Built-in sensor functions

Another way to achieve high detergency performance while keeping environmental impacts low is by making intelligent products with built-in sensor functions. In these applications, the products can respond to external stimuli and fulfil their task. The inherent sensoric principles of two recent innovations, detergent tablets and dishwashing tablets with built-in rinse aid, will be discussed. 

Detergent Products with Built in Sensor Functions 1109... 

In this paper, two qnestions are asked (1) what benefits do the new built-in digital functions of the discussed sensor chips offer for data sampling and (2) how can on-chip detectors be used in recognition tasks ... 

The light sensor rhodopsin is a heptahelical receptor with a built-in chromophore. Thanks to spectral changes in rhodopsin on illuminadon, discrete, sequential steps in the activation process could be distinguished. Since the relation of different activity states to their structural and molecular conformers, and finally to their functional states, is an issue that applies to all receptors, the information obtained with rhodopsin is considered in some detail (see ref. 57). 

However, by far most sensors convert nonelectrical (primary) input signals into electrical output. At first sight, the adoption of automated probe stations seems to be limited to purely electrical networks on sensor elements. However, electrical stimuli provided by the probe station may replace the nonelectrical primary input signals to the sensor transducer element, if a built-in self-test function (BITE) can be incorporated into the sensor design, allowing transducer-related (i.e., sensitivity-related) fundamental functional parameters to be tested. 

In addition, the sensor principle permits a complete self-test of the sensor, including the rotary oscillator (BITE = built in test). For the self-test, the rotary oscillator is deflected electrostatically via additional electrodes between the oscillator and the substrate. The sensor structure reacts to this in precisely the same way as if a turning movement has occurred the sensor detects a turning-rate signal. Testing is also possible during operation and thus permits a permanent function monitoring. 

While these studies have shown that polyaniline- and polypyrrole-coated fabrics can behave as chemi-resistive sensors, it is also expected that similar chemi-resistive behavior will be achieved using ICP monofilaments and yams, which are woven or stitched into an existing fabric. Ultimately, vapor detection fabric will need to comprise of an array of sensors with the patterned response fed into a microprocessor in order to identify and determine the concentration of the vapor. The relatively low cost of production and subsequent processing of these electrically conductive fabrics provide the opportunity for designing a textile-based electronic nose, which possess built-in massive redundancy for the sensor array so that device will still function even if there is a localized break (rip, tear hole, etc.) in the fabric network. Specifically, in case some area of the sensor system fads, there is a massive redundancy in the garment such that other areas may stdl be able to acquire the information. This will greatly increase the rehabdity of the system. 

Neuronal networks (Rojas 1993) simulate brain functions. In sensor science, they are used to construct non-parametric, non-linear models of the results of sensor arrays. Neuronal networks are made homogeneously of elements having the same basic structure, the so-called neurons. Often three-layer networks of the feed-forward type are built, where neurons are arranged in layers (Fig. 10.6, left). The number of input neurons in such networks corresponds to the number of received sensor signals. The numbers of hidden neurons and of output neuronSy respectively, depend on conditions. The network is trained by standard samples. In this way, the number of hidden neurons can be optimized. Neuronal networks are suited pimarily to obtain qualitative information, but less to a lesser extent for quantitative analysis. Graphical representation in the form of radar plots (Fig. 10.6, right) has proven useful. 

Video cameras can also be implemented to measure the bending amplitude of an IPMC (Fig. 4b). The biggest advantage of using a video camera is the possibility to record the mechanical response of the whole shape of the actuator. However, extracting relevant data from the video can be rather complicated, especially in comparison with laser displacement sensor. However, the state of the art of video imaging systems and their built-in functionality are rapidly... 

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