Determinação do estado fundamental de compostos antiferromagnéticos da série Tb(1-x)Y(x)RhIn(5): aproximação teórico experimental

We present the results of electrical resistivity, magnetic susceptibility, specific heat and x-ray absorption spectroscopy measurements in Tb1−xYxRhIn5 (x = 0.00, 0.15, 0.4.0, 0.50 e 0.70) single crystals. Tb1−xYxRhIn5 is an antiferromagnetic AFM compound with ordering temperature TN ≈ 46 K, the higher TN within the RRhIn5 serie (R : rare earth). We evaluate the physical properties evolution and the supression of the AFM state considering doping and Crystalline Electric Field (CEF) effects on magnetic exchange interaction between Tb3+ magnetic ions. CEF acts like a perturbation potential, breaking the (2J + 1) multiplet s degeneracy. Also, we studied linear-polarization-dependent soft x-ray absorption at Tb M4 and M5 edges to validate X-ray Absorption Spectroscopy as a complementary technique in determining the rare earth CEF ground state. Samples were grown by the indium excess flux and the experimental data (magnetic susceptibility and specific heat) were adjusted with a mean field model that takes account magnetic exchange interaction between first neighbors and CEF effects. XAS experiments were carried on Total Electron Yield mode at Laborat´onio Nacional de Luz S´ıncrotron, Campinas. We measured X-ray absorption at Tb M4,5 edges with incident polarized X-ray beam parallel and perpendicular to c-axis (E || c e E ⊥ c). The mean field model simulates the mean behavior of the whole system and, due to many independent parameters, gives a non unique CEF scheme. XAS is site- and elemental- specific technique and gained the scientific community s attention as complementary technique in determining CEF ground state in rare earth based compounds. In this work we wil discuss the non conclusive results of XAS technique in TbRhIn5 compounds.

Estratificação de solos em camadas horizontais utilizando evolução diferencial

This work's objective is the development of a methodology to represent an unknown soil through a stratified horizontal multilayer soil model, from which the engineer may carry out eletrical grounding projects with high precision. The methodology uses the experimental electrical apparent resistivity curve, obtained through measurements on the ground, using a 4-wire earth ground resistance tester kit, along with calculations involving the measured resistance. This curve is then compared with the theoretical electrical apparent resistivity curve, obtained through calculations over a horizontally strati ed soil, whose parameters are conjectured. This soil model parameters, such as the number of layers, in addition to the resistivity and the thickness of each layer, are optimized by Differential Evolution method, with enhanced performance through parallel computing, in order to both apparent resistivity curves get close enough, and it is possible to represent the unknown soil through the multilayer horizontal soil model fitted with optimized parameters. In order to assist the Differential Evolution method, in case of a stagnation during an arbitrary amount of generations, an optimization process unstuck methodology is proposed, to expand the search space and test new combinations, allowing the algorithm to nd a better solution and/or leave the local minima. It is further proposed an error improvement methodology, in order to smooth the error peaks between the apparent resistivity curves, by giving opportunities for other more uniform solutions to excel, in order to improve the whole algorithm precision, minimizing the maximum error. Methodologies to verify the polynomial approximation of the soil characteristic function and the theoretical apparent resistivity calculations are also proposed by including middle points among the approximated ones in the verification. Finally, a statistical evaluation prodecure is presented, in order to enable the classication of soil samples. The soil stratification methodology is used in a control group, formed by horizontally stratified soils. By using statistical inference, one may calculate the amount of soils that, within an error margin, does not follow the horizontal multilayer model.