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Synthetic replication of the precise mesoscale control found in natural systems poses substantial experimental challenges due to the need for manipulation across multiple length scales (from nano- to millimeter). 

We report the use of fluorinated polymer zwitterions to build hybrid systems for efficient CO₂electroreduction. 

Research involving polymer zwitterions typically involves the preparation of ammonium-based structures and their study as coatings or gels that impart hydrophilicity and/or antifouling properties to substrates and materials. More recent synthetic advances have produced a significant expansion in polymer zwitterion chemistry, especially with respect to the composition of the cationic moieties that open new possibilities to examine polymer zwitterions as amphiphiles, functional surfactants, and components of complex emulsions. This article describes the synthesis of new zwitterionic sulfonium sulfonate monomers and their use as starting materials in controlled free radical polymerization to yield the corresponding polymers. These novel polymer zwitterions bear sulfonium sulfonate groups, that possess an inverted dipole directionality relative to prior examples that yields different and unexpected physical and chemical properties. For example, the polymer zwitterions described here are soluble in a wide range of nonaqueous solvents and possess significantly greater stability against nucleophiles relative to their dipole-inverted counterparts. Additionally, the amphiphilic character of these sulfonium sulfonate polymers makes them amenable to use as surfactants for stabilizing oil-in-water emulsions, a feature that is not possible using conventional ultrahydrophilic polymer zwitterions. 

Abstract As a class of semiconductor nanocrystals that exhibit high photoluminescence quantum yield (PLQY) at tunable wavelengths, perovskite nanocrystals (PNCs) are attractive candidates for optoelectronic and light‐emitting devices. However, attempts to optimize PNC integration into such applications suffer from PNC instability and loss of PL over time. Here, we describe the impact of organic and polymeric N‐oxides when used in conjunction with PNCs, whereby a significant increase in PNC quantum yield is observed in solution, and stable PL emission is obtained in polymeric nanocomposites. Specifically, when using aliphatic N‐oxides in ligand exchange with CsPbBr₃PNCs in solution, a substantial boost in PNC brightness is observed (~40% or more PLQY increase), followed by an alteration of the perovskite chemistry. When N‐oxide substituents are positioned pendent to a poly(n‐butyl methacrylate) backbone, the optically clear flexible nanocomposite films obtained have bright PL emission and maintain optical clarity for months. X‐ray diffraction is useful for characterizing the PNC crystalline structure following exposure to aliphatic N‐oxides, while electron microscopy (EM) and small‐angle X‐ray scattering (SAXS) measurements of the PNC‐polymer nanocomposites show this polymeric N‐oxide platform to cleanly disperse PNCs in flexible polymer films. 

In the field of materials science, microscopy is the first and often only accessible method for structural characterization. There is a growing interest in the development of machine learning methods that can automate the analysis and interpretation of microscopy images. Typically training of machine learning models requires large numbers of images with associated structural labels, however, manual labeling of images requires domain knowledge and is prone to human error and subjectivity. To overcome these limitations, we present a semi-supervised transfer learning approach that uses a small number of labeled microscopy images for training and performs as effectively as methods trained on significantly larger image datasets. Specifically, we train an image encoder with unlabeled images using self-supervised learning methods and use that encoder for transfer learning of different downstream image tasks (classification and segmentation) with a minimal number of labeled images for training. We test the transfer learning ability of two self-supervised learning methods: SimCLR and Barlow-Twins on transmission electron microscopy (TEM) images. We demonstrate in detail how this machine learning workflow applied to TEM images of protein nanowires enables automated classification of nanowire morphologies ( e.g. , single nanowires, nanowire bundles, phase separated) as well as segmentation tasks that can serve as groundwork for quantification of nanowire domain sizes and shape analysis. We also extend the application of the machine learning workflow to classification of nanoparticle morphologies and identification of different type of viruses from TEM images. 

Abstract Filamentous bundles are ubiquitous in Nature, achieving highly adaptive functions and structural integrity from assembly of diverse mesoscale supramolecular elements. Engineering routes to synthetic, topologically integrated analogs demands precisely coordinated control of multiple filaments’ shapes and positions, a major challenge when performed without complex machinery or labor-intensive processing. Here, we demonstrate a photocreasing design that encodes local curvature and twist into mesoscale polymer filaments, enabling their programmed transformation into target 3-dimensional geometries. Importantly, patterned photocreasing of filament arrays drives autonomous spinning to form linked filament bundles that are highly entangled and structurally robust. In individual filaments, photocreases unlock paths to arbitrary, 3-dimensional curves in space. Collectively, photocrease-mediated bundling establishes a transformative paradigm enabling smart, self-assembled mesostructures that mimic performance-differentiating structures in Nature (e.g., tendon and muscle fiber) and the macro-engineered world (e.g., rope).