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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 Please do not cite the old paper anymore.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 oxygen (O) → Esters (RCOOR) → pentyl ethanoate

FORMULA:CH3COOC5H11
TRIVIAL NAME: amyl acetate
CAS RN:628-63-7
STRUCTURE
(FROM NIST):
InChIKey:PGMYKACGEOXYJE-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
2.5×10−2 7200 Brockbank (2013) L 1)
2.7×10−2 6700 Plyasunov et al. (2004) L
1.3×10−1 5000 Meynier et al. (2003) M 38)
9.3×10−3 Kaneko et al. (1994) M 14)
3.4×10−2 Hellmann (1987) M 88)
2.8×10−2 6500 Kieckbusch and King (1979b) M 503)
2.4×10−2 Mackay et al. (2006c) V
2.4×10−2 Mackay et al. (1995) V
2.5×10−2 Hine and Mookerjee (1975) V
2.8×10−2 Yaws (2003) X 259)
2.8×10−2 Yaws (2003) X 238)
2.5×10−2 Meynier et al. (2003) C
3.3×10−2 Dupeux et al. (2022) Q 260)
5.3×10−2 Keshavarz et al. (2022) Q
8.6×10−2 Duchowicz et al. (2020) Q
1.4×10−2 Gharagheizi et al. (2012) Q
2.5×10−2 Raventos-Duran et al. (2010) Q 244) 272)
2.0×10−2 Raventos-Duran et al. (2010) Q 245)
2.0×10−2 Raventos-Duran et al. (2010) Q 246)
2.3×10−2 Gharagheizi et al. (2010) Q 247)
2.0×10−2 Hilal et al. (2008) Q
2.8×10−2 Modarresi et al. (2007) Q 68)
2.7×10−2 Yaffe et al. (2003) Q 249) 250)
2.1×10−2 Yao et al. (2002) Q 230) 268)
2.3×10−2 English and Carroll (2001) Q 231) 261)
3.8×10−2 Katritzky et al. (1998) Q
2.1×10−2 Nirmalakhandan et al. (1997) Q
2.6×10−2 Russell et al. (1992) Q 280)
1.9×10−2 Suzuki et al. (1992) Q 233)
2.1×10−2 Nirmalakhandan and Speece (1988) Q
2.3×10−2 Taft et al. (1985) Q
2.5×10−2 Duchowicz et al. (2020) ? 21) 186)
2.9×10−2 Yaws (1999) ? 21)
2.8×10−2 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).
  • 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).
  • Hellmann, H.: Model tests on volatilization of organic trace substances in surfaces waters, Fresenius J. Anal. Chem., 328, 475–479, doi:10.1007/BF00475967 (1987).
  • 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).
  • Hine, J. & Mookerjee, P. K.: The intrinsic hydrophilic character of organic compounds. Correlations in terms of structural contributions, J. Org. Chem., 40, 292–298, doi:10.1021/JO00891A006 (1975).
  • Kaneko, T., Wang, P. Y., & Sato, A.: Partition coefficients of some acetate esters and alcohols in water, blood, olive oil, and rat tissues, Occup. Environ. Med., 51, 68–72, doi:10.1136/OEM.51.1.68 (1994).
  • 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).
  • Kieckbusch, T. G. & King, C. J.: An improved method of determining vapor liquid equilibria for dilute organics in aqueous solution, J. Chromatogr. Sci., 17, 273–276, doi:10.1093/CHROMSCI/17.5.273 (1979b).
  • 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. III of Oxygen Containing Compounds, CRC/Taylor & Francis Group, doi:10.1201/9781420044393 (2006c).
  • Meynier, A., Garillon, A., Lethuaut, L., & Genot, C.: Partition of five aroma compounds between air and skim milk, anhydrous milk fat or full-fat cream, Lait, 83, 223–235, doi:10.1051/LAIT:2003012 (2003).
  • 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. N. & Speece, R. E.: QSAR model for predicting Henry’s constant, Environ. Sci. Technol., 22, 1349–1357, doi:10.1021/ES00176A016 (1988).
  • 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).
  • Plyasunov, A. V., Plyasunova, N. V., & Shock, E. L.: Group contribution values for the thermodynamic functions of hydration of aliphatic esters at 298.15 K, 0.1 MPa, J. Chem. Eng. Data, 49, 1152–1167, doi:10.1021/JE049850A (2004).
  • 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).
  • Russell, C. J., Dixon, S. L., & Jurs, P. C.: Computer-assisted study of the relationship between molecular structure and Henry’s law constant, Anal. Chem., 64, 1350–1355, doi:10.1021/AC00037A009 (1992).
  • Suzuki, T., Ohtaguchi, K., & Koide, K.: Application of principal components analysis to calculate Henry’s constant from molecular structure, Comput. Chem., 16, 41–52, doi:10.1016/0097-8485(92)85007-L (1992).
  • Taft, R. W., Abraham, M. H., Doherty, R. M., & Kamlet, M. J.: The molecular properties governing solubilities of organic nonelectrolytes in water, Nature, 313, 384–386, doi:10.1038/313384A0 (1985).
  • 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.
14) Value at T = 310 K.
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.
88) Value at T = 295 K.
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.
238) Value given here as quoted by Gharagheizi et al. (2010).
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.
259) Value given here as quoted by Dupeux et al. (2022).
260) Calculated using the COSMO-RS method.
261) Value from the validation dataset.
268) Value from the test set.
272) Value from the validation dataset.
280) Value from the training set.
503) The same data were also published in Kieckbusch and King (1979a).

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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