Picric acid (PA) is a lethal explosive substance that is easily soluble in water and harmful to the environment. Here, a supramolecular polymer material BTPY@Q[8] with aggregation induced emission (AIE) was prepared by supramolecular self-assembly of cucurbit uril (Q[8]) and 1,3,5-tris[4-(pyridin-4-yl) phenyl] benzene derivative (BTPY), which exhibited aggregation-induced fluorescence enhancement. To this supramolecular self-assembly, the addition of a number of nitrophenols was found to have no obvious effect on the fluorescence, however on addition of PA, the fluorescence intensity underwent a dramatic quench. For PA, BTPY@Q[8] had sensitive specificity and effective selectivity. Based on this, a quick and simple on-site visual PA fluorescence quantitative detection platform was developed using smart phones, and the platform was used to monitor temperature. Machine learning (ML) is a popular pattern recognition technology, which can accurately predict the results from data. Therefore, ML has much more potential for analyzing and improving sensing data than the widely used statistical pattern recognition method. In the field of analytical science, the sensing platform offers a reliable method for the quantitative detection of PA that can be applied to other analytes or micropollutant screening.
Keywords: Cucurbit[8]uril; Fluorescent sensor; Host-guest; Picric acid; Self-assemble.
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