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Characterization of metal oxide gas sensors via optical techniques
Metal oxide (MOX) sensors are increasingly gaining attention in analytical applications. Their fundamental operation principle is based on conversion reactions of selected molecular species at their semiconducting surface. However, the exact turnover of analyte gas in relation to the concentration h...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7329784/ https://www.ncbi.nlm.nih.gov/pubmed/32548766 http://dx.doi.org/10.1007/s00216-020-02705-6 |
Sumario: | Metal oxide (MOX) sensors are increasingly gaining attention in analytical applications. Their fundamental operation principle is based on conversion reactions of selected molecular species at their semiconducting surface. However, the exact turnover of analyte gas in relation to the concentration has not been investigated in detail to date. In the present study, two optical sensing techniques—luminescence quenching for molecular oxygen and infrared spectroscopy for carbon dioxide and methane—have been coupled for characterizing the behavior of an example semiconducting MOX methane gas sensor integrated into a recently developed low-volume gas cell. Thereby, oxygen consumption during MOX operation as well as the generation of carbon dioxide from the methane conversion reaction could be quantitatively monitored. The latter was analyzed via a direct mid-infrared gas sensor system based on substrate-integrated hollow waveguide (iHWG) technology combined with a portable Fourier transform infrared spectrometer, which has been able to not only detect the amount of generated carbon dioxide but also the consumption of methane during MOX operation. Hence, a method based entirely on direct optical detection schemes was developed for characterizing the actual signal generating processes—here for the detection of methane—via MOX sensing devices via near real-time online analysis. [Figure: see text] |
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