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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 Please do not cite the old paper anymore.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 Constants → Organic species with nitrogen (N) → Amines (C, H, N) → aminobenzene

FORMULA:C6H7N
TRIVIAL NAME: aniline
CAS RN:62-53-3
STRUCTURE
(FROM NIST):
InChIKey:PAYRUJLWNCNPSJ-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
4.9 6800 Brockbank (2013) L 1)
4.4 Chao et al. (2017) M
5.2 Altschuh et al. (1999) M
1.2 Heal et al. (1995) M 375)
5.0 Jayasinghe et al. (1992) M
1.1 Dallos et al. (1983) M 555)
4.4 Chao et al. (2017) V
4.6 6500 Bernauer et al. (2006) V 1)
6.0 Mackay et al. (2006d) V
6.0 Schüürmann (2000) V
7.1×10−5 Lide and Frederikse (1995) V
6.0 Mackay et al. (1995) V
5.5 Hwang et al. (1992) V
3.4 Yoshida et al. (1983) V
5.8 Yaws (2003) X 259)
7.1×10−5 Howard (1989) X 366)
5.7 Yaws (2003) X 238)
8.2×10−2 Howard (1989) X 414)
2.4 Dupeux et al. (2022) Q 260)
3.3 Keshavarz et al. (2022) Q
5.1 Duchowicz et al. (2020) Q 300)
1.8×101 Gharagheizi et al. (2012) Q
5.5 Gharagheizi et al. (2010) Q 247)
5.1 Hilal et al. (2008) Q
4.2 Modarresi et al. (2007) Q 68)
6200 Kühne et al. (2005) Q
4.8 Yaffe et al. (2003) Q 249) 250)
2.4 Yao et al. (2002) Q 230)
3.0 Katritzky et al. (1998) Q
4.9 Duchowicz et al. (2020) ? 21) 186)
8.2×10−2 Mackay et al. (2006d) ?
7100 Kühne et al. (2005) ?
5.8 Yaws (1999) ? 21)
4.3 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).
  • Altschuh, J., Brüggemann, R., Santl, H., Eichinger, G., & Piringer, O. G.: Henry’s law constants for a diverse set of organic chemicals: Experimental determination and comparison of estimation methods, Chemosphere, 39, 1871–1887, doi:10.1016/S0045-6535(99)00082-X (1999).
  • Bernauer, M., Dohnal, V., Roux, A. H., Roux-Desgranges, G., & Majer, V.: Temperature dependences of limiting activity coefficients and Henry’s law constants for nitrobenzene, aniline, and cyclohexylamine in water, J. Chem. Eng. Data, 51, 1678–1685, doi:10.1021/JE060136Y (2006).
  • 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).
  • Chao, H.-P., Lee, J.-F., & Chiou, C. T.: Determination of the Henry’s law constants of low-volatility compounds via the measured air-phase transfer coefficients, Wat. Res., 120, 238–244, doi:10.1016/J.WATRES.2017.04.074 (2017).
  • Dallos, A., Ország, I., & Ratkovics, F.: Liquid-liquid and vapour-liquid equilibrium data and calculations for the system aniline + water in the presence of NaCl, NaI, NH4Cl and NH4I, Fluid Phase Equilib., 11, 91–102, doi:10.1016/0378-3812(83)85008-0 (1983).
  • 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).
  • 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).
  • Heal, M. R., Pilling, M. J., Titcombe, P. E., & Whitaker, B. J.: Mass accommodation of aniline, phenol and toluene on aqueous droplets, Geophys. Res. Lett., 22, 3043–3046, doi:10.1029/95GL02944 (1995).
  • 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).
  • Howard, P. H.: Handbook of Environmental fate and exposure data for organic chemicals. Vol. I: Large production and priority pollutants, Lewis Publishers Inc. Chelsea, Michigan, ISBN 0873711513 (1989).
  • 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).
  • Jayasinghe, D. S., Brownawell, B. J., Chen, H., & Westall, J. C.: Determination of Henry’s constants of organic compounds of low volatility: methylanilines in methanol-water, Environ. Sci. Technol., 26, 2275–2281, doi:10.1021/ES00035A028 (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).
  • Lide, D. R. & Frederikse, H. P. R.: CRC Handbook of Chemistry and Physics, 76th Edition, CRC Press, Inc., Boca Raton, FL, ISBN 0849304768 (1995).
  • 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).
  • Schüürmann, G.: Prediction of Henry’s law constant of benzene derivatives using quantum chemical continuum-solvation models, J. Comput. Chem., 21, 17–34, doi:10.1002/(SICI)1096-987X(20000115)21:1<17::AID-JCC3>3.0.CO;2-5 (2000).
  • 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).
  • Yoshida, K., Shigeoka, T., & Yamauchi, F.: Non-steady state equilibrium model for the preliminary prediction of the fate of chemicals in the environment, Ecotoxicol. Environ. Saf., 7, 179–190, doi:10.1016/0147-6513(83)90064-7 (1983).

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.
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.
259) Value given here as quoted by Dupeux et al. (2022).
260) Calculated using the COSMO-RS method.
300) Value from the test set for true external validation.
366) Value given here as quoted by Haynes (2014).
375) Value at T = 283 K.
414) Value given here as quoted by Mackay et al. (1995).
555) Value at T = 323 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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