OPTICAL SENSING AND CHEMOMETRIC ANALYSIS OF CHIRAL COMPOUNDS

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With support of the Chemical Structure, Dynamic & Mechanism B Program of the Chemistry Division, Professor Christian Wolf’s research group of the Department of Chemistry at Georgetown University is developing optical sensing methodology that accelerates and broadens chiral compound analysis by introducing widely applicable small-molecule probes, conceptually new molecular recognition strategies, artificial intelligence tools, and automated high-throughput experimentation technology. This research has the potential to push current research and development boundaries, transform cumbersome analysis protocols, and unfold new opportunities, for example, continuous asymmetric reaction development workflows that eliminate slow and work-intensive laboratory practice. The proposed work is highly collaborative involving industrial and academic partners and provides multiple training opportunities for graduate, undergraduate and high school students from groups underrepresented in science.Chiroptical sensing assays for primary and secondary amines, amino alcohols, amino acids, alcohols and hydroxy acids have been introduced to date. However, comprehensive sensing--determination of absolute configuration, selectivity, and concentration--remains a challenge for a number of systems. These include (a) compounds devoid of functionalities such as nitriles, esters, amides, tertiary amines or aromatic scaffolds, (b) substrates that possess more than one chirality center and (c) multicomponent mixtures. To overcome these limitations, new molecular probe designs that quickly capture currently elusive chiral target compounds in solution and report the molecular recognition event via spontaneous chirality amplification across stereodynamic receptor scaffolds into a distinct and quantifiable chiroptical (circular dichroism and UV/vis absorption) response will be introduced. In addition, organic reaction based multi-modal optical chirality sensing methodology and chemometrics capable of orthogonal data fusion and spectral deconvolution will be developed to achieve comprehensive sensing of complicated mixtures by eliminating the common need for physical separation prior to the analysis. Finally, machine learning will be integrated with chiroptical high-throughput screening technology to enable original asymmetric catalysis discoveries.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.