Steady-state sensor response

The steady-state response of each sensor ( 5 min) was recorded for each substance. A training pattern was formed by converting each set of steady-state sensor responses to a bit pattern. Bit patterns generally consisted of no more than 200 bits. The low resolution of the training pattern maximized process speed for the test in an actual instrument, a small pattern size would also help to... 

The steady-state sensor luminescent response (t —> °°) as a time function is written in the following form... 

The results shown in Figures 7 and 9 also indicate that the sensors based on the poly(ethylene oxide) and siloxane-ethylene oxide branch polymer systems can operate efficiently at relatively low applied potentials. In fact, the sensors containing these polymers show steady-state glucose responses at a potential of +100 mV (vs. SCE) which are similar to the response of the best poly(siloxane)-based sensor at +300 mV. This is an important consideration because lower operating potentials are often advantageous in real measurements, where easily oxidizable interfering species are usually present. 

Figure 12. (left) Steady-state current response of acetylcholine sensors based on polymer C, in pH 7.0 phosphate buffer under N2-saturated conditions. Each point is the mean result for five electrodes. 

Clifford P. K. and Tuma D. T., Characteristics of semiconductor gas sensors I. Steady state gas response. Sens. Actuators B, 3, 233-254, 1983. 

The ion-sensor together with double junction reference electrode was dipped in the stirred electrochemical cell with a working volume of 15 ml. The electrode potential was monitored with a Orion pH meter model SA 520 and recorded. At the steady-state potentiometric response, varying concentrations of the ionic solution ( KCl, NH4CI, NaCl) were inj ted into the cell and the new steady-state potential was recorded. The measurements were made with the sol-gel modified electrode with and without the ionsensing membrane. 

Fig. 1.18 Steady-state current response of a microamperometric sensor to glucose and galactose.

For completeness it should be mentioned that some of the theoretical conclusions for SECMIT are analogous to earlier treatments for the transient and steady-state response for a membrane-covered inlaid disk UME, which was investigated for the development of microscale Clark oxygen sensors [62-65]. An analytical solution for the steady-state diffusion-limited problem has also been proposed [66,67]. 

The principle of antioxidant detection is shown in Fig. 17.3. Superoxide was enzymatically produced and dismutated spontaneously to oxygen and H202. Under controlled conditions of superoxide generation such as air saturation of the buffer, optimal hypoxanthine concentration (100 pM) and XOD activity (50mU ml-1) a steady-state superoxide level could be obtained for several min (580-680 s). Since these steady-state superoxide concentrations can be detected by the cyt c-modified gold electrode, the antioxidate activity can be quantified from the response of the sensor electrode by the percentage of the current decrease. 

To define a feature extraction procedure it is necessary to consider that the output signal of a chemical sensor follows the variation of the concentration of gases at which it is exposed with a certain dynamics. The nontrivial handling of gas samples complicates the investigation of the dynamics of the sensor response. Generally, sensor response models based on the assumption of a very rapid concentration transition from two steady states results in exponential behaviour. 

NN applications, perhaps more important, is process control. Processes that are poorly understood or ill defined can hardly be simulated by empirical methods. The problem of particular importance for this review is the use of NN in chemical engineering to model nonlinear steady-state solvent extraction processes in extraction columns [112] or in batteries of counter-current mixer-settlers [113]. It has been shown on the example of zirconium/ hafnium separation that the knowledge acquired by the network in the learning process may be used for accurate prediction of the response of dependent process variables to a change of the independent variables in the extraction plant. If implemented in the real process, the NN would alert the operator to deviations from the nominal values and would predict the expected value if no corrective action was taken. As a processing time of a trained NN is short, less than a second, the NN can be used as a real-time sensor [113]. 

Biochemical oxygen demand (BOD) is one of the most widely determined parameters in managing organic pollution. The conventional BOD test includes a 5-day incubation period, so a more expeditious and reproducible method for assessment of this parameter is required. Trichosporon cutaneum, a microorganism formerly used in waste water treatment, has also been employed to construct a BOD biosensor. The dynamic system where the sensor was implemented consisted of a 0.1 M phosphate buffer at pH 7 saturated with dissolved oxygen which was transferred to a flow-cell at a rate of 1 mL/min. When the current reached a steady-state value, a sample was injected into the flow-cell at 0.2 mL/min. The steady-state current was found to be dependent on the BOD of the sample solution. After the sample was flushed from the flow-cell, the current of the microbial sensor gradually returned to its initial level. The response time of microbial sensors depends on the nature of the sample solution concerned. A linear relationship was foimd between the current difference (i.e. that between the initial and final steady-state currents) and the 5-day BOD assay of the standard solution up to 60 mg/L. The minimum measurable BOD was 3 mg/L. The current was reproducible within 6% of the relative error when a BOD of 40 mg/L was used over 10 experiments [128]. 

C, when the gas surface reactions can be expected to occur at a faster rate. Now it is seen that the response has reached a steady-state value after exposure to the ammonia atmosphere. The extra dip in the response curve seen in the oxygen environment might be due to the slow diffusion of ammonia. Some gas molecules might still be left under the sensor surface in this experiment when hit by the oxygen gas outlet. 

In principle, there are two possible ways to measure this effect. First, there is the end-point measurement (steady-state mode), where the difference is calculated between the initial current of the endogenous respiration and the resulting current of the altered respiration, which is influenced by the tested substances. Second, by kinetic measurement the decrease or the acceleration, respectively, of the respiration with time is calculated from the first derivative of the currenttime curve. The first procedure has been most frequently used in microbial sensors. These biosensors with a relatively high concentration of biomass have a longer response time than that of enzyme sensors. Response times of comparable magnitude to those of enzyme sensors are reached only with kinetically controlled sensors. 

First, an estimate can be made of expected response time from Equation (11). Tube length is L = 10 cm and D = 0.175 cm2/sec. Using these values, t = 1 3 seconds was computed. Secondly, the steady state current was estimated for 1 ppm CO by means of Equation (12). Note that tube area A = 2.85 cm2, nF = 2 x 96,500 coul/mole and 1 ppm CO is equivalent to k.OQ x 10 11 moles/cm3. Using these numbers, a sensor response of 0.29 /ppm CO was calculated. The simplified diffusion model provided predictions of operating characteristics which were sufficiently promising to proceed with an experimental study. 

The actual solution for both transient and steady-state response of any zero-flux-boundary sensor can be obtained by solving (2.26) through (2.33) for the appropriate boundary and initial conditions. Fitting of the experimental calibration curves (Fig. 2.10) and of the time response curves (Fig. 2.11) to the calculated ones, validates the proposed model. 

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