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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 Please do not cite the old paper anymore.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 fluorine (F) → Organic fluorine → fluorobenzene

FORMULA:C6H5F
CAS RN:462-06-6
STRUCTURE
(FROM NIST):
InChIKey:PYLWMHQQBFSUBP-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
1.5×10−3 4100 Brockbank (2013) L 1)
1.6×10−3 Mackay and Shiu (1981) L
1.6×10−3 3900 Hiatt (2013) M
1.4×10−3 4300 Dewulf et al. (1999) M
1.1×10−3 Li and Carr (1993) M
1.5×10−3 4400 Hartkopf and Karger (1973) M
1.6×10−3 Schüürmann (2000) V
1.4×10−3 Mackay et al. (1993) V
1.6×10−3 Yaws (2003) X 238)
1.6×10−3 Sieg et al. (2008) C
7.4×10−3 Keshavarz et al. (2022) Q
8.6×10−3 Duchowicz et al. (2020) Q 300)
1.6×10−3 Li et al. (2014) Q 242)
2.2×10−3 Li et al. (2014) Q 242)
4.1×10−3 Gharagheizi et al. (2012) Q
2.5×10−3 Raventos-Duran et al. (2010) Q 243) 244)
1.6×10−3 Raventos-Duran et al. (2010) Q 245)
1.6×10−3 Raventos-Duran et al. (2010) Q 246)
1.6×10−3 Gharagheizi et al. (2010) Q 247)
2.0×10−3 Hilal et al. (2008) Q
1.4×10−3 Modarresi et al. (2007) Q 68)
3700 Kühne et al. (2005) Q
1.6×10−3 Yaffe et al. (2003) Q 249) 250)
2.3×10−3 Yao et al. (2002) Q 230) 268)
1.3×10−3 Katritzky et al. (1998) Q
5.0×10−3 Nirmalakhandan et al. (1997) Q
1.6×10−3 Duchowicz et al. (2020) ? 21) 186)
3800 Kühne et al. (2005) ?
1.6×10−3 Yaws (1999) ? 21)
1.2×10−3 Hoff et al. (1993) ? 21)
1.6×10−3 Yaws and Yang (1992) ? 21)
1.5×10−3 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).
  • 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).
  • Dewulf, J., van Langenhove, H., & Everaert, P.: Determination of Henry’s law coefficients by combination of the equilibrium partitioning in closed systems and solid-phase microextraction techniques, J. Chromatogr. A, 830, 353–363, doi:10.1016/S0021-9673(98)00877-2 (1999).
  • 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).
  • 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).
  • Hartkopf, A. & Karger, B. L.: Study of the interfacial properties of water by gas chromatography, Acc. Chem. Res., 6, 209–216, doi:10.1021/AR50066A006 (1973).
  • Hiatt, M. H.: Determination of Henry’s law constants using internal standards with benchmark values, J. Chem. Eng. Data, 58, 902–908, doi:10.1021/JE3010535 (2013).
  • 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).
  • Hoff, J. T., Mackay, D., Gillham, R., & Shiu, W. Y.: Partitioning of organic chemicals at the air–water interface in environmental systems, Environ. Sci. Technol., 27, 2174–2180, doi:10.1021/ES00047A026 (1993).
  • 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).
  • Li, J. & Carr, P. W.: Measurement of water-hexadecane partition coefficients by headspace gas chromatography and calculation of limiting activity coefficients in water, Anal. Chem., 65, 1443–1450, doi:10.1021/AC00058A023 (1993).
  • Li, H., Wang, X., Yi, T., Xu, Z., & Liu, X.: Prediction of Henry’s law constants for organic compounds using multilayer feedforward neural networks based on linear salvation energy relationship, J. Chem. Pharm. Res., 6, 1557–1564 (2014).
  • Mackay, D. & Shiu, W. Y.: A critical review of Henry’s law constants for chemicals of environmental interest, J. Phys. Chem. Ref. Data, 10, 1175–1199, doi:10.1063/1.555654 (1981).
  • Mackay, D., Shiu, W. Y., & Ma, K. C.: Illustrated Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals, vol. III of Volatile Organic Chemicals, Lewis Publishers, Boca Raton, ISBN 0873719735 (1993).
  • 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).
  • Raventos-Duran, T., Camredon, M., Valorso, R., Mouchel-Vallon, C., & Aumont, B.: Structure-activity relationships to estimate the effective Henry’s law constants of organics of atmospheric interest, Atmos. Chem. Phys., 10, 7643–7654, doi:10.5194/ACP-10-7643-2010 (2010).
  • 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).
  • Sieg, K., Fries, E., & Püttmann, W.: Analysis of benzene, toluene, ethylbenzene, xylenes and n-aldehydes in melted snow water via solid-phase dynamic extraction combined with gas chromatography/mass spectrometry, J. Chromatogr. A, 1178, 178–186, doi:10.1016/J.CHROMA.2007.11.025 (2008).
  • 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).

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).
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.
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.
300) Value from the test set for true external validation.

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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