Design parameters Resonance

Applying harmonic filters requires careful consideration. Series-tuned filters appear to be of low impedance to harmonic currents but they also form a parallel resonance circuit with the source impedance. In some instances, a situation can be created that is worse than the condition being corrected. It is imperative that computer simulations of the entire power system be performed prior to applying harmonic filters. As a first step in the computer simulation, the power system is modeled to indicate the locations of the harmonic sources, then hypothetical harmonic filters are placed in the model and the response of the power system to the filter is examined. If unacceptable results are obtained, the location and values of the filter parameters are changed until the results are satisfactory. When applying harmonic filters, the units are almost never tuned to the exact harmonic frequency. For example, the 5th harmonic frequency may be designed for resonance at the 4.7th harmonic frequency. 

Various physical transducers can be used to detect interaction of the immobilized DNA probe with the analyte. Commonly used detection systems include optical (MarvUc, 1997 Jordan, 1997 Lee, 2001), electrochemical (Wang, 2001 Palecek, 2001) or mass sensitive devices (Storri, 1998 Ebara, 2000). Considering the growing number of potential applications, simphcity and low cost are some of the major design parameters in both microarray and other biosensor apphcations. Therefore, increased attention has been given to relatively simple detection or sensing techniques such as fluorescence measurements. This method offers faster assays without the need for specific substrate properties such as in the mass-sensitive quartz-crystal microbalance (Storri, 1998 Ebara, 2000) or in more complex optical techniques such as surface plasmon resonance (Jordan, 1997 Lee, 2001). 

Most hydrocarbon resins are composed of a mixture of monomers and are rather difficult to hiUy characterize on a molecular level. The characteristics of resins are typically defined by physical properties such as softening point, color, molecular weight, melt viscosity, and solubiHty parameter. These properties predict performance characteristics and are essential in designing resins for specific appHcations. Actual characterization techniques used to define the broad molecular properties of hydrocarbon resins are Fourier transform infrared spectroscopy (ftir), nuclear magnetic resonance spectroscopy (nmr), and differential scanning calorimetry (dsc). 

It is therefore recommended that a small resistance of a low- /- R loss be introduced into the filter circuits as shown in Figure 23.15(a) to limit such an excessive flow of currents through them. Knowledge of the system parameters (resistance and reactance) is also essential to design an appropriate filter circuit to avoid a possible resonance in the first instance. If this occurs the resistance thus introduced will limit the excessive flow of current. 

K. R. Minard, R. A. Wind 2001, (Sole-noidal microcoil design - Part II Optimizing winding parameters for maximum signal-to-noise performance), Concepts Magn. Reson. 13, 190. 

Taft and Topsom151 have fairly recently written an extensive review of the electronic effects of substituents in the gas phase. This article includes a tabulation of substituent inductive and resonance parameters. The inductive parameters (designated Op) are based on measured spectroscopic properties in either the gas phase or in hydrocarbon or similar solvents. The resonance parameters were arrived at through the treatment of 38 gas-phase reactivity series by iterative multiple regression, using the cr values of Bromilow and coworkers155 as the starting point. The of value for NO2 was found to be 0.65 (quoted... 

The sensor consists of a patented resonator which is designed to have one side make physical contact with the sample under test The shift in resonant frequency of the cavity, as well as the change in amplitude of reflection are a measure of the dielectric properties, e and e" of the sample. By measuring these two parameters and correlating them to process parameters, one is able to develop an algorithm which can be used for process monitoring. 

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