Understanding mechanistic underpinnings of chemical processes by probing them in real time under relevant laboratory conditions helps scientists improve synthetic methodologies without undue costs and lengthy optimization studies. As of today, the number of methods for robust and simple processing of “big data” in chemical reactions and processes remains limited and often requires employment of complex computational software. However, simulation protocols frequently provide poor correlation of experimental data with computed values, resulting in unconformity with proposed mechanistic model. In the current work, mathematical analysis protocols for processing high-density experimental data were developed and applied for quantitative and qualitative investigation of copper(I)-catalyzed azide-iodoalkyne (ICuAAC) and -bismuth(III)acetylide (BiCuAAC) cycloaddition transformations. As such, high throughput statistical analysis employing off-line 1H NMR spectroscopy and heat flow calorimetry experimental data was performed to obtain reaction rate parameters and obtain insight into the fundamental interactions that are responsible for the observed reactivity. This analysis revealed substrate dependent change in the turnover-limiting step as well as speciation of key intermediates.

High throughput quantitative and qualitative analysis of high-density kinetic data leads to improved understanding of complex catalytic reactions

2019 Fall National Meeting

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