Imagine being tasked with baking a soufflé, except the only instruction provided is an ingredient list without any measurements or temperatures.
It would likely take an enormous amount of time, effort and ingredients to bake the perfect soufflé. It would require trial and error – tweaking ingredient measurements, altering the temperature and baking duration – but what if you had a model that could predict the final product before anything ever went into the mixing bowl? It would not only save weeks' worth of time and resources but could also provide useful details like why and how the soufflé rose and collapsed when it did or why the texture didn't turn out how you expected.
Researchers at the Beckman Institute for Advanced Science and Technology aren't quite baking soufflés. Instead, they developed a computational model that digs into the chemical "recipe" of polymer manufacturing to provide predictive control over how materials self-organize to give rise to new textures and properties.
"That means manufacturers can design and simulate materials with built-in patterns – enhancing toughness, reducing weight, or enabling new functions – before ever mixing chemicals in the lab," said Jeffrey Moore , the Stanley O. Ikenberry Endowed Chair Emeritus in the Department of Chemistry at the University of Illinois Urbana-Champaign.
The interdisciplinary team was led by Philippe Geubelle , executive associate dean of the Grainger College of Engineering and a professor in the Department of Aerospace Engineering, Moore, and primary researchers Anna Cramblitt, a graduate student in the Department of Materials Science and Engineering, and Donald Bistri, a postdoctoral researcher in the Department of Aerospace Engineering. Their work appears in the journal, the Proceedings of the National Academy of Sciences.
Frontal polymerization is a method of rapidly converting monomer subunits to polymer complexes by propagating a localized reaction wave. Polymer materials include plastic, rubber or resins. Moore compares this process to a storm front and the predictive model they developed to the modeling forecasts of a meteorologist describing the anticipated path of a storm.
A liquid mixture of chemicals is combined and transforms into a solid when heat is applied, a reaction that spreads like a weather front. This technique is a chemical system that combines strongly coupled reaction and transport phenomena.
The team identified frontal ring-opening metathesis polymerization, or FROMP, as a novel method to create tunable materials with diverse forms and function through reactive processing.
"Frontal polymerization can be used to make polymer materials and under certain conditions, materials with periodic patterning akin to high-performance materials found in nature," Cramblitt said.
In contrast, other types of synthetic manufacturing techniques often require labor-intensive and user-controlled, multi-step methods that lack the self-organizing abilities of emergent patterns found in natural systems.
Patterns are ubiquitous in nature; rippled sand dunes, trees and blood vessels that use fractal branching to optimize nutrient and oxygen distribution, or spiral patterns found in DNA, seashells and hurricanes. These designs arise from the interaction of smaller components without any central control.
Many emergent patterns are associated with specific functionalities. The markings found on a zebra, for example, contribute to thermoregulation and camouflage. Alternating regions of stiff and flexible material of dragonfly wings contribute to strong yet compliant wings that enable complex aerial maneuvers.
Inspired by various emergent behaviors in biological systems, the research team developed an integrated computational and experimental framework to understand and induce pattern formation in frontally polymerized synthetic materials.
"Much like balancing weights on a scale, we demonstrate how small shifts in chemical equilibrium can tip the balance between reaction kinetics and heat transport to drive pattern formation. In a way, we discovered the cooking recipe for making patterned materials," Bistri said.
FROMP enables precise control of the key polymerization reaction steps which include inhibition, initiation and propagation. By investigating the chemistry at each step, the researchers uncovered that near-equilibrium dynamics, paired with far-from-equilibrium kinetics, drive pattern formation in frontally polymerized synthetic materials.
The researchers determined a particular chemical equilibrium which "gates" or controls pattern formation during polymerization.
In this case, the researchers leveraged chemistry and thermodynamics in FROMP systems to drive exothermic, self-sustaining reactions. A balance between heat generation, reaction kinetics and thermal transport results in a reaction front that propagates steadily to create a uniform material.
When the chemistry or thermal input is altered, the equilibrium is disrupted, which results in a non-uniform propagation front that modifies the material's micro- and macro- structure during the polymerization process.
For example, rather than producing a material of uniform stiffness, the material can be fabricated to have alternating bands of stiff and soft polymer material.
Taking their research a step further, the team integrated computational modeling to create a mechanism-based FROMP model, based in the principles of reaction kinetics, to understand the chemical origins of emergent behavior in frontally polymerized synthetic materials.
So rather than baking a thousand soufflés to get the perfect one, the team's model predicts how changes in the recipe and baking temperature will affect the ultimate product before the ingredients ever touch the mixing bowl.
"I'm excited for the design freedom enabled by our improved understanding of the system. I think it will open doors for a lot of new experiments. I'm looking forward to creating patterns in materials with a wide range of properties and characterizing their behavior," Cramblitt said.
The team hopes to use its findings to create patterned materials with tunable properties that mimic natural materials to achieve higher toughness.
Collaborating with Massachusetts Institute of Technology researchers, Rafael Gómez-Bombarelli and Lauren Chua, who are well-versed in density functional theory modeling, Geubelle hopes to combine their DFT modeling with the newly developed FROMP phenomena modeling to manufacture patterns in a variety of different materials, opening a broad design space for property optimization.
The modeling system can be compared to a recipe guide, whereas DFT is more comparable to the ingredient information. The recipe needs to be calibrated using details about ingredients, like the energy of each molecular component.
"Through this work, we are getting a taste of linking the atomistic scale to the continuum for the computationally driven design of sustainable materials. We can now discover new formulations of catalyst, monomer, and inhibitor de novo that give rise to these chemical fronts," Chua said.
This expanded design space increases the flexibility to create reactive, self-organizing systems and provides insights for researchers to fabricate sustainable, bioinspired materials with improved functionality.
