Gd2O3: Eu3+/PPO/POPOP/PS composites for digital imaging radiation detectors

Polymer based scintillator composites have been produced by combining polystyrene (PS) and Gd2O3:Eu3+ scintillator nanoparticles. Polystyrene has been used since it is a flexible and stable binder matrix, resistant to thermal and light deterioration and with suitable optical properties. Gd2O3:Eu3+ has been selected as scintillator material due to its wide band gap, high density and visible light yield. The optical, thermal and electrical characteristics of the composites were studied as a function of filler content, together with their performance as scintillator material. Additionally 1wt.% of 2,5 dipheniloxazol (PPO) and 0.01wt.% of (1,4-bis(2-(5-phenioxazolil))-benzol (POPOP) were introduced in the polymer matrix in order to strongly improve light yield, i.e. the measured intensity of the output visible radiation, under X-ray irradiation. Whereas increasing scintillator filler concentration (from 0.25wt.% to 7.5wt.%) increases scintillator light yield, decreases the optical transparency of the composite. The addition of PPO and POPOP, strongly increased the overall 2 transduction performance of the composite due to specific absorption and re-emission processes. It is thus shown that Gd2O3:Eu3+/PPO/POPOP/PS composites in 0.25 wt.% of scintillator content with fluorescence molecules is suitable for the development of innovate large area X-ray radiation detectors with huge demand from the industries.

MOEA/VAN: Multiobjective Evolutionary Algorithm based on Vector Angle Neighborhood

Natural selection favors the survival and reproduction of organisms that are best adapted to their environment. Selection mechanism in evolutionary algorithms mimics this process, aiming to create environmental conditions in which artificial organisms could evolve solving the problem at hand. This paper proposes a new selection scheme for evolutionary multiobjective optimization. The similarity measure that defines the concept of the neighborhood is a key feature of the proposed selection. Contrary to commonly used approaches, usually defined on the basis of distances between either individuals or weight vectors, it is suggested to consider the similarity and neighborhood based on the angle between individuals in the objective space. The smaller the angle, the more similar individuals. This notion is exploited during the mating and environmental selections. The convergence is ensured by minimizing distances from individuals to a reference point, whereas the diversity is preserved by maximizing angles between neighboring individuals. Experimental results reveal a highly competitive performance and useful characteristics of the proposed selection. Its strong diversity preserving ability allows to produce a significantly better performance on some problems when compared with stat-of-the-art algorithms.