Dual‐Mode Ratiometric ESIPT Probes for Vapor‐Phase Detection of a Pulmonary Agent: Leveraging Machine Learning for High‐Precision Quantitative Analysis

Benzimidazole-based ESIPT probes have been developed toward dual-mode ratiometric sensing of a pulmonary agent, phosgene both in solution phase and solid state. Machine-learning based polynomial regression models (quadratic and cubic) demonstrated exceptional accuracy, capturing the nonlinear fluorescence response of triphosgene with high precision, as evidenced by R-squared values nearing 1.

Abstract

The present work reports design and synthesis of two benzimidazole based ESIPT probes with variable number of receptor sites (monomer, 1 and dimer, 2). Both compounds exhibited dual-mode ratiometric response toward a pulmonary agent, phosgene both in solution phase and solid state. Mechanistic investigations indicated formation of benzoxazolone derivatives, which enhanced the emission from corresponding enol form. Not only that, the chemodosimetric adduct, formed as result of phosgene interaction, showed improved self-assembly behavior than that of the precursor. Though the mode of interaction appeared to be similar, the dimeric compound showed larger response toward phosgene. Machine-learning based polynomial regression models (quadratic and cubic) demonstrated exceptional accuracy, capturing the nonlinear fluorescence response of triphosgene with high precision, as evidenced by R-squared values nearing 1. Especially, cubic polynomial regression was found to be particularly effective. Well-characterized, chemically-modified paper strips were developed for vapor-phase detection of phosgene below its permissible level.

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