Youli Li

Charge and energy transport within living systems are fundamental processes that enable the autonomous function of excitable cells and tissues. To date, localized control of these transport processes has been enabled by genetic modification approaches to render light sensitivity to cells. Here, we present peptidic nanoassemblies as constituents of a cardiac biomaterial platform that leverages complementary sequence interactions to direct photoinduced energy transport at the cellular interface. Photophysical characterizations and conductivity measurements confirm the occurrence of energy/charge transfer and photocurrent generation upon optical excitation in both dry and electrolytic environments. Comparing an electrostatic sequence pair against a sequence-matched donor–acceptor coassembly, we demonstrate that the sequence design with charge complementarity shows more prominent photocurrent behavior. With the flanking bioadhesive units, the primary and stem cell–derived cardiomyocytes interfaced with covalently stabilized films of the optoelectronic nanostructures exhibited material-stimulated genotypic, structural, or functional cardiac features. Collectively, our findings introduce an optoelectronic cardiac biomaterial where coassembled peptide nanostructures are molecularly designed to induce light sensitivity in excitable cells without gene modification, influencing in vitro cardiac contractile behavior and expression of cardiac markers.

We examine the swelling and dehydration-rehydration response of triblock polyelectrolyte complex (PEC) hydrogels using small-angle X-ray scattering and rheology to demonstrate that PEC hydrogel microstructures equilibrate readily into morphologies corresponding to pristine PEC hydrogels with comparable polymer contents. As such, we show that PEC hydrogels possess identical microstructures independent of the route employed to make them. We propose PEC domain fission as the dominant mechanism for these microstructural rearrangements. Furthermore, we show that mixing two fully formed PEC hydrogels with distinct microstructures leads to microstructural rearrangements, resulting in hydrogels with intermediate microstructure and viscoelastic properties of the individual PEC hydrogels. Our findings provide explicit experimental proof that PEC hydrogel microstructures are not kinetically trapped and have implications for their processing in diverse applications.

Block copolymers play a vital role in materials science due to their diverse self‐assembly behavior. Traditionally, exploring the block copolymer self‐assembly and associated structure–property relationships involve iterative synthesis, characterization, and theory, which is labor‐intensive both experimentally and computationally. Here, we introduce a versatile, high‐throughput workflow toward materials discovery that integrates controlled polymerization and automated chromatographic separation with a novel physics‐informed machine‐learning algorithm for the rapid analysis of small‐angle X‐ray scattering data. Leveraging the expansive and high‐quality experimental data sets generated by fractionating polymers using automated chromatography, this machine‐learning method effectively reduces data dimensionality by extracting chemical‐independent features from SAXS data. This new approach allows for the rapid and accurate prediction of morphologies without repetitive and time‐consuming manual analysis, achieving out‐of‐sample predictive accuracy of around 95% for both novel and existing materials in the training data set. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy. Collectively, the synergistic combination of controlled synthesis, automated chromatography, and data‐driven analysis creates a powerful workflow that markedly expedites the discovery of structure–property relationships in advanced soft materials.

Block copolymers play a vital role in materials science due to their diverse self-assembly behavior. Traditionally, exploring the phase space of block copolymer self-assembly and associated structure-property re- lationships involves iterative synthesis, characterization, and theory, which is labor-intensive both experimentally and computationally. Here, we intro- duce a versatile, high-throughput workflow towards materials discovery that integrates controlled polymerization and automated chromatographic separa- tion with a novel physics-informed machine learning algorithm for the rapid analysis of small-angle X-ray scattering data. Leveraging the expansive and high-quality experimental datasets generated by fractionating polymers using automated chromatography, this machine learning method effectively reduces data dimensionality by extracting chemical-independent features from SAXS data. This new approach allows for the rapid and accurate prediction of mor- phologies without repetitive and time-consuming manual analysis, achieving out-of-sample predictive accuracy of around 95% for both novel and existing materials in the training dataset. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy. Collectively, the synergistic combi- nation of controlled synthesis, automated chromatography, and data-driven analysis creates a powerful workflow that markedly expedites the discovery of structure-property relationships in advanced soft materials.

Biomacromolecular networks with multiscale fibrillar structures are characterized by exceptional mechanical properties, making them attractive architectures for synthetic materials. However, there is a dearth of synthetic polymeric building blocks capable of forming similarly structured networks. Bottlebrush polymers (BBPs) are anisotropic graft polymers with the potential to mimic and replace biomacromolecules such as tropocollagen for the fabrication of synthetic fibrillar networks; however, a longstanding limitation of BBPs has been the lack of rigidity necessary to access the lyotropic ordering that underpins the formation of collagenous networks. While the correlation between BBP rigidity and grafting density is well established, synthetic approaches to rigidify BBPs by increased grafting density are underdeveloped. To address this gap in synthetic capability, we report the synthesis of novel macroinitiators that provide well-defined BBPs with an unprecedentedly high grafting density. A suite of light scattering techniques are used to correlate macromolecular rigidity with grafting architecture and density and demonstrate for the first time that poly(norbornene) BBPs exhibit long-range lyotropic ordering as a result of their rodlike character. Specifically, the newly reported ultradensely grafted structures, preparable on multigram scale, form hexagonal arrays while conventional BBPs do not, despite showing long-range spatial correlations. These results implicate the central role of density and entanglement in the solution phase assembly of BBPs and provide new fundamental insight that is broadly relevant to the fabrication and performance of BBP-derived materials, spanning biomedical research to photonic materials and thermal management technologies. Furthermore, these newly reported liquid crystalline BBPs provide a structural template to explore the untapped potential of the bottom-up assembly of semiflexible networks and are ultimately intended to provide a modular route to hierarchically structured biomimetic materials.

Biomacromolecular networks with multiscale fibrillar structures are characterized by exceptional mechanical properties, mak-ing them attractive architectures for synthetic materials. However, there is a dearth of synthetic polymeric building blocks capable of forming similarly structured networks. Bottlebrush polymers (BBPs) are anisotropic graft polymers with the po-tential to mimic and replace biomacromolecules such as tropocollagen for the fabrication of synthetic fibrillar networks; however, a longstanding limitation of BBP’s has been the lack of rigidity necessary to access the lyotropic ordering that underpins the formation of collagenous networks. While the correlation between BBP rigidity and grafting density is well-established, synthetic approaches to rigidify BBPs by increased grafting density are underdeveloped. To address this gap in synthetic capability, we report the synthesis of novel macroinitiators that provide well-defined BBPs with unprecedentedly high grafting density. A suite of light scattering techniques are used to correlate macromolecular rigidity with grafting archi-tecture and density, and demonstrate for the first time that poly(norbornene) BBPs exhibit long-range lyotropic ordering as a result of their rod-like character. Specifically, the newly reported ultra-densely grafted structures, preparable on multigram scale, form hexagonal arrays while conventional BBPs do not, despite showing long range spatial correlations. These results implicate the central role of density and entanglement in the solution phase assembly of BBPs and provides new fundamen-tal insight that is broadly relevant to the fabrication and performance of BBP derived materials, spanning biomedical research to photonic materials and thermal management technologies. Furthermore, these newly reported liquid crystalline BBPs pro-vide a structural template to explore the untapped potential of the bottom-up assembly of semiflexible networks and are ul-timately intended to provide a modular route to hierarchically structured biomimetic materials.