Working range dynamic

Hochachka, P.W., and G.O. Matheson (1992). Regulation of ATP turnover over broad dynamic muscle work ranges. J. Appl. Physiol. 73 570-575. 

Sensor linearity may concern primary measurand (concentration of analyte) or refractive index and defines the extent to which the relationship between the measurand and sensor output is linear over the working range. Linearity is usually specified in terms of the maximum deviation from a hn-ear transfer function over the specified dynamic range, hi general, sensors with linear transfer functions are desirable as they require fewer calibration points to produce an accurate sensor calibration. However, response of SPR biosensors is usually a non-linear function of the analyte concentration and therefore calibration needs to be carefully considered. 

The dynamic (working) range of an automatic or automated instrumental technique must obviously fit the working range of concentrations to which it is being applied. In a continuous or automatic titration, for example, the dynamic range and span are governed by the sample size (the volume of the sample and the concentration of desired species in it), the size of the buret, and the concentration of the titrant. The presence of a second titratable species (interference) in the system reduces the usable span by the amount of the second species, since the titration will measure both species. 

The dynamic range corresponds to the valid range of the functional relationship between the signal and the concentration or mass. The analytical or working range denotes the interval between the lowest and highest concentrations, for which accurate measurements are feasible for evaluation of random and systematic errors. Outside this interval, the measurements are considered uncertain. 

Define the analytical performance characteristics precision, trueness, accuracy, selectivity, dynamic range, working range, recovery, robustness, detection limit, and limit of determination. 

Space-charge effects (ion—ion repulsion) limit the inherent dynamic range of the ion trap. This is usually handled by auto-ranging a pre-scan is performed to determine the ion current and the ionising electron current is then adjusted to reduce the number of ions formed to within the working range. This can be done wherever the ion formation event can be manipulated to control the number of ions formed. 

Dynamical aspects of Q2D suspensions of spherical colloids and asymmetric colloid mixtures with long-range repulsive forces have been investigated by Brownian dynamics simulations [193-195]. These works emphasize dynamic scaling and the importance of hydrodynamic interactions on self- and tracer diffusion. 

Adsorption immobilization of the reagent in chemooptical interface was utilized in fiber optic chemical sensors i.e. pH sensor, redox titrator and calcium sensitive optrode. The choice of the indicator as well as the support and the immobilization procedure governs the working and dynamic ranges of the pH sensor. The optrode based on optimized redox optomembrane consisting of PETP additionally covered with PVC) may be used in various redox titrations e.g. Fe(II) with MnfVII) or As(HI) with Cr(VI). Hardness of water can be measured by the fiber optic calcium sensor directly in the studied medium without any sample preparation. 

A wide variety of solvent vapors can be detected with this new QMB with extremely high sensitivity (starting in the ppb range) and showing a dynamic working range of up to 4 decades with response times below 10 s, indicating the potential of this approach to gas-phase monitors and sensors for applications in environmental analysis or process control. 

Most spraying processes work under dynamic conditions and improvement of their efficiency requires the use of surfactants that lower the liquid surface tension yLv under these dynamic conditions. The interfaces involved (e.g. droplets formed in a spray or impacting on a surface) are freshly formed and have only a small effective age of some seconds or even less than a millisecond. The most frequently used parameter to characterize the dynamic properties of liquid adsorption layers is the dynamic surface tension (that is a time dependent quantity). Techniques should be available to measure yLv as a function of time (ranging firom a fraction of a millisecond to minutes and hours or days). To optimize the use of surfactants, polymers and mixtures of them specific knowledge of their dynamic adsorption behavior rather than equilibrium properties is of great interest [28]. It is, therefore, necessary to describe the dynamics of surfeictant adsorption at a fundamental level. The first physically sound model for adsorption kinetics was derived by Ward and Tordai [29]. It is based on the assumption that the time dependence of surface or interfacial tension, which is directly proportional to the surface excess F (moles m ), is caused by diffusion and transport of surfeictant molecules to the interface. This is referred to as the diffusion controlled adsorption kinetics model . This diffusion controlled model assumes transport by diffusion of the surface active molecules to be the rate controlled step. The so called kinetic controlled model is based on the transfer mechanism of molecules from solution to the adsorbed state and vice versa [28]. 

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