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Program Highlights

High-entropy engineering of the crystal and electronic structures in a Dirac material

Quantum materials have the potential to revolutionize technologies ranging from sensing to telecommunication and computation. However, advancement has been limited by the development of topological and Dirac materials. IRG2 researchers demonstrated a novel and widely applicable strategy to engineer relativistic electron states to develop such materials through a high-entropy approach.

Superlubricious Hydrogels from Oxidation Gradients

Hydrogels are hydrated three-dimensional networksof hydrophilic polymers that are commonly used in the biomedical industry due to their mechanical and structural tunability, biocompatibility, and similar water content to biological tissues.

Rapid Generation and Screening of Complex Polymer Morphologies

Block copolymers, with their complex morphologies, are widely used in many applications. A grand challenge associated with these materials is accelerating their design and discovery.

Record Voltage-Based Tuning of Thermal Conductivity in La0.5Sr0.5CoO3-d

A team from IRG-1, working with collaborators at Argonne National Laboratory and the University of Utah, have demonstrated continuous room-temperature electrical tuning of the thermal conductivity of La0.5Sr0.5CoO3-d by a factor of more than five (a record for a single-step process) via ion-gel gating. Application of a gate voltage in these devices drives a transformation from a metallic perovskite phase to an insulating brownmillerite phase via the formation and migration of oxygen vacancies, realizing the record range of measured thermal conductivities.

Unprecedented Nanoscale Morphology in Self-Assembled Bottlebrush Block Polymers

Soft materials known as molecular bottlebrush block polymers comprise a polymer backbone with densely grafted polymer side chains. These materials have attracted much attention for their ability to self-assemble into ordered structures with relatively large periodicities (over 50 nm), which are rarely achieved with simpler linear polymers. However, only self-assembly into lamellar and cylindrical phases has been reported in diblock bottlebrush materials.

Endotaxial stabilization of 2D charge density waves with long-range order

Rather than the typical approach of exfoliating and peeling off individual atomic layers to make a 2D material, the researchers grew the 2D material inside of another matrix. The work has dubbed this new class of materials "endotaxial" from the Greek roots "endo", meaning within, and "taxis", meaning in an ordered manner.

Thermomechanical Properties of Squid Sucker Proteins

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.

MEM-C IRG-1: Spin-Photonic Coupling in a Ferromagnetic Hybrid Layered Perovskite, (PEA)2CrCl4

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.

MEM-C SEED: Expanding Data Automation Using a Jubilee Robotic Platform

UW Chemical Engineering Prof. Lilo Pozzo’s ‘23/’24 Seed project aims to serve the materials community by advancing AI-driven experimentation and analysis for broad adoption and acceleration of materials research. Pozzo has engaged in highly collaborative projects to advance self-driving laboratory (SDL) technologies and to help others adopt them for their own workflows.

Graph Machine Learning for Polycrystals

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.

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