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Real-Time Monitoring of Gas-Phase and Dissolved CO 2 Using a Mixed-Matrix Composite Integrated Fiber Optic Sensor for Carbon Storage Application.

Ki-Joong KimJeffrey T CulpJames E EllisMatthew D Reeder
Published in: Environmental science & technology (2022)
Novel chemical sensors that improve detection and quantification of CO 2 are critical to ensuring safe and cost-effective monitoring of carbon storage sites. Fiber optic (FO)-based chemical sensor systems are promising field-deployable systems for real-time monitoring of CO 2 in geological formations for long-range distributed sensing. In this work, a mixed-matrix composite integrated FO sensor system was developed with a purely optical readout that reliably operates as a detector for gas-phase and dissolved CO 2 . A mixed-matrix composite sensor coating consisting of plasmonic nanocrystals and hydrophobic zeolite embedded in a polymer matrix was integrated on the FO sensor. The mixed-matrix composite FO sensor showed excellent reversibility/stability in a high humidity environment and sensitivity to gas-phase CO 2 over a large concentration range. This remarkable sensing performance was enabled by using plasmonic nanocrystals to significantly enhance the sensitivity and a hydrophobic zeolite to effectively mitigate interference from water vapor. The sensor exhibited the ability to sense CO 2 in the presence of other geologically relevant gases, which is of importance for applications in geological formations. A prototype FO sensor configuration, which possesses a robust sensing capability for monitoring dissolved CO 2 in natural water, was demonstrated. Reproducibility was confirmed over many cycles, both in a laboratory setting and in the field. More importantly, we demonstrated on-line monitoring capabilities with a wireless telemetry system, which transferred the data from the field to a website. The combination of outstanding CO 2 sensing properties and facile coating processability makes this mixed-matrix composite FO sensor a good candidate for practical carbon storage applications.
Keyphrases
  • computed tomography
  • magnetic resonance imaging
  • magnetic resonance
  • room temperature
  • machine learning
  • high resolution
  • mass spectrometry
  • deep learning
  • quantum dots