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Prediction of Quartz Crystal Microbalance Gas Sensor Responses Using Grand Canonical Monte Carlo Method

  • Takamichi Nakamoto
Chapter
Part of the Integrated Analytical Systems book series (ANASYS)

Abstract

Our group has studied an odor sensing system using an array of Quartz Crystal Microbalance (QCM) gas sensors and neural-network pattern recognition. In this odor sensing system, it is important to know the properties of sensing films coated on Quartz Crystal Microbalance electrodes. These sensing films have not been experimentally characterized well enough to predict the sensor response. We have investigated the predictions of sensor responses using a computational chemistry method, Grand Canonical Monte Carlo (GCMC) simulations. We have successfully predicted the amount of sorption using this method. The GCMC method requires no empirical parameters, unlike many other prediction methods used for QCM based sensor response modeling. In this chapter, the Grand Canonical Monte Carlo method is reviewed to predict the response of QCM gas sensor, and the modeling results are compared with experiments.

Keywords

Sensor Response Quartz Crystal Microbalance Odor Intensity Grand Canonical Monte Carlo Quartz Resonator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The author wishes to thank his former student, Dr. K. Nakamura (Anritsu Corp) for his previous work on the odor sensing system.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Graduate school of Science and EngineeringTokyo Institute of TechnologyTokyoJapan

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