Spectral normalisation by error minimisation for prediction of conversion in solvent-free catalytic chain transfer polymerisations

Oligomers are useful chemicals for a number of synthetic and industrial applications. Catalytic chain transfer (CCT) polymerisation has been shown to be an extremely effective methodology for the synthesis of oligomers. Monitoring the conversion of monomer during the production of oligomers can present challenges using conventional analytical techniques such as IR or Raman spectroscopy, due to overlap from spectral features from the retained alkene groups at the chain terminus of the oligomers. This can cause ambiguity when assigning monomeric and oligomeric peaks in the vibrational spectra. In addition to this, such reactions are often carried out in solvent-free systems making the normalisation of spectra difficult. Multivariate analysis offers a useful methodology to quantify monomer conversion using Raman spectroscopy, despite a high double bond content within the polymerisation mixture. Chemometric models were also used to determine suitable points at which to normalise the spectra by a process of error minimisation, since conventional normalisation methods are not effective when Raman bands of constant intensity are not present. A number of partial least squares regression (PLSR) models were used to predict conversion for a range of commercially important monomers, such as methyl methacrylate (MMA), tert -butyl methacrylate ( t -BMA) and hydroxyethyl methacrylate (HEMA), with goodness-of-fit R 2 values typically above 0.99, and root-mean-square error of cross-validation (RMSECV) between 1-3% or within 5% of the maximum conversion. Additionally, the ability to detect the concentrations of dimer and trimer formed in the CCT polymerisation of MMA has been demonstrated. This work provides a robust method to determine spectral normalization points in reactions with no known constant responses.