Superconducting Quantum Materials and Systems (SQMS) Center: Novel 3D Platforms for Quantum Computing and Sensing
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Roy, Tanay [Fermilab]↗
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Experimental Characterization and Modeling of High Hole Mobility GeSn Quantum Wells: The Role of Alloy Disorder Scattering
Understanding mechanisms influencing electrical transport in material systems not only provides a scientific explanation for observed behavior but also offers insight into ways to enhance transport in devices. This study reports experimental hole mobility of 8 x 10 4 cm 2 V -1 s -1 in a Ge 0.92 Sn 0.08 , the highest recorded mobility for the GeSn system. A study of the material's quality is presented using structural and electrical characterization techniques, with transport data being supported by simulations using an extensive modeling framework. Quantum Hall measurements further indicate the material's high quality and potential spintronic applications, with extracted values of 0.0689$m$ 0 and 13.6 for the effective mass and effective g‐factor, respectively. It is observed that transport is limited by alloy disorder scattering at cryogenic temperatures. A comparative study between the presented structure and similar quantum well heterostructures revealed that the difference in hole mobilities is captured by a disparity in the reduced nominal alloy disorder scattering potential (Δ U alloy = 0.8 eV), that is lower than the value of a fully random alloy (Δ U alloy = 1.4–1.7 eV) potential. The difference in Δ U alloy suggests that heterostructures with similar geometries and alloy compositions can have different alloy disorder scattering, implying that an underlying mechanism, such as short‐range order, may be responsible and warrants further investigation.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Kinetics of amorphous defect phases measured through ultrafast nanocalorimetry
Recognition of the role of extended defects on local phase transitions has led to the conceptualization of the defect phase, localized thermodynamically stable interfacial states that have since been applied in a myriad of material systems to realize significant enhancements in material properties. Here, we explore the kinetics of grain boundary confined amorphous defect phases, utilizing the high temperature and scanning rates afforded by ultrafast differential scanning calorimetry to apply targeted annealing/quenching treatments at high rates capable of capturing the kinetic behavior. Four Al-based nanocrystalline alloys, including two binary systems, Al–Ni and Al–Y, and two ternary systems, Al–Mg–Y and Al–Ni–Y, are selected to probe the materials design space (enthalpy of mixing, enthalpy of segregation, chemical complexity) for amorphous defect phase formation and stability, with correlative transmission electron microscopy applied to link phase evolution and grain stability to nanocalorimetry signatures. A series of targeted isothermal annealing heat treatments is utilized to construct a Time–Temperature-Transformation curve for the Al–Ni system, from which a critical cooling rate of 2400 °C/s was determined for the grain boundary confined disordered-to-ordered transition. Finally, a thermal profile consisting of 1000 repeated annealing sequences was created to quantify the recovery of the amorphous defect phase following sequential annealing treatments, with results indicating remarkable microstructural stability after annealing at temperatures above 90% of the melting temperature. This work contributes to a deeper understanding of grain boundary localized thermodynamics and kinetics, with potential implications for the design and optimization of advanced materials with enhanced stability and performance.
Amorphous defect phases↗
TuFF internal WRAP for Rapid Pipeline Repair (TuFF iWRAP)
The goal of “TuFF internal WRAP for Rapid Pipeline Repair” (TuFF iWRAP) program was to develop a novel material system and placement process to fabricate structural pipe within the existing deteriorated pipelines without disruption of gas delivery. The team (University of Delaware – Center for Composite Materials (UD-CCM) and Plitzie Inc.) addressed this challenge by developing a new material feedstock and pipe in pipe (PIP) repair strategy. This allows the potential for significant cost reduction and has minimum operational impact on gas customers. The new robotic based placement design allows discontinuous placement of pipe sections creating a stand-alone structural liner within the legacy pipeline without the need for pipe shutdown. Here, the material is supplied using a tethered material feeding system and is placed and UV cured with the internal Wound Rapid Automated Placement (iWRAP) system. This provides maximum placement efficiency capable of traversing 90 angle bends in 12-inch pipe and overall design customization to meet pipe repair requirements (e.g., variable wall thickness, bridging gaps, etc.). UV-curable fiber reinforced composite material has been optimized to meet structural performance and placement/cure times. Superior strength and fatigue life has been demonstrated by improving fiber-matrix adhesion using new fiber sizing for UV resins. Rapid cure approaches using new liner and resins have been evaluated with industry. The appropriate design of the section joints has been developed and tested. We estimate coating time to be ~100 hours per mile enabling typical pipe repair within 1 week.
03 NATURAL GAS↗
Direct CO 2 Reduction to CO with an Fe 4 S 4 -Based Coordination Polymer
Fe 4 S 4 clusters play essential roles in nature, classically in electron transport but increasingly in newly discovered reactivity or catalysis. These roles have spurred interest in developing synthetic Fe 4 S 4 systems and while several molecular and material systems built from Fe 4 S 4 clusters have been developed, comparatively few examples of synthetic Fe 4 S 4 cluster-based catalysts exist. Herein, we present the use of an Fe 4 S 4 -based coordination polymer as a catalyst for the direct and selective electroreduction of CO 2 to CO. Computational studies suggest that the reaction proceeds through CO 2 binding to a reduced Fe 4 S 4 cluster, followed by a series of protonation, reduction, and H 2 O loss steps to yield a CO-bound cluster that can finally exchange with CO 2 to restart the catalytic cycle. CO bound clusters are predicted to be thermodynamically stable, suggesting that carbonyl species might be off-cycle intermediates. Mechanistic CV studies as well as in situ studies by IR spectroscopy provide evidence for carbonyl-ligated clusters, supporting these compounds as unusual examples of small molecule binding to Fe 4 S 4 clusters. Finally, this work establishes Fe 4 S 4 cluster-based coordination polymers as direct electrocatalysts for CO 2 reduction and provides mechanistic insights into how these species mediate catalytic conversions of small molecules.
cluster chemistry↗
Thermodynamics of grain-boundary phases
The past decade has seen a significant increase in research efforts aimed at understanding the thermodynamics of low-dimensional phases existing in many materials systems, ranging from two-dimensional materials to core regions of extended defects in crystalline solids. We review the current status of theoretical, computational, and experimental research on the “defect phases,” focusing on grain boundaries (GBs) in elemental and multicomponent polycrystalline materials. After reviewing the generalized concept of a phase of any dimensionality, we discuss recent progress in atomistic computer simulations of GB phase transformations and phase coexistences, including the observation of one-dimensional defects separating GB phases (defects in defects). Computational predictions compare well with experimental observations of multiple GB phases and segregation-induced phase transformations. An intriguing open question of GB thermodynamics is whether the GB free energy can be driven to a zero value by increasing solute segregation. We review recent efforts to understand this ultimate thermodynamic stabilization of GB phases and the possible polycrystalline microstructures that may arise. An outlook for future research in the field is discussed.
Materials science↗
An Investigation of Thermal Properties of 2D Materials [Dissertation]
Studying the thermal conductivity of 2D materials is important due to the applications of 2D materials in fields such as thermal management, thermoelectricity, renewable energy, and sensors. As such, measurements of the thermal conductivity of these 2D materials become important to measure. Thermal conductivity is often difficult to measure for 2D materials due to their atomically thin nature and many experimental methods for doing so requiring contact with the sample, which can alter the thermal properties. A non-contact method for calculating the thermal conductivity of 2D materials supported on substrates in order to model the thermal conductivity of 2D materials for devices, is proposed and experimentally performed in this dissertation. The optothermal Raman technique is a useful non-contact diagnostic technique useful in determining the thermal conductivity of 2D materials. The optothermal Raman typically does not account for heat losses due to convection or radiation or substrate resistance, which are shown to be important factors to consider when developing an optothermal Raman model. Additionally, the calculation of the interfacial thermal conductance between the bottom surface of the sample and the top surface of the substrate, plays an important role in determining the final value of the thermal conductivity of a supported sample, and will yield differing results based on whether or not the conductance is calculated using an approach such as the Diffuse Mismatch Model (DMM) or calculated directly by varying the laser heating profile (usually done by changing the laser objective). This is shown to be the case for both graphene on Ni, graphene on Cu, and SnSe 2 on Cu. In addition to experimentally calculating the thermal conductivity of a 2D material with the optothermal Raman technique, the thermal conductivity of 2D materials can also be calculated using computational methods. The three-phonon method is a method which can be used to simulate phonon scattering processes and determine the thermal conductivity of semiconductors, wherein phonon scattering is the dominant mechanism which determines the thermal conductivity. The three-phonon method uses relaxation times for phonon scattering with other phonons, electrons, and other material system elements, such as isotopes or material defects, in order to create a single-mode relaxation time approximation (SMRTA), which is used to calculate the final value of the thermal conductivity. An important consideration when determining the thermal conductivity of a 2D material using this method is the device geometry, which is reflected in this work as the phonon-boundary scattering relaxation time. This inclusion is important along with the inclusion of phonon-electron scattering in accurately determining the thermal conductivity of a 2D material. In both the optothermal Raman experiments and the three-phonon method computations, strain is shown to have a demonstrable effect on the thermal conductivity of 2D materials. When a 1.1% strain was applied to the mechanical properties of SnSe, the three-phonon processes yielded a lower thermal conductivity than the no-strain case. For the optothermal Raman experiments, the strain induced in the Cu substrate and transferred to a single-layer graphene (SLG) sample yields a trend where the thermal conductivity of the SLG decreases with respect to strain applied. In the case where the interfacial thermal conductance was calculated directly, the conductance increased with respect to strain applied. This presents strain as a reliable and viable method for tuning the thermal properties of 2D materials for device applications.
36 MATERIALS SCIENCE↗
Planar Systems for Quantum Information
This project aims to develop two‐dimensional (2D) moiré materials as a quantum simulator to implement model Hamiltonians and their phase diagrams. Progress in quantum information science (QIS) requires the development of advanced quantum materials systems. The rich family of layered van der Waals materials and their heterostructures present opportunities to create previously unrealized types of applications for QIS. Specifically, when two layers of van der Waals materials are overlaid with a small twist angle or/and lattice mismatch, a moiré superlattice with a period of about ten nanometers is formed. This provides a periodic trapping potential for electrons. Electrons can tunnel between the traps and repel each other by their mutual Coulomb interactions. The platform of 2D moiré materials provides many attractive features, including tunability of length and energy scales, charge density, and even lattice symmetry. It presents new possibilities for realizing quantum simulation of the many-body physics in a solid-state platform. This integrated team of six investigators seeks to develop relevant theoretical treatments to link ab-initio studies of 2D moiré materials to model Hamiltonians and to evaluate correlated phases predicted by these model Hamiltonians in the relevant regimes. On the experimental side, the team aims to develop methods to realize a homogeneous and highly controlled potential landscape for the electrons and to initiate, protect, and measure their quantum many-body states.
36 MATERIALS SCIENCE↗
Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations
We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.
Density functional theory↗
Generalized representative structures for atomistic systems
A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g. the chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, along with the restriction to crystal systems, results from using the fixed-lattice cluster correlations to describe structural characteristics. A more general description of the structural characteristics of atomic systems is obtained using complete sets of atomic environment descriptors. These are used within for generating representative atomic structures without restriction to fixed lattices. A general data-driven approach is provided here utilizing the atomic cluster expansion (ACE) basis. The N-body ACE descriptors are a complete set of atomic environment descriptors that span both chemical and spatial degrees of freedom and are used within for describing atomic structures. The generalized representative structure (GRS) method presented within generates small atomic structures that reproduce ACE descriptor distributions corresponding to arbitrary structural and chemical complexity. It is shown that systematically improvable representations of crystalline systems on fixed parent lattices, amorphous materials, liquids, and ensembles of atomic structures may be produced efficiently through optimization algorithms. With the GRS method, we highlight reduced representations of atomistic machine-learning training datasets that contain similar amounts of information and small 40–72 atom representations of liquid phases. The ability to use GRS methodology as a driver for informed novel structure generation is also demonstrated. The advantages over other data-driven methods and state-of-the-art methods restricted to high-symmetry systems are highlighted.
atomic cluster expansion↗
Electrical Detection of Spin-Hall-Induced Auto-oscillations in Lithium Aluminate Ferrite Thin Films
Ferrimagnetic insulators with ultralow damping are of great interest for their potential applications in energy-efficient computing devices. Here, we report the direct electrical detection of magnetic auto-oscillations in unpatterned ultralow damping ferrimagnetic insulator epitaxial Li 0.5 Al 0.5 Fe 2 O 4 thin films, driven by a current in a proximal Pt nanowire. Auto-oscillations occur for only one current polarity, consistent with the spin-Hall effect inducing the oscillation state. Micromagnetic modeling shows good agreement with the experimental frequency and field dispersions, showing only one dominant oscillation mode, in contrast to the multiple modes typically observed in transition-metal nanowire-type spin-Hall nanooscillators. This study illustrates a new material system for neuromorphic computing and magnonics, a simple material platform with the direct-current generation of high-frequency (~10 GHz) signals and their electrical detection.
36 MATERIALS SCIENCE↗
Shielded magnetic small-angle neutron scattering for characterization of radioactive samples
The development of a Pb-shielded fixture for the execution of a small-angle neutron scattering (SANS)-based workflow for interrogation of highly irradiated nuclear materials has been explored. The Pb shielding was specially designed to reduce the detected radioactivity from the specimen during SANS experiments, and the overall configuration is termed shielded magnetic SANS (SM-SANS). Two FeCrAl-based alloys, C35M and 125YF, were examined with the SM-SANS technique using a free-form size distribution locally monodisperse model in both the as-received and irradiated states. Quantitative values derived from the free-form size distribution were compared with atom probe tomography experiments. Microstructural and compositional parameters determined using the two characterization techniques were complements of each other. The results demonstrate that the SM-SANS technique is an effective means of characterizing nanoscale clustering in irradiated material systems and provides new avenues for investigating radioactive material microstructures.
FeCrAl↗
MixPI: Mixed-time slicing path integral software for quantized molecular dynamics simulations
We introduce the MixPI software to implement path integral molecular dynamics (PIMD) simulations for the study of condensed phase systems where nuclear quantum effects (NQEs) are important. In contrast to existing PIMD simulation software, MixPI enables the implementation of mixed quantum–classical path integral simulations where only a subset of system degrees of freedom (dofs) are treated quantum mechanically in an extended phase space while the remaining dofs are described classically. We expect this software to be particularly useful for simulations of electron and proton transfer in condensed phase systems, as well as for the study of biological and material systems where only a handful of dofs contribute significantly to the observed NQEs. We demonstrate the use of MixPI in two different systems. The first is a simple water model where we implement a set of mixed quantum–classical simulations to compute average energy and radial distribution functions. We use these simulations to benchmark the effectiveness of MixPI and to demonstrate how it enables systematic investigation into the origin of observed NQEs. We then compute radial distribution functions for a system where MixPI is essential: a solvated metal (M 2+ ) cation described using an explicit quantized electron localized on an M 3+ ion in water.
chemical physics↗
Electronic structure and effective mass of pristine and Cl-doped CsPbBr 3
Organic–inorganic lead halide perovskites (LHPs) have attracted great interest owing to their outstanding optoelectronic properties. Typically, the underlying electronic structure would determinate the physical properties of materials. But as for now, limited studies have been done to reveal the underlying electronic structure of this material system, comparing to the huge amount of investigations on the material synthesis. The effective mass of the valance band is one of the most important physical parameters which plays a dominant role in charge transport and photovoltaic phenomena. In pristine CsPbBr 3 , the Fröhlich polarons associated with the Pb–Br stretching modes are proposed to be responsible for the effective mass renormalization. In this regard, it would be very interesting to explore the electronic structure in doped LHPs. Here, we report high-resolution angle-resolved photoemission spectroscopy (ARPES) studies on both pristine and Cl-doped CsPbBr 3 . The experimental band dispersions are extracted from ARPES spectra along both $\overline{\Gamma}$–$\overline{M}$–$\overline{\Gamma}$ and $\overline{X}$–$\overline{M}$–$\overline{X}$ high symmetry directions. DFT calculations are performed and directly compared with the ARPES data. Our results have revealed the band structure of Cl-doped CsPbBr 3 for the first time, which have also unveiled the effective mass renormalization in the Cl-doped CsPbBr 3 compound. Doping dependent measurements indicate that the chlorine doping could moderately tune the renormalization strength. These results will help understand the physical properties of LHPs as a function of doping.
36 MATERIALS SCIENCE↗
Evolution from topological Dirac metal to flat-band-induced antiferromagnet in layered K x Ni 4 S 2 (0 ≤ x ≤ 1)
Condensed matter systems with coexisting Dirac cones and flat bands and a switchable control between them within a single system are desirable but remarkably uncommon. Here, we report a layered quantum material system, K x Ni 4 S 2 (0 <= x <= 1), that simultaneously hosts both characteristics without involving typical Kagome/honeycomb lattices. Enabled by a topochemical K-deintercalation process, the Fermi surface can be fine-tuned continuously over a wide range of energies. Consequently, a non-magnetic Dirac-metal state with a topological nontrivial Z 2 index of 1;(000), supported by first-principles calculations and high mobility up to 1,471 cm 2 V -1 s -1 , is observed on the K-rich x = 1 side, whereas a flat-band-induced antiferromagnetic state with T N up to 10.1 K emerges as the K-content approaches 0. The K x Ni 4 S 2 system offers a versatile platform for exploring emerging phenomena and underscores aviable pathway for in situ control of quantum materials dominated by Dirac cones, flat bands, and their interplay.
dirac metal↗
Application of lipid-stabilized liquid-liquid interfaces in 3D printing of biomaterials
Developing strategies to stabilize liquid-liquid interfaces is essential for advancing applications in various biomedical systems. This study introduces a novel biocompatible in situ-forming material in which lipid self-assembly stabilizes water–oil interfaces, enabling controlled structuring of liquids through liquid-in-liquid 3D printing. The stabilization process, driven by the formation of nanostructures at the interface, is thoroughly analyzed through small-angle X-ray scattering (SAXS), rheometry, and microscopy techniques. This material system enables the fabrication of complex 3D constructs, including fibers, substrates, and microneedle patches, which exhibit outstanding mechanical properties and biocompatibility, as confirmed by tensile testing and cell viability tests. Here, by leveraging the unique properties of lipid-stabilized interfaces, this work demonstrates the potential of this approach for diverse biomedical applications such as drug delivery and tissue engineering while establishing a foundation for future advancements in liquid-in-liquid 3D printing technology.
36 MATERIALS SCIENCE↗
Critical impact of experimentally-driven strut level anisotropic material models in advanced stress analysis of additively manufactured lattice structures
The rapid acceleration in materials discovery may overshadow the importance of thoroughly understanding the mechanical performance of newly developed materials in demanding environments. The recent interest in combining parametric studies with machine learning techniques to explore how changes in specific processing parameters or model inputs affect the overall behavior of a material system can only be truly beneficial if the governing constitutive relations describing material behavior are accurately established. In this study, we demonstrate the critical impact of accurately representing strut-level anisotropic material behavior in advanced stress analysis of additively manufactured lattice structures (AMLS). We introduce a systematic experimental and modeling approach for developing strut-level anisotropic elastoplastic material models that account for the influence of microstructural features such as porosity, texture, and surface roughness on the development of local anisotropic mechanical properties, which vary with strut orientation relative to the build direction (BD). As a result the presented material model captures and relates the statistics of spatially varying struts’ microstructural features to the local stress distribution. Our findings suggest that incorporating strut-level anisotropic material behavior into unit cell analysis significantly influences the load distribution and evolution of local stresses within the structure. Therefore, accounting for this anisotropy is critical for developing an understanding of unit cell behavior and performance, including subsequent topology/component design optimization based on this analysis.
Sahoo, Subhadip [University of Arizona]↗
Designing Antifouling and Antimicrobial Interfaces: Structural Characterization using CryoEM, Automated Microscopy, and AI Image Segmentation
The design of functionalized surfaces for interactions with biological systems is critical across sectors such as healthcare, energy, and agriculture. Tailoring materials for specific applications, such as antifouling and antimicrobial surfaces, demands a comprehensive understanding of topology and chemistry across multiple length and time scales on both biological and materials systems. This work presents the development and characterization of nanostructured surfaces with controlled topographies and chemistries that enhance bacterial membrane disruption, reduce biofilm formation, and improve antimicrobial and antifouling capabilities. Two specific use cases will be presented - the use of cellulose nanocrystals (CNCs) for bacterial growth inhibition and the development of antifouling surfaces to prevent protein and bacterial adsorption [1-4]. By leveraging large language models (LLMs) for image segmentation and training [5], we enable automated analysis of terabyte-scale cryogenic electron microscopy (cryoEM) datasets. This analysis provides statistical insights into the biotic/abiotic interface and facilitates automated electron microscopy experiments to mitigate time and dose. The integration of cryogenic electron tomography (cryoET) and cryogenic focused ion beam (cryoFIB) milling enables high-resolution, near-native-state imaging and 3D reconstructions of bio/material interfaces [6]. Orthogonal characterization techniques and computational modeling further enhances our understanding, offering a robust platform for the design and optimization of next-generation functional surfaces [7].
Williams, Alexis [ORNL] (ORCID:0000000252835822)↗