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Mesoscale hierarchical helical structures with diverse functions are abundant in nature.

Researchers in the Wisconsin MRSEC have shown that depositing onto an alignment substrate creates better glass films that are anisotropic biaxially, meaning they are aligned in the plane of the substrate as well as out of plane. The in-plane orientation of the molecules affect how they interact with light and conduct electricity. In general, more alignment is better for applications ranging from flexible transistors to OLEDs to organic photovoltaics.

We describe hierarchical assemblages of colloidal rods that mimic some of the complexity and reconfigurability of biological structures. In particular, we show that chiral rod-like inclusions dissolved in an achiral colloidal membrane assemble into rafts, which are adaptable finite-sized liquid droplets that exhibit two distinct chiral states of opposite handedness. Interconverting between these two states switches the membrane-mediated raft interactions between long-ranged repulsions and attractions.

Current active matter systems, such as self propelled colloids or migrating cells, are inherently 2D, which limits the potential engineering applications. Brandeis developed the first 3D active nematic material by mixing an isotropic active fluid (Microtubules + kinesin motors) with a passive nematic colloidal liquid crystal (fd viruses). Using multiview light sheet microscopy, they explored the structure and the dynamics of topological defects in a 3D active nematic.

Fifty years of Moore’s Law scaling in microelectronics have brought remarkable opportunities for the rapidly-evolving field of microscopic robotics. Electronic, magnetic, and optical systems now offer an unprecedented combination of complexity, small size, and low cost, and could readily be appropriated to form the intelligent core of microscopic robots. But one major roadblock exists: there is no micron-scale actuator system that seamlessly integrates with semiconductor processing and responds to standard electronic control signals.

Atomically-thin sheets of semiconductors have been of immense interest since the Nobel-Prize-winning discovery of graphene or two-dimensional (2D) carbon. Such materials represent the ultimate limit of “scaling” to small sizes, of vital importance in the semiconductor device industry. A particularly exciting recent (2017) finding is that the elemental semiconductor tellurium can be created in 2D sheets, with highly mobile electrons.

Wisconsin MRSEC researchers have leveraged the power of machine learning to tame the complexity of polycrystalline materials and predict their properties. They have developed a graph neural network approach that predicts materials properties with >98% accuracy 90,000 times faster than competing methods. They applied this model to predict magnetostriction, which quantifies the size change of a material induced by a magnetic field.