Gamma-ray energy spectrum unfolding of plastic scintillators using artificial neural network.

In this study, the unfolding of the plastic scintillator spectrum was undertaken using the artificial neural networks tools of MATLAB. To this purpose, the response matrix of the plastic scintillator was generated for 145 energy groups and in 512 pulse-height channels using the MCNPX2.6 code. The results confirmed that the relative error in the gamma-ray energy unfolding with artificial neural networks is less than 3.8%.

Angular distribution of scattered neutrons as a tool for soil moisture measurement: A feasibility study.

The conceptual design of a soil-moisture measurement instrument using a rectangular soil sample and an almost collimated 241Am-9Be source was proposed. Unlike previous studies and in a different simulation approach, the soil moisture was determined using the angular distribution of thermal neutrons using MCNPX2.6 Monte Carlo code, where a cylindrical BF3 proportional counter located at different polar angles was responsible for thermal neutron detection. Both Monte Carlo library least-squares method (MCLLS) and artificial neural networks (ANN) were used to calculate the soil moisture based on BF3 count rates with small relative error, about 2% and 10% maximum relative errors, respectively.

Ab initio calculations for industrial materials engineering: successes and challenges.

Computational materials science based on ab initio calculations has become an important partner to experiment. This is demonstrated here for the effect of impurities and alloying elements on the strength of a Zr twist grain boundary, the dissociative adsorption and diffusion of iodine on a zirconium surface, the diffusion of oxygen atoms in a Ni twist grain boundary and in bulk Ni, and the dependence of the work function of a TiN-HfO(2) junction on the replacement of N by O atoms. In all of these cases, computations provide atomic-scale understanding as well as quantitative materials property data of value to industrial research and development. There are two key challenges in applying ab initio calculations, namely a higher accuracy in the electronic energy and the efficient exploration of large parts of the configurational space. While progress in these areas is fueled by advances in computer hardware, innovative theoretical concepts combined with systematic large-scale computations will be needed to realize the full potential of ab initio calculations for industrial applications.

Improving the safety of a body composition analyser based on the PGNAA method.

The 252Cf radioisotope and 241Am-Be are intense neutron emitters that are readily encapsulated in compact, portable and sealed sources. Some features such as high flux of neutron emission and reliable neutron spectrum of these sources make them suitable for the prompt gamma neutron activation analysis (PGNAA) method. The PGNAA method can be used in medicine for neutron radiography and body chemical composition analysis. 252Cf and 241Am-Be sources generate not only neutrons but also are intense gamma emitters. Furthermore, the sample in medical treatments is a human body, so it may be exposed to the bombardments of these gamma-rays. Moreover, accumulations of these high-rate gamma-rays in the detector volume cause simultaneous pulses that can be piled up and distort the spectra in the region of interest (ROI). In order to remove these disadvantages in a practical way without being concerned about losing the thermal neutron flux, a gamma-ray filter made of Pb must be employed. The paper suggests a relatively safe body chemical composition analyser (BCCA) machine that uses a spherical Pb shield, enclosing the neutron source. Gamma-ray shielding effects and the optimum radius of the spherical Pb shield have been investigated, using the MCNP-4C code, and compared with the unfiltered case, the bare source. Finally, experimental results demonstrate that an optimised gamma-ray shield for the neutron source in a BCCA can reduce effectively the risk of exposure to the 252Cf and 241Am-Be sources.