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Optimization of thin film solid phase microextraction and data deconvolution methods for accurate characterization of organic compounds in produced water

  • University of Toledo
  • University of Texas at Arlington

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

The continued rise in the extraction of unconventional oil and gas across the globe poses many questions about how to manage these relatively new waste-streams. Produced water, the primary waste by-product, contains a diverse number of anthropogenic additives together with the numerous hydrocarbons extracted from the well. Due to potential environmental hazards, it is critical to characterize the chemical composition of this type of waste before proper disposal or remediation/reuse. In this work, a thin film solid phase microextraction approach was developed and optimized to characterize produced water. The thin film device consisted of hydrophilic-lipophilic balance particles embedded in polydimethylsiloxane and immobilized on a carbon mesh surface. These devices were chosen to provide broad extraction coverage and high reusability. Various parameters were evaluated to ensure reproducible results while minimizing analyte loss. This optimized protocol, consisting of a 15 min extraction followed by a short (3 s) rinsing step, enabled the reproducible analysis of produced water without any sample pretreatment. Extraction efficiency was suitable for both produced water additives and hydrocarbons. The developed approach was able to tentatively identify a total of 201 compounds from produced water samples, by using one-dimensional gas chromatography hyphenated to mass spectrometry and data deconvolution.

Original languageEnglish
Pages (from-to)1915-1924
Number of pages10
JournalJournal of Separation Science
Volume43
Issue number9-10
DOIs
StatePublished - May 1 2020

Keywords

  • data deconvolution
  • hydraulic fracturing
  • thin film solid phase microextraction
  • unconventional environmental contaminants
  • untargeted analysis

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