Environmental Monitoring of Methane with Quartz-Enhanced Photoacoustic Spectroscopy Exploiting an Electronic Hygrometer to Compensate the H2O Influence on the Sensor Signal

A dual-gas sensor based on the combination of a quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor and an electronic hygrometer was realized for the simultaneous detection of methane (CH4) and water vapor (H2O) in air. The QEPAS sensor employed an interband cascade laser operating at 3.34 μm capable of targeting a CH4 absorption line at 2988.8 cm−1 and a water line at 2988.6 cm−1. Water vapor was measured with both the electronic hygrometer and the QEPAS sensor for comparison. The measurement accuracy provided by the hygrometer enabled the adjustment of methane QEPAS signal with respect to the water vapor concentration to retrieve the actual CH4 concentration. The sensor was tested by performing prolonged measurements of CH4 and H2O over 60 h to demonstrate the effectiveness of this approach for environmental monitoring applications.

Quartz Tuning Fork Resonance Tracking and application in Quartz Enhanced Photoacoustics Spectroscopy

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.