Abstract

Molecular cruciforms are X-shaped systems in which two conjugation axes intersect at a central core. If one axis of these molecules is substituted with electron-donors, and the other with electron-acceptors, cruciforms' HOMO will localize along the electron-rich and LUMO along the electron-poor axis. This spatial isolation of cruciforms' frontier molecular orbitals (FMOs) is essential to their use as sensors, since analyte binding to the cruciform invariably changes its HOMO-LUMO gap and the associated optical properties. Using this principle, Bunz and Miljanić groups developed 1,4-distyryl-2,5-bis(arylethynyl)benzene and benzobisoxazole cruciforms, respectively, which act as fluorescent sensors for metal ions, carboxylic acids, boronic acids, phenols, amines, and anions. The emission colors observed when these cruciform are mixed with analytes are highly sensitive to the details of analyte's structure and - because of cruciforms' charge-separated excited states - to the solvent in which emission is observed. Structurally closely related species can be qualitatively distinguished within several analyte classes: (a) carboxylic acids; (b) boronic acids, and (c) metals. Using a hybrid sensing system composed from benzobisoxazole cruciforms and boronic acid additives, we were also able to discern among structurally similar: (d) small organic and inorganic anions, (e) amines, and (f) phenols. The method used for this qualitative distinction is exceedingly simple. Dilute solutions (typically 10(-6) M) of cruciforms in several off-the-shelf solvents are placed in UV/Vis vials. Then, analytes of interest are added, either directly as solids or in concentrated solution. Fluorescence changes occur virtually instantaneously and can be recorded through standard digital photography using a semi-professional digital camera in a dark room. With minimal graphic manipulation, representative cut-outs of emission color photographs can be arranged into panels which permit quick naked-eye distinction among analytes. For quantification purposes, Red/Green/Blue values can be extracted from these photographs and the obtained numeric data can be statistically processed.

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