Self-assembly of block copolymer blends
Description
A block copolymer (BCP) consists of two or more covalently-bound chemically distinct homopolymer blocks. These macromolecules have emerging applications in energy storage, membrane separations, and nanolithography stemming from their propensity to self-assemble into regular nanoscale structures. For a pure BCP, this self-assembly is dictated entirely by the polymer’s degree of polymerization (N), chemistry (χ), composition (f), and chain architecture. Blending together different types of these polymers provides a simpler, synthesis-free method for tuning nanoscale morphology and feature size. This dissertation describes the use of dissipative particle dynamic (DPD) simulation to develop a fundamental understanding of phase behavior in BCP/homopolymer and cyclic-linear BCP blends. Block copolymer-homopolymer blends offer a simple method for tuning nanostructure sizes to meet application-specific demands. We systematically investigated morphology and feature size in ternary blends of symmetric linear copolymers and their constituent homopolymers (A-b-B/A/B), finding a close match between simulation results and known experimental behavior. Having established DPD simulation as a valid model, we used the simulation results to explore the relationship between polymer chain length, molecular packing, and the degree of lamellar swelling with homopolymer addition in ternary blends. A consensus of theoretical and simulation work suggests that cyclic BCPs form features up to 40% smaller than their linear analogues - while also exhibiting superior thin film stability and assembly dynamics – making them intriguing candidates for nanolithography. However, the complex syntheses required to produce these molecules mean that a need for pure cyclic BCPs would present a challenge to large-scale manufacturing. Thus, we aspired to understand the self-assembly of cyclic-linear copolymer blends. We first combined DPD simulation results and strong segregation theory to develop a scaling prediction for neat BCP feature size based on experimentally-tunable parameters (χ, N, f, and polymer architecture). The resulting Revised Scaling Law quantitatively predicts domain spacings over a wide range of BCP chain lengths, segregation strengths, and compositions and offers an explanation for the significant discrepancies between prior theoretical predictions and experimental results of cyclic BCP feature size. Next, we investigated the dimensions and interfacial roughness of nanofeatures formed by cyclic-linear BCP blends. For mixtures of symmetric cyclic and linear polymers of equivalent N, up to 10% synthetic impurity had minimal impact on cyclic BCP feature dimensions. On the other hand, we found that adding small amounts of cyclic BCP was an effective method for “fine-tuning” linear BCP feature sizes. We also analyzed our simulated blend domain spacings in the context of the revised scaling law, and found deviations between simulation and theory that arose from molecular-level packing motifs not included in theory. Finally, we investigated the impact of blending polymer architectures on the BCP order-disorder transition, finding that a mismatch in molecular size and architecture can strongly inhibit ordering. These insights into blend self-assembly will assist experimentalists in the rational design of BCP materials for advanced nanolithography applications.