(319g) Prediction of 1-Octanol/Water Partition Coefficients for Ionic Liquids by the Application of Adaptive Biasing Force Method | AIChE

(319g) Prediction of 1-Octanol/Water Partition Coefficients for Ionic Liquids by the Application of Adaptive Biasing Force Method

Authors 

Potoff, J. - Presenter, Wayne State University
Bhatnagar, N., Wayne State University
Baker, G. A., Oak Ridge National Laboratory


Ionic liquids are fast emerging as compounds representing a new class of solvents with a variety of applications in the chemical industry such as organic synthesis [1], transition metal catalysis [2], liquid phase exfoliation of graphene [3] and carbon capture [4] to name a few. Ionic liquids are tagged as environmental friendly substitutes for volatile organic solvents as they possess low vapor pressures and also have the ability to act as bio catalysts. Other advantageous properties include high chemical and thermal stability, high ionic conductivity and non-inflammability [1]. Also, with a plethora of cations and anions available for generation of ionic liquids, a large set of permutations is available depending on the problem at hand. However, ILs are also associated with certain eco-toxicological concerns as well marked by factors like sorption, hydrolysis and degradation. One prime parameter to quantify the environmental impact of any compound is the 1-octanol/water partition coefficient which provides a measure of solute hydrophobicity and also serves as a rough guideline for predicting “enzyme compatible” media for non-aqueous biocatalysis [5]. Experimental methods such as shake-flask followed by concentration determination using UV–vis spectroscopy or high-performance liquid chromatography (HPLC) have been used primarily for imidazolium-based ILs due to the ability to detect the imidazolium ring by UV–vis [6]. Theoretical methods such as linear free energy relation correlations (LFER) [7] and COSMO-RS [8] have been used to predict log Kow for ILs albeit with a disadvantage that these require an extensive training set of experimentally-determined Kow to parameterize the coefficients for solvation free energy correlation.

In this work, difference in Gibbs free energy of solute transfer between two solvent phases is calculated by the application of a recently developed method by Darve et al. [9-11] known as adaptive biasing force (ABF). This free energy is then used for estimation of log Kow for imidazolium based ILs. ABF follows an unconstrained molecular dynamics based approach where the system is allowed to evolve freely enabling the solute to visit multiple states encountered between the initial and final state points. Molecular dynamics simulations were performed with NAMD [12] and trajectory visualization and analysis was done with VMD [13]. All atom force field developed by Lopes et al.[14] was employed to model imidazolium cations and anions. TraPPE-UA mode developed for alcohols [15] and SPC/E [16] model was used to describe the interactions of 1-octanol and water respectively. ILs made up of 1-butyl-3-methylimidazolium ([bmim]+) cation and paired with six different anions were studied in this work. Free energy of hydration and solvation are calculated initially depending on whether the solute is transferred into vacuum from the water or 1-octanol phase respectively. Results were found to be in good agreement with the experimental data with a marked improvement when water saturated 1-octanol phase is used instead of dry 1-octanol.  

References

  1. Zhao, H and Malhotra, S. V. Aldrichimica Acta (2002) 35, 75-83
  2. Wasserscheid, P. and Keim, W. Angew. Chem. Int. Ed. (2000) 39, 3772-3789
  3. X. Wang, P. F. Fulvio, G. A. Baker, G. M. Veith, R. R. Unocic, S. M. Mahurin, M. Chi and S. Dai, Chem. Commun., 2010, 46, 4487-4489.
    1. E. D. Bates, R. D. Mayton, I. Ntai and J. H. Davis, JACS Commun., 2001, 124, 926-927.
    2. U. Kragl, M. Eckstein and N. Kaftzik, Curr. Opin. Biotechnol., 2002, 13, 565-571.
    3. L. Ropel, L. S. Belvèze, S. N. V. K. Aki, M. A. Stadtherr and J. F. Brennecke, Green Chem., 2005, 7, 83-90.
    4. M. H. Abraham, A. M. Zissimos, J. G. Huddleston, H. D. Willauer and R. D. Rogers, Ind. Eng. Chem. Res, 2003, 42, 413-418.
    5. C.-W. Cho, U. Preiss, C. Jungnickel, S. Stolte, J. Arning, Ranke, A. J. Klamt, I. Krossing and J. Thoming, J. Phys. Chem. B, 2011, 115, 6040-6050.
    6. E. Darve and A. Pohorille, J. Chem. Phys., 2001, 115, 9169-9183.
    7. E. Darve, M. A. Wilson and A. Pohorille, Mol. Sim., 2002, 28, 113-144.
    8. E. Darve, David Rodríguez-Gómez and A. Pohorille, J Chem Phys, 2008, 128, 144120.
    9. J. C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R. D. Skeel, L. Kale and K. Schulten, J. Comp. Chem., 2005, 26, 1781-1802.
    10. Humphrey, W., Dalke, A. and Schulten, K., J. Molec. Graphics, 1996, vol. 14, pp. 33-38.
    11. J. Lopes, J. Deschamps and A. Padua, J. Phys. Chem. B, 2004, 108, 2038-2047.
    12. B. Chen, J. J. Potoff and J. I. Siepmann, J. Phys. Chem. B 2001, 105, 3093.
    13. H. J. C. Berendsen, J. R. Grigera and T. P. Straatsma, J. Phys. Chem., 1987, 91, 6269-6271.
See more of this Session: Thermophysical Properties and Phase Behavior I

See more of this Group/Topical: Engineering Sciences and Fundamentals