Category: Research Highlights

The paper describes the use of a short protein (peptide) sequence feature to control self-assembly using pH. We showed that this control allows access to structures unprecedented in nature or computationally designed protein assemblies. The resulting nanowires are electronically conductive, with promising applications in biosensors, implants, and bioenergy devices. Learn more Grosvirt-Dramen, A.; Urbach, Z….Continue Reading 2025 IRG-2: Hierarchical Assembly of Conductive Fibers from Coiled-Coil Peptide Building Blocks

Spin–orbit torques enable energy-efficient manipulation of magnetization by electric current and hold promise for applications ranging from non-volatile memory to neuromorphic computing. A team lead by Prof. Ilya Krivorotov of the CCAM seed project discovered a new type of giant spin–orbit torque induced by the anomalous Hall effect in ferromagnetic conductors. This anomalous Hall torque is self-generated…Continue Reading 2025 Seed: Anomalous Hall Spin Current Drives Self-Generated Spin–Orbit Torque in a Ferromagnet

Disulfide hydrogels, based on cysteine-driven redox systems, exhibit remarkable self-assembly properties through reversible disulfide bond formation, making them a promising platform for dynamic material design. A research team from IRG-2, led by Prof. Joseph Patterson and Prof. Douglas Tobias, employed advanced cryogenic electron microscopy (cryo-EM) to reveal a consistent fiber diameter of 5.4 nm. Using these structural insights,…Continue Reading 2025 IRG-2: Cryo-EM Informed Molecular Dynamics Simulations to Investigate the Disulfide Hydrogel Self-Assembly

An IRG2 team, led by Prof. Sahar Sharifzadeh (Boston University) and Prof. Stacy Copp (University of California, Irvine), has developed an approach that integrates machine learning with density functional theory (DFT) calculations to study dynamic electronic structure fluctuations in the de novo peptide ACC-Dimer. In biomolecular environments, it remains unclear which chemical and structural dynamics support electronic conductivity, which…Continue Reading 2025 IRG-2: Dynamic Electronic Structure Fluctuations in the De Novo Peptide ACC-Dimer Revealed by First-Principles Theory and Machine Learning

Impact of Entropy Stabilization on Electrical Conductivity Entropy-stabilized oxide (ESO) research has primarily focused on discovering unprecedented structures, chemistries, and properties in the single-phase state. However, few studies discuss the impacts of entropy stabilization and secondary phases on functionality and in particular, electrical conductivity.  The IRG 1 collaborative work between Bowman (Hasti Vahidi), Schoenung (Justin Cortez and Alex Dupuy)…Continue Reading Impact of Entropy Stabilization on Electrical Conductivity

Observing the Dynamics of an Electrochemically Driven Active Material with Liquid Electron Microscopy The IRG-2 team led by Assistant Professor Joe Patterson (Department of Chemistry) and his student Wyeth Gibson, have pioneered the observation of molecular active materials using liquid electron microscopy. This technique, primarily applied to inorganic materials, is now being used to explore the self-assembly dynamics…Continue Reading Observing the Dynamics of an Electrochemically Driven Active Material with Liquid Electron Microscopys

Exceptional Electronic Transport & Quantum Oscillations in Thin Bismuth Crystals Grown Inside vdW Materials The seed team, led by Assistant Professor Javier D. Sanchez-Yamagishi, has developed an innovative approach to thin crystal synthesis by growing bismuth within a nanoscale mold composed of van der Waals (vdW) materials. This atomically-flat vdW mold templates the bismuth crystals, resulting…Continue Reading Exceptional Electronic Transport & Quantum Oscillations in Thin Bismuth Crystals Grown Inside vdW Materials

Sensitive Thermochromic Behavior of InSeI Optical thermometry measures temperature using the optical properties of materials, such as luminescence, absorbance, or fluorescence. In a study published in Advanced Materials, the seed team led by Assistant Prof. Maxx Q. Arguilla of Chemistry, along with co-workers Dmitri Leo Mesoza Cordova, Yinong Zhou, Griffin M. Milligan, Leo Cheng, Tyler Kerr, Joseph Ziller, and Prof. Ruqian Wu,…Continue Reading Sensitive Thermochromic Behavior of InSeI

Neural Network Kinetics Method Predicts Atomic Diffusion in Complex Materials IRG-1 researchers, Assistant Professor Penghui Cao of Mechanical and Aerospace Engineering and his student Bin Xing, have developed a groundbreaking Neural Network Kinetics (NNK) method to model and predict atomic diffusion in compositionally complex materials. This innovative framework leverages artificial neural networks to simulate the chemical and structural…Continue Reading Neural Network Kinetics Method Predicts Atomic Diffusion in Complex Materials

The main achievement of this research is revealing the atomic-scale origin of the low grain-boundary (GB) resistance in air-quenched (Li0.375Sr0.4375)(Ta0.375Nb0.375Zr0.125Hf0.125)O3 (LSTNZH) perovskite solid electrolyte in charge perspective and providing insights on overcoming the ubiquitous bottleneck of high GB resistance by elemental selection and material processing. Significance of this scientific achievement · Applying four-dimensional scanning transmission electron microscopy (4D-STEM)…Continue Reading 2024 IRG-1: Elucidating Electrostatics at Grain Boundaries in Perovskite Solid Electrolytes Using 4D-STEM