Biotech Breakthrough: Enzymatic Recycling of Plastics Goes Industrial

Enzymatic Plastics Recycling

A major step forward in sustainable materials science has been achieved: a new study shows that enzyme-based recycling of PET plastics—the common material used in soda bottles and food packaging—can be cost-competitive at an industrial scale. This could revolutionize the way the world handles plastic waste.

The research, a collaborative effort by scientists from the National Renewable Energy Laboratory (NREL), University of Massachusetts Lowell, and the University of Portsmouth, was published in Nature Chemical Engineering. It provides an in-depth techno-economic analysis and process design blueprint for enzymatic recycling that is both scalable and affordable.

From Soda Bottles to Sustainable Feedstock

Polyethylene terephthalate (PET) is used globally in billions of products. Traditional mechanical and chemical recycling methods often fall short when dealing with low-quality, colored, or contaminated waste. However, PETase enzymes—biologically engineered catalysts that break down PET into its original monomers—can work even under such challenging conditions.

The breakthrough lies not only in the biological power of these enzymes, but in process innovations that reduce energy consumption, chemical additives, and operating costs. The study showcases how this technology can match or even beat the cost of producing virgin PET: an estimated $1.51/kg versus $1.87/kg for new PET in the U.S.

Key Innovations That Enable Scale-Up

To make enzymatic recycling viable at scale, the team introduced a number of critical advancements:

  • Redesigned reaction conditions that eliminate over 99% of acid/base use
  • New separation technologies that reduce energy consumption by 65%
  • Process improvements that cut operational costs by 74%

The system begins with PET deconstruction into monomers via engineered PETases, followed by monomer purification and reuse for producing either recycled PET or higher-value upcycled materials. These steps are now streamlined and more energy-efficient than earlier models.

Environmental and Economic Impact

In the U.S., over 86% of plastics were landfilled in 2019, representing a vast reservoir of untapped embodied energy. By recovering and upcycling these materials, enzymatic recycling could dramatically reduce our dependence on fossil-based feedstocks while contributing to the circular economy.

As global plastic production is expected to rise 2–4 times by 2050, the demand for innovative recycling methods has never been greater. This study proves that enzyme-based technologies are not only environmentally superior but also industrially viable—a critical milestone in the fight against plastic pollution.

Looking Forward

By demonstrating commercial feasibility, the research opens the door to public-private partnerships, pilot plants, and full-scale industrial facilities for enzyme-powered recycling. Further research will refine enzyme performance and scale up engineering to enable adoption across packaging, textiles, and beyond.

📖 Read the original article on Phys.org: https://phys.org/news/2025-06-enzyme-based-plastics-recycling-industrial.html

Sponsored by PWmat (Lonxun Quantum) – a leading developer of GPU-accelerated materials simulation software for cutting-edge quantum, energy, and semiconductor research. Learn more about our solutions at: https://www.pwmat.com/en

📘 Download our latest company brochure to explore our software features, capabilities, and success stories: PWmat PDF Brochure

📞 Phone: +86 400-618-6006
📧 Email: support@pwmat.com

#PlasticsRecycling #EnzymaticRecycling #PETase #CircularEconomy #GreenChemistry #MaterialsScience #NREL #Biocatalysis #SustainablePlastics #PWmat #QuantumServerNetworks #NatureChemicalEngineering #TechForGood

Comments

Post a Comment

Popular posts from this blog

Water Simulations Under Scrutiny: Researchers Confirm Methodological Errors

Image
Accurate water simulations are vital for research in biomolecular structures, drug discovery, and materials science. But a recent study from Oak Ridge National Laboratory (ORNL) has revealed a potential pitfall in long-standing computational methods, raising concerns about the reliability of countless molecular dynamics studies. The Time Step Problem in Simulations At the heart of the issue is the “time step” used in simulations—the small intervals at which calculations are performed. Since the 1970s, scientists have used time steps of 2 femtoseconds (quadrillionths of a second) to simulate molecular motion efficiently. However, the ORNL team has shown that using this standard can introduce significant errors when modeling liquid water, potentially leading to inaccurate predictions of properties such as density, pressure, and hydration energies. “Using longer time steps is tempting because it reduces computational cost,” explained senior scientist Dilip Asthagiri. “But a...
Read more

Quantum Chemistry Meets AI: A New Era for Molecular Machine Learning

Image
In a powerful leap forward for computational chemistry and materials design, researchers from Carnegie Mellon University have developed a new kind of molecular machine learning model—one that goes beyond simplistic molecular graphs and integrates fundamental principles of quantum chemistry. Their work, published in Nature Machine Intelligence , could radically enhance our ability to predict chemical behavior, optimize catalysts, and discover new drugs, all while using less data and gaining more scientific insight. The Problem: Traditional Molecular Representations Fall Short Molecular machine learning (ML) models underpin key processes in drug discovery, materials science, and catalysis. However, most existing models use simplified representations such as SMILES strings, 2D graphs, or coordinate matrices. These techniques often lack the quantum-chemical information that governs how molecules truly behave—particularly electron interactions that define reactivity, stability, and...
Read more

CrystalGPT: Redefining Crystal Design with AI-Driven Predictions

Image
July 18, 2025 In a major leap for materials chemistry, researchers in the UK have developed Molecular Crystal Representation from Transformers (MCRT) —an AI model likened to ChatGPT for its transformative impact. Dubbed "CrystalGPT" , this foundation model rapidly predicts the physical properties of molecular crystals, revolutionizing how chemists design materials in silico . The AI Revolution in Crystal Design Understanding and predicting the properties of molecular crystals is critical for designing new materials for applications ranging from pharmaceuticals to energy storage. Traditional computational approaches, though powerful, are often resource-intensive and slow. Machine learning methods such as Smooth Overlap of Atomic Positions (SOAP) and atom-centred symmetry functions (ACSFs) have offered improvements, but they struggle with large datasets and subtle structural nuances. CrystalGPT, developed by a team led by Andy Cooper at the University of Liverpool, o...
Read more