CAREER: Computational crystallization of polymer blends: application to recycled plastics and more

NON-TECHNICAL SUMMARY

This CAREER award supports computational and theoretical research for modeling crystallization in semicrystalline polymer blends, such as recycled commodity plastics. In these blends, crystallization is impacted by phase separation between different polymeric species during processing, which results in undesired final morphologies and degraded material properties.

The inferior properties of recycled plastics make them less competitive than virgin polymers, leading to a low recycling rate. Understanding and quantitatively modeling crystallization in these phase-separated polymer blends is key to enhancing their material properties and potentially helping resolve the environmental crisis caused by plastics.

The PI plans to combine computer simulations and theoretical tools to directly probe how different polymers crystallize near the interfaces in phase-separated blends. The project will also reveal the effects of interfacial compatibilizers, which can bridge different polymer domains across the interfaces, on the sample mechanical properties. Based on the molecular simulation results, the team will construct a numerical simulation algorithm to predict crystallization near the interfaces in polymer blends, which will permit efficient prediction of material structures and properties for semicrystalline polymer blends.

Overall, the research will help guide the development and optimization of a wide range of semicrystalline polymers, including recycled plastics and functional polymers.

The education and outreach components of this project will provide training on basic programming skills and simulation techniques to high school, community college, and undergraduate students via a series of in-house designed open-source training modules. To disseminate knowledge about polymer sustainability and recycling to the general public, including K-12 students, the research team will develop a novel mobile app.

This app will utilize machine learning to recognize commodity polymers from recycling symbols and then introduce their molecular information and recycling strategies to the users. TECHNICAL SUMMARY

This award supports the development of a multi-scale framework for quantitatively modeling polymer crystallization, one of the grand challenges in polymer physics. Motivated by the lack of a mechanistic understanding of crystallization in polymer blends, the research will reveal the crystallization mechanism for semicrystalline polymer blends and develop a novel computational approach for predicting polymer crystallization in phase-separated blends.

Specifically, the project consists of four interrelated aims: 1) predict the free energy landscapes of crystallization for polymer blends, including polyethylene (PE) and isotactic polypropylene (iPP), 2) reveal the crystallization mechanism near phase-separated interfaces for PE/iPP and semiflexible bead-spring mixtures, which mimic other semicrystalline blends, 3) elucidate the effects of amorphous stress transmitters near interfaces, including entanglements and additives such as block copolymer compatibilizers, on the sample mechanical properties, and 4) develop a phase-field model to predict the semicrystalline morphologies of polymer blends after a long crystallization time. By predicting the crystallization, semicrystalline structures, and mechanical properties, this project will lay the foundation for optimizing the properties of recycled polyolefins, and in turn, promote a more sustainable plastics industry.

The multi-scale approach will also guide material design for other materials, such as (semi)conducting polymers, for which the electronic properties are governed by tie chains and interfaces.

The broader impact of the research includes providing training and education on computational and theoretical research to high school, community college, and undergraduate students using in-house designed open-source training modules. These training modules will prepare students for performing simulations and data analysis to solve research problems.

The PI team will also develop a phone app to promote polymer recycling and sustainability. This app can identify the type of plastics by scanning the recycling symbol using the phone camera. After identification, the app will introduce the molecular features and properties and the recycling strategy and challenges of this polymer.

The PI will use the mobile app in outreach activities to help enhance public awareness of sustainability. STATEMENT OF MERIT REVIEW

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.