(179d) Thermophysical Properties of Binary Mixtures of Polyethylene and Higher n-Alkanes

Current efforts in chemical recycling technologies have led to a new paradigm where plastic waste has the potential to play a “resource-oriented” role in a circular economy. Catalytic reactions can break down plastics into building blocks to produce new chemicals and plastics at reasonable temperatures. However, in order to increase the selectivity and efficiency for the implementation of these technologies, a better understanding of the deconstruction process is imperative. During the reaction, the polymer environment evolves and the complex dynamic interactions between polymer chains and the array of products are poorly understood because of the multiphase and multiscale nature of the mixtures. Therefore, predictive models are required to accurately describe ensemble structure and dynamics of complex mixtures of polymer chains at various stages of deconstruction.

To simplify the understanding of polymer deconstruction, smaller model systems are studied to clearly define the kinetics, mechanisms, and product selectivity before exploring more complex polymeric systems. In this work, we present a model for the prediction of thermophysical properties of binary mixtures of linear polyethylene (PE) and higher n-alkanes. By integrating high-throughput molecular dynamics simulations and machine learning techniques, we accurately model density and diffusion coefficients at different concentrations (C), molecular weights, pressures (P), and temperatures (T) in ranges relevant to chemical recycling. Our model provides new insights into methods to control the selectivity of the reactions and properties of the final products.

*This work was supported as part of the Center for Plastics Innovation, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science.