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At least 721 records · Page 40

Strongly correlated and topological states in [111] grown transition metal oxide thin films and heterostructures

We highlight recent advances in the theory, materials fabrication, and experimental characterization of strongly correlated and topological states in [111] oriented transition metal oxide thin films and heterostructures, which are notoriously difficult to realize compared to their [001] oriented counterparts. We focus on two classes of complex oxides, with the chemical formulas ABO 3 and A 2 B 2 O 7 , where the B sites are occupied by an open-shell transition metal ion with a local moment and the A sites are typically a rare earth element. The [111] oriented quasi-two-dimensional lattices derived from these parent compound lattices can exhibit peculiar geometries and symmetries, namely, a buckled honeycomb lattice, as well as kagome and triangular lattices. These lattice motifs form the basis for emergent strongly correlated and topological states expressed in exotic magnetism, various forms of orbital ordering, topological insulators, topological semimetals, quantum anomalous Hall insulators, and quantum spin liquids. For transition metal ions with high atomic number, spin–orbit coupling plays a significant role and may give rise to additional topological features in the electronic band structure and in the spectrum of magnetic excitations. We conclude this perspective by articulating open challenges and opportunities in this actively developing field.

36 MATERIALS SCIENCE↗

Mind the gap: Bridging the divide between AI aspirations and the reality of autonomous microscopy

What does materials science look like in the “Age of Artificial Intelligence?” Each material’s domain—synthesis, characterization, and modeling—has a different answer to this question, motivated by unique challenges and constraints. This work focuses on the tremendous potential of autonomous characterization within electron microscopy. We present our recent advancements in developing domain-aware, multimodal models for microscopy analysis capable of describing complex atomic systems. We then address the critical gap between the theoretical promise of autonomous microscopy and its current practical limitations, showcasing recent successes while highlighting the necessary developments to achieve robust, real-world autonomy.

2D materials↗

Entanglement Witness for Indistinguishable Electrons Using Solid-State Spectroscopy

Characterizing entanglement in quantum materials is crucial for advancing next-generation quantum technologies. Despite recent strides in witnessing entanglement in magnetic materials with distinguishable spin modes, quantifying entanglement in systems formed by indistinguishable electrons remains a formidable challenge. To solve this problem, we introduce a method to extract various four-fermion correlations by analyzing the nonlinearity in resonant inelastic x-ray scattering spectra. These correlations constitute the primary components of the cumulant two-particle reduced density matrix. We further derive bounds for its eigenvalues and demonstrate the linear scaling with fermionic entanglement depth, providing a reliable witness for entanglement. Using the material-relevant strongly correlated models as examples, we show how this entanglement witness can efficiently quantify multipartite entanglement across different phase regions, highlighting its advantage over quantum Fisher information. Published by the American Physical Society 2025

Liu, Tongtong (ORCID:0000000295324061)↗

Quantum-enhanced detection of viral cDNA via luminescence resonance energy transfer using upconversion and gold nanoparticles

Abstract The COVID-19 pandemic has profoundly impacted global economies and healthcare systems, revealing critical vulnerabilities in both. In response, our study introduces a sensitive and highly specific detection method for cDNA, leveraging Luminescence Resonance Energy Transfer (LRET) between upconversion nanoparticles (UCNPs) and gold nanoparticles (AuNPs), and achieves a detection limit of 242 fM for SARS-CoV-2 cDNA. This innovative sensing platform utilizes UCNPs conjugated with one primer and AuNPs with another, targeting the 5′ and 3′ ends of the SARS-CoV-2 cDNA, respectively, enabling precise differentiation of mismatched cDNA sequences and significantly improving detection specificity. Through rigorous experimental analysis, we established a quenching efficiency range from 10.4 % to 73.6 %, with an optimal midpoint of 42 %, thereby demonstrating the superior sensitivity of our method. Our work uses SARS-CoV-2 cDNA as a model system to demonstrate the potential of our LRET-based detection method. This proof-of-concept study highlights the adaptability of our platform for future diagnostic applications. Instrumental validation confirms the synthesis and formation of AuNPs, addressing the need for experimental verification of the preparation of nanomaterial. Our comparative analysis with existing SARS-CoV-2 detection methods revealed that our approach provides a low detection limit and high specificity for target cDNA sequences, underscoring its potential for targeted COVID-19 diagnostics. This study demonstrates the superior sensitivity and adaptability of using UCNPs and AuNPs for cDNA detection, offering significant advances in rapid, accessible diagnostic technologies. Our method, characterized by its low detection limit and high precision, represents a critical step forward in developing next-generation biosensors for managing current and future viral outbreaks. By adjusting primer sequences, this platform can be tailored to detect other pathogens, contributing to the enhancement of global healthcare responsiveness and infectious disease control.

Esmaeili, Shahriar [Institute for Quantum Science ↗

On the Nature of Surface Defects Found in 2/0 N-Doped 9-Cell Cavities

In this contribution, we present a systematic study on the microstructure of 1.3 GHz 9-cell TESLA type SRF cavity, processed with 2/0 Nitrogen-doping surface treatment, to explain the premature quench phenomena commonly observed in N-doping treated cavities. The microstructure characterization was carried out using Secondary electron images, advanced metallurgical techniques such as EBSD in parallel with chemical information obtained from spectroscopic techniques. The most remarkable difference is observed in the ends-cavities (1 and 9), which showed roughening of the surface, revealing a series of morphologies associated with Nb cubic phase. The cell-to-cell analysis also showed standard features such as pits with different geometry and distribution, located in grains and grain boundaries. The defects found in this system suggest that the standard electropolishing chemical etching was insufficient to eliminate history defects produced during the manufacture of the cavity, without discarding the role of the impurities, N and O, that could have induced the growth of these morphologies.

43 PARTICLE ACCELERATORS↗

An Experimental Study of Design and Performance for the Water-Based Reactor Cavity Cooling System

The document summarizes the test activity conducted at the Texas A&M University water-cooled Reactor Cavity Cooling System experimental facility. Flow and temperature characterization of the risers' panel is conducted using advanced measurement techniques. Steady-state and transient scenarios are studied, analyzed, and discussed in the report. The experimental work is complemented with modeling and simulations using system level codes.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Efficient Scalable Tomography of Many-Qubit Quantum Processors

Quantum computing has the potential to realize powerful and revolutionary applications. A quantum computer can, in theory, solve certain problems exponentially faster than its classical counterparts. The current state of the art devices, however, are too small and noisy to practically realize this goal. An important tool for the advancement of quantum hardware, called model-based characterization, seeks to learn what types of noise are exhibited in a quantum processor. This technique, however, is notoriously difficult to scale up to even modest numbers of qubit, and has been limited to just 2 qubits until now. In this report, we present a novel method for performing model-based characterization, or tomography, on a many-qubit quantum processor. We consider up to 10 qubits, but the technique is expected to scale to even larger systems.

97 MATHEMATICS AND COMPUTING↗

Microwave Cast Material Examination

Microwave-cast material dating from the latest prototype development period and subsequent TRL 7 advancement testing was sent to LANL for additional characterization. The initial characterization described here is considered a supplement to and verification of that performed by Y-12 during the Technology Readiness Assessment process. The carbon chemistry results from Y-12 and LANL regarding early prototype microwave cast test object (CTO) and cylinder castings match within expectations. Only one fully intact as-cast CTO was available for examination in which carbon chemistry does not appear to vary spatially, at least at relatively low carbon levels (hypoeutectic). The large as-cast grain size leads to relatively poor grain size statistics and similar difficulties were encountered during the TRA. Small, isolated gas pores were observed in four of the five metallographic samples in prototype CTO casting PT-01. Finally, the next phase of characterization work, which focuses on corrosion behavior, has started and is described briefly.

36 MATERIALS SCIENCE↗

Plant-Based Ingredients from the Ocean (CRADA Final Report)

During the LEEP fellowship, our research was focused on optimizing methods to extract protein from red seaweeds to create a protein concentrate that can be used as an ingredient in delicious foods with an emphasis on creating plant-based meat products. We came into the fellowship with a preliminary method for extracting protein from red seaweeds. Over the course of the fellowship, we optimized the bench process, scaled the bench process to pilot scale equipment, and characterized our protein concentrate. The fellowship helped to advance our business in several ways. First, it provided a stipend, additional funding, and lab space to work. This financial aspect provided us with the ability to focus on time to refine our business and technology plan. In its absence, we likely would have pitched for pre-seed money at too early of a stage, before we had mapped out a successful plan. The fellowship helped us to craft and refine our message and our overall company direction. This in turn, helped us to find the right investors that matched our goals, which helped us to close our seed round and move forward. Lastly, the fellowship gave us access to an amazing network, allowing us to learn from the successes and challenges of a variety of startups at various stages of growth.

59 BASIC BIOLOGICAL SCIENCES↗

Increased Reliability for Near-Term Quantum Computers via Low-Level Control (CRADA Final Report)

This project helped narrow the gap between near-term hardware and practical applications of quantum computing through the development and implementation of optimized compilation and error mitigation techniques. These advances were enabled by low-level control available on the Advanced Quantum Testbed, and led to methods that characterized and improved the platform’s performance on quantum algorithms. Results of this work were published in the scientific literature and used to inform internal software development of partner Super.tech.

97 MATHEMATICS AND COMPUTING↗

Identifying Critical Mineral Binding Mechanisms and Distribution in Acid Mine Drainage Treatment Solids to Inform Targeted Recovery Methods

In this study, micro-X-ray Fluorescence and micro/bulk X-ray Adsorption Near Edge Spectroscopy were collected at Stanford Synchrotron Radiation Lightsource and the Advanced Photon Source to: (1) gain a detailed characterization of critical minerals in Fe/Mn (hydr)oxide host phases; and (2) investigate the Co/Ni coordination and redox speciation associated with Fe/Mn (hydr)oxides phases in AMD solids with diverse composition (e.g., Al-rich, Mn-rich, Al/Mn/Fe-rich). Preliminary results show co-location of Co/Ni within Mn-rich hotspots, while Fe-rich hotspots were associated with heavy REEs. In Al-rich solids, Mn speciation was mostly comprised of Mn+3/+4 oxides, while the Mn-rich solids contain predominately Mn+4. These results suggest the AMD matrix plays an important role in how Co/Ni are coordinated, which aids in evaluating the efficacy to utilize AMD solids as a resource, and informs other novel sorbent and recovery technologies.

Hoffman, Colleen↗

Investigating Material Properties of Subsurface Rock Formations Modified by Engineering Mineral Precipitation (Final Scientific and Technical Report)

Montana State University’s (MSU) Energy Research Institute (ERI), in collaboration with the Center for Biofilm Engineering (CBE) and the Department of Civil Engineering (CE), has conducted a long‐term research program aimed at developing a novel cementing agent to address wellbore integrity and reduce the unwanted upward migration of fluids and greenhouse gases from the subsurface. The primary technology developed through this research program is known as ureolysis‐induced calcite precipitation (UICP), which harnesses bio‐chemical processes to precipitate calcium carbonate (CaCO 3 ). The same general process can also be called microbially-induced calcium carbonate precipitation (MICP) when microbes provide the process-catalyzing urease enzyme. Both terms are used in this report. Results have conclusively demonstrated that, if properly controlled, UICP can successfully seal fractures, high permeability zones, and compromised cement in the vicinity of wellbores and in nearby caprock. This technology has been successfully deployed to mitigate annular leakage in two test wells and over sixty commercial wells with a 100% success rate. This success in downhole deployment generates consideration of other subsurface applications where UICP could provide benefit to the energy sector, such as shale property modification for unconventional oil and gas recovery. The focus of this research project was to investigate fundamental material and mechanical properties of select shale cores and analyze how these properties change due to engineered mineral precipitation with the intent to control these properties to achieve a range of engineering objectives. Ultimately, the project aim was to identify valuable new areas where application of UICP might contribute to national energy security and environmental protection. The research workplan coupled UICP treatment of core samples, nuclear magnetic resonance (NMR) characterization, and mechanical strength testing at MSU with advanced X‐Ray micro-computed tomography (μCT) imaging and numerical modeling performed by collaborators at two national laboratories, the National Energy Technology Laboratory (NETL) and Lawrence Berkeley National Laboratory (LBNL). Experimental results are useful to inform geo-mechanical models which could be applied to predict mineralized rock formation behavior at field scale. Our findings suggest that NMR and μCT methods to detect and quantify biomineral formation in shale fractures are complementary and consistent with each other. Either could be used to estimate the volume of new mineral formed by UICP in shale fractures. The use of surfactants and guar gum to enhance biomineral precipitation in shale fractures merits further research. UICP can, under some conditions, increase the tensile strength of sealed shale fractures beyond that of the intact shale. These findings demonstrate that continued research in this area may be valuable to understanding and improving shale resource recovery techniques.

58 GEOSCIENCES↗

reVRt (reV Routing) [SWR-25-112]

The reV Routing (reVRt) tool is a computational framework for modeling and optimizing transmission infrastructure requirements for electrical grid connections. By employing a spatially-aware least-cost-path methodology, it allows users to incorporate a wide range of factors including siting constraints, regional component costs, land composition costs, point-of-interconnection costs, and network upgrade costs. Additionally, the tool enables advanced follow-on analyses, such as land characterization for potential transmission line routes, to support informed decision-making. Although it's designed to integrate seamlessly with the reV model, the reV Routing tool is versatile and can also be utilized independently for standalone analyses in transmission planning and resource assessment scenarios.

Pinchuk, Pavlo (Paul) [National Renewable Energy L↗

Untangling the heavy-flavor mess: status of the Fermilab-MILC calculation of the $B_{(s)}\to D^{(\ast)}_{(s)}\ellν$ form factors

We present the status of calculations of the form factors of the most relevant heavy-to-heavy and heavy-to-light decay channels. Using seven $N_f = 2+1+1$ HISQ ensembles, with lattice spacings ranging from 0.15 fm down to 0.06 fm, we calculate the form factors of the decays, including correlations among them. More than half of our ensembles feature physical pion masses, and the heavy quarks are simulated at their physical masses using the Wilson-clover action with the Fermilab interpretation. Even though we have recently seen huge qualitative and quantitative leaps in the characterization of heavy-to-heavy decays, these advances have failed to translate into improvements for the inclusive vs exclusive question, or the matter of the Lepton Flavor Universality ratios. In particular, in the $B\to D^{\ast}\ellν$ channel, the current situation of the lattice-QCD form factors is far from clear. Further, the latest lattice-QCD results on the heavy-to-light form factors display unexplained tensions that must urgently be resolved. The work presented here is an attempt to address these issues.

Vaquero, Alejandro [Zaragoza U.]↗

TEM Characterization of Neutron Irradiated HfAl3-Al Composite Specimens

Particles comprised of a thermal neutron absorbing material (HfAl3) are dispersed in a metal matrix material with high thermal conductivity (aluminum) to conduct the heat generated by neutron capture away from the fuel and materials. This metal matrix composite is very promising for use as a conduction-cooled neutron absorber and has the potential to be useful as a shroud or heat sink for testing advanced fast reactor fuels and materials in an existing thermal reactor. To design and optimize the absorber block system for advanced reactor designs, fundamental understanding of the irradiation effect on material properties is necessary. This dataset contains TEM characterization results of neutron irradiated HfAl3-Al composite specimens. It is focused on the irradiation induced defects (dislocation lines and loops) characterization using the on-zone axis bright field STEM technique. This data was collected using a FEI Tecnai G2 F30 S/TEM at the Microscopy and Characterization Suite (MaCS), Center for Advanced Energy Studies (CAES).

Guillen, Donna↗

TEM Characterization of Neutron Irradiated HfAl3-Al Composite Specimens

Particles comprised of a thermal neutron absorbing material (HfAl3) are dispersed in a metal matrix material with high thermal conductivity (aluminum) to conduct the heat generated by neutron capture away from the fuel and materials. This metal matrix composite is very promising for use as a conduction-cooled neutron absorber and has the potential to be useful as a shroud or heat sink for testing advanced fast reactor fuels and materials in an existing thermal reactor. To design and optimize the absorber block system for advanced reactor designs, fundamental understanding of the irradiation effect on material properties is necessary. This dataset contains TEM characterization results of neutron irradiated HfAl3-Al composite specimens. It is focused on the irradiation induced defects (dislocation lines and loops) characterization using the on-zone axis bright field STEM technique. This data was collected using a FEI Tecnai G2 F30 S/TEM at the Microscopy and Characterization Suite (MaCS), Center for Advanced Energy Studies (CAES).

Guillen, Donna↗

Cold Testing for Characterization of Oxide Feed Impurities in Support of the ARIES Program

The Advanced Recovery and Integrated Extraction System (ARIES) Program converts surplus plutonium metal components into an oxide powder form and packages the material for final disposition. In support of the ARIES program, over a metric ton (MT) of plutonium has been removed from surplus nuclear weapons since 1998 and packaged for long-term storage in 3013 cans. The oxide production rate is anticipated to increase substantially over the next decade in support of dilute and dispose goals. Analytical chemistry data from destructive analysis of the resulting 70+ blend lots to date has been reported for contaminants in the product oxide. Changes in the Pu product specification for Pu oxide product, induced by a different pit mix, the LANL swap program and processing Alternate (non-Pit Pu) Feed Stock (AFS) metals, will affect the level and the mix of these contaminants in the upcoming years. With these changes in feed material, it will be important to provide fast and efficient measurement of elemental impurities in the oxide to be certain that the process is under control and to monitor the anticipated changes from the historical blend lot data. Non-destructive methods need to be evaluated for the characterization of the plutonium oxide product and the quantification of the impurities at the current and anticipated levels. A feasibility study has begun to investigate the applicability of new spectroscopic methods for oxide impurity characterization. To this end, here we describe the preparation of surrogate materials to support these investigations, and the results of a feasibility study investigating the use of LIBS (Laser Induced Breakdown Spectroscopy) to characterize the surrogate materials as calibration standards.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗