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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Thermally unstable roosts influence winter torpor patterns in a threatened bat species

Abstract Many hibernating bats in thermally stable, subterranean roosts have experienced precipitous declines from white-nose syndrome (WNS). However, some WNS-affected species also use thermally unstable roosts during winter that may impact their torpor patterns and WNS susceptibility. From November to March 2017–19, we used temperature-sensitive transmitters to document winter torpor patterns of tricolored bats (Perimyotis subflavus) using thermally unstable roosts in the upper Coastal Plain of South Carolina. Daily mean roost temperature was 12.9 ± 4.9°C SD in bridges and 11.0 ± 4.6°C in accessible cavities with daily fluctuations of 4.8 ± 2°C in bridges and 4.0 ± 1.9°C in accessible cavities and maximum fluctuations of 13.8 and 10.5°C, respectively. Mean torpor bout duration was 2.7 ± 2.8 days and was negatively related to ambient temperature and positively related to precipitation. Bats maintained non-random arousal patterns focused near dusk and were active on 33.6% of tracked days. Fifty-one percent of arousals contained passive rewarming. Normothermic bout duration, general activity and activity away from the roost were positively related to ambient temperature, and activity away from the roost was negatively related to barometric pressure. Our results suggest ambient weather conditions influence winter torpor patterns of tricolored bats using thermally unstable roosts. Short torpor bout durations and potential nighttime foraging during winter by tricolored bats in thermally unstable roosts contrasts with behaviors of tricolored bats in thermally stable roosts. Therefore, tricolored bat using thermally unstable roosts may be less susceptible to WNS. More broadly, these results highlight the importance of understanding the effect of roost thermal stability on winter torpor patterns and the physiological flexibility of broadly distributed hibernating species.

Biodiversity & Conservation↗
DOE OSTI2026

Probabilistic inference in very large universes

Our current favored cosmological theories allow for the striking and controversial possibility that the observable universe is just a small part of a much larger universe in which parameters that describe the effective, low-energy laws of physics vary from one region to another. The controversy is largely driven by the fact that such a “very large universe” is mostly observationally inaccessible to us, so the issue arises of how we can reasonably assess a theory that describes such a universe. In this paper, we propose a Bayesian method for theory assessment based on theory-generated probability distributions for our observations. We focus on the principles that define this method, leaving aside concerns about how, in practice, one would carry out the required calculations. (One important issue that we set aside is the measure problem.) We argue that cosmological theories can be tested by the standard method of Bayesian updating, but we need to use theoretical predictions for “first-person” probabilities—that is, probabilities that we should use for our observations, taking into account all relevant selection effects. These selection effects can vary from one observer to another and can vary with time, so, in principle, first-person probabilities are defined for each observer instant—an observer at a specific instant of time. Calculations of first-person probabilities should take into account everything that the observer believes about herself and her surroundings, which we refer to as her subjective state. If the universe is very large, a theory might predict that there are many observer instants in the same subjective state; we argue that first-person probabilities should be calculated using a principle of self-locating indifference (PSLI), the assumption that any real observer should make predictions for her future as if she were chosen randomly and uniformly from the theoretically predicted observer instants that share her subjective state. We believe the PSLI is intuitively very reasonable, but we also argue that, if the theory is correct, the use of this principle maximizes the expected fraction of observers who will make correct predictions. A further complication is that cosmological theories are not expected to fully predict the detailed properties of the universe, but rather will predict a set of possible universes, each with a probability. Different possible universes will generically have different numbers of observers. We argue that, in the calculation of first-person probabilities, the probability for each possible universe should be weighted by the number of observer instants in the specified subjective state that it contains. These issues have been controversial in the literature, so we also provide a rebuttal to the claim that principles like the PSLI involve a “selection fallacy”; a rebuttal to what we dub the principle of required certainty; an argument rejecting theories that predict a preponderance of Boltzmann brains; a rebuttal to a parable about humans and Jovians used by Hartle and Srednicki to argue that assumptions of typicality can lead to absurd consequences; and, finally, a discussion about how the use of “old evidence” can be fit into a Bayesian mold.

Azhar, Feraz [University of Notre Dame, IN (United↗
DOE OSTI2023

Protecting and Defending against Autonomous Control Systems and Digital Twin Cyber Attacks: Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Models in Nuclear Power Plants (Final)

Navigating through the complex tapestry of technological advancements, "Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Model in Nuclear Power Plants" stands at the intersection of cybersecurity and nuclear power plant operations, embarking on a journey through the intricacies of securing digital twins against malicious cyber activities. As nuclear power plants progressively integrate digital twin technology and machine learning models to optimize operations and ensure system reliability, they inadvertently expose themselves to a new spectrum of vulnerabilities, notably in the realm of hyperparameter attacks. Hyperparameters, integral in machine learning model tuning and optimal performance of digital twins, have emerged as a target for adversaries aiming to destabilize the predictive capabilities and therefore, the operational accuracy of these digital entities within critical infrastructures like nuclear plants. This paper, therefore, meticulously threads the needle through the development of a robust response strategy, poised to shield these digital reflections against calculated hyperparameter manipulations, ensuring that the digital twin can effectively and securely function as a reliable proxy for its physical counterpart. The ensuing sections delve into the orchestrated maelstrom of multi-rate time-changing intelligent coordinated hyperparameter attacks and the implementation of event-triggered predictive control, laying down a structured, predictive, and responsive framework that safeguards the nexus where the digital and physical realms of nuclear power plants coalesce. The operational integrity of digital twins in nuclear power plants depends critically on the security of machine learning hyperparameters. This study makes two different contributions. First, a decision-based idea known as a multi-rate time changing intelligent coordinated hyperparameter attack is put forth. In this attack, many hyperparameters are repeatedly changed using both random and intelligent optimal techniques by the attacker. These assaults introduce varied rates at different attack steps, compromise various amounts of hyperparameters, and improve stealth and flexibility. Second, a technique is developed for event triggered predictive control to rapidly respond to potential hyperparameter attacks. This control integrates a sliding window framework, retaining a history of previous data points and employing linear regression to predict the next data point from the current dataset. The control gain K is determined using the Lyapunov-Krasovskii method, and subsequently, an action is developed. Finally, the outcome of the simulation demonstrates the viability of the proposed method for defending nuclear power plant digital twins from hyperparameter attacks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Turbulent dynamo action and its effects on the mixing at the convective boundary of an idealized oxygen-burning shell

Convection is one of the most important mixing processes in stellar interiors. Hydrodynamic mass entrainment can bring fresh fuel from neighboring stable layers into a convection zone, modifying the structure and evolution of the star. Because flows in stellar convection zones are highly turbulent, multidimensional hydrodynamic simulations are fundamental to accurately capture the physics of mixing processes. Under some conditions, strong magnetic fields can be sustained by the action of a turbulent dynamo, adding another layer of complexity and possibly altering the dynamics in the convection zone and at its boundaries. In this study, we used our fully compressible S EVEN -L EAGUE H YDRO code to run detailed and highly resolved three-dimensional magnetohydrodynamic simulations of turbulent convection, dynamo amplification, and convective boundary mixing in a simplified setup whose stratification is similar to that of an oxygen-burning shell in a star with an initial mass of 25 M ⊙ . We find that the random stretching of magnetic field lines by fluid motions in the inertial range of the turbulent spectrum (i.e., a small-scale dynamo) naturally amplifies the seed field by several orders of magnitude in a few convective turnover timescales. During the subsequent saturated regime, the magnetic-to-kinetic energy ratio inside the convective shell reaches values as high as 0.33, and the average magnetic field strength is ∼10 10 G. Such strong fields efficiently suppress shear instabilities, which feed the turbulent cascade of kinetic energy, on a wide range of spatial scales. The resulting convective flows are characterized by thread-like structures that extend over a large fraction of the convective shell. The reduced flow speeds and the presence of magnetic fields with strengths up to 60% of the equipartition value at the upper convective boundary diminish the rate of mass entrainment from the stable layer by ≈20% as compared to the purely hydrodynamic case.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Heavy-ion-induced displacement damage effects in magnetic tunnel junctions with perpendicular anisotropy

Here, we evaluate the resilience of CoFeB/MgO/CoFeB magnetic tunnel junctions (MTJs) with perpendicular magnetic anisotropy to displacement damage induced by heavy ion irradiation. MTJs were exposed to 3 MeV Ta 2+ ions at different levels of ion beam fluence spanning five orders of magnitude. The devices remained insensitive to beam fluences up to 10 11 ions/cm 2 , beyond which a gradual degradation in the device magnetoresistance, coercive magnetic field, and spin-transfer-torque switching voltage were observed, ending with a complete loss of magnetoresistance at very high levels of displacement damage (>0.035 displacements per atom). The loss of magnetoresistance is attributed to structural damage at the MgO interfaces, which allows electrons to scatter among the propagating modes within the tunnel barrier and reduces the net spin polarization. Ion-induced damage to the interface also reduces the perpendicular magnetic anisotropy. This study clarifies the displacement damage thresholds that lead to significant irreversible changes in the characteristics of spin-transfer-torque magnetic random access memory (STT-MRAM) and elucidates the physical mechanisms underlying the deterioration in device properties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Deterministic Current–Induced Perpendicular Switching in Epitaxial Co/Pt Layers without an External Field

Current–induced spin–orbit torques (SOTs) have emerged as a powerful tool to control magnetic elements and non–uniform magnetic textures such as domain walls and skyrmions. SOT–induced switching of perpendicular magnetization generally requires an external field to break the rotational symmetry of the spin–orbit effective fields responsible for the deterministic reversal. The proposed mechanisms to eliminate this requirement often rely on complex multilayer structures that necessitate laborious optimization in the material and spin transport properties, making them less attractive for applications. Herein, current–induced, external field–free switching of an epitaxial MgO/Pt/Co trilayer with an extremely large perpendicular anisotropy in excess of 3 Tesla is reported. It is found that switching occurs due to the interplay of strong SOTs, local anisotropy fluctuations, and the Dzyaloshinkii–Moriya interaction inherent to this epitaxial system. Finally, given that these layers constitute the base stack of a magnetic tunnel junction, this switching mechanism offers the most technologically viable path toward devices such as field–free SOT–based magnetic random–access memories.

36 MATERIALS SCIENCE↗
DOE OSTI2026

SCF Framework, HF Stability, and RPA Correlation for Jordan–Wigner-Transformed Spin Hamiltonians on Arbitrary Coupling Topologies

Mapping spins to fermions via the Jordan–Wigner (JW) transformation can render mean-field (Hartree–Fock, HF) descriptions effective for strongly correlated spin systems. As established in recent work, the application of such approaches is not limited by the nonlocal structure of JW strings or by site ordering because string operators can be absorbed into Thouless rotations of a Slater determinant, and the variational optimization of a unitary Lie-algebraic similarity transformation removes any ordering dependence. Leveraging these ideas, we develop a self-consistent field (SCF) scheme that expresses the mean-field energy as a functional of the single-particle density matrix, providing an alternative to gradient-based optimization of Thouless parameters. We derive the analytical orbital Hessian to diagnose HF stability and compute the ground-state correlation energy through the random-phase approximation (RPA). Benchmark results for the XXZ and J 1 –J 2 model on one- and two-dimensional lattices demonstrate that RPA significantly improves mean-field accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

NF-κB perturbation reveals unique immunomodulatory functions in Prx1 + fibroblasts that promote development of atopic dermatitis

Skin is composed of diverse cell populations that cooperatively maintain homeostasis. Up-regulation of the nuclear factor κB (NF-κB) pathway may lead to the development of chronic inflammatory disorders of the skin, but its role during the early events remains unclear. Here, through analysis of single-cell RNA sequencing data via iterative random forest leave one out prediction, an explainable artificial intelligence method, we identified an immunoregulatory role for a unique paired related homeobox-1 (Prx1) + fibroblast subpopulation. Disruption of Ikkb–NF-κB under homeostatic conditions in these fibroblasts paradoxically induced skin inflammation due to the overexpression of C-C motif chemokine ligand 11 (CCL11; or eotaxin-1) characterized by eosinophil infiltration and a subsequent T H 2 immune response. Because the inflammatory phenotype resembled that seen in human atopic dermatitis (AD), we examined human AD skin samples and found that human AD fibroblasts also overexpressed CCL11 and that perturbation of Ikkb–NF-κB in primary human dermal fibroblasts up-regulated CCL11. Monoclonal antibody treatment against CCL11 was effective in reducing the eosinophilia and T H 2 inflammation in a mouse model. Together, the murine model and human AD specimens point to dysregulated Prx1 + fibroblasts as a previously unrecognized etiologic factor that may contribute to the pathogenesis of AD and suggest that targeting CCL11 may be a way to treat AD-like skin lesions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Translation and rotation equivariant normalizing flow (TRENF) for optimal cosmological analysis

ABSTRACT Our Universe is homogeneous and isotropic, and its perturbations obey translation and rotation symmetry. In this work, we develop translation and rotation equivariant normalizing flow (TRENF), a generative normalizing flow (NF) model which explicitly incorporates these symmetries, defining the data likelihood via a sequence of Fourier space-based convolutions and pixel-wise non-linear transforms. TRENF gives direct access to the high dimensional data likelihood p(x|y) as a function of the labels y, such as cosmological parameters. In contrast to traditional analyses based on summary statistics, the NF approach has no loss of information since it preserves the full dimensionality of the data. On Gaussian random fields, the TRENF likelihood agrees well with the analytical expression and saturates the Fisher information content in the labels y. On non-linear cosmological overdensity fields from N-body simulations, TRENF leads to significant improvements in constraining power over the standard power spectrum summary statistic. TRENF is also a generative model of the data, and we show that TRENF samples agree well with the N-body simulations it trained on, and that the inverse mapping of the data agrees well with a Gaussian white noise both visually and on various summary statistics: when this is perfectly achieved the resulting p(x|y) likelihood analysis becomes optimal. Finally, we develop a generalization of this model that can handle effects that break the symmetry of the data, such as the survey mask, which enables likelihood analysis on data without periodic boundaries.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Stochastic quantum Krylov protocol with double-factorized Hamiltonians

Here we propose a class of randomized quantum Krylov diagonalization (rQKD) algorithms capable of solving the eigenstate estimation problem with modest quantum resource requirements. Compared to previous real-time evolution quantum Krylov subspace methods, our approach expresses the time evolution operator e –i$\widehat{H}$$\tau$ as a linear combination of unitaries and subsequently uses a stochastic sampling procedure to reduce circuit depth requirements. While our methodology applies to any Hamiltonian with fast-forwardable subcomponents, we focus on its application to the explicitly double-factorized electronic-structure Hamiltonian. To demonstrate the potential of the proposed rQKD algorithm on near-term quantum devices, we provide numerical benchmarks for a variety of molecular systems with circuit-based state-vector simulators including the effects of sampling noise, achieving ground-state energy errors of less than 1 kcal mol -1 with circuit depths orders of magnitude shallower than those required for low-rank deterministic Trotter-Suzuki decompositions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Machine-learning-aided cognitive reconfiguration for flexible-bandwidth HPC and data center networks [Invited]

This paper proposes a machine-learning (ML)-aided cognitive approach for effective bandwidth reconfiguration in optically interconnected datacenter/high-performance computing (HPC) systems. The proposed approach relies on a Hyper-X-like architecture augmented with flexible-bandwidth photonic interconnections at large scales using a hierarchical intra/inter-POD photonic switching layout. We first formulate the problem of the connectivity graph and routing scheme optimization as a mixed-integer linear programming model. A two-phase heuristic algorithm and a joint optimization approach are devised to solve the problem with low time complexity. Then, we propose an ML-based end-to-end performance estimator design to assist the network control plane with intelligent decision making for bandwidth reconfiguration. Numerical simulations using traffic distribution profiles extracted from HPC applications traces as well as random traffic matrices verify the accuracy performance of the ML design estimator ( < <#comment/> 9 % <#comment/> error) and demonstrate up to 5 × <#comment/> throughput gain from the proposed approach compared with the baseline Hyper-X network using fixed all-to-all intra/inter-portable data center interconnects.

Chen, Xiaoliang (ORCID:0000000278056237)↗
DOE OSTI2021

Quantifying the Influence of Charge Rate and Cathode-Particle Architectures on Degradation of Li-Ion Cells Through 3D Continuum-Level Damage Models

In this article, we develop a 3D, continuum-level damage model implemented on statistically generated LiNi 0.5 Mn 0.3 Co 0.2 O (NMC 532) secondary cathode particles. The primary motivation of the particle-level model is to inform cathode-particle design through detailed exploration of the influence of secondary and primary particle sizes on the damage predicted during operation, and determine charging profiles that reduce cathode fracture. The model considers NMC 532 secondary particles containing an agglomeration of anisotropic, randomly oriented grains. These brittle, Ni-based cathodes are prone to mechanical degradation, which reduces overall battery cycle life. The model predicts that secondary-particle fracture is primarily due to non-ideal grain interactions and high-rate charge demands. The model predicts that small secondary-particles with large grains develop significantly less damage than larger secondary particles with small grains. The model predicts most of the chemo-mechanical damage accumulates in the first few cycles. The chemo-mechanical model predicts monotonically increasing capacity fade with cycling and rate. Comparing to experimental results, the model is well suited for capturing initial capacity fade mechanisms, but additional physics is required to capture long-term capacity fade effects.

25 ENERGY STORAGE↗
DOE OSTI2021

Modeling Coupled Chemo-Mechanical Behavior of Randomly Oriented NMC811 Polycrystalline Li-Ion Battery Cathodes

This paper develops a three-dimensional, transient, chemo-mechanical model that predicts the performance of single secondary particle Li-ion battery cathodes. The secondary particles are composed of numerous (approximately 60) randomly oriented single-crystal primary particles. The model incorporates concentration-dependent and anisotropic material properties. As much as possible, electrochemical, transport, and structural properties for crystalline NMC811 (Li x Ni 0.8 Mn 0.1 Co 0.1 O 2 ) are taken from prior publications. Weak Van der Waals bonding between primary particles is modeled empirically using a spring analogy, which enables local primary-particle separations (disintegration) and subsequent reattachments. The model fully couples Li diffusion and the mechanical response. Results include predictions of local Li-concentrations and stresses. High stresses are found near grain boundaries, especially when the lattice orientations are greatly misaligned. Particle separations are characterized in terms of a damage parameter. The model is used to predict the effects of design and operating conditions, including charge/discharge rates, cycling scenarios, and particle sizes.

25 ENERGY STORAGE↗
DOE OSTI2022

Holographic measurement and bulk teleportation

Holography has taught us that spacetime is emergent and its properties depend on the entanglement structure of the dual theory. In this paper, we describe how changes in the entanglement due to a local projective measurement (LPM) on a subregion A of the boundary theory modify the bulk dual spacetime. We find that LPMs destroy portions of the bulk geometry, yielding post-measurement bulk spacetimes dual to the complementary unmeasured region A c that are cut off by end-of-the-world branes. Using a bulk calculation in AdS 3 and tensor network models of holography (in particular, the HaPPY code and random tensor networks), we show that the portions of the bulk geometry that are preserved after the measurement depend on the size of A and the state we project onto. The post-measurement bulk dual to A c includes regions that were originally part of the entanglement wedge of A prior to measurement. This suggests that LPMs performed on a boundary subregion A teleport part of the bulk information originally encoded in A into the complementary region A c . In semiclassical holography an arbitrary amount of bulk information can be teleported in this way, while in tensor network models the teleported information is upper-bounded by the amount of entanglement shared between A and A c due to finite-N effects. When A is the union of two disjoint subregions, the measurement triggers an entangled/disentangled phase transition between the remaining two unmeasured subregions, corresponding to a connected/disconnected phase transition in the bulk description. Our results shed new light on the effects of measurement on the entanglement structure of holographic theories and give insight on how bulk information can be manipulated from the boundary theory. They could also represent a first step towards a holographic description of measurement-induced phase transitions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

One of the open problems in scientific computing is the long-time integration of nonlinear stochastic partial differential equations (SPDEs), especially with arbitrary initial data. We address this problem by taking advantage of recent advances in scientific machine learning and the spectral dynamically orthogonal (DO) and borthogonal (BO) methods for representing stochastic processes. The recently introduced DO/BO methods reduce the SPDE to solving a system of deterministic PDEs and a system of stochastic ordinary differential equations. Specifically, we propose two new physics-informed neural networks (PINNs) for solving time-dependent SPDEs, namely the neural network (NN)-DO/BO methods. The proposed methods incorporate the DO/BO constraints into the loss function (along with the modal decomposition of the SPDE) with an implicit form instead of generating explicit expressions for the temporal derivatives of the DO/BO modes. Hence, the NN-DO/BO methods can overcome some of the drawbacks of the original DO/BO methods. For example, we do not need the assumption that the covariance matrix of the random coefficients is invertible as in the original DO method, and we can remove the assumption of no eigenvalue crossing as in the original BO method. Moreover, the NN-DO/BO methods can be used to solve time-dependent stochastic inverse problems with the same formulation and same computational complexity as for forward problems. Furthermore, we demonstrate the capability of the proposed methods via several numerical examples, namely: (1) A linear stochastic advection equation with deterministic initial condition: we obtain good results with the proposed methods, while the original DO/BO methods cannot be applied directly in this case. (2) Long-time integration of the stochastic Burgers' equation: we show the good performance of NN-DO/BO methods, especially the effectiveness of the NN-BO approach for such problems with many eigenvalue crossings during the whole time evolution, while the original BO method fails. (3) Nonlinear reaction diffusion equation: we consider both the forward problem and the inverse problems, including very noisy initial point values, to investigate the flexibility of the NN-DO/BO methods in handling inverse and mixed type problems. Taken together, these simulation results demonstrate that the NN-DO/BO methods can be employed to effectively quantify uncertainty propagation in a wide range of physical problems, but future work should address the efficiency issue of PINNs for forward problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Prospects for Antiferromagnetic Spintronic Devices

This article examines recent advances in the field of antiferromagnetic spintronics from the perspective of potential device realization and applications. We discuss advances in the electrical control of antiferromagnetic order by current-induced spin–orbit torques, particularly in antiferromagnetic thin films interfaced with heavy metals. We also review possible scenarios for using voltage-controlled magnetic anisotropy as a more efficient mechanism to control antiferromagnetic order in thin films with perpendicular magnetic anisotropy. Next, we discuss the problem of electrical detection (i.e., readout) of antiferromagnetic order and highlight recent experimental advances in realizing anomalous Hall and tunneling magnetoresistance effects in thin films and tunnel junctions, respectively, which are based on noncollinear antiferromagnets. Understanding the domain structure and dynamics of antiferromagnetic materials is essential for engineering their properties for applications. For this reason, we then provide an overview of imaging techniques as well as micromagnetic simulation approaches for antiferromagnets. Finally, we present a perspective on potential applications of antiferromagnets for magnetic memory devices, terahertz sources, and detectors.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Methods for the robust computation of the long-period seismic spectrum of broad-band arrays

SUMMARY We describe array methods to search for low signal-to-noise ratio (SNR) signals in long-period seismic data using Fourier analysis. This is motivated by published results that find evidence of solar free oscillations in the Earth's seismic hum. Previous work used data from only one station. In this paper, we describe methods for computing spectra from array data. Arrays reduce noise level through averaging and provide redundancy that we use to distinguish coherent signal from a random background. We describe two algorithms for calculating a robust spectrum from seismic arrays, an algorithm that automatically removes impulsive transient signals from data, a jackknife method for estimating the variance of the spectrum, and a method for assessing the significance of an entire spectral band. We show examples of their application to data recorded by the Homestake Mine 3-D array in Lead, SD and the Piñon Flats PY array. These are two of the quietest small aperture arrays ever deployed in North America. The underground Homestake data has exceptionally low noise, and the borehole sensors of the PY array also have very low noise, making these arrays well suited to finding very weak signals. We find that our methods remove transient signals effectively from the data so that even low-SNR signals in the seismic background can be found and tested. Additionally, we find that the jackknife variance estimate is comparable to the noise floor, and we present initial evidence for solar g-modes in our data through the T2 test, a multivariate generalization of Student's t-test.

Caton, Ross C.↗
DOE OSTI2020

Complex Disorder in Type-I Clathrates: Synthesis and Structural Characterization of A8GaxSn46−x (A = Rb, Cs; 6.9 < x < 7.5)

Exploratory studies in the systems Rb–Ga–Sn and Cs–Ga–Sn yielded the cubic type-I clathrates with refined compositions Rb8GaxSn46−x and Cs8GaxSn46−x (6.9 < x < 7.5). Nearly single-phase materials with good crystallinity were obtained from stoichiometric reactions of the elements. The structures were characterized by means of single-crystal X-ray diffraction methods. Both Rb8GaxSn46−x and Cs8GaxSn46−x represents cases, where a Group 13 element randomly substitutes a Group 14 element in the structure. The extent of Ga/Sn mixing is apparently governed by the drive of the system to achieve an optimal valence electron count, and hence, Rb8GaxSn46−x and Cs8GaxSn46−x (x ≈ 8) can be regarded as Zintl phases. This notion is supported by structure refinements on a multitude of single-crystal X-ray diffraction data, which also confirm that both types of cages in the cubic type-I structure are fully occupied by Rb and Cs atoms. The open-framework, comprised of 46 nodes per formula unit, adapts to the incorporation of nearly eight Ga atoms within the matrix of Sn, whereby small, short-range distortions result. The exact nature of these effects is still unclear, as so far, the structural variations could only be modeled as both positional and occupational disorder at one of three framework sites. Since vacancies in the structures of the binary type-I clathrates A8Sn46−x☐x (A = Rb, Cs; ☐ = missing Sn atom) are also known to cause local distortions, the latter were also synthesized with the same protocols used for the synthesis of A8GaxSn46−x and structurally re-analyzed. The results from the latter studies confirm that homogeneity issues abound, and that the final structures/compositions are an intricate function of the experimental conditions.

36 MATERIALS SCIENCE↗