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2D-QSPR Studies of PNP-Cr-based Catalyst System for Highly Selective Ethylene Tri-and Tetramerization
Author: ZhanXingWen
Tutor: LiuBaiPing
School: East China University of Science and Technology
Course: Material Chemical Engineering
Keywords: QSAR/QSPR 1-hexene/l-octene 1-octene the heuristic method (HM) Radial basis function neural networks (RBFNN)
CLC: TQ325.1
Type: Master's thesis
Year: 2011
Downloads: 48
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Abstract
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1-hexene/1-octene was important comonomer of the production of high-performance polyolefin. PNP-Cr-based catalyst system for highly selective ethylene tri- and tetramerization has become a hot research orientation in the field of polyolefin. In this work, we assessed the ability of common molecular descriptors to predict the selectivity of 1-hexene/1-octene and 1-octene in the PNP-Cr-based ethylene tri- and tetra-merization catalyst systems. Quantitative structure-property relationship (QSPR) models were developed based on both the linear heuristic method (HM) and radial basis function neural networks (RBFNN). After the DFT geometry optimization of each structure of the data set with Gaussian 03, four classes’ descriptors (constitutional, topological, geometrical, and electrostatic) were calculated by the software CODESSA in this study. HM was used both for pre-selecting molecular descriptors and for developing the linear models, while nonlinear models were developed by RBFNN. By interpretingthe descriptors in the models, we can conclude that higher steric hindrance of substituent groups and more electron-donating groups attached to the nitrogen atom leads to higher selectivity of 1-hexene/1-octene, while the substituent on the N atom had little effect on the 1-octene selectivity. We also predicted 19 kinds of new structures and designed the reaction route of synthesis of new ligands.
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CLC: > Industrial Technology > Chemical Industry > Synthetic resins and plastics industry > Polymer resin and plastic > Polyolefin plastic
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