Automated flow and real-time analytics approach for screening functional group tolerance in heterogeneous catalytic reactions. Issue 6 (16th February 2022)

• Record Type: Journal Article
• Authors: Simon, Kevin; Sagmeister, Peter; Munday, Rachel; Leslie, Kevin; Hone, Christopher A.; Kappe, C. Oliver
• Is Part Of: Catalysis science & technology. Volume 12:Issue 6(2022)
• Page Start: 1799
• Page End: 1811
• Publication Date: 2022-02-16
• Subjects: Catalysis -- Periodicals; 541.395
• DOI: 10.1039/d2cy00059h
• Languages: English
• ISSNs: 2044-4753

Abstract

Abstract : An automated continuous flow and real-time analytics platform facilitates the generation of quantitative data to understand the sensitivity of the reaction performance in the presence of different functional groups and heterocycles. Abstract : Heterogeneous hydrogenation reactions are widely used in synthesis, and performing them using continuous flow technologies addresses many of the safety, scalability and sustainability issues. However, one of the main potential drawbacks is catalyst deactivation by substrate inhibition. The efficient and rapid evaluation of functional group/heterocycle tolerance of an heterogeneous hydrogenation reaction using an automated continuous flow and real-time analytics platform is achieved. The information obtained is important for benchmarking catalytic reactions, particularly in the preparation of complex molecules. The methodology is applied to an aromatic nitro group reduction using hydrogen gas and palladium on alumina (Pd/Al2 O3 ) as a heterogeneous catalyst. The system utilizes catalytic static mixer (CSM) technology within a thermoregulated shell-and-tube reactor. The flow approach is configured to collect large datasets for a catalytic system with different additives (12 in total). The generation of data in real-time for complex reaction mixtures is a significant challenge. The flow setup integrates inline FT-IR and online UHPLC as orthogonal analytical methods for rapid data acquisition and for quantification of the main chemical species (substrate, product and all the additives). Thus, the change in the reaction outcome is assessed along with the stability of the additive to the reaction conditions. In particular, advanced data analysis models (partial least squares regression) were used to quantify the chemical species by FT-IR in real time. Our approach facilitates the generation of quantitative data early in development to understand the sensitivity of the reaction performance in the presence of different functional groups and heterocycles, greatly reducing experimental effort.