A design approach for mmWave switches in substrate integrated waveguide (SIW) technology is demonstrated. The switch is based on a photoconductive element (PE) which represents a piece of an intrinsic silicon wafer with light modulated conductivity. Using both dielectric (high-resistivity) and conductive states of the PE, we can design a low-loss mmWave switching element. Owing to the light actuation, the control circuitry of the photoconductive switch (PS) is electrically separated from the highfrequency elements of the device. This solves the bottleneck of conventional mmWave switches based on PIN diodes, MOSFETs, MEMS, etc., which are bulky and lossy due to decoupling filters and matching elements of control and biasing circuits. The proposed approach is generic and can be applied to many mmWave applications within 10-100 GHz such as 5G, WiGig, automotive radars, and others.
This paper presents a scheme of analysis for bottom sediments based on inductively coupled plasma‐atomic fluorescence spectrometry and arc discharge atomic emission spectrometry combined with instrumental pyrolysis and gas chromatographic methods (head space analysis) for determining organic matter. The experimental investigation was performed on bottom sediments of the Kuybyshev and Ivankovo reservoirs on the Volga River, used as a drinking water supply. The selective sample dissolution method followed by the inductively coupled plasma‐atomic fluorescence spectrometry liquid phase analysis and the arc discharge atomic emission spectrometry solid phase analysis was used to obtain quantitative information on heavy metal speciation in bottom sediments. In the present work we have taken the direct extraction scheme for analysis. It is shown that the proposed combination of instrumental methods is highly effective for revealing natural and anthropogenic components in the composition of organic matter, for conducting preliminary estimation of the binding of heavy metals with organic matter and for assessing activity of organic matter biochemical transformation. The proposed approach enlarges the capabilities for prediction and modelling of the processes in environmental‐geochemical investigations.
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