4 resultados para density model
em Universidad de Alicante
Resumo:
Characterization of sound absorbing materials is essential to predict its acoustic behaviour. The most commonly used models to do so consider the flow resistivity, porosity, and average fibre diameter as parameters to determine the acoustic impedance and sound absorbing coefficient. Besides direct experimental techniques, numerical approaches appear to be an alternative to estimate the material’s parameters. In this work an inverse numerical method to obtain some parameters of a fibrous material is presented. Using measurements of the normal incidence sound absorption coefficient and then using the model proposed by Voronina, subsequent application of basic minimization techniques allows one to obtain the porosity, average fibre diameter and density of a sound absorbing material. The numerical results agree fairly well with the experimental data.
Resumo:
The solubility, density, refractive index, and viscosity data for the ethylene glycol + CsBr + H2O, 1,2-propanediol + CsBr + H2O, and glycerin + CsBr + H2O ternary systems have been determined at (288.15, 298.15, and 308.15) K. In all cases, the solubility of CsBr in aqueous solutions was decreased significantly due to the presence of polyhydric alcohol. The liquid–solid equilibrium experimental data were correlated using the NRTL (nonrandom two-liquid) activity coefficient model, considering nondissociation of the dissolved salt in the liquid phase, and new interaction parameters were estimated. The mean deviations between calculated and experimental compositions were low, showing the good descriptive quality and applicability of the NRTL model. The refractive indices, densities, and viscosities for the unsaturated solutions of the three ternary systems have also been measured at three temperatures. Values for all of the properties were correlated with the salt concentrations and proportions of polyhydric alcohol in the solutions.
Resumo:
Purpose: Retinitis pigmentosa includes a group of progressive retinal degenerative diseases that affect the structure and function of photoreceptors. Secondarily to the loss of photoreceptors, there is a reduction in retinal vascularization, which seems to influence the cellular degenerative process. Retinal macroglial cells, astrocytes, and Müller cells provide support for retinal neurons and are fundamental for maintaining normal retinal function. The aim of this study was to investigate the evolution of macroglial changes during retinal degeneration in P23H rats. Methods: Homozygous P23H line-3 rats aged from P18 to 18 months were used to study the evolution of the disease, and SD rats were used as controls. Immunolabeling with antibodies against GFAP, vimentin, and transducin were used to visualize macroglial cells and cone photoreceptors. Results: In P23H rats, increased GFAP labeling in Müller cells was observed as an early indicator of retinal gliosis. At 4 and 12 months of age, the apical processes of Müller cells in P23H rats clustered in firework-like structures, which were associated with ring-like shaped areas of cone degeneration in the outer nuclear layer. These structures were not observed at 16 months of age. The number of astrocytes was higher in P23H rats than in the SD matched controls at 4 and 12 months of age, supporting the idea of astrocyte proliferation. As the disease progressed, astrocytes exhibited a deteriorated morphology and marked hypertrophy. The increase in the complexity of the astrocytic processes correlated with greater connexin 43 expression and higher density of connexin 43 immunoreactive puncta within the ganglion cell layer (GCL) of P23H vs. SD rat retinas. Conclusions: In the P23H rat model of retinitis pigmentosa, the loss of photoreceptors triggers major changes in the number and morphology of glial cells affecting the inner retina.
Resumo:
Model Hamiltonians have been, and still are, a valuable tool for investigating the electronic structure of systems for which mean field theories work poorly. This review will concentrate on the application of Pariser–Parr–Pople (PPP) and Hubbard Hamiltonians to investigate some relevant properties of polycyclic aromatic hydrocarbons (PAH) and graphene. When presenting these two Hamiltonians we will resort to second quantisation which, although not the way chosen in its original proposal of the former, is much clearer. We will not attempt to be comprehensive, but rather our objective will be to try to provide the reader with information on what kinds of problems they will encounter and what tools they will need to solve them. One of the key issues concerning model Hamiltonians that will be treated in detail is the choice of model parameters. Although model Hamiltonians reduce the complexity of the original Hamiltonian, they cannot be solved in most cases exactly. So, we shall first consider the Hartree–Fock approximation, still the only tool for handling large systems, besides density functional theory (DFT) approaches. We proceed by discussing to what extent one may exactly solve model Hamiltonians and the Lanczos approach. We shall describe the configuration interaction (CI) method, a common technology in quantum chemistry but one rarely used to solve model Hamiltonians. In particular, we propose a variant of the Lanczos method, inspired by CI, that has the novelty of using as the seed of the Lanczos process a mean field (Hartree–Fock) determinant (the method will be named LCI). Two questions of interest related to model Hamiltonians will be discussed: (i) when including long-range interactions, how crucial is including in the Hamiltonian the electronic charge that compensates ion charges? (ii) Is it possible to reduce a Hamiltonian incorporating Coulomb interactions (PPP) to an 'effective' Hamiltonian including only on-site interactions (Hubbard)? The performance of CI will be checked on small molecules. The electronic structure of azulene and fused azulene will be used to illustrate several aspects of the method. As regards graphene, several questions will be considered: (i) paramagnetic versus antiferromagnetic solutions, (ii) forbidden gap versus dot size, (iii) graphene nano-ribbons, and (iv) optical properties.