Erbium doped materials for a Si-based microphotonics

We have investigated the role of the Si excess on the photoluminescence properties of Er doped substoichiometric SiOx layers. We demonstrate that the Si excess has two competing roles: when agglomerated to form Si nanoclusters (Si-nc) it enhances the Er excitation efficiency but it also introduces new non-radiative decay channels. When Er is excited through an energy transfer from Si-nc, the beneficial effect on the enhanced excitation efficiency prevails and the Er emission increases with increasing Si content. Nevertheless the maximum excited Er fraction is only of the order of percent. In order to increase the concentration of excited Er ions, a different approach based on Er silicate thin film has been explored. Under proper annealing conditions, an efficient luminescence at 1535 nm is found and all of the Er ions in the material is optically active. The possibility to efficiently excite Er ions also through electron-hole mediated processes is demonstrated in nanometer-scale Er-Si-O/Si multilayers. These data are presented and discussed.

Linear multiscale analysis and finite element validation of stretching and bending dominated lattice materials

The paper presents a multiscale procedure for the linear analysis of components made of lattice materials. The method allows the analysis of both pin-jointed and rigid-jointed microtruss materials with arbitrary topology of the unit cell. At the macroscopic level, the procedure enables to determine the lattice stiffness, while at the microscopic level the internal forces in the lattice elements are expressed in terms of the macroscopic strain applied to the lattice component. A numeric validation of the method is described. The procedure is completely automated and can be easily used within an optimization framework to find the optimal geometric parameters of a given lattice material. © 2011 Elsevier Ltd. All rights reserved.

Structural optimization of lattice materials

Lattice materials are characterized at the microscopic level by a regular pattern of voids confined by walls. Recent rapid prototyping techniques allow their manufacturing from a wide range of solid materials, ensuring high degrees of accuracy and limited costs. The microstructure of lattice material permits to obtain macroscopic properties and structural performance, such as very high stiffness to weight ratios, highly anisotropy, high specific energy dissipation capability and an extended elastic range, which cannot be attained by uniform materials. Among several applications, lattice materials are of special interest for the design of morphing structures, energy absorbing components and hard tissue scaffold for biomedical prostheses. Their macroscopic mechanical properties can be finely tuned by properly selecting the lattice topology and the material of the walls. Nevertheless, since the number of the design parameters involved is very high, and their correlation to the final macroscopic properties of the material is quite complex, reliable and robust multiscale mechanics analysis and design optimization tools are a necessary aid for their practical application. In this paper, the optimization of lattice materials parameters is illustrated with reference to the design of a bracket subjected to a point load. Given the geometric shape and the boundary conditions of the component, the parameters of four selected topologies have been optimized to concurrently maximize the component stiffness and minimize its mass. Copyright © 2011 by ASME.

Magnetocaloric effect and magnetic properties of Tb0.9 Sn0.1 MnO3

The magnetocaloric effect in magnetic materials is of great interest nowadays. In this article we present an investigation about the magnetic properties near the magnetic transition in a polycrystalline sample of a manganite Tb0.9 Sn0.1 MnO3. Particularly, we are interested in describing the nature of the magnetic interactions and the magnetocaloric effect in this compound. The temperature dependence of the magnetization was measured to determine the characteristics of the magnetic transition and the magnetic entropy change was calculated from magnetization curves at different temperatures. The magnetic solid is paramagnetic at high temperatures. We observe a dominant antiferromagnetic interaction below Tn =38 K for low applied magnetic fields; the presence of Sn doping in this compound decreases the Ńel temperature of the pure TbMnO3 system. A drastic increase in the magnetization as a function of temperature near the magnetic transition suggests a strong magnetocaloric effect. We found a large magnetic entropy change Δ SM (T) of about -4 J/kg K at H=3 T. We believe that the magnetic entropy change is associated with the magnetic transition and we interpret it as due to the coupling between the magnetic field and the spin ordering. This relatively large value and broad temperature interval (about 35 K) of the magnetocaloric effect make the present compound a promising candidate for magnetic refrigerators at low temperatures. © 2007 American Institute of Physics.

An example of the use of neural computing techniques in materials science - The modelling of fatigue thresholds in Ni-base superalloys

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. © 1999 Elsevier Science S.A.