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Henry's Law Constants

www.henrys-law.org

Rolf Sander

Atmospheric Chemistry Division

Max-Planck Institute for Chemistry
Mainz, Germany

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Henry's Law Constants

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When referring to the compilation of Henry's Law Constants, please cite this publication:

R. Sander: Compilation of Henry's law constants (version 5.0.0) for water as solvent, Atmos. Chem. Phys., 23, 10901-12440 (2023), doi:10.5194/acp-23-10901-2023

The publication from 2023 replaces that from 2015, which is now obsolete. Please do not cite the old paper anymore.

Henry's Law Constants → Hydrocarbons (C, H) → Alkanes → ethane

FORMULA:C2H6
CAS RN:74-84-0
STRUCTURE
(FROM NIST):
InChIKey:OTMSDBZUPAUEDD-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
1.9×10−5 2400 Burkholder et al. (2019) L 1)
1.9×10−5 2400 Burkholder et al. (2015) L 1)
1.9×10−5 2400 Sander et al. (2011) L 1)
1.9×10−5 2400 Sander et al. (2006) L 1)
1.9×10−5 2400 Fernández-Prini et al. (2003) L 3)
1.9×10−5 2300 Plyasunov and Shock (2000) L
1.9×10−5 2300 Abraham and Matteoli (1988) L
1.9×10−5 2300 Hayduk (1982) L 1)
2.0×10−5 Mackay and Shiu (1981) L
1.8×10−5 2400 Wilhelm et al. (1977) L
2.0×10−5 2300 Reichl (1995) M 235)
1.3×10−5 Guitart et al. (1989) M 14)
1.8×10−5 2700 Ben-Naim and Battino (1985) M
1.9×10−5 2300 Rettich et al. (1981) M
1.8×10−5 2700 Cosgrove and Walkley (1981) M 11)
2.0×10−5 McAuliffe (1966) M 226)
2.0×10−5 2400 Wetlaufer et al. (1964) M
2.0×10−5 McAuliffe (1963) M 227)
1.7×10−5 2100 Morrison and Billett (1952) M 236)
1.8×10−5 2400 Winkler (1901) M 237)
2.0×10−5 Duchowicz et al. (2020) V 187)
2.0×10−5 HSDB (2015) V
2.0×10−5 Hine and Mookerjee (1975) V
1.7×10−5 2000 Wauchope and Haque (1972) V
1.0×10−4 Butler and Ramchandani (1935) V
4.0×10−5 Pierotti (1965) T
2.0×10−5 Yaws (2003) X 238)
1.8×10−5 Deno and Berkheimer (1960) C
1.8×10−5 Hayer et al. (2022) Q 20)
1.3×10−3 Duchowicz et al. (2020) Q
1.1×10−4 Wang et al. (2017) Q 81) 239)
1.1×10−5 Wang et al. (2017) Q 81) 240)
2.0×10−5 Wang et al. (2017) Q 81) 241)
2.0×10−5 Li et al. (2014) Q 242)
3.3×10−6 Gharagheizi et al. (2012) Q
2.0×10−5 Raventos-Duran et al. (2010) Q 243) 244)
1.2×10−5 Raventos-Duran et al. (2010) Q 245)
2.0×10−5 Raventos-Duran et al. (2010) Q 246)
4.1×10−5 Gharagheizi et al. (2010) Q 247)
2.0×10−5 Hilal et al. (2008) Q
7.8×10−6 Modarresi et al. (2007) Q 68)
2600 Kühne et al. (2005) Q
4.6×10−6 Modarresi et al. (2005) Q 248)
2.1×10−5 Yaffe et al. (2003) Q 249) 250)
2.1×10−5 Yao et al. (2002) Q 230)
1.8×10−5 English and Carroll (2001) Q 231) 232)
1.5×10−5 Katritzky et al. (1998) Q
2.4×10−5 Suzuki et al. (1992) Q 233)
2.2×10−5 Nirmalakhandan and Speece (1988) Q
1.1×10−5 Irmann (1965) Q
2500 Kühne et al. (2005) ?
2.0×10−5 Yaws (1999) ? 21)
1.9×10−5 2300 Yaws et al. (1999) ? 21)
1.5×10−5 Abraham and Weathersby (1994) ? 21)
1.8×10−5 2400 Dean and Lange (1999) ? 23) 251)
2.0×10−5 Yaws and Yang (1992) ? 21)
1.9×10−5 Abraham et al. (1990) ?

Data

The first column contains Henry's law solubility constant Hscp at the reference temperature of 298.15 K.
The second column contains the temperature dependence d ln Hs cp / d (1/T), also at the reference temperature.

References

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  • Hilal, S. H., Ayyampalayam, S. N., & Carreira, L. A.: Air-liquid partition coefficient for a diverse set of organic compounds: Henry’s law constant in water and hexadecane, Environ. Sci. Technol., 42, 9231–9236, doi:10.1021/ES8005783 (2008).
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  • HSDB: Hazardous Substances Data Bank, TOXicology data NETwork (TOXNET), National Library of Medicine (US), URL https://www.nlm.nih.gov/toxnet/Accessing_HSDB_Content_from_PubChem.html (2015).
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  • Kühne, R., Ebert, R.-U., & Schüürmann, G.: Prediction of the temperature dependency of Henry’s law constant from chemical structure, Environ. Sci. Technol., 39, 6705–6711, doi:10.1021/ES050527H (2005).
  • Li, H., Wang, X., Yi, T., Xu, Z., & Liu, X.: Prediction of Henry’s law constants for organic compounds using multilayer feedforward neural networks based on linear salvation energy relationship, J. Chem. Pharm. Res., 6, 1557–1564 (2014).
  • Mackay, D. & Shiu, W. Y.: A critical review of Henry’s law constants for chemicals of environmental interest, J. Phys. Chem. Ref. Data, 10, 1175–1199, doi:10.1063/1.555654 (1981).
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  • Modarresi, H., Modarress, H., & Dearden, J. C.: Henry’s law constant of hydrocarbons in air–water system: The cavity ovality effect on the non-electrostatic contribution term of solvation free energy, SAR QSAR Environ. Res., 16, 461–482, doi:10.1080/10659360500319869 (2005).
  • Modarresi, H., Modarress, H., & Dearden, J. C.: QSPR model of Henry’s law constant for a diverse set of organic chemicals based on genetic algorithm-radial basis function network approach, Chemosphere, 66, 2067–2076, doi:10.1016/J.CHEMOSPHERE.2006.09.049 (2007).
  • Morrison, T. J. & Billett, F.: 730. The salting-out of non-electrolytes. Part II. The effect of variation in non-electrolyte, J. Chem. Soc., pp. 3819–3822, doi:10.1039/JR9520003819 (1952).
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  • Raventos-Duran, T., Camredon, M., Valorso, R., Mouchel-Vallon, C., & Aumont, B.: Structure-activity relationships to estimate the effective Henry’s law constants of organics of atmospheric interest, Atmos. Chem. Phys., 10, 7643–7654, doi:10.5194/ACP-10-7643-2010 (2010).
  • Reichl, A.: Messung und Korrelierung von Gaslöslichkeiten halogenierter Kohlenwasserstoffe, Ph.D. thesis, Technische Universität Berlin, Germany (1995).
  • Rettich, T. R., Handa, Y. P., Battino, R., & Wilhelm, E.: Solubility of gases in liquids. 13. High-precision determination of Henry’s constants for methane and ethane in liquid water at 275 to 328 K, J. Phys. Chem., 85, 3230–3237, doi:10.1021/J150622A006 (1981).
  • Sander, S. P., Friedl, R. R., Golden, D. M., Kurylo, M. J., Moortgat, G. K., Keller-Rudek, H., Wine, P. H., Ravishankara, A. R., Kolb, C. E., Molina, M. J., Finlayson-Pitts, B. J., Huie, R. E., & Orkin, V. L.: Chemical Kinetics and Photochemical Data for Use in Atmospheric Studies, Evaluation Number 15, JPL Publication 06-2, Jet Propulsion Laboratory, Pasadena, CA, URL https://jpldataeval.jpl.nasa.gov (2006).
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  • Suzuki, T., Ohtaguchi, K., & Koide, K.: Application of principal components analysis to calculate Henry’s constant from molecular structure, Comput. Chem., 16, 41–52, doi:10.1016/0097-8485(92)85007-L (1992).
  • Wang, C., Yuan, T., Wood, S. A., Goss, K.-U., Li, J., Ying, Q., & Wania, F.: Uncertain Henry’s law constants compromise equilibrium partitioning calculations of atmospheric oxidation products, Atmos. Chem. Phys., 17, 7529–7540, doi:10.5194/ACP-17-7529-2017 (2017).
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  • Yaws, C. L.: Chemical Properties Handbook, McGraw-Hill, Inc., ISBN 0070734011 (1999).
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Type

Table entries are sorted according to reliability of the data, listing the most reliable type first: L) literature review, M) measured, V) VP/AS = vapor pressure/aqueous solubility, R) recalculation, T) thermodynamical calculation, X) original paper not available, C) citation, Q) QSPR, E) estimate, ?) unknown, W) wrong. See Section 3.1 of Sander (2023) for further details.

Notes

1) A detailed temperature dependence with more than one parameter is available in the original publication. Here, only the temperature dependence at 298.15 K according to the van 't Hoff equation is presented.
3) The vapor pressure for water from Wagner and Pruss (1993) was used to calculate Hs.
11) Measured at high temperature and extrapolated to T⊖ = 298.15 K.
14) Value at T = 310 K.
20) Calculated using machine learning matrix completion methods (MCMs).
21) Several references are given in the list of Henry's law constants but not assigned to specific species.
23) The partial pressure of water vapor (needed to convert some Henry's law constants) was calculated using the formula given by Buck (1981). The quantities A and α from Dean and Lange (1999) were assumed to be identical.
68) Modarresi et al. (2007) use different descriptors for their calculations. They conclude that a genetic algorithm/radial basis function network (GA/RBFN) is the best QSPR model. Only these results are shown here.
81) Value at T = 288 K.
187) Estimation based on the quotient between vapor pressure and water solubility, extracted from HENRYWIN.
226) The same value was also published in McAuliffe (1963).
227) The same value was also published in McAuliffe (1966).
230) Yao et al. (2002) compared two QSPR methods and found that radial basis function networks (RBFNs) are better than multiple linear regression. In their paper, they provide neither a definition nor the unit of their Henry's law constants. Comparing the values with those that they cite from Yaws (1999), it is assumed that they use the variant Hvpx and the unit atm.
231) English and Carroll (2001) provide several calculations. Here, the preferred value with explicit inclusion of hydrogen bonding parameters from a neural network is shown.
232) Value from the training dataset.
233) Calculated with a principal component analysis (PCA); see Suzuki et al. (1992) for details.
235) The data from Reichl (1995) were fitted to the three-parameter equation: Hscp= exp( −109.51433 +6313.03876/T +13.60483 ln(T)) mol m−3 Pa−1, with T in K.
236) The data from Morrison and Billett (1952) were fitted to the three-parameter equation: Hscp= exp( −215.51394 +10861.98666/T +29.50128 ln(T)) mol m−3 Pa−1, with T in K.
237) The data from Winkler (1901) were fitted to the three-parameter equation: Hscp= exp( −277.60377 +13887.90452/T +38.63046 ln(T)) mol m−3 Pa−1, with T in K.
238) Value given here as quoted by Gharagheizi et al. (2010).
239) Calculated using linear free energy relationships (LFERs).
240) Calculated using SPARC Performs Automated Reasoning in Chemistry (SPARC).
241) Calculated using COSMOtherm.
242) Temperature is not specified.
243) Value from the training dataset.
244) Calculated using the GROMHE model.
245) Calculated using the SPARC approach.
246) Calculated using the HENRYWIN method.
247) Calculated using a combination of a group contribution method and neural networks.
248) Modarresi et al. (2005) use different descriptors for the QSPR models. They conclude that their "COSA" method and the artificial neural network (ANN) are best. However, as COSA is not ideal for hydrocarbons with low solubility, only results obtained with ANN are shown here.
249) Yaffe et al. (2003) present QSPR results calculated with the fuzzy ARTMAP (FAM) and with the back-propagation (BK-Pr) method. They conclude that FAM is better. Only the FAM results are shown here.
250) Value from the training set.
251) The data from Dean and Lange (1999) were fitted to the three-parameter equation: Hscp= exp( −249.13770 +12672.58357/T +34.34947 ln(T)) mol m−3 Pa−1, with T in K.

The numbers of the notes are the same as in Sander (2023). References cited in the notes can be found here.

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