Effect of Internal Donors on Raman and IR Spectroscopic Fingerprints of MgCl2/TiCl4 Nanoclusters Determined by Machine Learning and DFT

Understanding the structure and properties of MgCl2/TiCl4/ID nanoclusters is a key to uncover the origin of Ziegler-Natta catalysis. In this work MgCl2/TiCl4 nanoplatelets derived by machine learning and DFT calculations have been used to model the interaction with ethyl-benzoate EB (as internal donor) with available exposed sites of binary TixCly/MgCl2 systems. The influence of vicinal Ti2Cl8 and coadsorbed TiCl4 on energetic, structural and spectroscopic behaviour of EB has been considered. The adsorption of homogeneous-like TiCl4EB and TiCl4(EB)2 at the various surface sites have been also simulated. Calculations have been carried out by employing B3LYP-D2 and M06 functionals. The adducts have been characterized by computing IR and Raman spectra that have been found to provide specific fingerprints useful to identify surface species; IR spectra have been successfully compared to available experimental data.

Towards the design of a mixture of diether and succinate as an internal donor in a MgCl2-supported Ziegler–Natta catalyst

An investigation into competitions of mixtures of internal donors for interaction with MgCl2 was carried out and the subsequent influence on catalyst performance was studied.