Defects in Persistent Luminescence Materials

Persistent luminescence materials can store energy from solar radiation or artificial lighting and release it over a period of several hours without a continuous excitation source. These materials are widely used to improve human safety in emergency and traffic signalization. They can also be utilized in novel applications including solar cells, medical diagnostics, radiation detectors and structural damage sensors. The development of these materials is currently based on methods based on trial and error. The tailoring of new materials is also hindered by the lack of knowledge on the role of their intrinsic and extrinsic lattice defects in the appropriate mechanisms. The goal of this work was to clarify the persistent luminescence mechanisms by combining ab initio density functional theory (DFT) calculations with selected experimental methods. The DFT approach enables a full control of both the nature of the defects and their locations in the host lattice. The materials studied in the present work, the distrontium magnesium disilicate (Sr₂MgSi₂O₇) and strontium aluminate (SrAl₂O₄) are among the most efficient persistent luminescence hosts when doped with divalent europium Eu²⁺ and co-doped with trivalent rare earth ions R³⁺ (R: Y, La-Nd, Sm, Gd-Lu). The polycrystalline materials were prepared with the solid state method and their structural and phase purity was confirmed by X-ray powder diffraction. Their local crystal structure was studied by high-resolution transmission electron microscopy. The crystal and electronic structure of the nondoped as well as Eu²⁺, R^2+/3+ and other defect containing materials were studied using DFT calculations. The experimental trap depths were obtained using thermoluminescence (TL) spectroscopy. The emission and excitation of Sr₂MgSi₂O₇:Eu²+,Dy³⁺ were also studied. Significant modifications in the local crystal structure due to the Eu²⁺ ion and lattice defects were found by the experimental and DFT methods. The charge compensation effects induced by the R³⁺ co-doping further increased the number of defects and distortions in the host lattice. As for the electronic structure of Sr₂MgSi₂O₇ and SrAl₂O₄, the experimental band gap energy of the host materials was well reproduced by the calculations. The DFT calculated Eu²⁺ and R^2+/3+ 4fn as well as 4f^n-15d¹ ground states in the Sr₂MgSi₂O₇ band structure provide an independent verification for an empirical model which is constructed using rather sparse experimental data for the R³⁺ and especially the R²⁺ ions. The intrinsic and defect induced electron traps were found to act together as energy storage sites contributing to the materials’ efficient persistent luminescence. The calculated trap energy range agreed with the trap structure of Sr₂MgSi₂O₇ obtained using TL measurements. More experimental studies should be carried out for SrAl₂O₄ to compare with the DFT calculations. The calculated and experimental results show that the electron traps created by both the rare earth ions and vacancies are modified due to the defect aggregation and charge compensation effects. The relationships between this modification and the energy storage properties of the solid state materials are discussed.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.