Uncovering the relationship between microstructure and mechanical properties in polymer–nanoparticle hydrogels through fluorescent and super-resolution optical microscopy

Characterization and understanding the structure of hydrogel networks is usually accomplished through measuring mechanical properties and relating them to theoretical models of the expected network structure. However, several classes of hydrogels are comprised of more complex network architectures, such as non-covalent molecular cross-links and secondary macromolecular interactions, which deviate from ideal polymer networks. In the case of polymer–nanoparticle (PNP) hydrogels, the hydrogel network is formed through polymer–nanoparticle interactions or nanoparticle–nanoparticle interactions, which imbues them with the observed bulk viscoelasticity. While these materials are often used as injectable hydrogels for the delivery of therapeutics and 3D printing, the origin of their dynamic mechanical properties remains poorly understood, which can be addressed through the development of characterization techniques that are able to resolve the microstructure of the networks. Here, we used total internal reflection fluorescence (TIRF) microscopy and direct stochastic optical reconstruction microscopy (dSTORM) to investigate the nanoparticle distribution within PNP hydrogels to relate microstructure to macroscopic properties in this class of materials. We formulated PNP hydrogels with varying α-cyclodextrin (αCD) concentrations and employed dye-labelled nanoparticles for imaging. At low concentrations of αCD, we observed a homogeneous, network-like distribution of the nanoparticles with minimal aggregation, while at higher concentrations aggregates of increased size formed within the network indicative of increased nanoparticle–nanoparticle interactions. This work lays the foundation for advanced microscopy techniques to be employed to fill the gap between understanding of the molecular behavior, changes in topology, and macroscopic properties in hydrogel characterization.