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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 Please do not cite the old paper anymore.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) → nonanal

FORMULA:C8H17CHO
CAS RN:124-19-6
STRUCTURE
(FROM NIST):
InChIKey:GYHFUZHODSMOHU-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
1.1×10−2 6800 Brockbank (2013) L
1.0×10−2 6700 Zhou and Mopper (1990) M 458)
1.3×10−2 Buttery et al. (1969) M
7.1×10−2 Buttery et al. (1965) M
1.3×10−2 Amoore and Buttery (1978) V
1.0×10−2 Yaws (2003) X 259)
1.0×10−2 Yaws (2003) X 38) 238)
1.4×10−2 Sieg et al. (2008) C
1.8×10−2 Dupeux et al. (2022) Q 260)
9.6×10−2 Keshavarz et al. (2022) Q
9.7×10−2 Duchowicz et al. (2020) Q 185)
1.8×10−3 Gharagheizi et al. (2012) Q
2.0×10−2 Raventos-Duran et al. (2010) Q 243) 244)
3.9×10−2 Raventos-Duran et al. (2010) Q 245)
2.0×10−2 Raventos-Duran et al. (2010) Q 246)
1.1×10−2 Gharagheizi et al. (2010) Q 247)
2.4×10−2 Hilal et al. (2008) Q
5.9×10−2 Modarresi et al. (2007) Q 68)
1.3×10−2 Yaffe et al. (2003) Q 249) 250)
1.8×10−2 Yao et al. (2002) Q 230)
2.3×10−2 English and Carroll (2001) Q 231) 232)
2.8×10−2 Nirmalakhandan et al. (1997) Q
2.1×10−2 Suzuki et al. (1992) Q 233)
2.0×10−2 Meylan and Howard (1991) Q
1.3×10−2 Duchowicz et al. (2020) ? 21) 186)
1.5×10−2 Yaws (1999) ? 21) 38)
6.9×10−3 Yaws and Yang (1992) ? 21) 38)
1.3×10−2 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).
  • Buttery, R. G., Guadagni, D. G., & Okano, S.: Air–water partition coefficients of some aldehydes, J. Sci. Food Agric., 16, 691–692, doi:10.1002/JSFA.2740161110 (1965).
  • 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).
  • 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).
  • 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).
  • 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).
  • Meylan, W. M. & Howard, P. H.: Bond contribution method for estimating Henry’s law constants, Environ. Toxicol. Chem., 10, 1283–1293, doi:10.1002/ETC.5620101007 (1991).
  • 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).
  • 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).
  • Sieg, K., Fries, E., & Püttmann, W.: Analysis of benzene, toluene, ethylbenzene, xylenes and n-aldehydes in melted snow water via solid-phase dynamic extraction combined with gas chromatography/mass spectrometry, J. Chromatogr. A, 1178, 178–186, doi:10.1016/J.CHROMA.2007.11.025 (2008).
  • 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).
  • Yaws, C. L. & Yang, H.-C.: Henry’s law constant for compound in water, in: Thermodynamic and Physical Property Data, edited by Yaws, C. L., pp. 181–206, Gulf Publishing Company, Houston, TX, ISBN 0884150313 (1992).
  • Zhou, X. & Mopper, K.: Apparent partition coefficients of 15 carbonyl compounds between air and seawater and between air and freshwater; Implications for air-sea exchange, Environ. Sci. Technol., 24, 1864–1869, doi:10.1021/ES00082A013 (1990).

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.
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.
185) Value from the validation set for checking whether the model is satisfactory for compounds that are absent from the training set.
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.
238) Value given here as quoted by Gharagheizi et al. (2010).
243) Value from the training dataset.
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.
458) Data from Table 1 by Zhou and Mopper (1990) were used to redo the regression analysis. The data for acetone in their Table 2 are incorrect.

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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