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.

The Cr2+-based compounds, A2CrX4, where A = M+ (e.g., K+, Cs+, Rb+) or RNH3+ (e.g., MeNH3+) and X = Cl-, Br-, are an underexplored family of lead-free layered metal-halide perovskites. These compounds attracted a great deal of interest in the 1970s and 1980s after their "transparent ferromagnetism" was discovered, but they have received virtually no attention since, perhaps because they are extremely unstable in air. Further investigation into their chemistry and properties is warranted.

This article studies the reversible structure and mechanical properties of a biological dynamic polymer network. This biological material based on structural protein polymers has a glass transition at 35 °C, causing a reversible thermomechanical transition and a change in modulus spanning several orders of magnitudes.

The atomic structure along step edges on metal oxide surfaces is crucial for the growth of overlayers in complex oxide devices. No experimental techniques are yet capable of resolving that structure. Theoretical calculations of step structures on metal oxides are complex and have not been reported to date.

Researchers in the Center for the Science and Engineering of Materials (CSEM) at the California Institute of Technology are developing a new class of biomaterials for use in surgery and regenerative medicine. These new materials are produced by genetic engineering, and consist of pieces of natural proteins that have been stitched together in new ways to allow control of both their mechanical properties and their interactions with cells and tissues after surgical implantation.

R.M. Suter /CMU MRSEC, Carnegie Mellon University, NSF DMR- 0520425