A metal-organic framework nanocomposite was synthesized and applied to speciation analysis of Se(IV) and Se(VI). The sorbent is composed of MIL-101(Cr) and magnetite nanoparticles modified with dithiocarbamate. It is capably of selectively extracting Se(IV) at pH = 1.85, while Se(VI) remains in solution. The total amount of selenium can then be determined by reducing Se(VI) to Se(IV) and also extracting it. The extraction parameters were optimized by employing design-of-experiments methodology. Selenium was then quantified by electrothermal AAS. Figures of merit include (a) a 10 ng·L limit of detection, (b) a linear response in the 30 ng·L to 10 μg·L concentration range, and (a) a relative standard deviation of <11.5% for Se(IV). The method was validated by analyzing certified reference materials (water and tomato leaves). It was also applied to the speciation analysis of Se(IV) and Se(VI) in (spiked) water samples and of total selenium in agricultural samples. Graphical abstract Schematic of the synthesis of a metal-organic framework nanocomposite for speciation analysis of Se(IV) and Se(VI). The sorbent is composed of MIL-101(Cr) and magnetite nanoparticles modified with dithiocarbamate. Selenium can be quantified by electrothermal AAS with a 10 ng L detection limit.
In order to evaluate the quantitative traits of soybean (Glycine max L. Merill), 24 genotypes were studied at Baiekola Agricultural Research Station in 2008. The traits studied included phonological and morphological traits, yield components, seed yield, and seed oil and protein content. Results of analysis of variance showed that mean squares of the treatments were significant, indicating significant genetic variation among the genotypes regarding the above-mentioned traits. There was a significant positive correlation between the number of days to flowering and the number of days to maturity, indicating that selection based on a high number of days to flowering leads to indirect selection of late maturing varieties. Among the main yield components, there was a significant positive correlation between the 1000-seed weight and seed yield; therefore, any change in the 1000-seed weight will have significant effects on seed yield. Since the yield components and the qualitative traits do not have any significant correlation with seed yield, cluster analysis cannot be used to put these traits in one group together with high seed yield. The general conclusion is that the genotypes Ronak 2, L2002, and LB4 with seed yields of 2610, 2606, and 2597 kg/h, respectively, are the superior genotypes, and that they can be considered as one statistical group.
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