UC Berkeley

Nanocomposites are now a multi-billion-dollar market with products across various industries, including aerospace, automotive, biomedical, electronics, packaging, etc. The development of advanced nanocomposites to meet technological needs requires precise structure control, as the properties of the nanocomposites are determined by their structures across the atomic-nanoscopic-microscopic-macroscopic hierarchy. Although significant efforts have been made to design and investigate the ordered nanostructure by engineering favorable pair-interactions, we haven’t overcome the challenges associated with the organic/inorganic nanocomposites, such as the particles’ incompatibility with the polymer matrices, the size limitation of the nanofillers, and the precise requirement of the chemistries. In addition, it is challenging to modulate the structures beyond nanoscales. Because the micro- or macrostructure formation requires long-range diffusion of the building blocks, which are often prevented by the interfaces introduced by the preferential interactions that are designed for ordered nanostructure assembly. Moreover, most of the work has been focused on the final product, little is known about the structure evolution and the assembly kinetics at each stage across the structural hierarchy.

In this thesis, we have designed a multicomponent system by adding the small molecules to the mixture of block copolymer (BCP)-based supramolecules and nanoparticles (NPs) to solve the abovementioned challenges. By taking advantage of the mixing entropy, the small molecules distribute to allow the ordered nanostructure to form in a near-miscible state, which reduces the energy barrier associated with the interface diffusion, allowing microstructure modulation. Additionally, the enhanced miscibility enables large particles to sit across interfaces, solving the size limitation that plagues traditional BCP/nanoparticle blends. And the complex blend can adapt to impurities and retain structure fidelity. We performed detailed characterization and dynamic simulations to fully understand the hierarchically ordered structure and the spatial distribution of each component.

As we increase the variety of the component, the spatial distribution of each building block during the self-assembly process can be complicated. Also, given the different characteristics of the components and the resulting varied diffusion rates, combined with the intricate assembly process from low-tier to high-tier structures, the final assembled morphology can vary and is strongly related to the processing routes. We observed the pathway-dependent assemblies and performed systematic studies to map out the complete picture of structure evolution from the dilute solution to hierarchically ordered assembly over seven orders of relaxation time of NPs from 20 µs to 100 s. By performing in-situ X-ray photon correlation spectroscopy (XPCS) studies, we delineate each step of the assembly process, quantify the assembly dynamics, explain how each component in the complex blend self-regulates to form an ordered structure in a near-miscible state, and further assemble into hierarchical assemblies. Furthermore, we identify the critical parameter for successfully ordered hierarchical structure formation and explain the reason for the pathway-dependent assemblies.

The multicomponent blend also offers opportunities for new structure formation beyond the native BCP morphologies. We obtained a symmetry-breaking phase when the particle size is similar to the polymer microdomain size. Symmetry-breaking is commonly seen in 2-D materials to introduce exceptional properties but hasn’t been well-explored in soft matter. Here, the distribution of small molecules offers flexibility to arrange particles in a way to minimize the polymer chain deformation and maximize the favorable interactions. The structure only forms in a narrow range of particle size and supramolecule composition, and it provides experiment results for theoreticians to identify the new energy landscape in the multicomponent blend. Additionally, the system shows a self-sorting behavior based on only a few nanometers NP size differences, confirming the self-regulation and the hierarchical structural control abilities of the multicomponent nanocomposite.

These systematic studies allow us to build a phase diagram of the multicomponent nanocomposites as a function of particle size and composition. The blend can self-regulate based on the relative size between the NPs and the polymer microdomain, and the amount of the small molecules to tailor their distributions to form ordered structures. The dominant driving forces vary with the nanocomposites and can be separated into enthalpic polymer-guided assembly (NP size < polymer microdomain size) and the entropic co-assembly between particles and polymers (NP size > polymer microdomain size). Symmetry-breaking phase appears in the boundary between the enthalpic- and entropic-driven assemblies (NP size ≈ polymer microdomain size). Systematic structure evolution and assembly dynamics have also been investigated in each system.

Finally, we introduce the polymer-grafted colloidal particles in hundreds of nanometers to the multicomponent system to modulate the interparticle interactions to access tunable assemblies. We can tailor the interparticle interactions by tuning the colloidal particle size, grafted polymer chain length, and molecular weight of the BCPs. This enables us to access various ordered assemblies, including near-close packed hexagonal packing, non-closed packed morphology, anisotropic assembly, and hierarchical structures. Notably, it is challenging to obtain the non-close packed and anisotropic assemblies using simple particles in traditional methods without relying on templates. The self-assembly ranges from hard sphere-dominated assembly to coassembly between particles and polymer by varying the parameters mentioned above. This approach provides a way to design and modulate the interactions between particles in the multicomponent blend to achieve the target structures with desired complexity.