Issue 4, 2021

New insights into the biphasic “CO-free” Pauson–Khand cyclisation reaction through combined in situ spectroscopy and multiple linear regression modelling

Abstract

“CO-free” carbonylations are catalytic reactions, which strive to replace toxic synthesis gas with less harmful surrogate molecules like formaldehyde. This study sheds light on the mechanism of the biphasic, Rh-catalysed Pauson–Khand type cyclisation reaction by applying in situ Raman spectroscopy together with NMR, dynamic light scattering measurements, and quantum chemical calculations. The reaction is particularly challenging to analyse as the Rh-catalyst features different ligands in the aqueous and organic phase. To tackle this challenge, a novel approach was designed, coupling in situ Raman spectroscopy with an advanced data analysis model in the form of multiple linear regression. This fruitful combination of highly specific vibrational spectroscopy with state-of-the-art data analysis allowed not only identification of a new reaction mechanism, but also identification of parameters to accelerate the “CO-free” Pauson–Khand cyclisation, i.e. using mononuclear Rh–phosphine complexes with labile ligands, small organic micelles formed by an increased surfactant concentration, low formaldehyde concentration, and elevated temperature. This novel knowledge-driven optimisation protocol is superior to simple qualitative optimization routines and can be transferred to other reactions and spectroscopy techniques.

Graphical abstract: New insights into the biphasic “CO-free” Pauson–Khand cyclisation reaction through combined in situ spectroscopy and multiple linear regression modelling

Supplementary files

Article information

Article type
Paper
Submitted
25 ኖቬም 2020
Accepted
05 ጃንዩ 2021
First published
06 ጃንዩ 2021
This article is Open Access
Creative Commons BY license

Catal. Sci. Technol., 2021,11, 1626-1636

New insights into the biphasic “CO-free” Pauson–Khand cyclisation reaction through combined in situ spectroscopy and multiple linear regression modelling

R. Geitner, T. Huang, S. Kupfer, S. Gräfe, F. Meirer and B. M. Weckhuysen, Catal. Sci. Technol., 2021, 11, 1626 DOI: 10.1039/D0CY02267E

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