Polymeric Nanomaterials for Photocatalysis and Regenerative Medicine: Integrating Machine Learning and Synthetic Approaches for Materials Design

Bruckner, Eric Paul

doi:https://doi.org/10.21985/n2-ph0w-hr86

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Polymeric Nanomaterials for Photocatalysis and Regenerative Medicine: Integrating Machine Learning and Synthetic Approaches for Materials Design

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The ability to control the crystalline ordering and morphology of polymeric nanomaterials is a grand challenge in the field of materials science, which could enable the development of functional materials able to solve long-standing problems in renewable energy and medicine. In this work, we explore a combination of supramolecular chemistry and computational approaches to meet this challenge in systems ranging from organic chromophores for photocatalysis to peptide amphiphiles for extracellular matrix repair. In the first system, perylene monoimide (PMI) chromophore amphiphiles, known to form two-dimensional supramolecular polymers on their own, were co-crystallized with covalent polymers bearing pendant PMI motifs to form hybrid nanocrystals where different nanostructure morphologies and crystal structures could be formed simply by changing the ratio of the two components. This co-crystallization phenomenon was further explored in a second system, where ullazine chromophore amphiphiles were found to induce nanofiber formation in covalent polymers bearing pendant ullazine motifs that form collapsed, coiled chains on their own. Across both chromophore systems, the morphological changes in these hybrid nanostructures are dictated by a competition between the entropic driving force of the covalent polymer to form collapsed chains and the enthalpic driving force of the chromophore amphiphiles to form extended nanostructures. The combination of covalent polymers and self-assembling small molecules incorporated into these nanostructures led to emergent properties. In the PMI-based system, the incorporation of the covalent polymer into the hybrid nanocrystals led to an increase in the fracture toughness of single nanostructures and an increase in the photocatalytic production of hydrogen when the hybrid nanostructures were used as a photosensitizer for a thiomolybdate catalyst. A similar effect was observed in the ullazine-based systems where nanofibers formed by both covalent polymers and small molecules showed enhanced photocatalytic activity for the production of hydrogen peroxide due to an increase in the fluorescence emission. In the third system, the use of machine learning algorithms was explored to design bioactive supramolecular polymer nanostructures based on peptide amphiphiles (PAs). By combining a literature derived experimental dataset with results from high-throughput coarse-grained molecular dynamics simulations, a deep neural network was trained to accurately predict the nanostructure formation of various PAs directly from the peptide sequence. The neural network was then used to explore the chemical space of PAs to computationally design bioactive PA nanostructures containing the KTTKS peptide sequence known to stimulate the production of extracellular matrix proteins. The nanostructure predictions of the model were experimentally validated, and bioactivity studies revealed that the fibrous PA nanostructures increased collagen and fibronectin expression in human fibroblasts relative to micellar nanostructures and the KTTKS sequence alone. Taken together, this work provides novel methods to predict and control both the crystal structure and nanoscale morphology of various polymeric nanomaterials with applications in photocatalysis and regenerative medicine.

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