Sensors chemical logics

Turek, M., Heiden, W., Riesen, A., Chhabda, T. A., Schubert, J., Zander, W., Krueger, P., Keusgen, M., and Schoening, M. J. (2009). Artificial intelligence/fuzzy logic method for analysis of combined signals from heavy metal chemical sensors. Electrochim. Acta 54(25), 6082-6088. 

As discussed in the introduction, a major motivation for the development of methods to controllably functionalize silicon surfaces is the opportunity to create novel hybrid organic/silicon devices. By integrating organic molecules with silicon substrates it should be possible to expand the functionality of conventional microelectronic devices. Possibilities include high-density molecular memory and logic as well as chemical and biochemical sensors. Realization of these opportunities requires not only the development of the attachment chemistries, as discussed in the previous sections, but also detailed studies of the electronic properties of the resulting surfaces. 

To maintain the production rate, product quahty, and plant safety requires a data acquisition and control system. This system consists of tenperature, pressure, liquid level, flow rate, and conposition sensors. Computers record data and may control the process. Modem chemical plants use program logic controllers (PLC) extensively. According to Valle-Riestra [20], instrumentation cost is about 15% of pinchased equipment cost for little automatic control, 30% for full automatic control, and 40% for computer control. 

The fundamental idea is to utilize additional relevant process information for assessing the correctness of information generated by a sensor. This approach is known as the functional redundancy and it is more attractive than physical redundancy by duplicating sensors and using a voting logic to select the correct information. Several techniques based on statistics and system theory have been developed for validation of sensor information by functional redundancy. In most of these techniques, it is assumed that detailed process information is available a priori. Often, this knowledge is in the form of an accurate state-space model [39, 230]. In many cases, this type of accurate representation of a chemical process based on first principles is not available. 

Systems such as the one illustrated in figure 23.2 will also incorporate artificial intelligence. The information from the sensors will be used with fuzzy logic and neural networking to enable decisions by individual controllers based on the input from multiple sensors. Such systems will also incorporate sensor self-testing, self-calibration, and fault correction, resulting in reliable, automated systems applicable to any process or production line. These systems will significantly affect the productivity and profitability of food, chemical, and pharmaceutical production. 

In addition to chemical sensors, DNAzymes have found applications in molecular logic gates [68-70] and molecular motors [71, 72],... 

As discussed previously, many chemical threats have acid/base properties. Thus, polyaniline films, which have been demonstrated as sensor materials for acid ase compounds, are logical substrates for use in chemical detection systems. In addition, polyaniline films have the electrical properties of a conducting polymer, so that they can be used for transduction, as well as molecular recognition. However, due to sensitivity, time response, and weakness, these sensors have limitations. For these reasons, Virji and coauthors have worked to improve the polyaniline films and incorporate them into much more robust systems. 

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