Improving the Signal to Noise Ratio of QTF Preamplifiers Dedicated for QEPAS Applications

Piotr Z. Wieczorek , Tomasz Starecki , Frank K. Tittel


The signal-to-noise ratio (SNR) is a major factor that limits the detection sensitivity of quartz-enhanced photoacoustic spectroscopy (QEPAS) sensors. The higher the electrical signal level compared to the noise amplitude is the lower the concentration of gases that can be detected. For this reason the preamplifier circuits used in QEPAS should be optimized for low-frequency narrow-band applications. Moreover, special care should be taken when choosing a particular operational amplifier in either a transimpedance or voltage (differential) configuration. It turns out that depending on the preamp topology different operational amplifier parameters should be carefully considered when a high SNR of the whole QEPAS system is required. In this article we analyzed the influence of the crucial parameters of low-noise operational preamplifiers used in QEPAS applications and show the resulting limitations of transimpedance and voltage configurations.
Author Piotr Z. Wieczorek (FEIT / PE)
Piotr Z. Wieczorek,,
- The Institute of Electronic Systems
, Tomasz Starecki (FEIT / PE)
Tomasz Starecki,,
- The Institute of Electronic Systems
, Frank K. Tittel
Frank K. Tittel,,
Journal seriesApplied Sciences-Basel, [Applied Sciences (Switzerland)], ISSN 2076-3417
Issue year2020
Publication size in sheets205.25
Keywords in Englishquartz tuning fork; quartz enhanced photoacoustic; QEPAS sensor preamplifier; QTF front-end electronics
ASJC Classification1507 Fluid Flow and Transfer Processes; 1508 Process Chemistry and Technology; 1706 Computer Science Applications; 2200 General Engineering; 2500 General Materials Science; 3105 Instrumentation
Languageen angielski
Score (nominal)70
Score sourcejournalList
ScoreMinisterial score = 70.0, 18-08-2020, ArticleFromJournal
Publication indicators WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 0.985; WoS Impact Factor: 2018 = 2.217 (2) - 2018=2.287 (5)
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