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DOE OSTI2015

Beta (β) tungsten thin films: Structure, electron transport, and giant spin Hall effect

We use a simple magnetron sputtering process to fabricate beta (β) tungsten thin films, which are capable of generating giant spin Hall effect. As-deposited thin films are always in the metastable β-W phase from 3.0 to 26.7 nm. The β-W phase remains intact below a critical thickness of 22.1 nm even after magnetic thermal annealing at 280 °C, which is required to induce perpendicular magnetic anisotropy (PMA) in a layered structure of β-W/Co{sub 40}Fe{sub 40}B{sub 20}/MgO. Intensive annealing transforms the thicker films (>22.1 nm) into the stable α-W phase. We analyze the structure and grain size of both β- and α-W thin films. Electron transport in terms of resistivity and normal Hall effect is studied over a broad temperature range of 10 K to at least 300 K on all samples. Very low switching current densities are achieved in β-W/Co{sub 40}Fe{sub 40}B{sub 20}/MgO with PMA. These basic properties reveal useful behaviors in β-W thin films, making them technologically promising for spintronic magnetic random access memories and spin-logic devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗

DOE OSTI2025

Nanometer-thick ultraflat cantilever resonators

Nanomechanical devices made from ultrathin materials are transforming diverse fields, including sensing, signal processing, and quantum technologies. However, as these materials become thinner, their low bending rigidity poses significant fabrication challenges, and achieving nanometer-thick flat cantilevers with consistent and predictable mechanical responses has remained elusive despite decades of research. Here we present nanometer-thick, ultraflat cantilever resonators fabricated using atomic layer deposition. By effectively mitigating the effects of uncontrollable built-in strain and geometric disorder, the ultraflat nanocantilevers exhibit resonance frequencies closely aligned with thin-plate theory predictions and display low sample-to-sample variability. These cantilevers maintain mechanical stability in both vacuum and air environments, even at large length-to-thickness ratios of up to 3000. The ultraflat nanocantilevers are approaching the thickness limit, beyond which thermal fluctuations at room temperature can spontaneously induce random ripples in otherwise flat films.

nanofabrication↗

DOE OSTI2018

Improved Performance of Asymptotically Optimal Rapidly Exploring Random Trees

Three algorithms that improve the performance of the asymptotically optimal Rapidly exploring Random Tree (RRT*) are presented here. First, we introduce the Goal Tree (GT) algorithm for motion planning in dynamic environments where unexpected obstacles appear sporadically. The GT reuses the previous RRT* by pruning the affected area and then extending the tree by drawing samples from a shadow set. The shadow is the subset of the free configuration space containing all configurations that have geodesics ending at the goal and are in conflict with the new obstacle. Smaller, well defined, sampling regions are considered for Euclidean metric spaces and Dubins' vehicles. Next, the Focused-Refinement (FR) algorithm, which samples with some probability around the first path found by an RRT*, is defined. The third improvement is the Grandparent-Connection (GP) algorithm, which attempts to connect an added vertex directly to its grandparent vertex instead of parent. The GT and GP algorithms are both proven to be asymptotically optimal. Finally, the three algorithms are simulated and compared for a Euclidean metric robot, a Dubins' vehicle, and a seven degrees-of-freedom manipulator.

42 ENGINEERING↗

DOE OSTI2016

SpokeDarts

SpokeDarts software produces sample distributions with blue noise characteristics over arbitrary-dimensional spaces. More generally, spoke darts enable efficient searches and tailored distributions over high dimensional spaces. The software includes underlying geometric routines which are useful in their own right, such as efficient neighbor searches, random points on a sphere, and line-sphere intersections.

Mitchell, ScottA.↗

DOE OSTI2026

Machine learning identifies novel signatures of antifungal drug resistance in Saccharomycotina yeasts

Antifungal drug resistance is a major challenge in fungal infection management. Numerous genomic changes are known to contribute to acquired drug resistance in clinical isolates of specific pathogens, but whether they broadly explain natural resistance across entire lineages is unknown. We leveraged genomic, ecological, and phenotypic trait data from naturally sampled strains from nearly all known species in subphylum Saccharomycotina to examine the evolution of resistance to eight antifungal drugs. The phylogenetic distribution of drug resistance varied by drug; fluconazole resistance was widespread, while 5-fluorocytosine resistance was rare, except in Lipomycetales. A random forest algorithm trained on genomic data predicted drug-resistant yeasts with 54–75% accuracy. Fluconazole resistance was consistently predicted with the highest accuracy (75.2%). Furthermore, fluconazole resistance prediction accuracy was similar between models trained on genome-wide variation in the presence and number of InterPro protein annotations across Saccharomycotina (75.2%) and those trained on amino acid sequence alignment data of Erg11, a protein known to be involved in fluconazole resistance (74.3-74.9%). Interestingly, the top Erg11 residues for predicting fluconazole resistance across Saccharomycotina do not overlap with, are not spatially close to, and are less conserved than those previously linked to resistance in clinical isolates of Candida albicans. In silico deep mutational scanning of the C. albicans Erg11 protein reveals that amino acid variants implicated in clinical cases of resistance are almost universally destabilizing while variants in our most informative residues are energetically more neutral, explaining why the latter are much more common than the former in natural populations. Importantly, previous experimental analyses of C. albicans Erg11 have shown that amino acid variation in our most informative residues, despite having never been directly implicated in clinical cases, can directly contribute to resistance. Our results suggest that studies of natural resistance in yeast species never encountered in the clinic will yield a fuller understanding of antifungal drug resistance.

Harrison, Marie-Claire [Vanderbilt Univ., Nashvill↗

DOE OSTI2025

Covariance operator estimation: Sparsity, lengthscale, and ensemble Kalman filters

This paper investigates covariance operator estimation via thresholding. For Gaussian random fields with approximately sparse covariance operators, we establish non-asymptotic bounds on the estimation error in terms of the sparsity level of the covariance and the expected supremum of the field. We prove that thresholded estimators enjoy an exponential improvement in sample complexity compared with the standard sample covariance estimator if the field has a small correlation lengthscale. As an application of the theory, we study thresholded estimation of covariance operators within ensemble Kalman filters.

Covariance operator estimation↗

DOE OSTI2016

Radio-loud and radio-quiet QSOs

We discuss 6 GHz JVLA observations covering a volume-limited sample of 178 low-redshift (0.2 5 mJy beam{sup −1} (log(L)≳24). The radio luminosity function of optically selected QSOs and the extended radio emission associated with RLQs are both inconsistent with simple “unified” models that invoke relativistic beaming from randomly oriented QSOs to explain the difference between RLQs and RQQs. Some intrinsic property of the AGNs or their host galaxies must also determine whether or not a QSO appears radio-loud.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

GRB 221009A: Discovery of an Exceptionally Rare Nearby and Energetic Gamma-Ray Burst

We report the discovery of the unusually bright long-duration gamma-ray burst (GRB), GRB 221009A, as observed by the Neil Gehrels Swift Observatory (Swift), Monitor of All-sky X-ray Image, and Neutron Star Interior Composition Explorer Mission. This energetic GRB was located relatively nearby (z = 0.151), allowing for sustained observations of the afterglow. The large X-ray luminosity and low Galactic latitude (b = 4$^°_.$3) make GRB 221009A a powerful probe of dust in the Milky Way. Using echo tomography, we map the line-of-sight dust distribution and find evidence for significant column densities at large distances (≳10 kpc). We present analysis of the light curves and spectra at X-ray and UV–optical wavelengths, and find that the X-ray afterglow of GRB 221009A is more than an order of magnitude brighter at T 0 + 4.5 ks than that from any previous GRB observed by Swift. In its rest frame, GRB 221009A is at the high end of the afterglow luminosity distribution, but not uniquely so. In a simulation of randomly generated bursts, only 1 in 10 4 long GRBs were as energetic as GRB 221009A; such a large E γ,iso implies a narrow jet structure, but the afterglow light curve is inconsistent with simple top-hat jet models. Using the sample of Swift GRBs with redshifts, we estimate that GRBs as energetic and nearby as GRB 221009A occur at a rate of ≲1 per 1000 yr—making this a truly remarkable opportunity unlikely to be repeated in our lifetime.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1982

Designing with figer-reinforced plastics (planar random composites)

The use of composite mechanics to predict the hygrothermomechanical behavior of planar random composites (PRC) is reviewed and described. These composites are usually made from chopped fiber reinforced resins (thermoplastics or thermosets). The hygrothermomechanical behavior includes mechanical properties, physical properties, thermal properties, fracture toughness, creep and creep rupture. Properties are presented in graphical form with sample calculations to illustrate their use. Concepts such as directional reinforcement and strip hybrids are described. Typical data that can be used for preliminary design for various PRCs are included. Several resins and molding compounds used to make PRCs are described briefly. Pertinent references are cited that cover analysis and design methods, materials, data, fabrication procedures and applications.

Chamis, C. C.↗

NASA NTRS2017

Magnetic Flux Cancellation as the Trigger Mechanism of Solar Coronal Jets

Coronal jets are narrow eruptions in the solar corona, and are often observed in extreme ultraviolet (EUV) and X-Ray images. They occur everywhere on the solar disk: in active regions, quiet regions, and coronal holes (Raouafi et al. 2016). Recent studies indicate that most coronal jets in quiet regions and coronal holes are driven by the eruption of a minifilament (Sterling et al. 2015), and that this eruption follows flux cancellation at the magnetic neutral line under the pre-eruption minifilament (Panesar et al. 2016). We confirm this picture for a large sample of jets in quiet regions and coronal holes using multithermal extreme ultraviolet (EUV) images from the Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) and line-of-sight magnetograms from the SDO/Helioseismic and Magnetic Imager (HMI). We report observations of 60 randomly selected jet eruptions. We have analyzed the magnetic cause of these eruptions and measured the base size and the duration of each jet using routines in SolarSoft IDL. By examining the evolutionary changes in the magnetic field before, during, and after jet eruption, we found that each of these jets resulted from minifilament eruption triggered by flux cancellation at the neutral line. In agreement with the above studies, we found our jets to have an average base diameter of 7600 +/- 2700 km and an average jet-growth duration of 9.0 +/- 3.6 minutes. These observations confirm that minifilament eruption is the driver and that magnetic flux cancellation is the primary trigger mechanism for nearly all coronal hole and quiet region coronal jet eruptions.

McGlasson, Riley A.↗

NASA NTRSDate not supplied

Biological Insights at the Interface of Multiple Arabidopsis Legacy Datasets

The NASA GeneLab database includes an open-access collection of datasets yielded by space biology experiments. Six Gene Lab Data Sets (GLDS’s) performed in Arabidopsis were selected for analysis (7/17/44/121/205/213), all of which included transcriptome data from spaceflight and ground control environments. Hardware, ecotype, environmental conditions, and other experimental conditions varied, allowing the observations of overarching gene expression impacts of microgravity on plant life without focusing on effects of specific experimental conditions. Using GeneLab pre-processed datasets as the basis for the study, RNA microarray data were analyzed to identify genes that showed altered expression in microgravity when compared to control samples for each individual GLDS. All differentially expressed genes were compared to locate differentially expressed genes common between spaceflight experiments. The most noteworthy result is that not one gene shared differential expression among the six GLDS’s. However, gene expression was not influenced randomly by the microgravity environment, as there were several gene ontology terms that were significantly enriched across all experiments. These included 20 significantly enriched biological processes, and although the genes which enriched each term varied, there were many cases of specific genes common to clusters of multiple GLDS’s. Gene expression such as NAC92 and ERF011 or membrane structural element FFP6 provide insight and direction toward understanding the plant response to spaceflight. Characterizing these common processes and the shared differentially expressed genes has demonstrated potential targets for further study to understand and modulate the biological response of plants in microgravity. Life on Earth has never been subjected to the absence of gravity as a selective pressure, so observing how life forms react to a microgravity environment could provide insight to our shared fundamental biological processes. It is also feasible that the genetic modification of specific genes linked to the microgravity response could improve health and yield of space crops.

Joseph Emhof↗

DOE OSTI2016

SpokeDarts

Mitchell, Scott A.↗

NASA NTRS2014

Revised (Mixed-Effects) Estimation for Forest Burning Emissions of Gases and Smoke, Fire/Emission Factor Typology, and Potential Remote Sensing Classification of Types for Ozone and Black-Carbon Simulation

We summarize recent progress (a) in correcting biomass burning emissions factors deduced from airborne sampling of forest fire plumes, (b) in understanding the variability in reactivity of the fresh plumes sampled in ARCTAS (2008), DC3 (2012), and SEAC4RS (2013) airborne missions, and (c) in a consequent search for remotely sensed quantities that help classify forest-fire plumes. Particle properties, chemical speciation, and smoke radiative properties are related and mutually informative, as pictures below suggest (slopes of lines of same color are similar). (a) Mixed-effects (random-effects) statistical modeling provides estimates of both emission factors and a reasonable description of carbon-burned simultaneously. Different fire plumes will have very different contributions to volatile organic carbon reactivity; this may help explain differences of free NOx(both gas- and particle-phase), and also of ozone production, that have been noted for forest-fire plumes in California. Our evaluations check or correct emission factors based on sequential measurements (e.g., the Normalized Ratio Enhancement and similar methods). We stress the dangers of methods relying on emission-ratios to CO. (b) This work confirms and extends many reports of great situational variability in emissions factors. VOCs vary in OH reactivity and NOx-binding. Reasons for variability are not only fuel composition, fuel condition, etc., but are confused somewhat by rapid transformation and mixing of emissions. We use "unmixing" (distinct from mixed-effects) statistics and compare briefly to approaches like neural nets. We focus on one particularly intense fire the notorious Yosemite Rim Fire of 2013. In some samples, NOx activity was not so suppressed by binding into nitrates as in other fires. While our fire-typing is evolving and subject to debate, the carbon-burned delta(CO2+CO) estimates that arise from mixed effects models, free of confusion by background-CO2 variation, should provide a solid base for discussion. (c) We report progress using promising links we find between emissions-related "fire types" and promising features deducible from remote observations of plumes, e.g., single scatter albedo, Angstrom exponent of scattering, Angstrom exponent of absorption, (CO column density)/(aerosol optical depth).

Remote sensing↗

DOE OSTI2017

Deformation and damage of sintered low-porosity aluminum under planar impact: microstructures and mechanisms

In this paper, plate impact experiments are conducted to study compaction and spallation of 5% porosity aluminum. Free surface velocity histories, the Hugoniot elastic limit (HEL), and spall strengths are obtained at different peak stresses and pulse durations. Scanning electron microscopy, electron backscatter diffraction, and X-ray computed tomography are used to characterize 2D and 3D microstructures. 3D void topology analyses yield rich information on size distribution, shape, orientation, and connectivity of voids. HEL decreases/increases with sample thickness/impact velocity and approaches saturation. Its tensile strength increases with increasing peak stress and shock-induced densification. With the enhanced compaction under increasing impact velocities, spall damage modes change from growth of original voids to inter-particle crack propagation and to “random” nucleation of new voids. Such a change in damage mechanism also gives rise to a distinct decrease in damage extent at high impact velocities. Finally, compaction induces strain localizations around the original voids, while subsequent tension results in grain refinement, and shear deformation zones between staggered cracks.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Circ-UBR5: An exonic circular RNA and novel small nuclear RNA involved in RNA splicing

Highlights: • Low expression of circ-UBR5 in NSCLC was significantly related with tumor poor differentiation. • A highly efficient strategy for finding specific circRNA binding proteins using HuprotTM Protoarray was first be presented. • Circ-UBR5 could bind splicing regulatory factors QKI, and NOVA1 and U1 snRNA in the nucleus. • Our research first revealed the potential function of a non-functional phenotype circRNA in NSCLC cells. Circular RNAs (circRNAs) are class of non-coding RNAs formed by back-splicing events as loops, and could be found in all types of organisms. They play important and diverse roles in cell development, growth, and tumorigenesis, but functions of the majority of circRNAs remain enigmatic. Particularly functional phenotypes of great majority of circRNAs are not obvious. Here we randomly selected a circRNA circ-UBR5, which has no obvious functional phenotype in non-small cell lung cancer (NSCLC) cells from our previous research findings, to explore its potential function in cells. Differential expression of circ-UBR5 was detected in paired samples of tumorous tissues and adjacent nontumorous tissues from 59 patients with NSCLC by real-time quantitative reverse transcription-polymerase chain reactions (qRT-PCRs). Results showed circ-UBR5 expression was significantly downregulated in NSCLC tissues (p (p = 0.00126), suggesting circ-UBR5 might serve as an index of NSCLC differentiation. Our findings indicated circ-UBR5 could bind splicing regulatory factor QKI, KH domain containing RNA binding (QKI) and NOVA alternative splicing regulator 1 (NOVA1) and U1 small nuclear RNA (snRNA) in the nucleus, revealing circ-UBR5 might be a novel snRNA involved in RNA splicing regulatory process. Moreover, we first presented a highly efficient strategy for finding specific circRNA binding proteins using Human Protein Microarray (Huprot™ Protoarray).

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Deep melting reveals liquid structural memory and anomalous ferromagnetism in bismuth

As an archetypal semimetal with complex and anisotropic Fermi surface and unusual electric properties (e.g., high electrical resistance, large magnetoresistance, and giant Hall effect), bismuth (Bi) has played a critical role in metal physics. In general, Bi displays diamagnetism with a high volumetric susceptibility (~10 -4 ). Here in this paper, we report unusual ferromagnetism in bulk Bi samples recovered from a molten state at pressures of 1.4–2.5 GPa and temperatures above ~1,250 K. The ferromagnetism is associated with a surprising structural memory effect in the molten state. On heating, low-temperature Bi liquid (L) transforms to a more randomly disordered high-temperature liquid (L') around 1,250 K. By cooling from above 1,250 K, certain structural characteristics of liquid L' are preserved in L. Bi clusters with characteristics of the liquid L' motifs are further preserved through solidification into the Bi-II phase across the pressure-independent melting curve, which may be responsible for the observed ferromagnetism.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Entropy and its Relationship with Statistics

The purpose of our report is to discuss the notion of entropy and its relationship with statistics. Our goal is to provide a manner in which you can think about entropy, its central role within information theory and relationship with statistics. We review various relationships between information theory and statistics—nearly all are well-known but unfortunately are often not recognized. Entropy quantities the "average amount of surprise" in a random variable and lies at the heart of information theory, which studies the transmission, processing, extraction, and utilization of information. For us, data is information. What is the distinction between information theory and statistics? Information theorists work with probability distributions. Instead, statisticians work with samples. In so many words, information theory using samples is the practice of statistics.

97 MATHEMATICS AND COMPUTING↗