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CleanEx: A Versatile Automated Methane Preconcentration Device for High-Precision Analysis of 13 CH 4 , 12 CH 3 D, and 13 CH 3 D.

Ivan ProkhorovJoachim Mohn
Published in: Analytical chemistry (2022)
The relative abundance of methane isotopologues offers key insights into the global methane (CH 4 ) cycle. Advances in laser spectroscopy enable routine high-precision measurements even for rare deuterated methane isotopologues, 12 CH 3 D and 13 CH 3 D, provided there are sufficiently high methane amount fractions and reproducible measurement conditions, which can be achieved by CH 4 adsorption-desorption techniques. We present a new cryogen-free automated preconcentration device─CleanEx─designed for quantitative extraction of CH 4 from large volumes of sample gas and for cleaning by stepwise temperature-controlled desorption to separate interferant gases. We show that CleanEx has the capability to preconcentrate methane by almost 2000-fold from ∼18 L of air. The performance is demonstrated in a range of methane amount fractions between 2 ppm (μmol mol -1 ), which corresponds to the present-day ambient air, up to 1000 ppm, representative for close to source or process conditions. Advantages over existing devices are a significantly larger primary adsorption trap and a secondary cryo-focusing step, which ensures separation of methane from major atmospheric compounds, i.e., O 2 , Ar, and CO 2 . We have demonstrated quantitative extraction of methane, with no significant isotopic fractionation and high repeatability of 0.2‰, 0.6‰, and 0.8‰ ( n = 42) for the studied isotopologue ratios, 13 CH 4 / 12 CH 4 , 12 CH 3 D/ 12 CH 4 , and 13 CH 3 D/ 12 CH 4 , during cryogenic adsorption-desorption on HayeSep D material. The developed device in combination with a suitable laser spectrometer offers a robust and autonomous method for precise continuous monitoring of δ 13 C-CH 4 and δD-CH 4 in ambient air and optionally Δ 13 CH 3 D in process-derived methane.
Keyphrases
  • room temperature
  • anaerobic digestion
  • carbon dioxide
  • ionic liquid
  • air pollution
  • particulate matter
  • machine learning
  • high throughput
  • mass spectrometry