Predicting the Properties of Self-Associating Compounds

Predicting the Properties of Self-Associating Compounds

Author: Chemistry – A European Journal

Being able to predict physical properties from a chemical structure is important for the development of self-assembled nanostructures, gels, and organic frameworks. Using fast, low-level computational techniques is an attractive way to make such predictions.

Jennifer Hiscock, University of Kent, UK, and colleagues have studied the hydrogen-bonded self-association of 39 structurally similar (thio)urea‐anion amphiphiles (compounds with both hydrophilic and lipophilic properties) within the solid, gas, and solution states. The team used experiments such as X-ray diffraction and NMR spectroscopy, as well as both high- and low-level computational modeling. Depending on the exact chemical structure and the conditions, the monomers can self-assemble into, e.g., tape-like structures, dimers, or different types of stacks (pictured).

The experimental and computational exploration of these compounds allowed the researchers to find structure-property relationships. Surface charge parameters from simple low-level computational modeling techniques were found to correlate well with experimentally derived solution-state dimerization constants. A correlation was also identified between experimentally derived CMC (critical micelle concentration) values and calculated surface charge and LogP (partition coefficient) parameters. Together, these correlations demonstrate the potential for low-level modeling to predict the physical properties of self-associated systems.


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