Developing a Gel-Based Sensor Using Crystal Morphology Prediction.

The stimuli-responsive nature of molecular gels makes them appealing platforms for sensing. The biggest challenge is in identifying an appropriate gelator for each specific chemical or biological target. Due to the similarities between crystallization and gel formation, we hypothesized that the tools used to predict crystal morphologies could be useful for identifying gelators. Herein, we demonstrate that new gelators can be discovered by focusing on scaffolds with predicted high aspect ratio crystals. Using this morphology prediction method, we identified two promising molecular scaffolds containing lead atoms. Because solvent is largely ignored in morphology prediction but can play a major role in gelation, each scaffold needed to be structurally modified before six new Pb-containing gelators were discovered. One of these new gelators was developed into a robust sensor capable of detecting lead at the U.S. Environmental Protection Agency limit for paint (5000 ppm).