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Emulating the olfactory system to protect and enhance a chemical sensor
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What would it be worth to you to increase the sensitivity of your chemical detector, expand its dynamic range, and simultaneously protect it from overloading? Would a dollar or two be too much? In a new AC paper (2008, 80, 606–611), Francis Tsow, Erica Forzani, and N. J. Tao of Arizona State University report a procedure that uses an inexpensive microprocessor to control a chemical sensor intelligently. The processor watches the signal from the sensor and switches it on and off as necessary to prevent overloading. This approach emulates a strategy used in the mammalian olfactory system. The concentration of the analyte is determined as a function of the detector signal and the switching frequency. “For real-world chemical sensing applications—in addition to sensitivity and selectivity—the response time, dynamic range, and lifetime of the sensor are of great importance,” says Tao. “We take advantage of the capabilities of an inexpensive microprocessor to speed up the sensor response time and extend the dynamic range and lifetime, as well as improving its accuracy.” In recent years, the sensitivity of chemical sensors has increased markedly. Detectors based on biological sensors, in particular, have remarkably low detection limits—in some cases, down to a single molecule. This sensitivity has a downside, though: as the lower limit of detectability decreases, the limit of saturation often decreases as well. As the sensor is exposed to a large amount of the analyte, it reaches the limits of its dynamic range, and its response slows because of mass transport. Overexposure can limit the sensor’s performance or even cause irreversible damage. Standard ways to avoid saturation include restricting the dynamic range of the sensor or using multiple sensors with varying ranges. But with the new method of preventing saturation, the researchers extended the dynamic range of a model sensor and simultaneously
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The response of the sensor to ethanol vapors at (a) 47.3 mg/L and (b) 3.9 mg/L. Nitrogen was the purge gas.
increased the detector’s lifetime. The processor automatically adjusts the sampling duration according to the sensor’s response to the analytes. As long as the level is below a predetermined limit, the sensor continues to operate in its normal mode. However, when the element reaches the limit, the processor switches it off and routes a clean, purging gas to the sensing element so that it can undergo desorption. The model system used by Tsow, Forzani, and Tao was a microfabricated tuning-fork detector fitted with a polymer micro- or nanowire as a sensing element; ethanol, which binds reversibly to this sensor, was the analyte. Changes in the frequency of the tuning fork were correlated with the concentration of ethanol on the basis of the equilibrium of adsorption and desorption. The microprocessor controller switched between
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the sample-gas solution and a purge gas. The experimental results showed an order-of-magnitude increase in the dynamic range of the detector. If the controller was not used and the exposure time was not controlled, the polymer wire would break when exposed to high concentrations of the analyte. Tao emphasizes that the technique is not limited to the tuning-fork system used in the research. “It can be applied to any type of sensor that relies on chemical binding events,” he says. “There is a great advantage for sensors that tend to be nonlinear. There is always some range at which it responds in a linear manner, even if that range is small. By choosing the range that works, you can greatly extend the detection range.” The resulting signal manipulation resembles the approach used by neurons, which incorporate a shuttering mechanism in visual, tactile, and olfactory systems to sense small differences in neurotransmitter concentrations even at high concentrations. In addition to addressing saturation limitations and dynamic ranges, the system can combine the transient kinetics of adsorption and desorption with automatic switching to convert the amplitude response of the detector into an oscillation frequency that depends on concentration. For many types of noise, a frequency-modulated response provides better noise isolation and opportunities to filter the noise signal, thus increasing the sensitivity of the detector. In addition, frequency-modulated signals can be accumulated over time and analyzed by FT techniques; this approach further increases the dynamic range at the lower limits. “This technique can extend the capabilities of many types of detectors,” says Tao. “We are taking advantage of the fact [that] the microprocessors have become extremely cheap at the same time that detectors have become more sophisticated.” —Steve Miller
