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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 Please do not cite the old paper anymore.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) → methyl butanoate

FORMULA:C3H7COOCH3
TRIVIAL NAME: methyl butyrate
CAS RN:623-42-7
STRUCTURE
(FROM NIST):
InChIKey:UUIQMZJEGPQKFD-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
3.9×10−2 5700 Brockbank (2013) L 1) 513)
4.2×10−2 5700 Plyasunov et al. (2004) L
3.7×10−2 Aprea et al. (2007) M
3.6×10−2 Ioffe et al. (1984) M
4.8×10−2 Buttery et al. (1969) M
3.6×10−1 4400 Djerki and Laub (1988) V
3.7×10−2 Amoore and Buttery (1978) V
5800 Della Gatta et al. (1981) T
3.5×10−2 Yaws (2003) X 259)
3.6×10−2 Nahon et al. (2000) C 14)
4.4×10−2 Dupeux et al. (2022) Q 260)
2.9×10−2 Keshavarz et al. (2022) Q
2.3×10−1 Duchowicz et al. (2020) Q 300)
4.8×10−2 Li et al. (2014) Q 242)
1.2×10−2 Gharagheizi et al. (2012) Q
3.9×10−2 Raventos-Duran et al. (2010) Q 243) 244)
2.5×10−2 Raventos-Duran et al. (2010) Q 245)
3.1×10−2 Raventos-Duran et al. (2010) Q 246)
2.8×10−2 Hilal et al. (2008) Q
4.0×10−2 Modarresi et al. (2007) Q 68)
4.6×10−2 Yaffe et al. (2003) Q 249) 273)
3.2×10−2 Yao et al. (2002) Q 230)
4.0×10−2 English and Carroll (2001) Q 231) 232)
3.9×10−2 Katritzky et al. (1998) Q
1.5×10−1 Russell et al. (1992) Q 280)
3.4×10−2 Suzuki et al. (1992) Q 233)
3.2×10−2 Nirmalakhandan and Speece (1988) Q
4.8×10−2 Duchowicz et al. (2020) ? 21) 186)
3.5×10−2 Yaws (1999) ? 21)
4.8×10−2 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).
  • Amoore, J. E. & Buttery, R. G.: Partition coefficient and comparative olfactometry, Chem. Senses Flavour, 3, 57–71, doi:10.1093/CHEMSE/3.1.57 (1978).
  • Aprea, E., Biasioli, F., Märk, T. D., & Gasperi, F.: PTR-MS study of esters in water and water/ethanol solutions: Fragmentation patterns and partition coefficients, Int. J. Mass Spectrom., 262, 114–121, doi:10.1016/J.IJMS.2006.10.016 (2007).
  • 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).
  • Buttery, R. G., Ling, L. C., & Guadagni, D. G.: Volatilities of aldehydes, ketones, and esters in dilute water solutions, J. Agric. Food Chem., 17, 385–389, doi:10.1021/JF60162A025 (1969).
  • 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).
  • Djerki, R. A. & Laub, R. J.: Solute retention in column liquid chromatography. X. Determination of solute infinite-dilution activity coefficients in methanol, water, and their mixtures, by combined gas-liquid and liquid-liquid chromatography, J. Liq. Chromatogr., 11, 585–612, doi:10.1080/01483918808068333 (1988).
  • 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., 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).
  • 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).
  • Ioffe, B. V., Kostkina, M. I., & Vitenberg, A. G.: Preparation of standard vapor-gas mixtures for gas chromatography: discontinuous gas extraction, Anal. Chem., 56, 2500–2503, doi:10.1021/AC00277A053 (1984).
  • 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).
  • 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).
  • 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).
  • Nahon, D. F., Harrison, M., & Roozen, J. P.: Modeling flavor release from aqueous sucrose solutions, using mass transfer and partition coefficients, J. Agric. Food Chem., 48, 1278–1284, doi:10.1021/JF990464K (2000).
  • Nirmalakhandan, N. N. & Speece, R. E.: QSAR model for predicting Henry’s constant, Environ. Sci. Technol., 22, 1349–1357, doi:10.1021/ES00176A016 (1988).
  • 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).
  • 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.
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.
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.
233) Calculated with a principal component analysis (PCA); see Suzuki et al. (1992) for details.
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.
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.
259) Value given here as quoted by Dupeux et al. (2022).
260) Calculated using the COSMO-RS method.
273) Value from the test set.
280) Value from the training set.
300) Value from the test set for true external validation.
513) Values at 298 K in Tables C2 and C5 of Brockbank (2013) are inconsistent, with 5 % difference.

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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