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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 Please do not cite the old paper anymore.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) → 2-phenylethanol

FORMULA:C8H10O
CAS RN:60-12-8
STRUCTURE
(FROM NIST):
InChIKey:WRMNZCZEMHIOCP-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
1.7×101 7200 Brockbank (2013) L 1)
> 3.7×101 Altschuh et al. (1999) M
6.6×101 HSDB (2015) V
3.9×101 Abraham et al. (1994a) R
3.5 Yaws (2003) X 259)
1.6×101 Dupeux et al. (2022) Q 260)
3.0×101 Keshavarz et al. (2022) Q
2.0×10−3 Abney (2021) Q 401)
8.5 Duchowicz et al. (2020) Q 185)
8.5 Wang et al. (2017) Q 81) 239)
3.9×101 Wang et al. (2017) Q 81) 240)
3.9×101 Wang et al. (2017) Q 81) 241)
7.8×101 Raventos-Duran et al. (2010) Q 243) 244)
2.5×101 Raventos-Duran et al. (2010) Q 245)
3.1×101 Raventos-Duran et al. (2010) Q 246)
1.9×101 Hilal et al. (2008) Q
3.2×101 Modarresi et al. (2007) Q 68)
2.4×10−1 Emel’yanenko et al. (2007) Q 417)
2.4×10−1 Hertel and Sommer (2005) Q 417)
3.9×101 Yaffe et al. (2003) Q 249) 250)
1.0×101 Yao et al. (2002) Q 230)
2.8×101 English and Carroll (2001) Q 231) 275)
5.4×101 Katritzky et al. (1998) Q
5.3×101 Nirmalakhandan et al. (1997) Q
3.9×101 Duchowicz et al. (2020) ? 21) 186)
3.5 Yaws (1999) ? 21)

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

  • Abney, C. A.: Predicting Henry’s Law constants of volatile organic compounds present in bourbon using molecular simulations, Master’s thesis, University of Louisville, Kentucky, USA, doi:10.18297/etd/3440 (2021).
  • Abraham, M. H., Andonian-Haftvan, J., Whiting, G. S., Leo, A., & Taft, R. S.: Hydrogen bonding. Part 34. The factors that influence the solubility of gases and vapours in water at 298 K, and a new method for its determination, J. Chem. Soc. Perkin Trans. 2, pp. 1777–1791, doi:10.1039/P29940001777 (1994a).
  • 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).
  • 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).
  • 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).
  • Emel’yanenko, V. N., Dabrowska, A., Verevkin, S. P., Hertel, M. O., Scheuren, H., & Sommer, K.: Vapor pressures, enthalpies of vaporization, and limiting activity coefficients in water at 100C of 2-furanaldehyde, benzaldehyde, phenylethanal, and 2-phenylethanol, J. Chem. Eng. Data, 52, 468–471, doi:10.1021/JE060406C (2007).
  • 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).
  • Hertel, M. O. & Sommer, K.: Limiting separation factors and limiting activity coefficients for 2-phenylethanol and 2-phenylethanal in water at 100C, J. Chem. Eng. Data, 50, 1905–1906, doi:10.1021/JE050171P (2005).
  • 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).
  • HSDB: Hazardous Substances Data Bank, TOXicology data NETwork (TOXNET), National Library of Medicine (US), URL https://www.nlm.nih.gov/toxnet/Accessing_HSDB_Content_from_PubChem.html (2015).
  • 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).
  • 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).
  • Wang, C., Yuan, T., Wood, S. A., Goss, K.-U., Li, J., Ying, Q., & Wania, F.: Uncertain Henry’s law constants compromise equilibrium partitioning calculations of atmospheric oxidation products, Atmos. Chem. Phys., 17, 7529–7540, doi:10.5194/ACP-17-7529-2017 (2017).
  • 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

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.
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.
239) Calculated using linear free energy relationships (LFERs).
240) Calculated using SPARC Performs Automated Reasoning in Chemistry (SPARC).
241) Calculated using COSMOtherm.
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.
250) Value from the training set.
259) Value given here as quoted by Dupeux et al. (2022).
260) Calculated using the COSMO-RS method.
275) Value from the test dataset.
401) Calculated for an aqueous solution containing 60 % ethanol by volume as the solvent.
417) Value at T = 373 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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