New Linear Partitioning Models Based on Experimental Water

Supercritical CO2 Partitioning Data of Selected Organic Compounds

Aniela Burant, Christopher Thompson, Gregory V. Lowry, Athanasios Karamalidis

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Partitioning coefficients of organic compounds between water and supercritical CO2 (sc-CO2) are necessary to assess the risk of migration of these chemicals from subsurface CO2 storage sites. Despite the large number of potential organic contaminants, the current data set of published water-sc-CO2 partitioning coefficients is very limited. Here, the partitioning coefficients of thiophene, pyrrole, and anisole were measured in situ over a range of temperatures and pressures using a novel pressurized batch-reactor system with dual spectroscopic detectors: a near-infrared spectrometer for measuring the organic analyte in the CO2 phase and a UV detector for quantifying the analyte in the aqueous phase. Our measured partitioning coefficients followed expected trends based on volatility and aqueous solubility. The partitioning coefficients and literature data were then used to update a published poly parameter linear free-energy relationship and to develop five new linear free-energy relationships for predicting water-sc-CO2 partitioning coefficients. A total of four of the models targeted a single class of organic compounds. Unlike models that utilize Abraham solvation parameters, the new relationships use vapor pressure and aqueous solubility of the organic compound at 25 °C and CO2 density to predict partitioning coefficients over a range of temperature and pressure conditions. The compound class models provide better estimates of partitioning behavior for compounds in that class than does the model built for the entire data set.

Original languageEnglish (US)
Pages (from-to)5135-5142
Number of pages8
JournalEnvironmental Science and Technology
Volume50
Issue number10
DOIs
StatePublished - May 17 2016

Fingerprint

Organic compounds
organic compound
partitioning
Water
Free energy
Solubility
Ultraviolet detectors
Thiophenes
water
Pyrroles
Infrared spectrometers
Solvation
Batch reactors
Vapor pressure
solubility
Impurities
Detectors
Temperature
vapor pressure
energy

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Environmental Chemistry

Cite this

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title = "New Linear Partitioning Models Based on Experimental Water: Supercritical CO2 Partitioning Data of Selected Organic Compounds",
abstract = "Partitioning coefficients of organic compounds between water and supercritical CO2 (sc-CO2) are necessary to assess the risk of migration of these chemicals from subsurface CO2 storage sites. Despite the large number of potential organic contaminants, the current data set of published water-sc-CO2 partitioning coefficients is very limited. Here, the partitioning coefficients of thiophene, pyrrole, and anisole were measured in situ over a range of temperatures and pressures using a novel pressurized batch-reactor system with dual spectroscopic detectors: a near-infrared spectrometer for measuring the organic analyte in the CO2 phase and a UV detector for quantifying the analyte in the aqueous phase. Our measured partitioning coefficients followed expected trends based on volatility and aqueous solubility. The partitioning coefficients and literature data were then used to update a published poly parameter linear free-energy relationship and to develop five new linear free-energy relationships for predicting water-sc-CO2 partitioning coefficients. A total of four of the models targeted a single class of organic compounds. Unlike models that utilize Abraham solvation parameters, the new relationships use vapor pressure and aqueous solubility of the organic compound at 25 °C and CO2 density to predict partitioning coefficients over a range of temperature and pressure conditions. The compound class models provide better estimates of partitioning behavior for compounds in that class than does the model built for the entire data set.",
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New Linear Partitioning Models Based on Experimental Water : Supercritical CO2 Partitioning Data of Selected Organic Compounds. / Burant, Aniela; Thompson, Christopher; Lowry, Gregory V.; Karamalidis, Athanasios.

In: Environmental Science and Technology, Vol. 50, No. 10, 17.05.2016, p. 5135-5142.

Research output: Contribution to journalArticle

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