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NATURAL CHEM/BIO TAGS 11 TOPIC 3: NATURAL CHEM/BIO TAGS Two presentations were made in this session, one by Alan Gelperin, Monell Chemical Senses Center, and one by Steven Martin, Sandia National Laboratories. DESIGNING CHEMICAL SENSOR SYSTEMS FOR ELECTRONIC OLFACTION Alan Gelperin reviewed recent progress in electronic olfaction technology based on biological models. For example, moths are hypersensitive to a few specific compounds (e.g., pheromones), while a dog's nose has more general sensitivity. The general-purpose nose can be trained to detect new odors, such as TNT. The artificial nose requires an array of odor sensors, with diverse odor responses (there are some 1,000 different odor receptor classes in mice, 300 in humans), and a computational module for analyzing odor patterns. Hopfield published a paper3 showing that a larger number of different sensor classes in an array (up to ~100) gives a different and richer response than an array with a smaller number of sensor classes. Gelperin felt that an algorithm developed by Hopfield is quietly revolutionizing this field. The algorithm allows a system to recognize a new odor pattern in terms of known odor patterns. Gelperin focused on organic field effect transistors in which the odor vapor is flowed over a chemically active organic layer between the source and drain of a transistor, and the degree of interaction between the odorant molecules and the active layer is reflected by changes in the current flow. This system has the advantage that the odor can be driven out (to reset the sensor) by reversing the gate voltage rather than having to flow fresh air over the sensor. The organic surface layer should be as thin as possible to maximize the influence of the surface. Another configuration demonstrated for the detection of O2 and CO gases uses changes in current flow through carbon nanotube wires (or nanowires made of other materials) as the sensor. Special challenges of these systems include the following: â¢ Ensuring that the identification of the odor does not depend on concentration; â¢ Separating odor âobjectsâ (multiple odors that arrive together); â¢ Identifying weak known odors against a background of strong unknown odors; â¢ Storing odor patterns for later pattern matches; and â¢ Subtracting constant background odors while remaining sensitive to new weak odor inputs.4 3J.J.Hopfield 1999. Odor space and olfactory processing: Collective algorithms and neural implementation, Proceedings of the National Academy of Sciences 96 (22):12506â12511. 4For example, dogs have to be trained on local background odors for about 2 weeks before they are used to detect land mines in a given region.