Computationally Driven Design of Shape-Controlled Polymeric Nanostructures

In the realm of shape control involving synthetic soft materials, block copolymers are ubiquitously exploited due to their ability to spontaneously self-assemble into a plethora of morphologies. The growing need in areas such as drug delivery, lithography, and microelectronics to generate new nanostructures demands ever finer control of copolymer self-assembly. It is desirable to better control the shape and size of soft nanostructures, with minimal post processing techniques to assert stability. While there is an abundance of literature on diblock copolymer assembly, few relationships exist for sequence-controlled copolymers. It is well known that biological macromolecules are highly sequence specific and exploit secondary interactions to direct the assembly into a hierarchy of structures with controlled shape and size. Single chain synthetic sequence-controlled copolymers that self-assemble into structures with well-defined shape, size, and stability could extend the design space of accessible shapes formed via traditional diblock copolymer self-assembly, and potentially offset the time and cost required for post-modification processes to assert stability. However, employing an in-depth study through trial and error is not practical; thus, emphasizing the need for the development of an efficient structure-searching strategy. Inspired by nature’s efficiency in self-assembly and shape recognition, the combined efforts of evolutionary algorithms, computer vision and Brownian dynamics simulations were used as a meta-heuristic approach to search and design sequence-controlled polymers that assemble into targeted shapes. This work is grouped into four main areas: Design of single chain nanoglobules: To provide a fundamental understanding on the design of single chain nanoglobules with targeted shapes Design of aggregation resistant globules: To design single chain vesicles that are resistant to aggregation at low concentrationDesign of hierarchial nanoglobules: To utilize single chain nanoglobules as building blocks for the design of hierarchical nanostructures Design and implementation of a structure-searching strategy In the first part of this work, the effects of the following length scales on nanoparticular shape were investigated based on a bead-spring polymer model: A/S Interfacial length (ε)* Kuhn length (b) Results yield a conformational diagram mapping molecular sequence to single molecule nanoparticular shapes ranging from vesicles to sponges to necklaces. Here, chain sequence can offer fine control of nanoparticulate dimensions, for example enabling control of vesicle cavity size. Within this model, complex shape control is dictated by a large molecular weight and operating in the limit of strong A-B segregation (high χAB). Further, single chain globules are designed that are resistant to aggregation at low concentration. Results indicate that the attachment of solvophilic loops to single chain vesicles in a sequence-specific manner permits steric repulsion that prevents aggregation; offering kinetic stabilization within the timescales probed. The single chain nanoglobules designed in the first part of the work provide an ‘alphabet’ of nanoparticular shapes for hierarchical shape design in the penultimate part of this work. The combination of multiple sequence ‘motifs’ consisting of vesicle and worm motifs demonstrated the design of poreated and tubular vesicles, pointing the way towards designer artificial enzymes and tubules. It was further shown that the pore size of poreated vesicles can be directly tuned by controlling the sequence of the worm-coding block. In the final part of the work, a shape-matching algorithm is developed to search for targeted shapes based on a 3D voxelization scheme. The shape-matching algorithm was tested with various model shapes and retrofitted for use in a genetic algorithm for inverse material design