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 Please do not cite the old paper anymore.
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References |
Type |
Notes |
[mol/(m3Pa)] |
[K] |
|
|
|
2.0×10−5 |
|
Hine and Mookerjee (1975) |
V |
|
2.0×10−5 |
|
Yaws (2003) |
X |
238)
|
2.0×10−5 |
|
Irmann (1965) |
C |
|
6.4×10−6 |
|
Keshavarz et al. (2022) |
Q |
|
5.3×10−5 |
|
Duchowicz et al. (2020) |
Q |
300)
|
4.7×10−5 |
|
Gharagheizi et al. (2012) |
Q |
|
1.8×10−5 |
|
Gharagheizi et al. (2010) |
Q |
247)
|
3.2×10−5 |
|
Hilal et al. (2008) |
Q |
|
1.1×10−5 |
|
Modarresi et al. (2007) |
Q |
68)
|
2.7×10−5 |
|
Nirmalakhandan and Speece (1988) |
Q |
|
5.6×10−6 |
|
Irmann (1965) |
Q |
|
2.0×10−5 |
|
Duchowicz et al. (2020) |
? |
21)
186)
|
2.1×10−5 |
|
Yaws (1999) |
? |
21)
|
Data
The first column contains Henry's law solubility constant
at the reference temperature of 298.15 K.
The second column contains the temperature dependence
, also at the
reference temperature.
References
-
Duchowicz, P. R., Aranda, J. F., Bacelo, D. E., & Fioressi, S. E.: QSPR study of the Henry’s law constant for heterogeneous compounds, Chem. Eng. Res. Des., 154, 115–121, doi:10.1016/J.CHERD.2019.12.009 (2020).
-
Gharagheizi, F., Abbasi, R., & Tirandazi, B.: Prediction of Henry’s law constant of organic compounds in water from a new group-contribution-based model, Ind. Eng. Chem. Res., 49, 10 149–10 152, doi:10.1021/IE101532E (2010).
-
Gharagheizi, F., Eslamimanesh, A., Mohammadi, A. H., & Richon, D.: Empirical method for estimation of Henry’s law constant of non-electrolyte organic compounds in water, J. Chem. Thermodyn., 47, 295–299, doi:10.1016/J.JCT.2011.11.015 (2012).
-
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).
-
Hine, J. & Mookerjee, P. K.: The intrinsic hydrophilic character of organic compounds. Correlations in terms of structural contributions, J. Org. Chem., 40, 292–298, doi:10.1021/JO00891A006 (1975).
-
Irmann, F.: Eine einfache Korrelation zwischen Wasserlöslichkeit und Struktur von Kohlenwasserstoffen und Halogenkohlenwasserstoffen, Chem.-Ing.-Tech., 37, 789–798, doi:10.1002/CITE.330370802 (1965).
-
Keshavarz, M. H., Rezaei, M., & Hosseini, S. H.: A simple approach for prediction of Henry’s law constant of pesticides, solvents, aromatic hydrocarbons, and persistent pollutants without using complex computer codes and descriptors, Process Saf. Environ. Prot., 162, 867–877, doi:10.1016/J.PSEP.2022.04.045 (2022).
-
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).
-
Nirmalakhandan, N. N. & Speece, R. E.: QSAR model for predicting Henry’s constant, Environ. Sci. Technol., 22, 1349–1357, doi:10.1021/ES00176A016 (1988).
-
Yaws, C. L.: Chemical Properties Handbook, McGraw-Hill, Inc., ISBN 0070734011 (1999).
-
Yaws, C. L.: Yaws’ Handbook of Thermodynamic and Physical Properties of Chemical Compounds, Knovel: Norwich, NY, USA, ISBN 1591244447 (2003).
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
21) |
Several references are given in the list of Henry's law constants but not assigned to specific species. |
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. |
186) |
Experimental value, extracted from HENRYWIN. |
238) |
Value given here as quoted by Gharagheizi et al. (2010). |
247) |
Calculated using a combination of a group contribution method and neural networks. |
300) |
Value from the test set for true external validation. |
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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