Determination of Zn in high-purity GaAs with neutron activation analysis
Neutron activation analysis was used to analyse Zn concentrations in high-purity GaAs. A combination of chemical separation of the neutron-induced 65Zn from the GaAs-matrix with anion-exchange chromatography and ultra low-level gamma-ray spectrometry in an underground laboratory was applied. Zn concentration ranged from 2.1 to 51 ng g(-1), and detection limits from 0.018 to 0.059 ng g(-1) were obtained.
Determination of trace impurities in gallium arsenide by NAA
GaAs is not an ideal matrix for INAA because elements yielding activation products with half-lives up to about 5 d cannot be measured due to the interference by72Ga and76As (t1/2=14.1 h and 26.4 h, respectively). The measurement of radionuclides with longer half-lives is interfered with by74As (t1/2=17.7 d), generated by fast neutrons. However, using an irradiation facility with a very low flux of fast neutrons, in which the generation of74As is minimal, five elements could be determined in GaAs (Cr, Co, Zn, Ag, and Hg). For 27 elements the detection limits were below 1 μg/g and for ten of them below 10 ng/g. The determination of nitrogen in GaAs has been carried out using the (n, p)-reaction on14N, which is induced by thermal neutrons. The activation product,14C, can be effectively separated and purified via14CO2 and counted with high efficiency in a liquid scintillation counter, and nitrogen can be determined with fairly low detection limits if sufficiently high neutron fluxes and long irradiation times are applied. The procedure described is based on a reactor irradiation with a thermal flux of 2·1014 n·cm−2·s−1 for 51 days. 0.16±0.09 μg/g N in GaAs were determined and the detection limit was about 3 ng/g.
Evidence of spurious results along with spatially autocorrelated errors in the context of geographically weighted regression for two independent SAR(1) processes
AbstractThe aim of this paper is to examine the performance of the geographically weighted regression (GWR) technique in terms of recognizing linear relationships at the local level. Using a Monte Carlo analysis with two independent spatially autocorrelated SAR(1) processes, this study finds that spurious behavior exists at the local level along with spatially autocorrelated errors. Therefore, GWR estimation results must be interpreted with caution.
