Henry's Law Constants

Rolf Sander

Atmospheric Chemistry Division

Max-Planck Institute for Chemistry
Mainz, Germany

Errata

Contact, Imprint, Acknowledgements

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 Please do not cite the old paper anymore.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 → Organic species with chlorine (Cl) → Chlorocarbons (C, H, Cl) → 1,1-dichlorobutane

FORMULA:	C₄H₈Cl₂
CAS RN:	541-33-3
STRUCTURE (FROM NIST):
InChIKey:	SEQRDAAUNCRFIT-UHFFFAOYSA-N

$H_{s}^{cp}$	$d ln H_{s}^{cp} / d (1/ T)$	References	Type	Notes
[mol/(m³Pa)]	[K]
1.3×10⁻³		Duchowicz et al. (2020)	V	187)
1.3×10⁻³		Hine and Mookerjee (1975)	V
2.7×10⁻³		Duchowicz et al. (2020)	Q
2.5×10⁻³		Hilal et al. (2008)	Q
9.2×10⁻⁴		Modarresi et al. (2007)	Q	68)
1.4×10⁻³		Yaffe et al. (2003)	Q	249) 250)
1.6×10⁻³		English and Carroll (2001)	Q	231) 261)
1.3×10⁻³		Katritzky et al. (1998)	Q
9.2×10⁻⁴		Nirmalakhandan and Speece (1988)	Q

Data

The first column contains Henry's law solubility constant

H_{s}^{cp}

at the reference temperature of 298.15 K.
The second column contains the temperature dependence

d ln H_{s}^{cp} / d
(1/ T)

, 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).
English, N. J. & Carroll, D. G.: Prediction of Henry’s law constants by a quantitative structure property relationship and neural networks, J. Chem. Inf. Comput. Sci., 41, 1150–1161, doi:10.1021/CI010361D (2001).
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).
Katritzky, A. R., Wang, Y., Sild, S., Tamm, T., & Karelson, M.: QSPR studies on vapor pressure, aqueous solubility, and the prediction of water-air partition coefficients, J. Chem. Inf. Comput. Sci., 38, 720–725, doi:10.1021/CI980022T (1998).
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).
Yaffe, D., Cohen, Y., Espinosa, G., Arenas, A., & Giralt, F.: A fuzzy ARTMAP-based quantitative structure-property relationship (QSPR) for the Henry’s law constant of organic compounds, J. Chem. Inf. Comput. Sci., 43, 85–112, doi:10.1021/CI025561J (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

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.
187)	Estimation based on the quotient between vapor pressure and water solubility, extracted from HENRYWIN.
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.
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.
261)	Value from the validation dataset.

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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