Two-Dimensional (2D) Correlation Analysis and the Search for Intermediates: A Strictly Mathematical Approach to an Important Mechanistic Question

Abstract

In situ spectroscopic studies of metal-mediated syntheses of new and previously unstudied systems are being increasingly used to better understand speciation and mechanistic aspects. These types of experiments give rise to an interesting question: namely, can one deduce from in situ data alone, and with no a priori chemical knowledge (i.e. chemical assignments), which pure component spectral estimates correspond to intermediates? In the present contribution, a statistical 2D correlation analysis is introduced to solve this problem for unicyclic catalytic systems. Such a methodological development achieves two goals: (1) it allows the experimentalist to concentrate on the most meaningful information at the outset of a new exploratory study (focus on the species directly associated with the catalysis), and (2) it helps to free the experimentalist from chemical bias and prejudice, i.e. believing that a specific organometallic species has to be an intermediate due to one or more chemical arguments, when in fact it may be just a side product or spectator species in the metal-mediated synthesis. The 2D correlation analysis is first tested with a numerically simulated data set and then with a real in situ FTIR data set from an unmodified rhodium-catalyzed hydroformylation. The resulting statistical 2D correlation analysis provides a clear and correct answer.