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Abstract Polyolefins account for more than half of global primary polymer production, however only a small fraction of these polymers are currently being recycled. Fragmentation of polymer chains into shorter chains with a targeted molecular weight distribution with the goal of reusing these fragments in subsequent chemical synthesis can potentially introduce an alternative approach to polyolefins recycling. Herein we develop a mesoscale framework to model degradation of polyethylene melts at a range of high temperatures. We use the dissipative particle dynamics approach with modified segmental repulsive potential to model the process of random scission in melts of linear polymer chains. We characterize the fragmentation process by tracking the time evolution of the distribution of degrees of polymerization of chain fragments. Specifically, we track the weight average and the number average degrees of polymerization and dispersity of polymer fragments as a function of the fraction of bonds broken. Furthermore, we track the number fraction distribution and the weight fraction distribution of polymer fragments with various degrees of polymerization as functions of the fraction of bonds broken for a range of high temperatures. Our results allow one to quantify to what extent the distribution of polymer chain fragments during random scission can be captured by the respective analytical distributions for the range of conversions considered. Understanding the thermal degradation of polyolefins on the mesoscale can result in the development of alternative strategies for recycling a range of thermoplastics.
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Methodical evaluation of Boyle temperatures using Mayer sampling Monte Carlo with application to polymers in implicit solvent
The Boyle temperature, TB, for an n-segment polymer in solution is the temperature where the second osmotic virial coefficient, A2, is zero. This characteristic is of interest for its connection to the polymer condensation critical temperature, particularly for n → ∞. TB can be measured experimentally or computed for a given model macromolecule. For the latter, we present and examine two approaches, both based on the Mayer-sampling Monte Carlo (MSMC) method, to calculate Boyle temperatures as a function of model parameters. In one approach, we use MSMC calculations to search for TB, as guided by the evaluation of temperature derivatives of A2. The second approach involves numerical integration of an ordinary differential equation describing how TB varies with a model parameter, starting from a known TB. Unlike general MSMC calculations, these adaptations are appealing because they neither invoke a reference for the calculation nor use special averages needed to avoid bias when computing A2 directly. We demonstrate these methods by computing TB lines for off-lattice linear Lennard-Jones polymers as a function of chain stiffness, considering chains of length n ranging from 2 to 512 monomers. We additionally perform calculations of single-molecule radius of gyration Rg and determine the temperatures Tθ, where linear scaling of Rg2 with n is observed, as if the polymers were long random-walk chains. We find that Tθ and TB seem to differ by 6% in the n → ∞ limit, which is beyond the statistical uncertainties of our computational methodology. However, we cannot rule out systematic error relating to our extrapolation procedure as being the source of this discrepancy.
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- Award ID(s):
- 2152946
- PAR ID:
- 10610605
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- The Journal of Chemical Physics
- Volume:
- 161
- Issue:
- 15
- ISSN:
- 0021-9606
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1063/5.0227411
