New discovery: Rapid preparation of high performance materials and coatings

Share this post:

From non-stick coatings on cookware to waterproof outdoor clothing, many of the things we use in our everyday lives rely on water-resistant or hydrophobic materials to function. In many cases, their ability to resist the absorption of water is due to their high fluorine content.

An excellent example of this is Teflon®, a highly fluorinated material used in many consumer products including electronics and non-stick coatings on pans. While effective for many applications, Teflon® and related materials may not be suited for all uses.

At IBM Research, we are working to develop the next-generation of hydrophobic materials for use in high performance applications. One important application is coatings for medical devices, which are often prone to colonization by bacteria that can be transmitted to patients.  Hydrophobic materials can serve as a protective layer for the medical instrument or implant by preventing the adhesion of bacteria to the surface, helping preserve patient health.

In order to prepare improved materials for medical device coatings and other applications, our work focused on the development of robust, highly fluorous polymers. In some cases, however, the standard approaches for preparing these materials requires a long reaction time and produces a large amount of salt as a byproduct, which must then be removed before processing into coatings or other devices. Because of these limitations, we were interested in developing a more streamlined approach to preparing new fluoropolymers.


IBM researcher Nathan Park prepares the reaction he discovered that generates fluoropolymers much faster and easier than traditional methods.

A few months ago, my colleague Gavin Jones and I were working together to make a new fluoropolymer using the traditional approach. But after reviewing some of the previous work Gavin published, I came up with a new direction for our current project.

I tested my idea in a lab at IBM-Almaden and was very excited by the result — a new process for fluoropolymers that was much faster and easier. Instead of producing a salt that must be removed during subsequent purification steps, the primary byproduct is an easy-to-remove gas. The reaction to create the polymer also turned out to be incredibly fast, reducing the time to prepare new materials from hours to just minutes or seconds.

As a researcher at IBM, I can collaborate closely with computational chemists to rationally design new catalysts and processes. In this project, I worked closely with Gavin, a computational chemist, to analyze the mechanism of the chemical reaction for deeper insights into the polymerization process.

In this case, we also reached out to IBM PhD Scholarship recipient Gabriel Gomes at Florida State University, who Gavin has been working with and co-advising together with his professor Igor Alabugin. Together, we discovered that the polymerization reaction proceeded via a unique mechanism with the catalysts used in our process, playing important, yet unexpected roles. This discovery is significant as it opens the door to further refinement of the polymerization process through computationally-guided catalyst design.

Going forward, such collaborations will be increasingly important as we continue to develop the process to prepare and manufacture high-performance coatings.





More Chemistry stories

We’ve moved! The IBM Research blog has a new home

In an effort better integrate the IBM Research blog with the IBM Research web experience, we have migrated to a new landing page:

Continue reading

New smartphone app to navigate blind people to stand in lines with distances

We have developed an AI-driven assistive smartphone app dubbed LineChaser, presented at CHI 2021, that navigates a blind or visually impaired person to the end of a line. It also continuously reports the distance and direction to the last person in the line, so that the blind user can follow them easily.

Continue reading

New research helps make AI fairer in decision-making

To tackle bias in AI, our IBM Research team in collaboration with the University of Michigan has developed practical procedures and tools to help machine learning and AI achieve Individual Fairness. The key idea of Individual Fairness is to treat similar individuals well, similarly, to achieve fairness for everyone.

Continue reading