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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 Please do not cite the old paper anymore.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)Ketones (RCOR) → 2,4-dimethyl-3-pentanone

FORMULA:C7H14O
TRIVIAL NAME: diisopropyl ketone
CAS RN:565-80-0
STRUCTURE
(FROM NIST):
InChIKey:HXVNBWAKAOHACI-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
2.3×10−2 6100 Brockbank (2013) L 1)
2.4×10−2 6500 Plyasunov and Shock (2001) L
2.8×10−2 Duchowicz et al. (2020) V 187)
4.1×10−2 Cabani et al. (1981) V
9.5×10−1 6400 Bagno et al. (1991) T 475)
6400 Della Gatta et al. (1981) T
2.8×10−2 Yaws (2003) X 238)
4.9×10−3 Duchowicz et al. (2020) Q
7.8×10−2 Raventos-Duran et al. (2010) Q 244) 272)
2.5×10−2 Raventos-Duran et al. (2010) Q 245)
6.2×10−2 Raventos-Duran et al. (2010) Q 246)
2.5×10−2 Gharagheizi et al. (2010) Q 247)
3.5×10−2 Hilal et al. (2008) Q
1.5×10−1 Modarresi et al. (2007) Q 68)
6000 Kühne et al. (2005) Q
2.8×10−2 Yaffe et al. (2003) Q 249) 250)
1.4×10−2 Yao et al. (2002) Q 230)
1.4×10−2 English and Carroll (2001) Q 231) 232)
1.3×10−2 Katritzky et al. (1998) Q
6.0×10−2 Nirmalakhandan et al. (1997) Q
4900 Kühne et al. (2005) ?
2.8×10−2 Yaws (1999) ? 21)
2.8×10−2 Yaws et al. (1998) ?

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

  • Bagno, A., Lucchini, V., & Scorrano, G.: Thermodynamics of protonation of ketones and esters and energies of hydration of their conjugate acids, J. Phys. Chem., 95, 345–352, doi:10.1021/J100154A063 (1991).
  • 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).
  • Cabani, S., Gianni, P., Mollica, V., & Lepori, L.: Group contributions to the thermodynamic properties of non-ionic organic solutes in dilute aqueous solution, J. Solution Chem., 10, 563–595, doi:10.1007/BF00646936 (1981).
  • Della Gatta, G., Stradella, L., & Venturello, P.: Enthalpies of solvation in cyclohexane and in water for homologous aliphatic ketones and esters, J. Solution Chem., 10, 209–220, doi:10.1007/BF00653098 (1981).
  • 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).
  • 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).
  • 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).
  • 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).
  • 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).
  • 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).
  • Plyasunov, A. V. & Shock, E. L.: Group contribution values of the infinite dilution thermodynamic functions of hydration for aliphatic noncyclic hydrocarbons, alcohols, and ketones at 298.15 K and 0.1 MPa, J. Chem. Eng. Data, 46, 1016–1019, doi:10.1021/JE0002282 (2001).
  • 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).
  • 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., Sheth, S. D., & Han, M.: Using solubility and Henry’s law constant data for ketones in water, Pollut. Eng., 30, 44–46 (1998).

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.
187) Estimation based on the quotient between vapor pressure and water solubility, 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).
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.
272) Value from the validation dataset.
475) Calculated under the assumption that ∆G and ∆H are based on [mol L−1] and [atm] as the standard states.

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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