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 Please do not cite the old paper anymore.
|
FORMULA: | C9H12O |
CAS RN: | 122-97-4 |
STRUCTURE
(FROM
NIST):
|
|
InChIKey: | VAJVDSVGBWFCLW-UHFFFAOYSA-N |
|
|
References |
Type |
Notes |
[mol/(m3Pa)] |
[K] |
|
|
|
> 1.8×102 |
|
Altschuh et al. (1999) |
M |
|
4.8×101 |
|
Abraham et al. (1994a) |
R |
|
4.1×101 |
|
Keshavarz et al. (2022) |
Q |
|
8.8 |
|
Duchowicz et al. (2020) |
Q |
300)
|
1.4×101 |
|
Hilal et al. (2008) |
Q |
|
2.0×101 |
|
Modarresi et al. (2007) |
Q |
68)
|
3.4×101 |
|
English and Carroll (2001) |
Q |
231)
232)
|
4.2×101 |
|
Nirmalakhandan et al. (1997) |
Q |
|
4.9×101 |
|
Duchowicz et al. (2020) |
? |
21)
186)
|
Data
The first column contains Henry's law solubility constant
at the reference temperature of 298.15 K.
The second column contains the temperature dependence
, also at the
reference temperature.
References
-
Abraham, M. H., Andonian-Haftvan, J., Whiting, G. S., Leo, A., & Taft, R. S.: Hydrogen bonding. Part 34. The factors that influence the solubility of gases and vapours in water at 298 K, and a new method for its determination, J. Chem. Soc. Perkin Trans. 2, pp. 1777–1791, doi:10.1039/P29940001777 (1994a).
-
Altschuh, J., Brüggemann, R., Santl, H., Eichinger, G., & Piringer, O. G.: Henry’s law constants for a diverse set of organic chemicals: Experimental determination and comparison of estimation methods, Chemosphere, 39, 1871–1887, doi:10.1016/S0045-6535(99)00082-X (1999).
-
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).
-
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).
-
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).
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. |
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. |
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. |
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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