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Henry's Law Constants

www.henrys-law.org

Rolf Sander

NEW: Version 5.0.0 has been published in October 2023

Atmospheric Chemistry Division

Max-Planck Institute for Chemistry
Mainz, Germany


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Henry's Law Constants

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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 Please do not cite the old paper anymore.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 ConstantsOrganic species with oxygen (O)Alcohols (ROH) → 1-butanol

FORMULA:C4H9OH
CAS RN:71-36-3
STRUCTURE
(FROM NIST):
InChIKey:LRHPLDYGYMQRHN-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
1.2 7500 Burkholder et al. (2019) L 1)
1.2 7500 Burkholder et al. (2015) L 1)
1.2 7500 Brockbank (2013) L 1)
1.2 7500 Sander et al. (2011) L 1)
1.3 7200 Sander et al. (2006) L
1.2 7500 Dohnal et al. (2006) L 1)
1.1 6300 Fogg and Sangster (2003) L
1.2 7400 Plyasunov and Shock (2000) L
1.0 7000 Wu et al. (2022a) M
2.0 Chao et al. (2017) M
1.0 6800 Shunthirasingham et al. (2013) M
1.3 Vitenberg and Dobryakov (2008) M
1.1 6000 Lei et al. (2007) M 397)
8.2×10−1 6200 Falabella et al. (2006) M 11) 340)
9.4×10−1 6100 Hovorka et al. (2002) M 11)
4.5×10−1 van Ruth et al. (2002) M 14)
4.4×10−1 van Ruth and Villeneuve (2002) M 14) 363)
4.8×10−1 van Ruth et al. (2001) M 14)
1.1 Kim et al. (2000) M
8.2×10−1 6200 Gupta et al. (2000) M
1.2 Altschuh et al. (1999) M
5.5×10−1 Eger et al. (1999) M 14)
1.1 Merk and Riederer (1997) M
1.4×10−1 Chaintreau et al. (1995) M
5.1×10−1 Kaneko et al. (1994) M 14)
1.1 Li and Carr (1993) M
6.1×10−1 5600 Kolb et al. (1992) M 278)
1.2 7200 Snider and Dawson (1985) M
5.3×10−1 Friant and Suffet (1979) M 38)
1.2 Rytting et al. (1978) M
1.1 Amoore and Buttery (1978) M
1.1 Buttery et al. (1969) M
1.4 Burnett (1963) M
1.2 Butler et al. (1935) M 390)
1.1 Chao et al. (2017) V
1.1 Mackay et al. (2006c) V
7.3×10−1 Mackay et al. (1995) V
8.3×10−1 Hwang et al. (1992) V
2.2×10−1 4700 Djerki and Laub (1988) V
7400 Abraham (1984) V
1.2 Amoore and Buttery (1978) V
1.2 Butler et al. (1935) V
1.2 Yaws (2003) X 259)
9.4×10−1 Dupeux et al. (2022) Q 260)
1.9 Hayer et al. (2022) Q 20)
1.2 Keshavarz et al. (2022) Q
1.3 Duchowicz et al. (2020) Q 185)
2.0×10−1 Wang et al. (2017) Q 81) 239)
9.1×10−1 Wang et al. (2017) Q 81) 240)
1.3 Wang et al. (2017) Q 81) 241)
1.2 Li et al. (2014) Q 242)
9.4×10−1 Gharagheizi et al. (2012) Q
7.8×10−1 Raventos-Duran et al. (2010) Q 243) 244)
6.2×10−1 Raventos-Duran et al. (2010) Q 245)
9.9×10−1 Raventos-Duran et al. (2010) Q 246)
5.6×10−1 Hilal et al. (2008) Q
1.0 Modarresi et al. (2007) Q 68)
7200 Kühne et al. (2005) Q
1.2 Yaffe et al. (2003) Q 249) 273)
1.1 Yao et al. (2002) Q 230)
9.5×10−1 English and Carroll (2001) Q 231) 261)
1.8 Katritzky et al. (1998) Q
1.1 Yaws et al. (1997) Q
8.6×10−1 Russell et al. (1992) Q 280)
8.4×10−1 Suzuki et al. (1992) Q 233)
9.9×10−1 Nirmalakhandan and Speece (1988) Q
1.1 Duchowicz et al. (2020) ? 21) 186)
6900 Kühne et al. (2005) ?
1.1 Yaws (1999) ? 21)
1.2 Abraham et al. (1990) ?
1.8 Mackay and Yeun (1983) ?

Data

The first column contains Henry's law solubility constant Hscp at the reference temperature of 298.15 K.
The second column contains the temperature dependence d ln Hs cp / d (1/T), also at the reference temperature.

References

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  • 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).
  • Kim, B. R., Kalis, E. M., DeWulf, T., & Andrews, K. M.: Henry’s Law constants for paint solvents and their implications on volatile organic compound emissions from automotive painting, Water Environ. Res., 72, 65–74, doi:10.2175/106143000X137121 (2000).
  • Kolb, B., Welter, C., & Bichler, C.: Determination of partition coefficients by automatic equilibrium headspace gas chromatography by vapor phase calibration, Chromatographia, 34, 235–240, doi:10.1007/BF02268351 (1992).
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  • Lei, Y. D., Shunthirasingham, C., & Wania, F.: Comparison of headspace and gas-stripping techniques for measuring the air–water partititioning of normal alkanols (C4 to C10) – effect of temperature, chain length and adsorption to the water surface, J. Chem. Eng. Data, 52, 168–179, doi:10.1021/JE060344Q (2007).
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  • 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).
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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

1) A detailed temperature dependence with more than one parameter is available in the original publication. Here, only the temperature dependence at 298.15 K according to the van 't Hoff equation is presented.
11) Measured at high temperature and extrapolated to T = 298.15 K.
14) Value at T = 310 K.
20) Calculated using machine learning matrix completion methods (MCMs).
21) Several references are given in the list of Henry's law constants but not assigned to specific species.
38) Value at T = 303 K.
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.
81) Value at T = 288 K.
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 Hvpx 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.
239) Calculated using linear free energy relationships (LFERs).
240) Calculated using SPARC Performs Automated Reasoning in Chemistry (SPARC).
241) Calculated using COSMOtherm.
242) Temperature is not specified.
243) Value from the training dataset.
244) Calculated using the GROMHE model.
245) Calculated using the SPARC approach.
246) Calculated using the HENRYWIN method.
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.
259) Value given here as quoted by Dupeux et al. (2022).
260) Calculated using the COSMO-RS method.
261) Value from the validation dataset.
273) Value from the test set.
278) Extrapolated from data measured between 40 °C and 80 °C.
280) Value from the training set.
340) Values for salt solutions are also available from this reference.
363) Effective Henry's law constants at several pH values are provided by van Ruth and Villeneuve (2002). Here, only the value at pH = 3 is shown.
390) This paper supersedes earlier work with more concentrated solutions (Butler et al., 1933).
397) Extrapolated from data above 298 K.

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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