Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 27 December 2021 / Approved: 28 December 2021 / Online: 28 December 2021 (14:04:10 CET)

A peer-reviewed article of this Preprint also exists.

D’Amore, M.; Taniike, T.; Terano, M.; Ferrari, A.M. Effect of Internal Donors on Raman and IR Spectroscopic Fingerprints of MgCl2/TiCl4 Nanoclusters Determined by Machine Learning and DFT. Materials 2022, 15, 909. D’Amore, M.; Taniike, T.; Terano, M.; Ferrari, A.M. Effect of Internal Donors on Raman and IR Spectroscopic Fingerprints of MgCl2/TiCl4 Nanoclusters Determined by Machine Learning and DFT. Materials 2022, 15, 909.

Abstract

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.

Keywords

polymerization catalysis; machine learning; nanoclusters; DFT; Raman spectrum; IR spectrum; Lewis bases

Subject

Chemistry and Materials Science, Physical Chemistry

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