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 Please do not cite the old paper anymore.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 (C, H, N) → 1-hexanamine

FORMULA:	C₆H₁₃NH₂
TRIVIAL NAME:	hexylamine
CAS RN:	111-26-2
STRUCTURE (FROM NIST):
InChIKey:	BMVXCPBXGZKUPN-UHFFFAOYSA-N

$H_{s}^{cp}$	$d ln H_{s}^{cp} / d (1/ T)$	References	Type	Notes
[mol/(m³Pa)]	[K]
3.5×10⁻¹		Brockbank (2013)	L
3.2×10⁻¹		Rytting et al. (1978)	M
3.7×10⁻¹		Christie and Crisp (1967)	M
1.0×10⁻¹		Yaws (2003)	X	238)
2.3×10⁻¹		Keshavarz et al. (2022)	Q
2.4		Duchowicz et al. (2020)	Q	185)
1.4×10⁻¹		Gharagheizi et al. (2010)	Q	247)
3.7×10⁻¹		Hilal et al. (2008)	Q
1.0		Modarresi et al. (2007)	Q	68)
3.2×10⁻¹		Yaffe et al. (2003)	Q	249) 250)
9.2×10⁻²		Yao et al. (2002)	Q	230) 268)
4.3×10⁻¹		English and Carroll (2001)	Q	231) 275)
3.3×10⁻¹		Katritzky et al. (1998)	Q
1.8×10⁻¹		Nirmalakhandan et al. (1997)	Q
3.4×10⁻¹		Russell et al. (1992)	Q	280)
4.6×10⁻¹		Suzuki et al. (1992)	Q	233)
3.7×10⁻¹		Duchowicz et al. (2020)	?	21) 186)
1.0×10⁻¹		Yaws (1999)	?	21)
3.2×10⁻¹		Abraham et al. (1990)	?
	7900	Abraham (1984)	?	21)

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.: Thermodynamics of solution of homologous series of solutes in water, J. Chem. Soc. Faraday Trans. 1, 80, 153–181, doi:10.1039/F19848000153 (1984).
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).
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).
Christie, A. O. & Crisp, D. J.: Activity coefficients on the n-primary, secondary and tertiary aliphatic amines in aqueous solution, J. Appl. Chem., 17, 11–14, doi:10.1002/JCTB.5010170103 (1967).
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).
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).
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).
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).
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., 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).
Russell, C. J., Dixon, S. L., & Jurs, P. C.: Computer-assisted study of the relationship between molecular structure and Henry’s law constant, Anal. Chem., 64, 1350–1355, doi:10.1021/AC00037A009 (1992).
Rytting, J. H., Huston, L. P., & Higuchi, T.: Thermodynamic group contributions for hydroxyl, amino, and methylene groups, J. Pharm. Sci., 69, 615–618, doi:10.1002/JPS.2600670510 (1978).
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).
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).
Yao, X., aand X. Zhang, M. L., Hu, Z., & Fan, B.: Radial basis function network-based quantitative structure-property relationship for the prediction of Henry’s law constant, Anal. Chim. Acta, 462, 101–117, doi:10.1016/S0003-2670(02)00273-8 (2002).
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.
185)	Value from the validation set for checking whether the model is satisfactory for compounds that are absent from the training set.
186)	Experimental value, extracted from HENRYWIN.
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 H_v^px 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.
233)	Calculated with a principal component analysis (PCA); see Suzuki et al. (1992) for details.
238)	Value given here as quoted by Gharagheizi et al. (2010).
247)	Calculated using a combination of a group contribution method and neural networks.
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.
268)	Value from the test set.
275)	Value from the test dataset.
280)	Value from the training 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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