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Novolex, Pactiv Evergreen to Form Packaging Powerhouse in $6.7-billion Deal
A new North American packaging powerhouse combines Novolex and Pactiv Evergreen in a deal valued at $6.7 billion.
Novolex, owned by Apollo Global Management, is paying $18 a share in cash for Pactiv, making the total cost about $3.22 billion. Novolex shares closed at $14.66 on Dec. 6. Apollo has been the majority shareholder of Novolex since 2022. Canada's Pension Plan Investment Board will contribute about $1 billion to the transaction to become a significant minority shareholder in the combined company.
The deal, which combines over 250 brands and 39,000 SKUs, including multiple types of fiber, resin, and post-consumer recycled content, is set to close by mid-2025.
Novolex develops and manufactures packaging for the food service, delivery and carryout, food processor, and industrial markets. Pactiv Evergreen specializes in fresh food service and food merchandising products and fresh beverage cartons —?many made with recycled, recyclable, or renewable materials — for restaurants, food service distributors, retailers, food and beverage producers, packers, and processors.
“This transaction reflects the continuation of our long-term growth strategy to create the industry’s most innovative, sustainable, and customer-focused company,” said Novolex CEO Stan Bikulege. “Pactiv Evergreen’s strong product portfolio, along with their talented team, will complement and add significant depth to Novolex’s diverse packaging solutions. We’re excited to continue our growth journey and are confident this combination will strengthen the future of food and beverage packaging.”
Both companies “serve attractive end markets, and . . . have made tremendous progress in recent years to enhance their operations and strengthen their focus on product innovation and sustainability leadership,” said Apollo partners Rob Seminara and Peter Sinensky.
Medical Molder Develops Innovative Neonatal Care Device
A contract development and manufacturing organization serving the medical technology sector, Europlaz has developed a proprietary CO2 sampling port that attaches to the side of its neonatal flow sensor. The device is designed to help improve neonatal care and save the lives of more babies born prematurely or in distress.
The sampling port enables clinicians to follow protocols related to the use of lung protective volume control and CO2 monitoring. This is achieved by combining the sensor for measuring flow volume with the optimally placed sampling port for safely extracting gas for tidal flow CO2 monitoring, reducing dead space found in existing products and ensuring more accurate readings, according to Europlaz.
A decade of neonatal flow-sensor expertise
Based in Chelmsford, UK, Europlaz has been manufacturing neonatal flow sensors for more than 10 years and said it has sold more than one million to date. The company added in the news release that it believes the time is ripe for ventilator manufacturers to consider switching to this neonatal flow sensor to save lives, reduce costs to the UK’s National Health Service, and avoid expensive litigation.
“Momentum is definitely growing, with two global manufacturers adopting our new flow sensors and trials about to begin with a famous London hospital,” said Europlaz Product Development Manager Frede Jensen. “This will provide the clinical evidence that many of the ventilator manufacturers have been asking for. We are dealing with the tiniest of lives, so it is imperative that we give clinical teams the best possible technology to help them do their jobs. This is exactly what this innovation does, with the side sampling port delivering the stable and accurate readings of CO2 being exhaled that they need to better inform the care of the child, and the level of ventilation support required,” added Jensen.
Europlaz assembles sophisticated production cell
Europlaz added that it is ramping up production for its new neonatal flow sensor, with a substantial investment in a new robotic welder. It’s described as the central element of a modern production cell that uses vision controlled micro-robotic wire welding to deliver highly consistent sensor quality, performance, and reliability.
“The robot is capable of welding a 13-micron precious metal wire —?one-third the thickness of a human hair —?to a surface smaller than a pinhead with 5-micron precision,” said Jensen. “It gives us the repeatable quality that we know is required and the confidence that our sensors will deliver accurate readings time and time again. We can also increase capacity, with the genuine expectation that we can supply 500,000 sensors every year,” said Jensen.
Europlaz offers a comprehensive suite of contract development and manufacturing services to medical device OEMs, including tooling design, prototyping, injection molding, cleanroom manufacturing, and sterilization.
Cyclyx Invests in Second Circularity Center for Plastics Recycling
By PlasticsToday Staff
Cyclyx International, LLC., a post-use plastic-to-feedstock innovator and joint venture of Agilyx, ExxonMobil, and LyondellBasell, has reached a final investment decision for a second Cyclyx Circularity Center (CCC2). The new facility will be located at an existing distribution center in the Fort Worth, TX, area. It will help to create further necessary infrastructure for a plastic circular economy.
Together, Agilyx, ExxonMobil, and LyondellBasell are investing $135 million into Cyclyx to fund the construction and operations for CCC2, which has an expected startup in the second half of 2026. When operational, the facility can produce around 300 million pounds of plastic waste feedstock per year for customers using mechanical and advanced recycling technologies. As with the CCC facility, CCC2 operations are expected to create more than 100 jobs in the local economy.?
“This new world-class facility will significantly expand our capability to supply the growing industry demand for feedstock for both mechanical and advanced recycling,” says Joe Vaillancourt, CEO of Cyclyx. “CCC2 will help enable the aggregation and recycling of post-consumer, commercial, and industrial plastic waste often destined for landfills or incineration. We look forward to working with the Dallas-Fort Worth Metroplex community as well as our feedstock customers to deliver sustainable solutions.”
Cyclyx Circularity Centers are uniquely designed to accept a wide range of plastic waste streams, including plastics that are not typically accepted in current industry recycling programs. Combined, CCC1 and CCC2 will have the capacity to produce an estimated 600 million pounds of plastic waste feedstock annually—another step forward to building a more circular economy for plastics.
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As part of its strategy, Cyclyx continues to explore the development of additional CCCs, each placed in major hubs across the United States.
Read more about Cyclyx at PlasticsToday.
AI, Machine Learning Accelerate Materials Research, Compress R&D Timelines
Two recent developments in artificial intelligence (AI) and machine learning have the potential to accelerate product development by streamlining materials research. Israel-based MaterialsZone, a cloud-based materials discovery platform, has introduced what it calls AI-Guided Product Development, which provides real-time experiment recommendations to guide researchers through iterative improvements in product design. Meanwhile, researchers in Japan have harnessed machine learning to optimize polymer production.
Aligning experimentation with R&D timelines
MaterialsZone leverages expertise in material science, data science, and software development to better help researchers align experimentation with R&D timelines, said the company, which was founded in 2018. The new AI-driven feedback loop reportedly transforms trial-and-error-based experimentation by building on successful use cases to gradually narrow parameters for a given application. It accelerates product development goals while considering critical material and process constraints, including cost optimization and carbon footprint reduction, according to MaterialsZone.
As each suggested experiment is completed and documented within the MaterialsZone platform, the AI model is used to refine recommendations according to the latest data, enhancing precision and efficiency. Available to researchers and technicians, this seamless cycle integrates data enrichment, machine learning, experiment synthesis, and feedback, optimizing development and reducing experimental cycles — all within a no-code framework.
Machine learning models polymerization process
The use of machine learning to reduce the need for time-consuming and expensive experimentation related to polymer production is at the heart of research conducted at Japan’s Nara Institute of Science and Technology. Researchers led by Professor Mikiya Fujii have used machine learning to mathematically model the polymerization process and more rapidly identify ideal manufacturing conditions.
Machine-learning algorithms need data, notes a release published in Asia Research News, so the researchers designed a polymerization process that would quickly and efficiently generate experimental data to feed into the mathematical model. The target molecule was a styrene-methyl methacrylate co-polymer, which was made by mixing styrene and methyl methacrylate monomers, both already dissolved in a solvent with an added initiator substance, then heating them in a water bath.
The two monomer solutions were mixed and heated in a constant flow — a process called flow synthesis —?to allow for better mixing, more efficient heating, and more precise control of heating time and flow rate.
The modeling evaluated the effect of five key variables in the polymerization process:
The goal was to have an end product with as close to 50% styrene as possible.
Five calculation cycles achieve successful outcome
The machine learning process took only five calculation cycles to reach the ideal proportion of styrene to methyl methacrylate. Key to this outcome was a lower temperature and longer time in the water bath, as well as lower relative concentration of the monomer in the solvent. The researchers said they were surprised to discover that the solvent concentration was just as important as the proportion of monomers going into the mix, notes the news release.
“Our results demonstrate that machine learning not only can explicitly reveal what humans may have implicitly taken for granted but can also provide new insights that weren’t recognized before,” said Fujii. “The use of machine learning in chemistry could open the door for smarter, greener manufacturing processes with reduced waste and energy consumption.”
The research is published in the peer-reviewed journal, Science and Technology of Advanced Materials: Methods.
Recommended Reads ??
? The US Path to Polypropylene Recycling Success: NextLooPP Americas’ plan mobilizes US organizations to close the loop for food-grade polypropylene using technologies including AI.
? Deadlocked Global Plastics Treaty Talks to Resume Next Year: Negotiations at what was supposed to be the final session reached an impasse on whether to impose limitations on plastics production and ban certain chemicals.
? Multifunctional Structural Battery Combines Energy Storage and Load-bearing Capacity: Korean research organization KAIST has developed a structural carbon-fiber-composite battery concept that it calls a “foundational technology for next-generation multifunctional energy.”
? Sustainable Manufacturing Expo Announces Key Industry Partners: Through educational programs, networking events, and exhibits, industry players will show how investing in sustainability today ensures the economic and environmental health of organizations and the planet.
? Graphene Gives PET Bottles a Lightweighting Boost and More: HydroGraph Clean Power targets three segments of the plastics supply chain for fractal graphene nanotechnology while developing the tech for other polymers.
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