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DOE OSTI2025

Anyon superconductivity from topological criticality in a Hofstadter–Hubbard model

We argue that the combination of strong repulsive interactions and high magnetic fields can generate electron pairing and superconductivity. Inspired by the large lattice constants of moiré materials, which make large flux per unit cell accessible at laboratory fields, we study the triangular lattice Hofstadter-Hubbard model at one-quarter flux quantum per plaquette, where previous literature has argued that a chiral spin liquid separates a weak-coupling integer quantum Hall phase and a strong-coupling topologically trivial antiferromagnetic insulator at a density of one electron per site. We argue that topological superconductivity emerges upon doping in the vicinity of the integer quantum Hall to chiral spin liquid transition. We employ exact diagonalization and density matrix renormalization group methods to examine this theoretical scenario and find that electronic pairing indeed occurs on both sides of criticality over a remarkably broad range of interaction strengths. On the chiral spin liquid side, our results provide a concrete model realization of the long-hypothesized mechanism of anyon superconductivity. Our study thus establishes a beyond-Bardeen-Cooper-Schrieffer route to electron pairing in a well-controlled limit, relying crucially on the interplay between electron correlations and band topology.

Hubbard model↗

DOE OSTI2022

Single crystal growth of iridates without platinum impurities

Iridates have attracted much interest in the last decade for their novel magnetism emerging in the limit of strong spin-orbit coupling and possible unconventional superconductivity. A standard for growing iridate single crystals has been the flux method using platinum crucibles. Here, in this work, we show that this widely used method compromises the sample quality by inclusion of platinum impurities. We find that Sr 2 ⁢IrO 4 single crystals grown in iridium crucibles show remarkable differences from those grown in platinum crucibles in their sample characterizations using Raman spectroscopy, resistivity, magnetization, optical third harmonic generation, resonant x-ray diffraction, and resonant inelastic x-ray scattering measurements. In particular, we show that several peaks of sizable intensities disappear in the Raman spectra of samples free of platinum impurities, and a significantly larger activation energy is extracted from the resistivity data compared to previously reported values. Furthermore, we find no evidence of the previously reported glide-symmetry-breaking structural distortions and confirm the I⁢ 4 1 / acd space group of the lattice symmetry. Although the platinum impurities are not apparent in the magnetic properties and thus went unnoticed in the stoichiometric insulating phase for a long time, their effects can be much more detrimental to transport properties in chemically doped compounds. Therefore, our result suggests using growth methods that avoid platinum impurities for an investigation of the intrinsic physical properties of iridates, and possible superconducting phases.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Probing liquid/liquid interfaces at and away from equilibrium using vibrational sum frequency generation

The self-assembly and transport of species at oil/aqueous interfaces represent key mechanistic steps in processes spanning from liquid extraction to soft-matter neuromorphic electrical devices. While technologically and fundamentally important, there is a notable lack of chemical understanding regarding these buried interfaces due to challenges in differentiating species located at the surface from the nearby bulk phases. In this work, we will describe some of the challenges in probing chemistry at these liquid/liquid interfaces and detail the approaches we have developed using vibrational sum frequency generation to enable studies of these complex interfaces at and away from equilibrium. From these spectroscopic developments, new insight into solvation, ion pairing, aggregation, complexation, and transport will be discussed in the context of solvent extraction chemistry. Notably, we show how the presence of both oil and aqueous phases provides an environment to drive fundamentally different phenomena from what is observed at model air/aqueous interfaces. This difference arises from key differences in solvation and bulk populations that feedback onto the surface to drive emergent and often functional molecular assemblies.

Doughty, Benjamin L↗

DOE OSTI2024

Material Discovery and Design Principles of Perovskite Oxides for Reversible Solid Oxide Cells (R-SOC)

Reversible solid oxide cells (R-SOCs) are highly efficient devices for energy conversion and storage, capable of operating for both hydrogen utilization and production. In fuel cell mode, an R-SOC consumes hydrogen or natural gas to generate electricity, while in electrolysis mode, it produces hydrogen from steam. The discover of new materials with rapid oxygen surface exchange kinetics and enduring stability is crucial for the economically viable commercialization of R-SOCs. To facilitate this pursuit, we conducted extensive Density Functional Theory (DFT) calculations and developed Machine Learning (ML) models to predict critical catalytic properties essential for R-SOCs, such as oxygen surface exchange/diffusivity, and area-specific resistance (ASR). BaCoxFeyZrzO3-d(BFCZ)(x+y+z=1) emerged as a promising family of electrode materials with high activity and stability, validated through systematic experimental study. Moreover, a robust numerical multiphysics model was developed to optimize materials and microstructure parameters, providing the ability to predict the performance of functional R-SOCs.

Liu, Jian↗

DOE OSTI2022

Machine learning pipeline to predict defect behavior in metallic alloy systems

The interaction between defect and solute atoms is critical to the thermodynamic and kinetic behavior of metallic alloys under exposure to high-energy radiation, causing irradiation damage in materials. Radiation can generate non-equilibrium concentrations of point defects such as vacancies and interstitials. The excess point defects not only accelerate diffusional processes such as precipitation that cause radiation embrittlement, but also change the pathway of phase transformations, including nucleation processes. Understanding these defect behaviors is complicated by the challenge and complexity of addressing each possible local and discrete distribution of environments and chemical interactions around targeted defects-solute or solute-solute complexes. To resolve the challenge, machine learning regression techniques have emerged as powerful tools that can train and construct an energy model to accurately describe the chemical interactions of solutes and defects. In Fiscal Year 2022, the work focused on the workflow development and demonstration using machine learning regression, density functional theory, cluster expansion, and Monte Carlo simulation to predict the effects of ternary solute elements (e.g., aluminum and molybdenum) and point defects on the Cr-rich $\alpha^{\prime}$ precipitation in multicomponent FeCr model alloys. The computational outcomes include the prediction of the ternary phase diagram, vacancy formation energy for different compositions, and the effect of vacancies on the nucleation of Cr-rich clusters. The simulations predict a pronounced change of Cr solubility in bcc Fe by the addition of Al and the rejection of Al atoms from $\alpha^{\prime}$ precipitates. Additionally, the simulations show the formation of Cr-vacancy clusters as the initial nuclei for stable nucleation and growth of $\alpha^{\prime}$ particles. The results demonstrate important outcomes and applications of using machine learning pipeline to study model or commercial alloys with multicomponent solute species and point defects.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Linking DSC/TGA to Cell Levels: Energetics, Evolved Gases, and Thermal Safety of NMC811‐Graphite Micro‐Cell

Thermochemical characterization of battery materials links intrinsic material properties to decomposition pathways, heat generation, and gas evolution that govern performance and safety. Despite extensive work on NMC811-Graphite, variability across partial configurations and the limited adoption of micro-cell architectures (cathode+anode+electrolyte+separator) hinder robust cell-scale interpretation. Accordingly, this work establishes a bottom-up, component-resolved methodology integrating DSC/TGA, evolved gas analysis (EGA), and in situ XRD to link decomposition pathways and energy release across partial and micro-cell configurations, providing a transferable assessment of safety and stability in emerging chemistries. In separator-free configurations, the gas–solid reaction between cathode-evolved O 2 and anode-leached Li dominates the net heat release (1139 J g −1 ). In contrast, in the micro-cell configuration, the separator hinders O 2 transport and alters the timing and pathways of other reactions, and reduces the net energy release to 618 J g −1 . Energy release was organized into defined temperature windows that provide a framework for a thermodynamic model combining quantified gas evolution with selected decomposition pathways and effective reaction enthalpies to estimate net specific energy release, with agreement between DSC and cell-level tests. Ex situ XPS of heat-treated samples extends post-mortem analysis to thermal-abuse regimes, supporting key pathway elements.

25 ENERGY STORAGE↗

DOE OSTI2026

Modeling and Experimental Validation of a Direct-Contact Counter-Flow Fluidized Bed Heat Exchanger for Thermal Energy Storage (TES) Applications

Particle-based thermal energy storage (TES) systems are an emerging energy storage technology. The technological advances have reduced costs, making TES more competitive and reliable in the marketplace but an efficient and reliable operation is heavily dependent on coherent heat transfer between air to particles or vice versa. The particle-based TES technologies provide an intermediate system that can store energy for short (0-10 h), long (10-200 h) and seasonal (> 200 h) timescales. The TES systems store energy by converting electricity to thermal energy; electricity can be directly sourced intermittent generation technologies and/or the grid, helping manage peak loads and other mismatches in supply and demand. The overall efficiency of the TES system depends on the performance of system components (particle storage silos and particle transfer mechanism etc.). The particle heat exchanger is one of the key system components that affects the system efficiency. The pressurized fluidized bed heat exchanger (PFB HX) performance is challenging to predict due to the chaotic behavior of particle and fluid interaction. This research presents a computational study of a novel direct-contact, counter-flow and air-to-particles PFB HX, that contributes in advancing the particle-based long-duration TES technologies. For the current analysis an unsteady Eulerian-Eulerian CFD model was developed and validated against experiments performed at the National Laboratory of the Rockies for two particle sizes (600 ..mu..m and 825 ..mu..m ). Following validation, parametric simulations were conducted to evaluate the effects of interphase drag models (Syamlal-O'Brien and Gidaspow), particle size, bed height and the influence of a frictional-viscosity term on hydrodynamics and heat transfer between the air & particles. The key findings from the analysis are: (1) for the studied operating window Syamlal-O'Brien provides superior agreement with measured gas temperatures (errors generally < 10%) while Gidaspow shows large deviations for the coarse particle case; (2) model predictions are most sensitive in the lower 0.2 m above the air distributor where bubble initiation and local mixing dominate interphase heat transfer; (3) representation of the distributor (number of inlet ports) materially affects predicted local mixing and temperature stratification; and (4) the Eulerian-Eulerian framework reproduces bulk thermal trends but shows regime dependent limitations for coarse particles, motivating mesoscale informed closures for scale-up analysis for future studies. These results provide validated guidance for drag selection and distributor design in particle-based thermal energy storage applications. Collectively, the validated model and parametric results quantify key drivers of PHB-HX performance and provide practical guidance for design and optimization. The results provide confidence in the model predictability and provide a step forward to improve on heat exchange performance. The demonstrated performance and modeling approach support the deployment and further development of this novel PHB-HX concept for robust, particle-based long-duration thermal energy storage systems.

25 ENERGY STORAGE↗

DOE OSTI2021

Classification and computation of extreme events in turbulent combustion

In the design of practical combustion systems, ensuring safety and reliability is an important requirement. For instance, reliably avoiding lean blowout, flame flashback or inlet unstart is critical for ensuring safe operation. Currently, the science of predicting such events is based on prior experience, limited modeling or diagnostic tools and purely statistical approaches. Even though computational and experimental tools for studying combustion devices have vastly advanced in the last three decades, the analysis of such failure events has not been pursued widely. While the use of data for model development and calibration is being widely accepted, the extension to failure events introduces numerous challenges. In particular, the focus here is on so-called data-poor problems, where the cost of generating data is extremely high and is not easily amenable to existing computational and experimental approaches. Data-poor problems are particularly relevant when related to extreme events (also called anomalous events) that can lead to catastrophic failure of the system. It is argued that transient events that describe such failure can have different causal mechanisms. To develop the scientific inference process, a classification of such problems is used to determine specific modeling paths as well as computational tools needed. Research opportunities in the emerging field of extreme event prediction are highlighted in order to identify critical and immediate needs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Decoherence from long-range forces in atom interferometry

Atom interferometers provide a powerful means of realizing quantum coherent systems with increasingly macroscopic extent in space and time. These systems provide an opportunity for a variety of novel tests of fundamental physics, including ultralight dark matter searches and tests of modifications of gravity, using long drop times and microgravity environments. However, as experiments operate with longer periods of free fall and become sensitive to smaller background effects, key questions start to emerge about the fundamental limits to future atom interferometry experiments. Here we study the effects on atomic coherence from hard-to-screen backgrounds due to baths of ambient particles with long-range forces, such as gravitating baths and charged cosmic rays. Our approach—working in the Heisenberg picture for the atomic motion—makes proper inclusion of the experimental apparatus feasible and clearly shows how to handle long-range forces. We find that these potential backgrounds are likely negligible for the next generation of interferometers, as aggressive estimates for the gravitational decoherence from a background bath of dark matter particles gives a decoherence timescale on the order of years.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2021

The three-dimensional construction of leaves is coordinated with water use efficiency in conifers

Conifers prevail in the canopies of many terrestrial biomes, holding a great ecological and economic importance globally. Current increases in temperature and aridity are imposing high transpirational demands and resulting in conifer mortality. Therefore, identifying leaf structural determinants of water use efficiency is essential for predicting physiological impacts due to environmental variation. Here, using synchrotron-generated microtomography imaging, we extracted leaf volumetric anatomy and stomatal traits in 34 species across conifers with a special focus on Pinus , the richest conifer genus. We show that intrinsic water use efficiency (WUE i ) is positively driven by leaf vein volume. Needle-like leaves of Pinus , as opposed to flat leaves or flattened needles of other genera, showed lower mesophyll porosity, decreasing the relative mesophyll volume. This led to increased ratios of stomatal pore number per mesophyll or intercellular airspace volume, which emerged as powerful explanatory variables, predicting both stomatal conductance and WUE i . Our results clarify how the three-dimensional organisation of tissues within the leaf has a direct impact on plant water use and carbon uptake. By identifying a suite of structural traits that influence important physiological functions, our findings can help to understand how conifers may respond to the pressures exerted by climate change.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Scoreboard

Emerging HPC machines have given rise to enhanced compute power that far outstrips the machine's ability to save large scale results for post-processing. To combat this, in situ data analysis techniques are slowly being adopted. With in situ data management favoring workflows composed of multiple simulations and analyses connected in transit on heterogeneous machines, scientists and engineers need a tool that enables them to create data extracts, visualizations, and interactively monitor and steer their simulations. Scoreboard Phase II is a next generation analysis software that supports composite in transit workflows on heterogeneous architectures and restores interactivity to in situ data analysis through simulation monitoring and computational steering. Scoreboard provides a simulation dashboard with graphs of metrics over time, controls for setting custom simulation steering parameters, controls for managing the set of data extracts being produced in the simulation, as well as the ability to explore data extracts, all from a web browser. Realizing the vision outlined in this project required research into making a system that integrates end to end from simulations all the way to the user. In situ tools generally suffer from complexity and excessive software dependencies. Scoreboard, by contrast, is easy to build and integrate into simulation codes and it provides first class FORTRAN support. The Scoreboard library is capable of in situ and in transit data analysis that can produce data extracts commonly needed for Computational Fluid Dynamics (CFD) analysis. Simulations can transparently stage data in transit to a Scoreboard Endpoint program, which can accept their data and produce the requested data extracts. This lets simulations return to their work while the Endpoint works on the analysis. Efficiently staging the data at scale was a topic of this research. Scoreboard provides the means to let the user manage data extracts and monitor/steer many simulations from a web browser. This area of the research focused on discovery of in transit network components to expose and control their steering parameters within an interactive browser-based user interface that includes: system topology, gathered metrics, notifications, dynamically-generated steering controls, and exploration of visualization data products.

Whitlock, BradJoseph [Intelligent Light] (00000001↗

DOE OSTI2024

Sustainable Aviation Fuel Combustion, Soot, and Contrail Formation

The aviation industry stands at a crossroads, facing the dual challenge of meeting the growing global demand for air travel while mitigating its environmental impact. As concerns over climate change intensify, sustainable aviation fuels (SAFs) have emerged as a promising solution to reduce the carbon footprint of air travel. The aviation sector has long been recognized as a contributor to greenhouse gas emissions, with carbon dioxide (CO 2 ) being a primary concern. SAFs, derived from renewable feedstocks such as biomass, waste oils, or synthetic processes, offer a promising avenue for reducing the net carbon emissions associated with aviation. While SAFs have shown potential in lowering CO 2 emissions, the combustion process introduces complexities related to soot particle formation and contrail generation that require comprehensive exploration. These aspects are pivotal not only for their environmental implications but also for their influence on atmospheric climate interactions. As the aviation industry increasingly embraces SAFs to meet sustainability goals, it is imperative to assess their combustion characteristics, unravel the mechanisms of soot formation, and scrutinize the factors influencing contrail development.

09 BIOMASS FUELS↗

DOE OSTI2025

A comparison of surrogate constitutive models for viscoplastic creep simulation of HT-9 steel

Mechanistic microstructure-informed constitutive models for the mechanical response of polycrystals are a cornerstone of computational materials science. However, as these models become increasingly more complex – often involving coupled differential equations describing the effect of specific deformation modes – their associated computational costs can become prohibitive, particularly in optimization or uncertainty quantification tasks that require numerous model evaluations. To address this challenge, surrogate constitutive models that balance accuracy and computational efficiency are highly desirable. Data-driven surrogate models, that learn the constitutive relation directly from data, have emerged as a promising solution. In this work, we develop two local surrogate models for the viscoplastic response of a steel: a piecewise response surface method and a mixture of experts model. These surrogates are designed to adapt to complex material behavior, which may vary with material parameters or operating conditions. The surrogate constitutive models are applied to creep simulations of HT-9 steel, an alloy of considerable interest to the nuclear energy sector due to its high tolerance to radiation damage, using training data generated from viscoplastic self-consistent (VPSC) simulations. In conclusion, we define a set of test metrics to numerically assess the accuracy of our surrogate models for predicting viscoplastic material behavior, and show that the mixture of experts model outperforms the piecewise response surface method in terms of accuracy.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Oceanic mixing and waves in the presence of a suspended canopy

Large-eddy simulations are analysed to determine the influence of suspended canopies, such as those formed in macroalgal farms, on ocean mixed layer (OML) deepening and internal wave generation. In the absence of a canopy, we show that Langmuir turbulence, when compared with wind-driven shear turbulence, results in a deeper OML and more pronounced internal waves beneath the OML. Subsequently, we examine simulations with suspended canopies of varying densities located in the OML, in the presence of a background geostrophic current. Intensified turbulence occurs in the shear layer at the canopy’s bottom edge, arising from the interaction between the geostrophic current and canopy drag. Structures resembling Kelvin–Helmholtz (KH) instability emerge as the canopy shear layer interacts with the underlying stratification, radiating internal waves beneath the OML. Both intensified turbulence and lower-frequency motions associated with KH-type structures are critical factors in enhancing mixing. Consequently, the OML depth increases by up to a factor of two compared with cases without a canopy. Denser canopies and stronger geostrophic currents lead to more pronounced KH-type structures and internal waves, stronger turbulence and greater OML deepening. Additionally, vertical nutrient transport is enhanced as the OML deepens due to the presence of the canopy. Considering that the canopy density investigated in this study closely represents offshore macroalgal farms, these findings suggest a mechanism for passive nutrient entrainment conducive to sustainable farming. Overall, this study reveals the intricate interactions between the suspended canopy, turbulent mixing and stratification, underscoring their importance in reshaping OML characteristics.

Bo, Tong (ORCID:0000000260300561)↗

DOE OSTI2022

Artificial intelligence inferred microstructural properties from voltage–capacity curves

Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn $$_2$$ 2 O $$_4$$ 4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 $$R^2$$ R 2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.

25 ENERGY STORAGE↗

DOE OSTI2025

Wide-field microwave magnetic field imaging with nitrogen-vacancy centers in diamond

Non-invasive imaging of microwave (MW) magnetic fields with microscale lateral resolution is pivotal for various applications, such as MW technologies and integrated circuit failure analysis. Diamond nitrogen-vacancy (NV) center magnetometry has emerged as an ideal tool, offering micrometer-scale resolution, millimeter-scale field of view, high sensitivity, and non-invasive imaging compatible with diverse samples. However, up until now, it has been predominantly used for imaging of static or low-frequency magnetic fields or, concerning MW field imaging, to directly characterize the same microwave device used to drive the NV spin transitions. In this work, we leverage an NV center ensemble in diamond for wide-field imaging of MW magnetic fields generated by a test device employing a differential measurement protocol. The microscope is equipped with a MW loop to induce Rabi oscillations between NV spin states, and the MW field from the device-under-test is measured through local deviations in the Rabi frequency. This differential protocol yields magnetic field maps of a 2.57 GHz MW field with a sensitivity of ∼9 μT Hz −1/2 for a total measurement duration of T=357 s, covering a 340 × 340 μm 2 field of view with a micrometer-scale spatial resolution and a device-under-test input power dynamic range of 30 dB. This work demonstrates a novel NV magnetometry protocol, based on differential Rabi frequency measurement, that extends NV wide-field imaging capabilities to imaging of weak MW magnetic fields that would be difficult to measure directly through standard NV Rabi magnetometry.

crystallographic defects↗

DOE OSTI2022

Links between autophagy and lipid droplet dynamics

Abstract Autophagy is a catabolic process in which cytoplasmic components are delivered to vacuoles or lysosomes for degradation and nutrient recycling. Autophagy-mediated degradation of membrane lipids provides a source of fatty acids for the synthesis of energy-rich, storage lipid esters such as triacylglycerol (TAG). In eukaryotes, storage lipids are packaged into dynamic subcellular organelles, lipid droplets. In times of energy scarcity, lipid droplets can be degraded via autophagy in a process termed lipophagy to release fatty acids for energy production via fatty acid β-oxidation. On the other hand, emerging evidence suggests that lipid droplets are required for the efficient execution of autophagic processes. Here, we review recent advances in our understanding of metabolic interactions between autophagy and TAG storage, and discuss mechanisms of lipophagy. Free fatty acids are cytotoxic due to their detergent-like properties and their incorporation into lipid intermediates that are toxic at high levels. Thus, we also discuss how cells manage lipotoxic stresses during autophagy-mediated mobilization of fatty acids from lipid droplets and organellar membranes for energy generation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Fidelity Comparison of Time-Bin and Fock State Encoding in Hybrid Quantum Systems Under Channel and Transduction Effects

Future quantum networks are expected to integrate a heterogeneous combination of quantum systems, owing to the diverse advantages offered by different physical platforms in terms of scalability, coherence times, and interfacing capabilities. Within the context of this emerging quantum communication paradigm, this paper presents an analytical comparison of two photonic encoding schemes—time-bin and Fock state (single rail)—in hybrid quantum systems where flying qubits are entangled with stationary matter qubits. We evaluate their resilience against fiber channel and quantum transduction effects by calculating the fidelity of the final states relative to their ideal forms. Employing the characteristic function approach, we derive analytical fidelity expressions and investigate their dependence on parameters such as transmissivity, noise levels, fiber length, and source generation success probability. Additionally, we simulate the scenario with a dedicated QuTiP software implementation to verify the validity of the theoretical models. Our findings reveal that due to its inherent single-mode structure, the Fock state encoding consistently outperforms time-bin encoding in fidelity, as this structure significantly minimizes susceptibility to losses compared to the two-mode nature of the time-bin scheme. This analysis offers valuable insights for future hybrid quantum communication and information processing applications.

Fiorini, Francesco [Pisa U.]↗