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DOE OSTI2024

Imaging Valley Excitons in a 2D Semiconductor with Scanning Tunneling Microscope-Induced Luminescence

Valley excitons dominate the optoelectronic response of transition-metal dichalcogenides and are drastically affected by structural and environmental inhomogeneities localized in these materials. Critical to understanding and controlling these nanoscale excitonic changes is the ability to correlate the imaging of excitonic states with crystalline structures on the atomic scale. Here, in this study, we apply scanning tunneling microscope-induced luminescence microscopy to image valley excitons in a semiconducting transition-metal dichalcogenide monolayer decoupled by a 10 nanometer-thick hexagonal-boron-nitride flake incorporated in a lateral homojunction on an Au electrode surface. This design enables the observation of chiral excitonic emission arising from neutral and charged valley excitons of the monolayer semiconductor at ambipolar voltages with a quantum efficiency up to similar to ~10 -5 photon/electron. The measured light helicity demonstrates considerable circular polarization dependent on the sample voltage, reaching as much as 40%. The real-space luminescence imaging maps$-$at subnanometer resolution$-$of the valley excitons reveal striking spatial variations associated with localized inhomogeneities, including surface impurities and possibly nanoscale dielectric and/or potential disorders in the monolayer. Our study introduces a promising format for 2D materials to explore and tailor their optoelectronic processes at the atomic scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Predicting synthesizable multi-functional edge reconstructions in two-dimensional transition metal dichalcogenides

Abstract Two-dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted tremendous interest as functional materials due to their exceptionally diverse and tunable properties, especially in their edges. In addition to the conventional armchair and zigzag edges common to hexagonal 2D materials, more complex edge reconstructions can be realized through careful control over the synthesis conditions. However, the whole family of synthesizable, reconstructed edges remains poorly studied. Here, we develop a computational approach integrating ensemble-generation, force-relaxation, and electronic-structure calculations to systematically and efficiently discover additional reconstructed edges and screen their functional properties. Using MoS 2 as a model system, we screened hundreds of edge-reconstruction to discover over 160 reconstructed edges to be more stable than the conventional ones. More excitingly, we discovered nine new synthesizable reconstructred edges with record thermodynamic stability, in addition to successfully reproducing three recently synthesized edges. We also find our predicted reconstructed edges to have multi-functional properties—they show near optimal hydrogen evolution activity over the conventional edges, ideal for catalyzing hydrogen-evolution reaction (HER) and also exhibit half-metallicity with a broad variation in magnetic moments, making them uniquely suitable for nanospintronic applications. Our work reveals the existence of a wide family of synthesizable, reconstructed edges in 2D TMDCs and opens a new materials-by-design paradigm of ‘intrinsic’ edge engineering multifunctionality in 2D materials.

Hu, Guoxiang (ORCID:0000000329428564)↗

DOE OSTI2020

Beyond Volume Variation: Anisotropic and Protrusive Lithiation in Bismuth Nanowire

Materials storing energy via an alloying reaction are promising anode candidates in rechargeable lithium-ion batteries (LIBs) due to their much higher energy density than the current graphite anode. Until now, the volumetric expansion of such electrode particles during lithiation has been considered as solely responsible for cycling-induced structural failure. Here, we report different structural failure mechanisms using single-crystalline bismuth nanowires as the alloying-based anode. The Li–Bi alloying process exhibits a two-step transition, that is, Bi–Li 1 Bi and Li 1 Bi–Li 3 Bi. Interestingly, the Bi–Li 1 Bi phase transition occurs not only in the bulk Bi nanowire but also on the particle surface showing its characteristic behavior. The bulk alloying kinetics favors a Bi-(012)-facilitated anisotropic lithiation, whose mechanism and energetics are further studied using the density functional theory calculations. More importantly, the protrusion of Li 1 Bi nanograins as a result of anisotropic Li–Bi alloying is found to dominate the surface morphology of Bi particles. The growth kinetics of Li 1 Bi protrusions is understood atomically with the identification of two different controlling mechanisms, that is, the dislocation-assisted strain relaxation at the Bi/Li 1 Bi interface and the short-range migration of Bi supporting the off-Bi growth of Li 1 Bi. As loosely rooted to the bulk substrate and easily peeled off and detached into the electrolyte, these nanoscale protrusions developed during battery cycling are believed to be an important factor responsible for the capacity decay of such alloying-based anodes at the electrode level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

An a priori evaluation of a principal component and artificial neural network based combustion model in diesel engine conditions

A principal component analysis (PCA) and artificial neural network (ANN) based chemistry tabulation approach is presented. ANNs are used to map the thermochemical state onto a low-dimensional manifold consisting of five control variables that have been identified using PCA. Three canonical configurations are considered to train the PCA-ANN model: a series of homogeneous reactors, a nonpremixed flamelet, and a two-dimensional lifted flame. The performance of the model in predicting the thermochemical manifold of a spatially-developing turbulent jet flame in diesel engine thermochemical conditions is a priori evaluated using direct numerical simulation (DNS) data. The PCA-ANN approach is compared with a conventional tabulation approach (tabulation using ad hoc defined control variables and linear interpolation). The PCA-ANN model provides higher accuracy and requires several orders of magnitude less memory. Here, these observations indicate that the PCA-ANN model is superior for chemistry tabulation, especially for modelling complex chemistries that present multiple combustion modes as observed in diesel combustion. The performance of the PCA-ANN model is then compared to the optimal estimator, i.e. the conditional mean from the DNS. The results indicate that the PCA-ANN model gives high prediction accuracy, comparable to the optimal estimator, especially for major species and the thermophysical properties. Higher errors are observed for the minor species and reaction rate predictions when compared to the optimal estimator. It is shown that the prediction of minor species and reaction rates can be improved by using training data that exhibits a variation of parameters as observed in the turbulent flame. The output of the ANN is analysed to assess mass conservation. It is observed that the ANN incurs a mean absolute error of 0.05% in mass conservation. Furthermore, it is demonstrated that this error can be reduced by modifying the cost function of the ANN to penalise for deviation from mass conservation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Sedimentation and shear-induced dynamics of spheroids in fluids with spatial viscosity variations

A generalized reciprocal theorem is used to relate the force and torque induced on a particle in an inertia-less fluid with small variation in viscosity to integrals involving Stokes flow fields and the spatial dependence of viscosity. These resistivity expressions are analytically evaluated using spheroidal harmonics and then used to obtain the mobility of the spheroid during sedimentation, and in linear flows, of a fluid with linear viscosity stratification. The coupling between the rotational and translational motion induced by stratification rotates the spheroid’s centerline, creating a variety of rotational and translational dynamics dependent upon the particle’s aspect ratio, κ, and the component of the stratification unit vector in the gravity direction, d g . Spheroids with 0.55 ⪅ κ ⪅ 2.0 exhibit the largest variety of settling behaviors. Interestingly, this range covers most microplastics and typical microorganisms. One of the modes include a stable orientation dependent only on κ and d g , but independent of initial orientation, thus allowing for the potential control of settling angles and sedimentation rates. In a simple shear flow, cross-streamline migration occurs due to the stratification-induced force generated on the particle. Similarly, a particle no longer stays at the stagnation point of a uniaxial extensional flow. While fully analytical results are obtained for spheroids, numerical simulations provide a source of validation. These simulations also provide additional insights into the stratification-induced force- and torque-producing mechanisms through the stratification-induced stress, which is not accessed in the reciprocal theorem-based analytical calculations.

Geophysical and Geological Flows: Stratified flows↗

DOE OSTI2025

Competitive Sorption of H 2 O and CO 2 in Clay Mineral Interlayers

The first-reported competitive CO 2 –H 2 O sorption isotherms on a naturally occurring expandable clay mineral (saponite) exchanged with Na, K, and Cs at near ambient CO 2 concentrations (1% CO 2 ) are reported by combining ATR-FTIR spectroscopy with gravimetric sorption methods. Little CO 2 sorption was observed at high relative humidity (RH) but increased significantly at low RH. Unlike prior work where CO 2 sorption was reported on samples that were previously subjected to heating and/or evacuation treatment to remove sorbed H 2 O, this study manipulated CO 2 sorption by controlling RH. CO 2 sorption increased in the order Cs- > K- > Na-exchanged saponite and was anticorrelated with the Gibbs energy of hydration of the exchangeable cation. As expected, H 2 O sorption followed the opposite trend, with increased H 2 O sorption occurring in the order Na > K > Cs. The amount of CO 2 sorbed using 1 atm CO 2 (p/p o = 1.0) ranged from 50 to 30 mg CO 2 /g clay and from 1.25 to 0.8 mg CO2 /g clay for 1% CO 2 (p/p o = 0.01) in N 2 . The sensitivity of the ATR-FTIR measurements is demonstrated by detecting the CO 2 stretch of adsorbed CO 2 from air containing 500 ppmv CO 2 . The spectral features of sorbed CO 2 were minimally affected by the nature of the exchangeable cation or variations in H 2 O content. However, the position of the HOH bending band shifted significantly from 1638 to 1610 cm –1 as H 2 O content decreased, reflecting changes in intermolecular hydrogen bonding between H 2 O molecules in the saponite interlayer. The shift in position of the HOH bending band to lower energy was coincident with increased CO 2 sorption. CO 2 sorption in clay mineral interlayers is consistent with sorption on weakly hydrated partially hydrophobic sites found on low charge density smectites and is enhanced by lower RH and by the presence of weakly hydrated exchangeable cations like Cs + or K + . In conclusion, the greatest CO 2 sorption occurs on Cs-saponite because this is the least hydrated cation of the three used in the study and indicates that CO2 sorption occurs on the neutral portion of the siloxane surface.

Johnston, Cliff T. [Purdue Univ., West Lafayette, ↗

DOE OSTI2024

Structural basis for the activity and specificity of the immune checkpoint inhibitor lirilumab

The clinical success of immune checkpoint inhibitors has underscored the key role of the immune system in controlling cancer. Current FDA-approved immune checkpoint inhibitors target the regulatory receptor pathways of cytotoxic T-cells to enhance their anticancer responses. Despite an abundance of evidence that natural killer (NK) cells can also mediate potent anticancer activities, there are no FDA-approved inhibitors targeting NK cell specific checkpoint pathways. Lirilumab, the most clinically advanced NK cell checkpoint inhibitor, targets inhibitory killer immunoglobulin-like receptors (KIRs), however it has yet to conclusively demonstrate clinical efficacy. Here we describe the crystal structure of lirilumab in complex with the inhibitory KIR2DL3, revealing the precise epitope of lirilumab and the molecular mechanisms underlying KIR checkpoint blockade. Notably, the epitope includes several key amino acids that vary across the human population, and binding studies demonstrate the importance of these amino acids for lirilumab binding. These studies reveal how KIR variations in patients could influence the clinical efficacy of lirilumab and reveal general concepts for the development of immune checkpoint inhibitors targeting NK cells.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Robust Implicit Adaptive Low Rank Time-Stepping Methods for Matrix Differential Equations

In this work, we develop implicit rank-adaptive schemes for time-dependent matrix differential equations. The dynamic low rank approximation (DLRA) is a well-known technique to capture the dynamic low rank structure based on Dirac–Frenkel time-dependent variational principle. In recent years, it has attracted a lot of attention due to its wide applicability. Our schemes are inspired by the three-step procedure used in the rank adaptive version of the unconventional robust integrator (the so called BUG integrator) (Ceruti et al. in BIT Numer Math 62(4):1149–1174, 2022) for DLRA. First, a prediction (basis update) step is made computing the approximate column and row spaces at the next time level. Second, a Galerkin evolution step is invoked using an implicit solves for the small core matrix. Finally, a truncation is made according to a prescribed error threshold. Since the DLRA is evolving the differential equation projected on to the tangent space of the low rank manifold, the error estimate of the BUG integrator contains the tangent projection (modeling) error which cannot be easily controlled by mesh refinement. This can cause convergence issue for equations with cross terms. To address this issue, we propose a simple modification, consisting of merging the row and column spaces from the explicit step truncation method together with the BUG spaces in the prediction step. In addition, we propose an adaptive strategy where the BUG spaces are only computed if the residual for the solution obtained from the prediction space by explicit step truncation method, is too large. Here, we prove stability and estimate the local truncation error of the schemes under assumptions. We benchmark the schemes in several tests, such as anisotropic diffusion, solid body rotation and the combination of the two, to show robust convergence properties.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Design of a separate effects MiniFuel irradiation experiment investigating microstructure evolution in high burnup UO 2

The microstructural evolution of UO 2 fuel pellets during commercial operation in light water reactors (LWRs) is known to vary significantly across the pellet radius due to spatial variations in local temperature and burnup. The primary obstacle to extending LWR refueling cycles to 24-month intervals is the susceptibility of certain high burnup fuel microstructures to fuel fragmentation, relocation, and dispersal (FFRD) during a loss of coolant accident (LOCA). Although FFRD of the high burnup structure in the rim region of a pellet is well studied, the fine fragmentation that has been observed in a second region, near the midradius of the pellet (termed the “dark zone”) following mock LOCA testing of high burnup commercial fuel rods is less understood. This paper describes the design, analysis, and execution of a separate effects MiniFuel irradiation experiment that aims to identify the specific temperature and burnup regimes under which FFRD-susceptible dark zone microstructures form. The small disc specimens (3 mm diameter by ∼0.3 mm thick) enable more precise control of the relatively uniform temperature and burnup conditions. A total of 42 specimens were fabricated with typical LWR fuel densities (∼96%–98% of theoretical density) and grain sizes (∼12 μm) and are being irradiated over a range of temperatures (600°C–1000°C) and discharge burnups (50–72 MWd/kg-U) that bound the midradius region of high burnup LWR fuel. Fuel specimens with identical 235 U enrichments were inserted in two irradiation locations in the High Flux Isotope Reactor and are currently undergoing irradiation to further evaluate the impact of rate effects (fission rate, time at temperature) on the microstructural evolution. The fuel fabrication and the thermal and neutronic simulations used for designing the experiment are detailed in this paper. A secondary objective of the experiment is to observe fission gas release (FGR) under the various irradiation conditions, and this work provides first-order predictions of FGR from all fuel specimens. The insights gained from these experiments will inform future high burnup core designs that could minimize the formation of susceptible microstructures and ultimately enable 24-month refueling cycles while minimizing the fraction of the fuel susceptible to FFRD.

FFRD↗

DOE OSTI2022

Divergent responses of soil microorganisms to throughfall exclusion across tropical forest soils driven by soil fertility and climate history

Model projections predict tropical forests will experience longer periods of drought and more intense precipitation cycles under a changing climate. Such transitions have implications for structure-function relationships within microbial communities. We examine how throughfall exclusion might reshape prokaryotic and fungal communities across four lowland forests in Panama with a wide variation in mean annual precipitation and soil fertility. Four sites were established across a 1000 mm span in Mean Annual Precipitation (MAP: 2335–3421 mm). We expected microbial communities at sites with lower MAP to be less sensitive to throughfall exclusion than sites with higher MAP and fungal communities to be more resistant to disturbance than prokaryotes. At each location, partial throughfall exclusion structures were established over 10 × 10 m plots to reduce direct precipitation input. After short-term (~3–9 months) throughfall exclusion, prokaryotic communities showed no change in composition. However, prolonged (12–18 months) throughfall exclusion resulted in divergent prokaryotic community responses, reflecting MAP and soil fertility. We observed the emergence of a “drought microbiome” within infertile sites, whereby the community structure of the experimental throughfall exclusion plots at the lower MAP sites diverged from their respective control sites and converged towards overlapping assemblages. Furthermore, under throughfall exclusion, taxa increasing in relative abundance at the wettest site reflected that endemic to control plots at the lowest MAP site, suggesting a shift toward communities with lifehistory traits selected for under a lower MAP. By contrast, fungal community composition across sites was resilient to throughfall exclusion; however, biomass diverged in response to throughfall exclusion, increasing at two sites while decreasing in the other two. Broadly, our results suggest that microbial communities’ sensitivity to frequent drying and rewetting periods in tropical forest soils will depend on climate history and soil fertility, with infertile sites likely to respond readily to changes in precipitation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Controls on Stable Methane Isotope Values in Northern Peatlands and Potential Shifts in Values Under Permafrost Thaw Scenarios

Abstract Northern peatlands are a globally significant source of methane (CH 4 ), and emissions are projected to increase due to warming and permafrost loss. Understanding the microbial mechanisms behind patterns in CH 4 production in peatlands will be key to predicting annual emissions changes, with stable carbon isotopes (δ 13 C‐CH 4 ) being a powerful tool for characterizing these drivers. Given that δ 13 C‐CH 4 is used in top‐down atmospheric inversion models to partition sources, our ability to model CH 4 production pathways and associated δ 13 C‐CH 4 values is critical. We sought to characterize the role of environmental conditions, including hydrologic and vegetation patterns associated with permafrost thaw, on δ 13 C‐CH 4 values from high‐latitude peatlands. We measured porewater and emitted CH 4 stable isotopes, pH, and vegetation composition from five boreal‐Arctic peatlands. Porewater δ 13 C‐CH 4 was strongly associated with peatland type, with δ 13 C enriched values obtained from more minerotrophic fens (−61.2 ± 9.1‰) compared to permafrost‐free bogs (−74.1 ± 9.4‰) and raised permafrost bogs (−81.6 ± 11.5‰). Variation in porewater δ 13 C‐CH 4 was best explained by sedge cover, CH 4 concentration, and the interactive effect of peatland type and pH (r 2 = 0.50,p < 0.001). Emitted δ 13 C‐CH 4 varied greatly but was positively correlated with porewater δ 13 C‐CH 4 . We calculated a mixed atmospheric δ 13 C‐CH 4 value for northern peatlands of −65.3 ± 7‰ and show that this value is more sensitive to landscape drying than wetting under permafrost thaw scenarios. Our results suggest northern peatland δ 13 C‐CH 4 values are likely to shift in the future which has important implications for source partitioning in atmospheric inversion models.

Environmental Sciences & Ecology↗

DOE OSTI2023

Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions

Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill , allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Maitner, Brian S.↗

DOE OSTI2021

Opportunities and Challenges to Capturing Distributed Battery Value via Retail Utility Rates and Programs

Distributed battery deployment is increasing with advanced metering, control, and communication technologies, leaving electric utilities with an under-utilized, flexible grid resource in aggregate. Rates can reflect locational and temporal prices while utility incentive-based programs allow DERs to provide direct grid services. However, utilities must balance accurately reflecting dynamic grid conditions versus simple and feasible design that encourages customer participation. Currently, most rates and incentive-based programs are simple, but as penetration of DER and advanced controls increase, dynamic designs could become prevalent. Utilities could encourage providing multiple services to optimize distributed battery dispatch and value streams, however, challenges persist when stacking services across distribution and bulk systems. A DER committed to multiple discrete services concurrently necessitates coordination between operators and a clear hierarchy of commitments. One way to address this is to separate commitments by time or capacity. For services that follow cyclic, predictable patterns, or those that are peak driven with predictability, an operator could ensure sufficient state of charge for participation, leaving time where a distributed battery could otherwise provide different services by segmenting participation temporally. To provide continuous or unexpected services, a battery operator may use state of charge management to reserve some percentage of the battery and segment participation by capacity. Macroeconomic trends, load patterns, generation profiles, and grid configurations drive variation in value and the subsequent implications for utility offerings and how a customer might participate. As distributed battery adoption increases, both regulators and utilities will need to ensure no adverse grid impacts and encourage provision of societal value beyond the customer domain.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Numerical and experimental investigation of the flame kernel growth in a methane/air mixture near the lean flammability limit

Lean combustion has the potential to improve the thermal efficiency of spark-ignition engines, but it faces the significant challenge of increased cycle-to-cycle variation due to low mixture reactivity and unstable flame dynamics. Computational fluid dynamics (CFD) employing predictive models can guide engine design and optimize operating strategies for lean combustion. However, ignition and combustion models have rarely been validated at fuel-lean conditions, and a fundamental understanding of the early flame kernel growth process is also lacking for a successful sub-model development. Here, the present study develops a numerical simulation framework used to investigate early flame kernel growth in methane/air mixtures. A nanosecond-pulsed discharge (NPD) approach is employed to effectively decouple the flame kernel growth from the electrical discharge due to their difference in timescales, and equivalence ratios near the experimentally measured lean flammability limit (LFL) are selected to focus on challenging mixture conditions. Three numerical investigations, such as the choice of turbulence modeling, grid size, and grid control strategies, are examined to match both LFL and flame kernel structure measured from experiments. It is demonstrated that a quasi-direct numerical simulation (QDNS) with a fixed grid embedding of 10 μm can predict the LFL as φ CFD =0.61 and match the displacement speed of the kernel’s boundary marked in schlieren images. To predict the LFL and flame kernel shape, a fine grid (Δ≤12.5 μm) is needed to capture the consumption of formaldehyde (CH 2 O) in kernel’s reaction branches attached to the anode, and adaptive mesh refinement is replaced with the fixed embedding due to loss of simulation accuracy. Also, it is found that a large-eddy simulation (LES) using the Dynamic Structure model is not suitable for the NPD-induced flame kernel simulation because artificial sub-grid turbulent kinetic energy induced by shock dynamics alters the flow velocity calculation, resulting in divergence of LES from QDNS. Lastly, the simulation well matches the experimental data for the flame kernel evolution in three mixture conditions (φ = 0.7, 0.61, 0.55), showing toroidal flame kernel expansion and flame kernel growth/extinction.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Omega-Limit Sets and Input-To-State Stability in Power Grids with Switching Equilibria

This paper studies a power transmission system with both conventional generators (CGs) and distributed energy assets (DEAs) providing frequency control. We consider an operating condition with demand aggregating two dynamic components: one that switches between different values on a finite set, and one that varies smoothly over time. Such dynamic operating conditions may result from protection scheme activations, external cyber-attacks, or due to the integration of dynamic loads, such as data centers. Mathematically, the dynamics of the resulting system are captured by a system that switches between a finite number of vector fields - or modes -, with each mode having a distinct equilibrium point induced by the demand aggregation. To analyze the stability properties of the resulting switching system, we leverage tools from hybrid dynamic inclusions and the concept of ..omega.. -limit sets from sets. Specifically, we characterize a compact set that is semi-globally practically asymptotically stable under the assumption that the switching frequency and load variation rate are sufficiently slow. For arbitrarily fast variations of the load, we use a level-set argument with multiple Lyapunov functions to establish input-to-state stability of a larger set and with respect to the rate of change of the loads. The theoretical results are illustrated via numerical simulations on the IEEE 39-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Understanding curvature effects on the magnetization reversal of patterned permalloy Archimedean spirals

Geometric curvature in magnetic systems can induce several unique magnetic interactions, the most notable of which are the curvature induced magnetic anisotropy and the curvature introduced Dzyaloshinski-Moria (DM) like interaction. Of the two, the DM-like interaction is particularly interesting as it provides a unique way to control domain wall motion, which is particularly relevant to the field of spintronics. In this work, we study the effect of geometric curvature on magnetization reversal in Permalloy strips patterned into Archimedean spirals with varying widths. Specifically, we simulated the magnetization reversal of Permalloy strips for several widths ranging from microscale to nanoscale to determine a lengthscale at which the curvature effects are strong. Simulations showed that in the microscale spirals, magnetization reversal primarily occurs through the appearance of magnetization ripple in which domains with reversed magnetization nucleate. On the other hand, the nanoscale spirals showed that reversal primarily proceeded through the motion of domain walls into the arms of the spiral. Our experimental in situ Lorentz transmission electron microscopy data of the microscale spirals, however, showed that magnetization reversal occurred with both mechanisms. At the nanoscale, the effect of local variation of curvature leads to hysteresis loops with stepped behavior. This behavior is characteristic of the curvature induced DM-like interaction affecting domain wall motion in the arms of the nanoscale spirals.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Spatial BCS-BEC crossover in superconducting $\textit{p–n}$ junctions

Here, we present a theory of superconducting $\textit{p–n}$ junctions. To this end, we consider a two band model of doped bulk semiconductors with attractive interactions between the charge carriers and derive the superconducting order parameter, the quasiparticle density of states, and the chemical potential as a function of the semiconductor gap $Δ_0$ and the doping level ε. We verify previous results for the quantum phase diagram for a system with constant density of states in the conduction and valence band, which show BCS-superconductor to Bose-Einstein-condensation (BEC) and BEC-to-insulator transitions as a function of doping level and the size of the band gap. Then, we extend this formalism to a density of states which is more realistic for 3D systems and derive the corresponding quantum phase diagram, where we find that a BEC phase can only exist for small band gaps $Δ_0 < Δ ^{\ast}_0$. For larger band gaps, we find rather a direct transition from an insulator to a BCS phase. Next, we apply this theory to study the properties of superconducting $\textit{p–n}$ junctions. We derive the spatial variation of the superconducting order parameter along the $\textit{p–n}$ junction. As the potential difference across the junction leads to energy band bending, we find a spatial crossover between a BCS and BEC condensate, as the density of charge carriers changes across the $\textit{p–n}$ junction. For the two-dimensional system, we find two possible regimes, when the bulk is in a BCS phase, a BCS-BEC-BCS junction with a single BEC layer in the space charge region, and a BCS-BEC-I-BEC-BCS junction with two layers of BEC condensates separated by an insulating layer. In three dimensions we find that there can also be a conventional BCS-I-BCS junction for semiconductors with band gaps exceeding $Δ^{\ast}_0$. Thus, we find that there can be BEC layers in the well controlled setting of doped semiconductors, where the doping level can be varied to change and control the thickness of BEC and insulator layers, making Bose-Einstein condensates thereby possibly accessible to experimental transport and optical studies in solid-state materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Best Practices for Grid Communications

As the grid evolves, the communications architecture will need to evolve with it. That architecture affords a structured means by which the evolving complexities of the modern electric grid can be managed. This document provides best practices that can be implemented in the grid of today and evolve towards the grid and grid architecture of the future. The evolving grid and its control communications increasingly rely on commercial communications providers and a variety of technologies, from wireless (e.g., 5G, microwave, Wi-Fi) to wireline (fiber, copper) to radio communications (P25, other repeater-based systems), and all these communications systems rely on electric power. A reliable and resilient grid must account for this complex set of interdependencies in its planning activities, especially those involving restoration and recovery. The participation of all relevant parties in both planning and exercising of plans can prevent unexpected conditions that impede the reliable operation and recovery of the grid. Best practices for grid communications include using a Network Management System to document the operational state, define and monitor baselines, detect changes, and accelerate response to abnormalities. If transitioning from SONET to IP/packet-based systems, translating grid requirements into communications requirements for latency, bandwidth and throughput, IP packet delay variation, packet loss, and availability should inform and drive technology planning and selection as well as that communication system’s Quality of Service (QoS) policies and Service Level Agreements (SLAs). Secure and reliable timing is another key component of a reliable and resilient grid that can operate through adverse events. A trusted internal NTP configuration, an integrated and diverse timing delivery system, optimizing the timing architecture based on the transport technologies of the communications system, and using established standards can deliver the level of timing accuracy required by a range of time-sensitive power system applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗