Coarse-Grained Model for Prediction of Hole Mobility in PolyethyleneShow others and affiliations
2023 (English)In: Journal of Chemical Theory and Computation, ISSN 1549-9618, E-ISSN 1549-9626, Vol. 19, no 21, p. 7882-7894Article in journal (Refereed) Published
Abstract [en]
Electrical conductivity measurements of polyethylene indicate that the semicrystalline structure and morphology influence the conductivity. To include this effect in atomistic charge transport simulations, models that explicitly or implicitly take morphology into account are required. In the literature, charge transport simulations of amorphous polyethylene have been successfully performed using short oligomers to represent the polymer. However, a more realistic representation of the polymer structure is desired, requiring the development of fast and efficient charge transport algorithms that can handle large molecular systems through coarse-graining. Here, such a model for charge transport simulations in polyethylene is presented. Quantum chemistry calculations were used to define six segmentation rules on how to divide a polymer chain into shorter segments representing localized molecular orbitals. Applying the rules to amorphous systems yields distributions of segments with mode and median segment lengths relatively close to the persistence length of polyethylene. In an initial test, the segments of an amorphous polyethylene were used as hopping sites in kinetic Monte Carlo (KMC) simulations, which yielded simulated hole mobilities that were within the experimental range. The activation energy of the simulated system was lower compared to the experimental values reported in the literature. A conclusion may be that the experimental result can only be explained by a model containing chemical defects that generate deep traps.
Place, publisher, year, edition, pages
American Chemical Society (ACS) , 2023. Vol. 19, no 21, p. 7882-7894
National Category
Polymer Chemistry
Identifiers
URN: urn:nbn:se:miun:diva-49872DOI: 10.1021/acs.jctc.3c00210ISI: 001092701300001PubMedID: 37842881Scopus ID: 2-s2.0-85176969089OAI: oai:DiVA.org:miun-49872DiVA, id: diva2:1812669
2023-11-162023-11-162024-08-13Bibliographically approved