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 Please do not cite the old paper anymore.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) → Amines, amides, amino acids (C, H, O, N) → 3-methoxy-benzenamine

FORMULA:	C₇H₉NO
TRIVIAL NAME:	3-methoxyaniline
CAS RN:	536-90-3
STRUCTURE (FROM NIST):
InChIKey:	NCBZRJODKRCREW-UHFFFAOYSA-N

$H_{s}^{cp}$	$d ln H_{s}^{cp} / d (1/ T)$	References	Type	Notes
[mol/(m³Pa)]	[K]
9.0×10¹		Abraham et al. (1994a)	R
9.0×10¹		HSDB (2015)	Q	100)
1.8×10²		Hilal et al. (2008)	Q
2.8×10¹		Modarresi et al. (2007)	Q	68)
6.4×10¹		English and Carroll (2001)	Q	231) 232)
1.5×10¹		Nirmalakhandan et al. (1997)	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

Abraham, M. H., Andonian-Haftvan, J., Whiting, G. S., Leo, A., & Taft, R. S.: Hydrogen bonding. Part 34. The factors that influence the solubility of gases and vapours in water at 298 K, and a new method for its determination, J. Chem. Soc. Perkin Trans. 2, pp. 1777–1791, doi:10.1039/P29940001777 (1994a).
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).
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).
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).

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.
100)	Calculated based on the method by Meylan and Howard (1991).
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.

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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