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Highlights

Highlights

(left) Polarized optical microscopy images showing that the ligands can align nanocrystal assembly, (middle) illustration of the proposed ligand arrangement, (right) Dendritic Promesogenic Ligands used in the study
(left) Polarized optical microscopy images showing that the ligands can align nanocrystal assembly, (middle) illustration of the proposed ligand arrangement, (right) Dendritic Promesogenic Ligands used in the study
May 21, 2024
University of Pennsylvania

Controlling Nanoparticle Assemblies with Dendritic Ligands

Christopher Murray and Chinedum Osuji, University of Pennsylvania
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.  
Shear bands formation in different disordered solids are well-captured by our mesoscopic StEP model. From left to right: simulated atomic glasses (left), experimental granular packing (middle), and a simulated polymer glass (right). The particles are colored according to their local strain with darker red indicating higher strain, and the applied strain localizes in all three cases. The bottom row has the stress/strain response of the three systems measured in simulations or experiments compared to the StEP model for the same three systems, showing very good agreement.
Shear bands formation in different disordered solids are well-captured by our mesoscopic StEP model. From left to right: simulated atomic glasses (left), experimental granular packing (middle), and a simulated polymer glass (right). The particles are colored according to their local strain with darker red indicating higher strain, and the applied strain localizes in all three cases. The bottom row has the stress/strain response of the three systems measured in simulations or experiments compared to the StEP model for the same three systems, showing very good agreement.
May 21, 2024
University of Pennsylvania

Understanding Deformation in Disordered Materials

Robert Riggleman, Douglas Durian and Andrea Liu, University of Pennsylvania
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.  
(A) Spatial distribution of particles with different machine-learned softness, strongly correlated with localized particle rearrangements within the glass. (B,C) Rearrangement barriers (entropic and energetic), rescaled to be dimensionless, against the ML-informed excess entropy of thermal configurations. Approximate collapse is observed in the data at the same temperatures relative to the onset of glassy dynamics. Agreement improves as one moves deeper into the supercooled regime.
(A) Spatial distribution of particles with different machine-learned softness, strongly correlated with localized particle rearrangements within the glass. (B,C) Rearrangement barriers (entropic and energetic), rescaled to be dimensionless, against the ML-informed excess entropy of thermal configurations. Approximate collapse is observed in the data at the same temperatures relative to the onset of glassy dynamics. Agreement improves as one moves deeper into the supercooled regime.
May 21, 2024
University of Pennsylvania

Predicting the Softness of Glasses from Thermodynamics

Paulo Arratia and Robert Riggleman (University of Pennsylvania)
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
Accessing pluripotent materials through tempering of dynamic covalent polymer networks
May 20, 2024
Big Idea: Understanding the Rules of Life

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.
Machine learning interpretable models of biomaterials from chemistry
Machine learning interpretable models of biomaterials from chemistry
May 20, 2024
Big Idea: Machine Learning / Artificial Intelligence, 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.
CryoEM finds complexity in structural evolution of active materials
CryoEM finds complexity in structural evolution of active materials
May 16, 2024
University of California, Irvine

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
Discovery of Ni Activated Sintering of MoNbTaW Guided by a Computed Grain Boundary “Phase” Diagram
May 16, 2024
University of California, Irvine

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
Sustainability efforts gain institutional support and international recognition
May 16, 2024
Pennsylvania State University

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
Interface-induced superconductivity in magnetic topological insulators
May 16, 2024
Big Idea: Quantum Leap

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.
High-entropy engineering of the crystal and electronic structures in a Dirac material
High-entropy engineering of the crystal and electronic structures in a Dirac material
May 16, 2024
Big Idea: Quantum Leap

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.

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