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A near-infrared carbon dioxide sensor system using a compact folded optical structure for deep-sea natural gas hydrates exploration

Abstract

The detection of dissolved gas (e.g. carbon dioxide (CO2), methane (CH4)) in seawater is important for deep-sea natural gas hydrates exploration, which requires that the sensor especially the optical structure should be of compact size and capable of operation under deep-sea environment. A compact optical structure with simple beam alignment and tracing method was proposed for tunable laser absorption spectroscopy (TLAS) based gas measurements, in order to minimize sensor size and ease beam alignment/tracing procedure for deep-sea operation. A near-infrared CO2 sensor system was developed based on the compact optical structure. A distributed feedback (DFB) laser centered at 6361.3 cm-1 and a multi-pass gas cell (MPGC) with an effective optical path length of 29.8 m were employed. The sensor system was integrated as standalone equipment by customizing an aluminum baseplate for a stable field operation. A series of experiments were carried out to assess the performance of the sensor system. A limit of detection (LoD) of ~ 7.1 parts-per-million in volume (ppmv) at a 0.4 s averaging time was obtained, and the LoD was reduced to ~ 277 ppbv at an optimum averaging time of 153.6 s. Considering gas mixing time, the rise and fall time were measured to be ~ 290 s and ~200 s, respectively. The proposed compact sensor structure provides the basis for the further development of a sensor system for dissolved CO2 detection with a LoD of parts-per-billion in volume (ppbv) via the use of a mid-infrared tunable laser.

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Publication details

The article was received on 09 Aug 2018, accepted on 10 Sep 2018 and first published on 10 Sep 2018


Article type: Paper
DOI: 10.1039/C8AY01776J
Citation: Anal. Methods, 2018, Accepted Manuscript
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    A near-infrared carbon dioxide sensor system using a compact folded optical structure for deep-sea natural gas hydrates exploration

    Z. Liu, C. Zheng, C. Chen, H. Xie, Q. Ren, W. Ye, Y. Wang and F. Tittel, Anal. Methods, 2018, Accepted Manuscript , DOI: 10.1039/C8AY01776J

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