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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 Please do not cite the old paper anymore.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) → Aldehydes (RCHO) → 2-methylpropanal

FORMULA:C4H8O
TRIVIAL NAME: isobutyraldehyde
CAS RN:78-84-2
STRUCTURE
(FROM NIST):
InChIKey:AMIMRNSIRUDHCM-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
5.1×10−2 Burkholder et al. (2019) L
5.1×10−2 Brockbank (2013) L
3.2×10−2 7600 Bruneel et al. (2016) M
5.9×10−3 4500 Strekowski and George (2005) M
3.3×10−2 Karl et al. (2003) M
3.4×10−2 Pollien et al. (2003) M
5.0×10−2 Amoore and Buttery (1978) M
5.5×10−2 Duchowicz et al. (2020) V 187)
5.5×10−2 HSDB (2015) V
6.7×10−2 Amoore and Buttery (1978) V
5.8×10−2 Yaws (2003) X 259)
4.5×10−2 Dupeux et al. (2022) Q 260)
3.8×10−2 Duchowicz et al. (2020) Q
7.4×10−2 Wang et al. (2017) Q 81) 239)
1.1×10−1 Wang et al. (2017) Q 81) 240)
4.3×10−2 Wang et al. (2017) Q 81) 241)
7.0×10−2 Hilal et al. (2008) Q
5000 Kühne et al. (2005) Q
5.2×10−2 Yaffe et al. (2003) Q 249) 250)
3.7×10−2 Yao et al. (2002) Q 230) 268)
5.2×10−2 English and Carroll (2001) Q 231) 275)
5.4×10−2 Katritzky et al. (1998) Q
8.2×10−2 Nirmalakhandan et al. (1997) Q
5100 Kühne et al. (2005) ?
5.7×10−2 Yaws (1999) ? 21)
5.1×10−2 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).
  • 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).
  • Bruneel, J., Walgraeve, C., Van Huffel, K., & Van Langenhove, H.: Determination of the gas-to-liquid partitioning coefficients using a new dynamic absorption method (DynAb method), Chem. Eng. J., 283, 544–552, doi:10.1016/J.CEJ.2015.07.053 (2016).
  • Burkholder, J. B., Sander, S. P., Abbatt, J., Barker, J. R., Cappa, C., Crounse, J. D., Dibble, T. S., Huie, R. E., Kolb, C. E., Kurylo, M. J., Orkin, V. L., Percival, C. J., Wilmouth, D. M., & Wine, P. H.: Chemical Kinetics and Photochemical Data for Use in Atmospheric Studies, Evaluation No. 19, JPL Publication 19-5, Jet Propulsion Laboratory, Pasadena, URL https://jpldataeval.jpl.nasa.gov (2019).
  • 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).
  • 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).
  • Karl, T., Yeretzian, C., Jordan, A., & Lindinger, W.: Dynamic measurements of partition coefficients using proton-transfer-reaction mass spectrometry (PTR-MS), Int. J. Mass Spectrom., 223-224, 383–395, doi:10.1016/S1387-3806(02)00927-2 (2003).
  • 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).
  • 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).
  • Pollien, P., Jordan, A., Lindinger, W., & Yeretzian, C.: Liquid-air partitioning of volatile compounds in coffee: dynamic measurements using proton-transfer-reaction mass spectrometry, Int. J. Mass Spectrom., 228, 69–80, doi:10.1016/S1387-3806(03)00197-0 (2003).
  • Strekowski, R. S. & George, C.: Measurement of Henry’s law constants for acetone, 2-butanone, 2,3-butanedione and isobutyraldehyde using a horizontal flow reactor, J. Chem. Eng. Data, 50, 804–810, doi:10.1021/JE034137R (2005).
  • 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

21) Several references are given in the list of Henry's law constants but not assigned to specific species.
81) Value at T = 288 K.
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.
239) Calculated using linear free energy relationships (LFERs).
240) Calculated using SPARC Performs Automated Reasoning in Chemistry (SPARC).
241) Calculated using COSMOtherm.
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.
268) Value from the test set.
275) Value from the test dataset.

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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