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The quartz tuning fork (QTF) is a piezoelectric transducer with a high quality factor that was successfully employed in sensitive applications such as atomic force microscopy or Quartz-Enhanced Photo-Acoustic Spectroscopy (QEPAS). The variability of the environment (temperature, humidity) can lead to a drift of the QTF resonance. In most applications, regular QTF calibration is absolutely essential. Because the requirements vary greatly depending on the field of application, different characterization methods can be found in the literature. We present a review of these methods and compare them in terms of accuracy. Then, we further detail one technique, called Beat Frequency analysis, based on the transient response followed by heterodyning. This method proved to be fast and accurate. Further, we demonstrate the resonance tracking of the QTF while changing the temperature and the humidity. Finally, we integrate this characterization method in our Resonance Tracking (RT) QEPAS sensor and show the significant reduction of the signal drift compared to a conventional QEPAS sensor.

We propose a new concept of photoacoustic gas sensing based on capacitive transduction which allows full integration while conserving the required characteristics of the sensor. For the sensor’s performance optimization, trial and error method is not feasible due to economic and time constrains. Therefore, we focus on a theoretical optimization of the sensor reinforced by computational methods implemented in a Python programming environment. We present an analytic model to optimize the geometry of a cantilever used as a capacitive transducer in photoacoustic spectroscopy. We describe all the physical parameters which have to be considered for this optimization (photoacoustic force, damping, mechanical susceptibility, capacitive transduction, etc.). These parameters are characterized by opposite trends. They are studied and compared to obtain geometric values for which the signal output and signal-to-noise ratio are maximized.

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