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Characterization of Synthetic Polymers via Matrix Assisted Laser Desorption Ionization Time of Flight MALDI-TOF Mass Spectrometry

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Computational study on acetophenone in amorphous polyethylene.

Shinya Iwata¹, Hiroaki Uehara², Tatsuo Takada³

¹Osaka Research Institute of Industrial Science and Technology, 7-1, Ayumino-2, Izumi-shi, Osaka, 5941157, Japan. iwata@tri-osaka.jp.

Journal of Molecular Modeling

|September 10, 2017

Summary

Acetophenone (AP) residues in polyethylene (PE) insulation can cause deterioration. This study used simulations to reveal that AP acts as a trap site, explaining its impact on electrical insulation performance.

Keywords:

Acetophenone Amorphous polyethylene Density functional theory External electric field Molecular dynamics simulation

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Area of Science:

Materials Science
Polymer Science
Computational Chemistry

Background:

Polyethylene (PE) is a key electrical insulator.
Acetophenone (AP) residues are linked to PE insulation degradation.
The physicochemical mechanisms of AP's influence remain unclear.

Purpose of the Study:

To investigate the behavior of AP molecules within amorphous PE.
To elucidate the physicochemical basis of AP's role in insulation deterioration.
To provide a molecular-level explanation for experimental observations.

Main Methods:

Molecular dynamics (MD) simulations of amorphous PE containing AP.
Quantum chemical calculations on PE-AP structures.
Analysis of molecular electrostatic potential (MEP), molecular orbitals, and energy levels of AP.
Evaluation of density and radius of gyration changes in PE.

Main Results:

AP does not significantly alter the density or radius of gyration of amorphous PE.
MD simulations and quantum calculations suggest AP acts as a trap site in PE.
External electric fields induce directional changes in the total density of states (DOS).

Conclusions:

AP's role as a trap site in amorphous PE is supported by computational evidence.
The study provides a physicochemical explanation for AP-induced insulation deterioration in PE.
Findings correlate computational results with existing experimental data.