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Program Highlights

New Configuration Transitions of Nematic Liquid Crystals in Drops Induced by Magnetic Fields

IRG-3 researchers Yodh, Kikkawa and Collings made a significant discovery about the behavior of liquid crystals (LCs) in droplets exposed to a magnetic field. LCs are unique materials that flow like liquids but also have some order (orientational order) like crystals. In this study, the researchers focused on a specific type of LC phase called a nematic. Field-induced “switching” of nematic liquid crystals (NLCs) in planar geometries is the basis of LC displays. Here, NLCs were put in spherical drops with special molecules (surfactants) on the drop surface that align the molecules, or NLC director, perpendicular to the droplet surface and force a topological hedgehog defect to form at the drop center. Field-induced switching in this case differs fundamentally from the planar cells due to confinement geometry and the topological defect.

Controlling Nanoparticle Assemblies with Dendritic Ligands

Liquid crystals are soft materials which see frequent use in optical displays and other smart devices. This is because they can change their optical properties (such as light transmission and polarization) when an electric field is applied. This allows them to selectively block or transmit light, creating the pixels that form images on the screen. Similarly, nanoparticles are materials that can have different optical properties that depend on their size.

In this work, Penn researchers have developed new liquid crystal-nanoparticle hybrid systems. They have integrated specially synthesized molecules known as “dendritic promesogenic ligands” that can attach to the nanoparticles.

 

Understanding Deformation in Disordered Materials

Disordered particulate solids are ubiquitous in items ranging from plastic to concrete. Despite their prevalence, applications can be limited because they are often brittle. In contrast, ductile materials can be deformed smoothly and significantly without fracturing. Strategies for tuning ductility of disordered solids are empirical and system-specific. 

Liu, Riggleman and Durian used computer simulations of atomic and polymeric glasses and laboratory experiments on granular packings to develop a general Structuro-Elasto-Plastic (StEP) framework for understanding large-scale deformation of disordered solids in terms of the system-specific interplay between local structure, local rearrangements and larger-scale elasticity.

 

Predicting the Softness of Glasses from Thermodynamics

The properties of glasses – disordered, amorphous materials – can be hard to predict because of this lack of long-range order and the associate properties of crystal symmetry. 

Work in this IRG has developed two fundamental descriptors to describe glass properties.  The first of these – softness – is a machine-learning derived descriptor that characterizes structural defects in glasses and predicts rearrangements or yield that will occur in disordered materials in response to applied loads. The second – excess entropy – is a thermodynamic quantity that is a simple function of that describes the deviation of atomic arrangements from what would be predicated from ideal gas theory.

Accessing pluripotent materials through tempering of dynamic covalent polymer networks

In this highlight, researchers at the University of Chicago MRSEC report the development of a polymeric, pluripotent material that can be tempered (akin to the process in metallurgy) to access a wide range of room temperature mechanical properties, from stiff and high strength to soft and extensible, from a single feedstock. The feedstock was composed of a benzalcyanoacetate-based Michael acceptor, a tetrathiol crosslinker, and a dithiol chain extender to form dynamic thia-Michael networks.

NSF Big Idea
Understanding the Rules of Life

Machine learning interpretable models of biomaterials from chemistry

This work, carried out by the University of Chicago MRSEC, shows how to integrate neural networks in the construction of predictive phenomenological models in cell biology, even when little knowledge of the underlying microscopic mechanisms exist.

NSF Big Idea
Machine Learning / Artificial Intelligence, Understanding the Rules of Life

CryoEM finds complexity in structural evolution of active materials

UCI MRSEC researchers have performed the first in-depth time-resolved cryo-electron microscopy study on molecular active materials formed under dissipative self-assembly conditions and compared the results to the same molecular formed under thermodynamic control. They found that the dissipative self-assembly conditions can stabilize the formation on transient, thermodynamically unstable phases and that these phases can be highly ordered.

Discovery of Ni Activated Sintering of MoNbTaW Guided by a Computed Grain Boundary “Phase” Diagram

This study, carried out by researchers at UCI MRSEC, demonstrated the first example of activated sintering of a high-entropy alloy. It also revealed a segregation-induced grain boundary prewetting (disordering) transition.

Sustainability efforts gain institutional support and international recognition

​The MRSEC’s sustainability initiative for research labs expanded in its second year to 29 labs across Penn State University Park and six branch campuses. Over 400 researchers have been involved thus far. Labs completing My Green Lab certification can be paired with one of 17 undergraduate Sustainable Lab Ambassadors who apply their sustainability training to the lab setting through engaged scholarship.

Interface-induced superconductivity in magnetic topological insulators

An IRG1 team employed molecular beam epitaxy to synthesize heterostructures stacking a ferromagnetic topological insulator with a quantum anomalous Hall state, Cr-doped (Bi, Sb)2Te3, and an antiferromagnetic iron chalcogenide, FeTe, with an atomically sharp interface. An unexpected phenomenon emerges: interface-induced superconductivity.

NSF Big Idea
Quantum Leap

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