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 Please do not cite the old paper anymore.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 nitrogen (N) → Nitriles (C, H, N) → pentanenitrile

FORMULA:	C₄H₉CN
TRIVIAL NAME:	butyl cyanide; valeronitrile
CAS RN:	110-59-8
STRUCTURE (FROM NIST):
InChIKey:	RFFFKMOABOFIDF-UHFFFAOYSA-N

$H_{s}^{cp}$	$d ln H_{s}^{cp} / d (1/ T)$	References	Type	Notes
[mol/(m³Pa)]	[K]
1.6×10⁻¹	6100	Brockbank (2013)	L
1.4×10⁻¹	5500	Plyasunov et al. (2006)	L
1.4×10⁻¹		Li and Carr (1993)	M
1.6×10⁻¹		Amoore and Buttery (1978)	V
2.7×10⁻¹		Hilal et al. (2008)	Q
1.3×10⁻¹		Modarresi et al. (2007)	Q	68)
1.5×10⁻¹		Yaffe et al. (2003)	Q	249) 273)
4.1×10⁻²		English and Carroll (2001)	Q	231) 232)
1.5×10⁻²		Nirmalakhandan et al. (1997)	Q
1.5×10⁻¹		Abraham et al. (1990)	?

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

Abraham, M. H., Whiting, G. S., Fuchs, R., & Chambers, E. J.: Thermodynamics of solute transfer from water to hexadecane, J. Chem. Soc. Perkin Trans. 2, pp. 291–300, doi:10.1039/P29900000291 (1990).
Amoore, J. E. & Buttery, R. G.: Partition coefficient and comparative olfactometry, Chem. Senses Flavour, 3, 57–71, doi:10.1093/CHEMSE/3.1.57 (1978).
Brockbank, S. A.: Aqueous Henry’s law constants, infinite dilution activity coefficients, and water solubility: critically evaluated database, experimental analysis, and prediction methods, Ph.D. thesis, Brigham Young University, USA, URL https://scholarsarchive.byu.edu/etd/3691/ (2013).
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).
Li, J. & Carr, P. W.: Measurement of water-hexadecane partition coefficients by headspace gas chromatography and calculation of limiting activity coefficients in water, Anal. Chem., 65, 1443–1450, doi:10.1021/AC00058A023 (1993).
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., Brennan, R. A., & Speece, R. E.: Predicting Henry’s law constant and the effect of temperature on Henry’s law constant, Wat. Res., 31, 1471–1481, doi:10.1016/S0043-1354(96)00395-8 (1997).
Plyasunov, A. V., Plyasunova, N. V., & Shock, E. L.: Group contribution values for the thermodynamic functions of hydration at 298.15 K, 0.1 MPa. 4. aliphatic nitriles and dinitriles, J. Chem. Eng. Data, 51, 1481–1490, doi:10.1021/JE060129+ (2006).
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.
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.
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.
273)	Value from the test set.

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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