the electric field gradient (EFG) provides information on the electronic charge distribution close to a given atomic site. In ferroelectric materials, the loss of inversion symmetry of the electronic charge distribution is necessary for the appearance of the electric polarization. We present first-principles density functional theory calculations of ferroelectrics such as BaTiO3, KNbO3, PbTiO3 and other oxides with perovskite structures, by focusing on both EFG tensors and polarization. We analyze the EFG tensor properties such as orientation and correlation between components and their link with electric polarization. This work supports previous studies of ferroelectric materials where a relation between EFG tensors and polarization was observed, which may be exploited to study ferroelectric order when standard techniques to measure polarization are not easily applied.Comment: 9 pages, 6 figures, 5 tables, corrected typos, as published in Phys. Rev.

Electroanalytical study of Prussian Blue modified glassy carbon paste electrodes

Two types of glassy carbon (GC) powder (i.e., Sigradur K and Sigradur G) have been mixed with mineral oil to obtain glassy carbon paste electrodes (GCPE's). The electrochemical behavior of such electrodes at different percentages of glassy carbon has been evaluated with respect to the electrochemistry of ferricyanide as revealed with cyclic voltammetry and the best paste composition was chosen. GC was then modified with Prussian Blue (PB), mixed at different percentages with unmodified GC and with a fixed amount of mineral oil in order to obtain PB modified glassy carbon paste electrodes (PB-GCPE's). PB-GCPE's with different percentages of GC modified with PB (PB-GC) were compared and the dependence on the amount of PB on their performances was evaluated by studying the parameters of cyclic voltammetry (i.e., current peak, Ep, anodic and cathodic current ratio, charge density) and the amperometric response to H2O2. Data interpretation based on the GC surface area is presented. GCPE's with a selected amount of PB-GC were then tested as H2O2 probes and all the analytical parameters together with the dependence on pH were evaluated. Some preliminary experiments with these electrodes assembled as glucose, lysine and lactate biosensors are also reported

Desenvolvimento inicial de pitangueira a partir de sementes de frutos em diferentes estágios de maturação.

bitstream/item/61611/1/Comunicado-278.pd

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

The Peculiar Phase Structure of Random Graph Bisection

The mincut graph bisection problem involves partitioning the n vertices of a graph into disjoint subsets, each containing exactly n/2 vertices, while minimizing the number of "cut" edges with an endpoint in each subset. When considered over sparse random graphs, the phase structure of the graph bisection problem displays certain familiar properties, but also some surprises. It is known that when the mean degree is below the critical value of 2 log 2, the cutsize is zero with high probability. We study how the minimum cutsize increases with mean degree above this critical threshold, finding a new analytical upper bound that improves considerably upon previous bounds. Combined with recent results on expander graphs, our bound suggests the unusual scenario that random graph bisection is replica symmetric up to and beyond the critical threshold, with a replica symmetry breaking transition possibly taking place above the threshold. An intriguing algorithmic consequence is that although the problem is NP-hard, we can find near-optimal cutsizes (whose ratio to the optimal value approaches 1 asymptotically) in polynomial time for typical instances near the phase transition.Comment: substantially revised section 2, changed figures 3, 4 and 6, made minor stylistic changes and added reference