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Researchers from Hokkaido University and Duke University have unveiled a groundbreaking hydrogel with the capability to autonomously repair and enhance its structure when subjected to stress or damage. This innovative approach could significantly advance the functionality of soft yet resilient materials, potentially benefiting applications in machinery, robotics, and medical fields.
The findings of this study were published online on February 26 in the journal Nature Materials.
Hydrogels may sound novel, but they are commonplace in everyday items, including soft contact lenses, gummy candies, and human cartilage. These materials are characterized by complex, web-like structures made of long polymer chains that can absorb and retain substantial amounts of water.
Creating hydrogels that are both soft and pliable yet resistant to tearing presents a formidable challenge. Consider the analogy of pressing on a Jell-O mold: the soft material deforms but ultimately loses its integrity when overstressed.
In 2003, Jian Ping Gong, a professor of soft and wet matter at Hokkaido University, developed the concept of double-network hydrogels. This design features an additional rigid, brittle framework embedded within the hydrogel to enhance its strength and durability.
“This concept is akin to how car tires are constructed,” explained Michael Rubinstein, a distinguished professor at Duke specializing in Mechanical Engineering and Materials Science. “The outer rubber is inherently soft, but the addition of a network of interconnected carbon particles strengthens it, making tires more resilient and durable.”
A notable limitation of both double-network hydrogels and traditional hydrogels is their inability to restore their internal networks once they have been compromised. For instance, you cannot revert a squished Jell-O back to its original form after it has been deformed.
To overcome this limitation, researchers have focused on creating self-repairing hydrogels designed to act in real time. Until now, however, the response times of these self-healing hydrogels have been far too slow for practical applications.
In their latest research, Gong, Rubinstein, and their team introduced a method to engineer double-network hydrogels that not only self-repair at a substantially faster rate than their predecessors but also enhance their strength during the process.
The innovation lies in incorporating quick-breaking sacrificial segments within the internal scaffolding of the hydrogel. When these segments break, they generate reactive radicals that interact with surrounding bifunctional and multifunctional monomers, leading to the formation of new polymer chains and crosslinks that recreate the internal network.
“Typical double-network hydrogels lose their structural support when the internal rigid framework collapses,” Rubinstein clarified. “In contrast, when these new structures break, they produce radicals that react with the available monomers, swiftly generating new networks. Thus, every fracture point initiates further reactions that reinforce the material and prevent complete disintegration.”
The outcome is a hydrogel capable of quickly and efficiently resisting cracking and other types of damage. Once a crack initiates—similar to how a Jell-O mold attempts to overflow—the hydrogel rapidly forms new bonds around the affected area, reinforcing itself and preventing further fracture.
This initial proof-of-concept is designed to handle crack propagation at approximately two inches per minute. Although this rate may seem slow, it is indeed advantageous for numerous use cases where gradual wear and degradation pose greater challenges than rapid mechanical failures.
This research marks just the start of a broader investigative path. Rubinstein and his team are advancing efforts to develop a comprehensive computational model to better understand these internal dynamics. This foundational knowledge will enable them to refine these materials further and accelerate their healing capabilities.
“This is just the first version of our research, and we are actively working towards a more advanced iteration,” Rubinstein added.
The study received support from various funding bodies, including JSPS KAKENHI, JST FOREST, JST PRESTO, and the National Science Foundation Center for the Chemistry of Molecularly Optimized Networks (MONET).
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