Thin films of functionalized single-wall carbon nanotubes were deposited on silicon chips by drop-coating and inkjet printing. These sensors were subjected to 1-100 ppm NOx, CO, H2S and H2O vapor in synthetic air. We have found that besides the expected changes in the electrical resistance of the film, there are also characterteristic differences in the noise pattern of the resistance vs. time function. This phenomenon is called fluctuation enhanced sensing and it can be used to increase the amount of information gathered from a carbon nanotube sensor device. The main advantage of fluctuation enhanced sensing is the improved selectivity of the sensor even if changes in electical resistance are rather low. Combined with differentiation based on modifying the adsorption characterstics of the nanotubes (e.g. by covalent functionalization), fluctuation enhanced sensing appears to be a very useful method for bringing cheap and reliable carbon nanotube based chemical sensors to the market.
Metal oxide gas sensors suffer from lack of selectivity and response drift. The use of sensor dynamics has been introduced to ameliorate sensor performance. The usual approach consists of modulating the operating temperature of gas sensors. Temperature modulation alters the kinetics of the adsorption and reaction processes taking place at sensors' surface. This results in response patterns that are characteristic of gas/sensor pairs. Despite the fact that a great deal of work has been done, the selection of the modulating frequencies remains an obscure and non-systematic method. A new approach to systematically select frequencies is discussed. The method is based on the use of pseudo-random binary sequences (MLS) to modulate the working temperature of gas sensors in a wide frequency range. The impulse response of a pair sensor-gas can be estimated from the circular cross-correlation of the MLS and the sensor response sequences. From the study of the impulse response in the frequency domain, an identification of the modulating frequencies that
convey important information to both identify and quantify gases is obtained.
Nanoparticle films of crystalline WO3, designed for gas sensing applications, were deposited on alumina substrates by reactive gas deposition. H2S, ethanol vapour, and binary mixtures of ethanol/H2S, ethanol/NO2 and H2S/NO2 were used in different concentrations for testing the performance of the sensor device. The sensor was operated in dynamic mode by modulating its temperature between 150 and 250 °C. Coefficients were extracted by applying Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) methods to the dynamic resistance response of the sensor. These coefficients were then used as inputs for pattern recognition methods to extract both quantitative (concentration) and qualitative (chemical selectivity) information about the test gases. After sensor calibration, it was possible to detect as little as 200 ppb of ethanol and 20 ppb of H2S with good accuracy. Furthermore, ethanol and H2S could be detected with good sensitivity and selectivity in the presence of both reducing and oxidising gases.