Predicting Product Distribution from Depolymerization of Plastic Waste
Plastics are essential to modern life due to their durability, versatility, and low cost. Their global consumption continues to rise, yet most plastics are derived from fossil‑fuel‑based olefin polymers that do not biodegrade. As a result, plastic waste accumulates in landfills and oceans, creating severe environmental and health impacts across ecosystems and human populations.
Controlled depolymerization offers a promising route to address this challenge by breaking down plastic waste into valuable small‑molecule olefins and hydrocarbons. These products can re‑enter the chemical supply chain, enabling circular and sustainable materials use. However, a fundamental knowledge gap remains: we do not yet understand how polymers decompose at different temperatures or which fragments are favored under specific conditions.
DepolSim aims to close this knowledge gap by developing a generalized computational framework capable of predicting:
Our approach integrates Density Functional Theory (DFT), machine learning (active learning with neural networks), statistical mechanics, and graph theory to compute thermodynamic properties (ΔG, ΔH, ΔS) of polymer fragmentation. These predictions will be validated through microwave‑assisted pyrolysis experiments.
Principal Investigator (PI)
co-Principal Investigator (co-PI)
Post-Doctoral Researcher
Post-Doctoral Researcher
PhD Student
Selected publications relevant to depolymerization, and computational modeling.
Coming Soon ...
This research project is implemented in the framework of the H.F.R.I. call “3rd Call for H.F.R.I.’s Research Projects to Support Faculty Members & Researchers” (H.F.R.I. Project Number: 23457).