Discovered: high-temperature remoldable gels

Share this post:

Mareva Fevre, IBM Research-Almaden

by Mareva Fevre, research post-doc at IBM Research-Almaden 

My team at IBM’s research lab in the Silicon Valley just discovered a new class of “self-healing” organogels that have unique recyclable properties – they are the first class of chemically-crosslinked gels that can be cooled to a solid, but then re-heated back to a liquid state. Imagine filling a mold with this liquid material, cooling it and discovering there was a mistake – with this material and its dynamic properties, we can start over until we get the desired shape, and then cure it to a permanent, hardened object. We describe how this process works in the paper, Melt-Processable Dynamic-Covalent Poly(Hemiaminal) Organogels as Scaffolds for UV-Induced Polymerization, published in the journal, Advanced Materials.

What is a gel? 

Gels are a peculiar family of materials. They exhibit properties between solids and liquids, and are composed of long polymer chains which link together and can trap some other smaller molecules. They are like a tiny (think nanoscale) fishing net that can retain liquids. The small molecules, which are trapped in the organogels that we developed, are monomers (the initial molecules which are used to prepare polymers). In our experiment, we also added some molecules called initiators in the gels, which under UV light transform the monomers into polymers. Thus, our gels have unique properties that allow them to behave at first like jello but after UV exposure become hard like plastic.

The elasticity of our organogel on display

After dozens of tests to measure the strength of the gels and figuring out the right compositions to obtain these properties, we created a modified gel that could melt at higher temperatures (80C) but recover its gel-like behavior when cooled down to 20C. The result is the first type of gel that could be molded, unmolded, and remolded several times before reaching that perfect final shape fixed by UV exposure.

Think of our gels like caulk used to repair cracks in objects, but liquid enough to penetrate into small cracks, and solid enough not to leak. A subsequent UV-curing step would allow for the material filling the crack to solidify and for the object to recover properties close to its original shape and strength. In the future, those gels could be used as a material for 3D-printing. If you think about today’s 3D-printing process, it requires layers of polymers stacked on top of each other. This leads to imperfect shapes that can have weakly bonded interfaces between its layers. Our new “self-healing” materials are not only fluid enough to be printed but also solid enough to hold their shape. By precisely controlling the printing temperature, we could ultimately get rid of the layers’ interface problem.

IBM’s gel (B), once heated, returns to its original state demonstrating
recyclable, remoldable properties. A typical gel (A) retains its form with heat.

A lab legacy 
This gel discovery stems from work by IBM scientists Jim Hedrick and Jeannette Garcia two years ago, when they discovered how to synthesize industrial polymers. We applied this chemistry to our gels using computational chemistry – co-author and IBMer Gavin Jones simulated the affinity our organogels’ crosslinks with the different monomers we used, and showed that they bound in a similar way as the original molecules used by Jim and Jeannette. Our team’s rheology expert, Nancy Zhang, also measured the gel’s ability to flow, and its strength. Her data explained how to perceive the gel’s hardness and softness, and also proved that the gel could be remolded multiple times before losing its strength.
Here’s Nancy explaining the gel’s ability to be molded multiple times:
More stories

A new supercomputing-powered weather model may ready us for Exascale

In the U.S. alone, extreme weather caused some 297 deaths and $53.5 billion in economic damage in 2016. Globally, natural disasters caused $175 billion in damage. It’s essential for governments, business and people to receive advance warning of wild weather in order to minimize its impact, yet today the information we get is limited. Current […]

Continue reading

DREAM Challenge results: Can machine learning help improve accuracy in breast cancer screening?

        Breast Cancer is the most common cancer in women. It is estimated that one out of eight women will be diagnosed with breast cancer in their lifetime. The good news is that 99 percent of women whose breast cancer was detected early (stage 1 or 0) survive beyond five years after […]

Continue reading

Computational Neuroscience

New Issue of the IBM Journal of Research and Development   Understanding the brain’s dynamics is of central importance to neuroscience. Our ability to observe, model, and infer from neuroscientific data the principles and mechanisms of brain dynamics determines our ability to understand the brain’s unusual cognitive and behavioral capabilities. Our guest editors, James Kozloski, […]

Continue reading