A Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem

In this article, we introduce a semi-parametric Bayesian approach based on Dirichlet process priors for the discrete calibration problem in binomial regression models. An interesting topic is the dosimetry problem related to the dose-response model. A hierarchical formulation is provided so that a Markov chain Monte Carlo approach is developed. The methodology is applied to simulated and real data.

Optical energy storage properties of Sr(2)MgSi(2)O(7):Eu(2+),R(3+) persistent luminescence materials

The details of the mechanism of persistent luminescence were probed by investigating the trap level structure of Sr(2)MgSi(2)O(7):Eu(2+),R(3+) materials (R: Y, La-Lu, excluding Pm and Eu) with thermoluminescence (TL) measurements and Density Functional Theory (DFT) calculations. The TL results indicated that the shallowest traps for each Sr(2)MgSi(2)O(7):Eu(2+),R(3+) material above room temperature were always ca. 0.7 eV corresponding to a strong TL maximum at ca. 90 A degrees C. This main trap energy was only slightly modified by the different co-dopants, which, in contrast, had a significant effect on the depths of the deeper traps. The combined results of the trap level energies obtained from the experimental data and DFT calculations suggest that the main trap responsible for the persistent luminescence of the Sr(2)MgSi(2)O(7):Eu(2+),R(3+) materials is created by charge compensation lattice defects, identified tentatively as oxygen vacancies, induced by the R(3+) co-dopants.