Investigating the effect of supramolecular gel phase crystallization on gel nucleation

Arnab Dawn, Marzieh Mirzamani, Christopher D. Jones, Dmitry S. Yufit, Shuo Qian, Jonathan W. Steed, Harshita Kumari

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Supramolecular gel phase crystallization offers a new strategy for drug polymorph screening and discovery. In this method, the crystallization outcome depends on the interaction between solute and gel fibre. While supramolecular gels have shown success in producing new polymorphs and crystals with novel morphologies, role of the gel and nature of gel-solute interaction remains largely unexplored. The present study aims to provide a comprehensive picture of the structural evolution of a supramolecular gel produced from a bis(urea) based gelator (G) in the presence of a polymorphic drug carbamazepine (CBZ). The structural aspects of the gel have been assessed by single crystal X-ray analysis, X-ray powder diffraction (XRPD) and solid state NMR spectroscopy. Small Angle Neutron Scattering (SANS) has been used to follow the changes in gel structure in the presence of CBZ. Visual evidence from morphological study and structural evolution observed at a macroscopic level from rheological measurements, shows good agreement with the SANS results. The concentration of the gelator and the relative proportion of G to CBZ were found to be crucial factors in determining the competitive nucleation events involving gelation and crystallization. At a critical G to CBZ ratio the effect of CBZ on gel structure was maximum and fiber bundling in the gel was found to be critically affected. This study offers important information about how the interplay of gelator assembly and gel-solute interactions can fine-tune the nucleation events in a supramolecular gel phase crystallization.

Original languageEnglish
Pages (from-to)9489-9497
Number of pages9
JournalSoft Matter
Volume14
Issue number46
DOIs
StatePublished - 2018

Funding

This work was funded by start-up funds (HK) provided by Office of Research, University of Cincinnati. This work benefited from the use of the SasView application, originally developed under NSF award DMR-0520547. SasView contains code developed with funding from the European Union’s Horizon 2020 research and innovation programme under the SINE2020 project, grant agreement No 654000. We thank Dr Boualem Hommouda for valuable discussions on SANS data analyses. We thank the Engineering and Physical Sciences Research Council for a studentship (to CDJ) and the Royal Society for a Wolfson Research Merit Award (to JWS).

FundersFunder number
Office of Research, University of Cincinnati
National Science FoundationDMR-0520547
Horizon 2020 Framework Programme654000
Engineering and Physical Sciences Research Council
Royal Society

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