(638e) Modeling the Chemocatalytic Depolymerization of Polyhydroxyalkanoates (PHAs) Using a Moment-Based Population Balance Model Informed By DFT

A potential long-term solution to the growing problem of sustainable disposal of plastic waste involves a transition to the use of intrinsically circular polymers (ICPs), which can be depolymerized back to their monomers with high selectivity. This chemical recycling strategy involving circular polymers represents a promising alternative to conventional mechanical recycling methods. An important consideration associated with depolymerization is maximizing monomer yield, which is often accomplished via chemocatalytic routes. However, in many cases, complete conversion and elimination of byproduct formation remain elusive.

Mechanistic models of depolymerization incorporating reaction mechanisms, energetics, and kinetics can highlight the roles of key catalytic intermediates and elementary steps in modulating experimental observables, and consequently, can guide experimental efforts toward maximizing monomer recovery while limiting byproduct formation. The complexity of such models can be tempered whilst retaining essential mechanistic information by employing a population balance-based approach that can be efficiently solved using the method of moments¹.

In this study, we develop a moment-based mechanistic model for the depolymerization of poly(3-hydroxy-2,2-dimethylbutyrate) [P3H(Me)₂B]—a circular polyhydroxyalkanoate (PHA) with high thermal stability and mechanical toughness²—catalyzed by NaOH. We consider elementary reactions leading to the formation of the lactone monomer [(Me)₂BL] and 2-methylbut-2-ene and CO₂ byproducts from two polymers with differing chain ends (-OH and -COOH) and utilize DFT to calculate kinetic barriers and estimate rate constants. Using the model, we probe catalyst binding at various end chain and midchain sites and elucidate the influence of competition between them on overall conversion and selectivity. Additionally, we investigate the effect of initial conditions like temperature, catalyst loading, and chain-end composition on the product distribution. Lastly, we compare computational predictions with experimental TGA, NMR studies of bulk depolymerization, and discuss strategies to increase monomer recovery.

(1) Nat. Chem. Eng. 2025, 2 (1), 8–10
(2) Science 2023, 380 (6640), 64–69