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Henry's Law Constants

www.henrys-law.org

Rolf Sander

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 Please do not cite the old paper anymore.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 Constants → Organic species with sulfur (S) → Sulfur (C, H, O, N, Cl, S) → 1-butanethiol

FORMULA:C4H9SH
TRIVIAL NAME: butyl mercaptan
CAS RN:109-79-5
STRUCTURE
(FROM NIST):
InChIKey:WQAQPCDUOCURKW-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
1.5×10−3 4300 Brockbank (2013) L 1)
1.4×10−3 4400 Plyasunova et al. (2004) L
1.5×10−3 4300 Haimi et al. (2006) M 807)
1.5×10−3 3600 Coquelet and Richon (2005) M
2.2×10−3 4100 Przyjazny et al. (1983) M
1.1×10−3 Mackay et al. (2006d) V
1.1×10−3 Mackay et al. (1995) V
1.4×10−3 Hwang et al. (1992) V
1.1×10−3 Yaws (2003) X 259)
1.1×10−3 Yaws (2003) X 238)
1.8×10−3 Dupeux et al. (2022) Q 260)
2.2×10−2 Keshavarz et al. (2022) Q
4.8×10−2 Duchowicz et al. (2020) Q 300)
6.1×10−4 Gharagheizi et al. (2012) Q
1.7×10−3 Gharagheizi et al. (2010) Q 247)
2.7×10−3 Hilal et al. (2008) Q
3.4×10−3 Modarresi et al. (2007) Q 68)
4300 Kühne et al. (2005) Q
2.2×10−3 Yaffe et al. (2003) Q 249) 273)
3.2×10−3 Yao et al. (2002) Q 230)
2.3×10−3 English and Carroll (2001) Q 231) 232)
2.5×10−3 Katritzky et al. (1998) Q
1.2×10−3 Nirmalakhandan et al. (1997) Q
2.2×10−3 Duchowicz et al. (2020) ? 21) 186)
1.1×10−3 Bartelt-Hunt et al. (2008) ? 21)
4200 Kühne et al. (2005) ?
1.1×10−3 Yaws et al. (2003) ? 21)
1.1×10−3 Yaws (1999) ? 21)
1.1×10−3 Yaws and Yang (1992) ? 21)
2.2×10−3 Abraham et al. (1990) ?

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

  • 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).
  • Bartelt-Hunt, S. L., Knappe, D. R. U., & Barlaz, M. A.: A review of chemical warfare agent simulants for the study of environmental behavior, Crit. Rev. Environ. Sci. Technol., 38, 112–136, doi:10.1080/10643380701643650 (2008).
  • 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).
  • Coquelet, C. & Richon, D.: Measurement of Henry’s law constants and infinite dilution activity coefficients of propyl mercaptan, butyl mercaptan, and dimethyl sulfide in methyldiethanolamine (1) + water (2) with w1 = 0.50 using a gas stripping technique, J. Chem. Eng. Data, 50, 2053–2057, doi:10.1021/JE050268B (2005).
  • 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).
  • Dupeux, T., Gaudin, T., Marteau-Roussy, C., Aubry, J.-M., & Nardello-Rataj, V.: COSMO-RS as an effective tool for predicting the physicochemical properties of fragrance raw materials, Flavour Fragrance J., 37, 106–120, doi:10.1002/FFJ.3690 (2022).
  • 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).
  • Gharagheizi, F., Eslamimanesh, A., Mohammadi, A. H., & Richon, D.: Empirical method for estimation of Henry’s law constant of non-electrolyte organic compounds in water, J. Chem. Thermodyn., 47, 295–299, doi:10.1016/J.JCT.2011.11.015 (2012).
  • Haimi, P., Uusi-Kyyny, P., Pokki, J.-P., Aittamaa, J., & Keskinen, K. I.: Infinite dilution activity coefficient measurements by inert gas stripping method, Fluid Phase Equilib., 243, 126–132, doi:10.1016/J.FLUID.2006.02.022 (2006).
  • 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).
  • Hwang, Y.-L., Olson, J. D., & Keller, II, G. E.: Steam stripping for removal of organic pollutants from water. 2. Vapor-liquid equilibrium data, Ind. Eng. Chem. Res., 31, 1759–1768, doi:10.1021/IE00007A022 (1992).
  • 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).
  • Kühne, R., Ebert, R.-U., & Schüürmann, G.: Prediction of the temperature dependency of Henry’s law constant from chemical structure, Environ. Sci. Technol., 39, 6705–6711, doi:10.1021/ES050527H (2005).
  • Mackay, D., Shiu, W. Y., & Ma, K. C.: Illustrated Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals, vol. IV of Oxygen, Nitrogen, and Sulfur Containing Compounds, Lewis Publishers, Boca Raton, ISBN 1566700353 (1995).
  • Mackay, D., Shiu, W. Y., Ma, K. C., & Lee, S. C.: Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals, vol. IV of Nitrogen and Sulfur Containing Compounds and Pesticides, CRC/Taylor & Francis Group, doi:10.1201/9781420044393 (2006d).
  • 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).
  • Plyasunova, N. V., Plyasunov, A. V., & Shock, E. L.: Group contribution values for the thermodynamic functions of hydration at 298.15 K, 0.1 MPa. 2. aliphatic thiols, alkyl sulfides, and polysulfides, J. Chem. Eng. Data, 50, 246–253, doi:10.1021/JE0497045 (2004).
  • Przyjazny, A., Janicki, W., Chrzanowski, W., & Staszewski, R.: Headspace gas chromatographic determination of distribution coefficients of selected organosulphur compounds and their dependence on some parameters, J. Chromatogr., 280, 249–260, doi:10.1016/S0021-9673(00)91567-X (1983).
  • 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).
  • Yaws, C. L. & Yang, H.-C.: Henry’s law constant for compound in water, in: Thermodynamic and Physical Property Data, edited by Yaws, C. L., pp. 181–206, Gulf Publishing Company, Houston, TX, ISBN 0884150313 (1992).
  • Yaws, C. L., Bajaj, P., Singh, H., & Pike, R. W.: Solubility & Henry’s law constants for sulfur compounds in water, Chem. Eng., pp. 60–64 (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

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.
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.
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.
232) Value from the training dataset.
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.
259) Value given here as quoted by Dupeux et al. (2022).
260) Calculated using the COSMO-RS method.
273) Value from the test set.
300) Value from the test set for true external validation.
807) The data from Haimi et al. (2006) were fitted to the three-parameter equation: Hscp= exp( −406.56800 +21428.82541/T +57.60207 ln(T)) mol m−3 Pa−1, with T in 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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