Chemical recycling of biodegradable films using microwave-assisted catalytic depolymerization

Overview

This technology enables rapid, selective chemical recycling of biodegradable films into their constituent monomers. By combining high-throughput computational screening of solvents and catalysts with artificial intelligence, the process identifies optimal conditions for depolymerization. The approach uses microwave heating with green electricity for energy efficiency and sustainability, offering a pathway to circular use of biodegradable packaging materials.

Technical specifications

Key features:

• High-throughput quantum and statistical mechanics methods screen thousands of solvents to selectively dissolve individual film layers and additives
• Solid catalysts with high surface area and selectivity enable depolymerization yields exceeding 90%
• Swelling solvents allow catalysts to penetrate film layers at programmed temperatures, enabling layer-by-layer separation with high purity
• Microwave heating provides rapid, energy-efficient processing using green electricity
• Active learning algorithms optimize process conditions to maximize monomer yield and purity while minimizing energy use
• Demonstrated complete depolymerization of polyesters in minutes with high monomer purity and oligomer byproducts suitable for novel material production
• Resilient catalyst concepts under development to resist additive interference, including pore-size catalysts that exclude additives from active sites

Technology readiness level

The core technology has been validated through demonstrated complete depolymerization of polyesters in minutes with high purity. The team has developed the most efficient heterogeneous catalysts for polyester depolymerization using microwaves and shown the impact of swelling on depolymerization rates. Future validation will involve screening solvents and catalysts using company-supplied films, testing model predictions experimentally, measuring monomer yields via chromatography and NMR, and optimizing process conditions using active learning. Phase II work will focus on developing resilient catalysts that perform despite the presence of additives.