Thermoplastic polyurethanes (TPUs) are versatile materials found in numerous applications, ranging from automotive components and medical devices to footwear and electronic equipment. These polymers exhibit desirable properties including elasticity, durability, and tunable mechanical characteristics, making them suitable for use as soft engineering plastics or as alternatives to traditional vulcanized rubber.
Chemical Engineering Assistant Professor Nader Taheri Qazvini recently secured funding from the American Chemical Society’s Petrochemical Research Fund, which enables investigators to pursue a new research direction.
"This two-year project aims to address a significant knowledge gap in our understanding of the mechanisms governing the behavior and structural evolution of thermoplastic polyurethane interfaces," Taheri Qazvini says. "I've been trained in polymer dynamics throughout my graduate studies and was looking for an opportunity to return to this research area."
TPUs represent a specific category within the broader class of materials known as thermoplastic elastomers. These thermoplastic elastomers are distinguished by their unique microphase-separated structure, containing both flexible and rigid components at the molecular level. After processing, these components separate into a specific structure consisting of a flexible matrix with embedded rigid domains, resulting in their characteristic mechanical properties.
TPUs complex structure consists of hard and soft segments, leading to complex chain dynamics during processing. But despite extensive research, a limited understanding of the correlation between chain dynamics, diffusion, and the rearrangements of soft and hard segment domains at TPU interfaces exists.
“Our research is to understand how the molecular structure of thermoplastic elastomers influences their specific properties and how these materials affect interdiffusion at the interface,” Taheri Qazvini says. “We want to see how variables such as the ratio of hard to soft segments, molecular weight, and other characteristics impact this process.”
A key advantage of thermoplastic elastomers is the ability to combine the elastic properties of rubber with the practical benefits of conventional plastics. Unlike traditional rubber, which cannot be remolded once it is formed, these materials can be repeatedly melted, reshaped and recycled. Furthermore, they have become important materials for additive manufacturing (3D printing) due to their favorable mechanical properties.
"In 3D printing applications, you're depositing one layer and then another on top of it. For proper bonding, you can't have two static surfaces simply in contact; they need to mix at their boundaries to integrate," Taheri Qazvini explains. “This integration is complicated by the complex structure and processing requirements of TPUs. The research aims to investigate these interrelated variables to address existing knowledge gaps."
Interdiffusion, the process by which polymers mix freely across an interface, is essential when printing successive layers of thermoplastic elastomers. The movement of polymer chains, known as chain dynamics, is temperature-dependent, with higher temperatures accelerating the diffusion process.
Since the project began in January, Taheri Qazvini has been working with commercial thermoplastic elastomers. "Since the properties of commercial products are generally well-characterized, we aim to correlate their molecular structure to their known properties," he says.
The long-term goal is to synthesize novel thermoplastic elastomers.
"We will systematically characterize their structure, durability, and mechanical properties, and then relate these findings back to their molecular architecture. This will inform our subsequent modifications based on the feedback from mechanical and application testing," Taheri Qazvini says.
Given the multiple structural parameters and properties involved in this research, Taheri Qazvini plans to incorporate artificial intelligence tools to optimize both the process and material design, potentially providing insights into new molecular architectures.
"We plan to implement an iterative machine learning approach where each experimental result informs the AI system, which then suggests new experiments," Taheri Qazvini explains. “This approach may significantly reduce the number of experiments required compared to traditional methods, which typically necessitate numerous trials for such optimization."
By the end of the project, Taheri Qazvini hopes to have developed a deeper understanding of how the complex structure of thermoplastic polyurethanes influences processing outcomes.
"Ultimately, we aim to design enhanced materials or at least establish a clearer correlation between molecular-level structure and final product performance," Taheri Qazvini says. "For instance, how can we develop products with superior mechanical properties and greater durability using these thermoplastic materials?"