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Classical challenges in the physical chemistry of polymer networks

Opening Session

Presented by Bradley Olsen


The design of polymer networks is one of the oldest and most important challenges in chemistry, impacting many of the highest volume chemical industries from rubber to adhesives to biomedical materials. However, more than any other branch of materials, networks have resisted precise characterization. This leaves many open challenges in understanding how their chemical design is linked to their physical properties. This lecture will discuss recent advances in our understanding of polymer networks held together by chemical and/or physical bonds and how this is leading to new advances in the design and application of these materials.

Stimulated by advances in optomechanochemistry, controlled polymerization of networks, and new discoveries in metrology, chemists are revisiting our classical understanding of polymer networks, leading to fundamentally new mechanisms and insights that are redefining our understanding. For example, Johnson and coworkers have developed a novel method that for the first time enables direct counting of loops within polymer gels. Using this new data, we have been able to develop and validate parameter-free theories for predicting the kinetics of network formation, accounting for defects. Using these theories, we can then develop a real elastic network theory built upon the classical phantom network theory that quantitatively accounts for network defects in calculating elastic response.

Second, new measurements of diffusion in polymer gels on short length scales have yielded the stunning observation of superdiffusive dynamics in several chemically unrelated physical hydrogel systems. Using forced Rayleigh scattering (FRS) to measure self-diffusion, we show that below a certain length scale, Fickian diffusion transitions to a super diffusive regime that occurs due to the interplay between chain association/dissociation with the network and chain diffusivity. This super-diffusive behavior is quantitatively captured by simple models that illustrate how diffusion can provide a new probe for previously inaccessible relaxation dynamics in associative polymers.

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