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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 Please do not cite the old paper anymore.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 Constants → Organic species with nitrogen (N) → Nitro compounds (RNO2) → nitromethane

FORMULA:CH3NO2
CAS RN:75-52-5
STRUCTURE
(FROM NIST):
InChIKey:LYGJENNIWJXYER-UHFFFAOYSA-N

Hscp d ln Hs cp / d (1/T) References Type Notes
[mol/(m3Pa)] [K]
3.4×10−1 4000 Burkholder et al. (2019) L
3.4×10−1 4000 Burkholder et al. (2015) L
3.6×10−1 3900 Brockbank (2013) L 1)
3.4×10−1 4000 Sander et al. (2011) L
3.4×10−1 4000 Sander et al. (2006) L
3.5×10−1 4000 Beneš and Dohnal (1999) M
3.6×10−1 Park et al. (1987) M
4.5×10−1 Rohrschneider (1973) M
3.4×10−2 Yaws (2003) X 238)
3.5×10−1 Gaffney and Senum (1984) X 391)
4.4×10−1 Hayer et al. (2022) Q 20)
2.5×10−1 Keshavarz et al. (2022) Q
6.2×10−1 Duchowicz et al. (2020) Q 185)
1.2×10−1 Raventos-Duran et al. (2010) Q 243) 244)
2.0×10−1 Raventos-Duran et al. (2010) Q 245)
2.5×10−1 Raventos-Duran et al. (2010) Q 246)
3.3×10−2 Gharagheizi et al. (2010) Q 247)
3.4×10−1 Hilal et al. (2008) Q
3.3×10−2 Modarresi et al. (2007) Q 68)
3700 Kühne et al. (2005) Q
2.3×10−1 Yaffe et al. (2003) Q 249) 273)
6.2×10−1 English and Carroll (2001) Q 231) 232)
2.8×10−2 Katritzky et al. (1998) Q
7.3×10−2 Nirmalakhandan et al. (1997) Q
3.5×10−1 Duchowicz et al. (2020) ? 21) 186)
3500 Kühne et al. (2005) ?
3.4×10−2 Yaws (1999) ? 21)
1.8×10−1 Abraham and Weathersby (1994) ? 21)
3.6×10−2 Yaws and Yang (1992) ? 21)
3.6×10−1 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. & Weathersby, P. K.: Hydrogen bonding. 30. Solubility of gases and vapors in biological liquids and tissues, J. Pharm. Sci., 83, 1450–1456, doi:10.1002/JPS.2600831017 (1994).
  • 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).
  • Beneš, M. & Dohnal, V.: Limiting activity coefficients of some aromatic and aliphatic nitro compounds in water, J. Chem. Eng. Data, 44, 1097–1102, doi:10.1021/JE9900326 (1999).
  • 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).
  • 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).
  • Gaffney, J. S. & Senum, G. I.: Peroxides, peracids, aldehydes, and PANs and their links to natural and anthropogenic organic sources, in: Gas-Liquid Chemistry of Natural Waters, edited by Newman, L., pp. 5–1–5–7, NTIS TIC-4500, UC-11, BNL 51757 Brookhaven National Laboratory (1984).
  • 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).
  • 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).
  • 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).
  • Park, J. H., Hussam, A., Couasnon, P., Fritz, D., & Carr, P. W.: Experimental reexamination of selected partition coefficients from Rohrschneider’s data set, Anal. Chem., 59, 1970–1976, doi:10.1021/AC00142A016 (1987).
  • 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).
  • Rohrschneider, L.: Solvent characterization by gas-liquid partition coefficients of selected solutes, Anal. Chem., 45, 1241–1247, doi:10.1021/AC60329A023 (1973).
  • 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).
  • 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).
  • 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).

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.
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.
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).
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.
273) Value from the test set.
391) Value given here as quoted by Gaffney et al. (1987).

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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