Synthesis and characterization of silver/alanine nanocomposites for radiation detection in medical applications: the influence of particle size on the detection properties

Silver/alanine nanocomposites with varying mass percentage of silver have been produced. The size of the silver nanoparticles seems to drive the formation of the nanocomposite, yielding a homogeneous dispersion of the silver nanoparticles in the alanine matrix or flocs of silver nanoparticles segregated from the alanine crystals. The alanine crystalline orientation is modified according to the particle size of the silver nanoparticles. Concerning a mass percentage of silver below 0.1%, the nanocomposites are homogeneous, and there is no particle aggregation. As the mass percentage of silver is increased, the system becomes unstable, and there is particle flocculation with subsequent segregation of the alanine crystals. The nanocomposites have been analyzed by transmission electron microscopy (TEM), UV-Vis absorption spectroscopy, X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy and they have been tested as radiation detectors by means of electron spin resonance (ESR) spectroscopy in order to detect the paramagnetic centers created by the radiation. In fact, the sensitivity of the radiation detectors is optimized in the case of systems containing small particles (30 nm) that are well dispersed in the alanine matrix. As the agglomeration increases, particle growth (up to 1.5 mu m) and segregation diminish the sensitivity. In conclusion, nanostructured materials can be used for optimization of alanine sensitivity, by taking into account the influence of the particles size of the silver nanoparticles on the detection properties of the alanine radiation detectors, thus contributing to the construction of small-sized detectors.

Synthesis and Characterization of Gold/Alanine Nanocomposites with Potential Properties for Medical Application as Radiation Sensors

Radiation dose assessment is essential for several medical treatments and diagnostic procedures. In this context, nanotechnology has been used in the development of improved radiation sensors, with higher sensitivity as well as smaller sizes and energy dependence. This paper deals with the synthesis and characterization of gold/alanine nanocomposites with varying mass percentage of gold, for application as radiation sensors. Alanine is an excellent stabilizing agent for gold nanoparticles because the size of the nanoparticles does not augment with increasing mass percentage of gold, as evidenced by UV-vis spectroscopy, dynamic light scattering, and transmission electron microscopy. X-ray diffraction patterns suggest that the alanine crystalline orientation undergoes alterations upon the addition of gold nanoparticles. Fourier transform infrared spectroscopy indicates that there is interaction between the gold nanoparticles and the amine group of the alanine molecules, which may be the reason for the enhanced stability of the nanocomposite. The application of the nanocomposites as radiation detectors was evaluated by the electron spin resonance technique. The sensitivity is improved almost 3 times in the case of the nanocomposite containing 3% (w/w) gold, so it can be easily tuned by changing the amount of gold nanoparticles in the nanocomposites, without the size of the nanoparticles influencing the radiation absorption. In conclusion, the featured properties, such as homogeneity, nanoparticle size stability, and enhanced sensitivity, make these nanocomposites potential candidates for the construction of small-sized radiation sensors with tunable sensitivity for application in several medical procedures.

Bayesian analysis of random regression models using B-splines to model test-day milk yield of Holstein cattle in Brazil

The objective of this paper is to model variations in test-day milk yields of first lactations of Holstein cows by RR using B-spline functions and Bayesian inference in order to fit adequate and parsimonious models for the estimation of genetic parameters. They used 152,145 test day milk yield records from 7317 first lactations of Holstein cows. The model established in this study was additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. Authors modeled the average lactation curve of the population with a fourth-order orthogonal Legendre polynomial. They concluded that a cubic B-spline with seven random regression coefficients for both the additive genetic and permanent environment effects was to be the best according to residual mean square and residual variance estimates. Moreover they urged a lower order model (quadratic B-spline with seven random regression coefficients for both random effects) could be adopted because it yielded practically the same genetic parameter estimates with parsimony. (C) 2012 Elsevier B.V. All rights reserved.