Lifetime sensor parameters

Depending on the gas type and application, different gas detection and sensor principles may be suitable. Beside the price, there are other parameters like accuracy, power consumption, long-term stability, lifetime, selectivity and sensitivity which have to be taken into account. However, whether a sensor is developed into a product depends on the relation between additional functionality and additional cost. In the subsequent chapters we intend to give an overview of common sensor principles and their potential use in household appliances. In order to give some information about existing (commercialized) sensors and sensor systems each subchapter is completed by an amendment listing a selection of relevant gas sensor products. 

From the practical point of view, the radiative decay rate kr may be assumed to be independent of the external parameters surrounding the excited sensor molecule. Its value is determined by the intrinsic inability of the molecule to remain in the excited state. The radiative decay rate kr is a function of the unperturbed electronic configuration of the molecule. In summary, for a given luminescent molecule, its unperturbed fluorescent or phosphorescent decay rate (or lifetime) may be regarded to be only a function of the nature of the molecule. 

Figure 9.2. Transient luminescence of a sensor as a function of the concentration of a [Parameter]. In the figure knr = jl (Eq, (9,12) with a = 0). Increasing values of [Parameter] result in shorter luminescence lifetimes. kr = Vw, k , = V a in units of time-1. The curves in the figure are determined by Eq. (9.3).

The dependence of knr on the value or concentration of a [Parameter], in the vicinity of the excited sensor, determines both the luminescence intensity and the excited state lifetime of the sensor. 

Figure 9.2 illustrates the transient luminescence of a sensor with k r determined by the ideal case of Eq. (9.12). The figure shows that, as the value of [Parameter] increases, the duration (or lifetime) of the luminescence decreases. 

Even though the temporal luminescence of a sensor cannot be uniquely represented in terms of lifetime distribution functions, the use of lifetime distributions provides a more convenient way to characterize the transient luminescence of sensors than the use of few discrete exponentials. Lifetime distribution functions require less parameters to describe the sensor luminescence response which is an advantage in the implementation of data analysis for real-time applications. 

The time constant or lifetime t of the sensor luminescence is determined by the value of [Parameter]. For sensor-carrier preparations with a uniform composition in which all sensor molecules return to the ground state with the same probability we have ... 

In the case in which the overall sensor luminescence is the result of isolated phases each with a different nonradiative decay rate, the lifetimes of each phase provides an independent measurement of [Parameter]... 

Significant curvature may be observed in the case of lifetime- (and intensity-) based sensors, mainly when the relation knri [Parameter]) is not linear. Figure 9.4 shows this type of nonlinear behavior for a fiberoptic oxygen sensor. The figure shows Stern-Volmer-type plots (r l versus [02]) at four different temperatures. The curvature is caused by the inability of the carrier to transport oxygen proportionally to the equilibrium partial pressure of oxygen. 

The photophysical properties (extinction coefficient, shifts in absorption and emission spectra, quantum yield, and lifetime) of a variety of probes are modified by pH changes, complexation by metal ions, or redox reactions. The resulting changes in photophysical parameters can be used to determine concentration of H+ and metal cations with suitably designed fluorophores. Most of these resulting sensors involve an equilibrium between the analyte, A, and the free probe (unprotonated or noncom-plexed by metal ion), Pf. If the stoichiometry of this reaction is 1 1, the reaction may be represented by... 

Table 10.3. Mean Lifetimes (r) in Solvents Purged by Nitrogen (N2), Air, and Oxygen (O2) and Sensing Parameters (Changes in Phase Angle, AO, and in Modulation, Am, "Air - N2 ) of Potential Oxygen Sensors 33 ...

The opportunities for near-IR fluorescence sensors are of course not only limited to analytical chemistry. Physical parameters such as temperature can also be measured. For example, Grattan and Palmer have used the fluorescence lifetime quenching of neodymium glass fluorescence at 1054 nm, excited at 810 nm with a gallium-alumi-... 

Fitting into the trend towards improvement of the availability and simplification the preparation of biocatalytic layers for biosensors, the use of crude materials has been explored. Arnold and coworkers investigated the feasibility of employing Jack bean meal in a urea sensor (Arnold and Glaizer, 1984) and rabbit muscle acetone powder in a sensor for adenosine monophosphate (Fiocchi and Arnold, 1984). Both sensors turned out to be serious contenders with the appropriate enzyme electrodes with respect to lifetime and slope of the calibration curves. Other parameters, such as response time and linear range, were quite similar. 

This section describes a methodical procedure that allows reliability issues to be approached efficiently. MEMS reveal specific reliability aspects, which differ considerably from the reliability issues of integrated circuits and macroscopic devices. A classification of typical MEMS-failure modes is given, as well as an overview of lifetime distribution models. The extraction of reliability parameters is a Tack of failures situation using accelerated aging and suitable models. In a case study, the implementation of the methodology is illustrated with a real-fife example of dynamic mechanical stress on a thin membrane in a hot-film mass-airflow sensor. 

The reliability parameters, such as the mean time to failure, have to be determined in experiments under well defined conditions. Failure rates of microsystems for automotive applications are typically in the range of a few ppm (parts per million). This may sound negligible, but due to the large number of sensors sold every year and their increasing numbers in each car, even this failure rate must be decreased further. However, the engineer who tries to investigate failure mechanisms is confronted with the problem of lack of failures in the sense that he finds too few defective samples for a thorough failure analysis. Thus, due to the lack of a statistical basis, the quality of lifetime predictions under normal in-use conditions would be poor. 

The sensor is installed in the air intake manifold of the car, measuring the aspirated air mass. The measuring principle requires the membrane to be exposed to the airflow and thereby also to dust and other particles, which are either not removed by the air filter or are inherent in the intake pipe. The impact of such particles on the membrane imposes a shock-type mechanical load, which can cause field failures as well as O-km failures due to membrane fracture. The task was to identify the relevant geometry and material parameters limiting the lifetime of the sensor element and to deduce a model for their effects on membrane stability [11]. 

Although the Weibull model allows qualitative description of the sensor reliability and thus the comparison Q15 to other variants, more specific experiments were needed to identify and understand the parameters of the failure mechanism itself. Thus, dedicated experiments, accompanied by simulations, were designed. With this setup, it became feasible to test several membranes differing in thickness, layer composition, and length/width ratio in a reasonable amount of time. The effects of particle size, velocity, and intensity Q16 on lifetime were also investigated. 

In contrast to engine oil, transmission (or gearbox) oil usually represents a lifetime filling unless unexpected contamination due to failures occurs, which, depending on the kind of contamination, may be detected potentially by all the physical parameters considered. For the detection of metallic wear particles in transmission oil, both ferromagnetic as well as non-ferromagnetic, the utilization of magnetic sensor principles would be near at hand [16, 17]. 

The recent analytical literature abounds with reports on the development of electrochemical, fiber optic, piezoelectric, and other sensors. Novel detection principles are frequently announced, and attainment of improved sensor quality in terms of selectivity, sensitivity, and lifetime are the goal of many established as well as newly formed research groups. The need for continuous monitoring of critical parameters in clinical chemistry, biotechnology, pharmaceutical, chemical, and nuclear industries, chemical warfare or environmental control is the driving force behind the sensor research. 

Dynamic properties, stability, and lifetime are generally better for amperometric than for i.s.e. sensors. These parameters vary case by case, and also depend strongly on measuring conditions, such as the flow rate of a sample. 

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