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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 Please do not cite the old paper anymore.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) → Alcohols (ROH) → 2-butanol

FORMULA:C4H10O
TRIVIAL NAME: sec-butanol
CAS RN:78-92-2
STRUCTURE
(FROM NIST):
InChIKey:BTANRVKWQNVYAZ-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
1.1 7600 Burkholder et al. (2019) L 1)
1.1 7600 Burkholder et al. (2015) L 1)
9.5×10−1 7600 Brockbank (2013) L 1)
1.1 7300 Sander et al. (2011) L
1.1 7300 Sander et al. (2006) L
1.0 7400 Fogg and Sangster (2003) L
9.9×10−1 7600 Plyasunov and Shock (2000) L
8.3×10−1 Merk and Riederer (1997) M
1.1 7300 Snider and Dawson (1985) M
9.8×10−1 Rytting et al. (1978) M
9.6×10−1 Butler et al. (1935) M
9.5×10−1 7600 Fenclová et al. (2007) V 1)
1.1 Mackay et al. (2006c) V
1.1 Mackay et al. (1995) V
9.1×10−1 7500 Cabani et al. (1975b) T
6.7×10−1 Yaws (2003) X 259)
1.1 Dupeux et al. (2022) Q 260)
1.2 Hayer et al. (2022) Q 20)
1.2 Keshavarz et al. (2022) Q
5.1×10−1 Duchowicz et al. (2020) Q 300)
2.3×10−1 Wang et al. (2017) Q 81) 239)
8.9×10−1 Wang et al. (2017) Q 81) 240)
7.3×10−1 Wang et al. (2017) Q 81) 241)
7.8×10−1 Raventos-Duran et al. (2010) Q 243) 244)
4.9×10−1 Raventos-Duran et al. (2010) Q 245)
9.9×10−1 Raventos-Duran et al. (2010) Q 246)
3.9×10−1 Hilal et al. (2008) Q
4.9×10−1 Modarresi et al. (2007) Q 68)
7200 Kühne et al. (2005) Q
1.2 Yaffe et al. (2003) Q 249) 250)
6.2×10−1 Yao et al. (2002) Q 230)
8.4×10−1 English and Carroll (2001) Q 231) 232)
5.7×10−1 Katritzky et al. (1998) Q
1.2 Yaws et al. (1997) Q
7.7×10−1 Suzuki et al. (1992) Q 233)
9.0×10−1 Nirmalakhandan and Speece (1988) Q
1.1 Duchowicz et al. (2020) ? 21) 186)
7100 Kühne et al. (2005) ?
1.2 Yaws (1999) ? 21)
9.9×10−1 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).
  • Burkholder, J. B., Sander, S. P., Abbatt, J., Barker, J. R., Huie, R. E., Kolb, C. E., Kurylo, M. J., Orkin, V. L., Wilmouth, D. M., & Wine, P. H.: Chemical Kinetics and Photochemical Data for Use in Atmospheric Studies, Evaluation No. 18, JPL Publication 15-10, Jet Propulsion Laboratory, Pasadena, URL https://jpldataeval.jpl.nasa.gov (2015).
  • 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).
  • Butler, J. A. V., Ramchandani, C. N., & Thomson, D. W.: The solubility of non-electrolytes. Part I. The free energy of hydration of some aliphatic alcohols, J. Chem. Soc., pp. 280–285, doi:10.1039/JR9350000280 (1935).
  • Cabani, S., Conti, G., Mollica, V., & Lepori, L.: Thermodynamic study of dilute aqueous solutions of organic compounds. Part 4. – Cyclic and straight chain secondary alcohols, J. Chem. Soc. Faraday Trans. 1, 71, 1943–1952, doi:10.1039/F19757101943 (1975b).
  • 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).
  • Fenclová, D., Dohnal, V., Vrbka, P., & Laštovka, V.: Temperature dependence of limiting activity coefficients, Henry’s law constants, and related infinite dilution properties of branched (C3 and C4) alkanols in water, J. Chem. Eng. Data, 52, 989–1002, doi:10.1021/JE600567Z (2007).
  • Fogg, P. & Sangster, J.: Chemicals in the Atmosphere: Solubility, Sources and Reactivity, John Wiley & Sons, Inc., ISBN 978-0-471-98651-5 (2003).
  • Hayer, N., Jirasek, F., & Hasse, H.: Prediction of Henry’s law constants by matrix completion, AIChE J., 68, e17 753, doi:10.1002/AIC.17753 (2022).
  • 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).
  • 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).
  • 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).
  • 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).
  • Merk, S. & Riederer, M.: Sorption of volatile C1 to C6 alkanols in plant cuticles, J. Exp. Bot., 48, 1095–1104, doi:10.1093/JXB/48.5.1095 (1997).
  • 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).
  • Plyasunov, A. V. & Shock, E. L.: Thermodynamic functions of hydration of hydrocarbons at 298.15K and 0.1MPa, Geochim. Cosmochim. Acta, 64, 439–468, doi:10.1016/S0016-7037(99)00330-0 (2000).
  • 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).
  • Rytting, J. H., Huston, L. P., & Higuchi, T.: Thermodynamic group contributions for hydroxyl, amino, and methylene groups, J. Pharm. Sci., 69, 615–618, doi:10.1002/JPS.2600670510 (1978).
  • Sander, S. P., Friedl, R. R., Golden, D. M., Kurylo, M. J., Moortgat, G. K., Keller-Rudek, H., Wine, P. H., Ravishankara, A. R., Kolb, C. E., Molina, M. J., Finlayson-Pitts, B. J., Huie, R. E., & Orkin, V. L.: Chemical Kinetics and Photochemical Data for Use in Atmospheric Studies, Evaluation Number 15, JPL Publication 06-2, Jet Propulsion Laboratory, Pasadena, CA, URL https://jpldataeval.jpl.nasa.gov (2006).
  • Sander, S. P., Abbatt, J., Barker, J. R., Burkholder, J. B., Friedl, R. R., Golden, D. M., Huie, R. E., Kolb, C. E., Kurylo, M. J., Moortgat, G. K., Orkin, V. L., & Wine, P. H.: Chemical Kinetics and Photochemical Data for Use in Atmospheric Studies, Evaluation No. 17, JPL Publication 10-6, Jet Propulsion Laboratory, Pasadena, URL https://jpldataeval.jpl.nasa.gov (2011).
  • Snider, J. R. & Dawson, G. A.: Tropospheric light alcohols, carbonyls, and acetonitrile: Concentrations in the southwestern United States and Henry’s law data, J. Geophys. Res., 90, 3797–3805, doi:10.1029/JD090ID02P03797 (1985).
  • 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).
  • 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).
  • Yaws, C. L., Hopper, J. R., Sheth, S. D., Han, M., & Pike, R. W.: Solubility and Henry’s law constant for alcohols in water, Waste Manage., 17, 541–547, doi:10.1016/S0956-053X(97)10057-5 (1997).

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.
20) Calculated using machine learning matrix completion methods (MCMs).
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.
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.
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.
300) Value from the test set for true external validation.

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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