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DOE OSTI2024

Collective neural network behavior in a dynamically driven disordered system of superconducting loops

Collective properties of complex systems composed of many interacting components such as neurons in our brain can be modeled by artificial networks based on disordered systems. We show that a disordered neural network of superconducting loops with Josephson junctions can exhibit computational properties like categorization and associative memory in the time evolution of its state in response to information from external excitations. Superconducting loops can trap multiples of fluxons in many discrete memory configurations defined by the local free energy minima in the configuration space of all possible states. A memory state can be updated by exciting the Josephson junctions to fire or allow the movement of fluxons through the network as the current through them surpasses their critical current thresholds. Simulations performed with a lumped element circuit model of a 4-loop network show that information written through excitations is translated into stable states of trapped flux and their time evolution. Experimental implementation on a high-Tc superconductor YBCO-based 4-loop network shows dynamically stable flux flow in each pathway characterized by the correlations between junction firing statistics. Neural network behavior is observed as energy barriers separating state categories in simulations in response to multiple excitations, and experimentally as junction responses characterizing different flux flow patterns in the network. The state categories that produce these patterns have different temporal stabilities relative to each other and the excitations. This provides strong evidence for time-dependent (short-to-long-term) memories, that are dependent on the geometrical and junction parameters of the loops, as described with a network model.

Josephson junctions↗

DOE OSTI2019

On the occurrence of fast neutrino flavor conversions in multidimensional supernova models

The dense neutrino medium in a core-collapse supernova or a neutron-star merger event can experience fast flavor conversions on time/distance scales that are much smaller than those of vacuum oscillations. It is believed that fast neutrino flavor transformation occurs in the region where the angular distributions of ν e and v¯ e cross each other. We present the first study of this crossing phenomenon and the fast neutrino flavor conversions in multidimensional (multi-D) supernova models. We examine the neutrino distributions obtained by solving the Boltzmann transport equation for several fixed profiles which are representative snapshots taken from separate 2D and 3D supernova simulations with an 11.2 M ⊙ progenitor model. Our research shows that the spherically asymmetric patterns of the ν e and v¯ e fluxes in multi-D models can assist the appearance of the crossing between the ν e and v¯ e angular distributions. In the models that we have studied, there exist unstable neutrino oscillation modes in and beyond the neutrino decoupling region which have amplitude growth rates as large as an e-fold per nanosecond. Here, this finding can have important consequences for the explosion mechanism, nucleosynthesis, and neutrino signals of core-collapse supernovae.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Self-generated electrokinetic flows from active-charged boundary patterns

We develop a hydrodynamic description of self-generated electrolyte flow in capillaries whose bounding walls feature nonuniform distributions of charge nonuniform active ionic fluxes. The hydrodynamic velocity arising in such a system has components that are forbidden by symmetry in the absence of charge and fluxes. However, when these two boundary mechanisms are simultaneously present, they can lead to a symmetry broken state where steady flows with both unidirectional and circulatory components emerge. We show that these flow states arise when modulated boundary patterns of charge and fluxes are offset by a flux-charge phase difference, which is associated with the separation between sites of their peak densities on the wall. Mismatch in diffusivity of cationic and anionic species can modify the flow states and becomes an enhancing factor when fluxes of both ion species are being produced together at the same site. We demonstrate that this mechanism can be realized with a microfluidic generator that is powered by enzyme-coated patches that catalyze reactants in the solution to produce fluxes of ions. The local ionic elevation or depletion, which disrupts a nonuniform double layer, promotes self-induced gradients yielding persistent body forces to generate bulk fluid motion. Our work quantifies a boundary-driven mechanism behind self-sustained electrolyte flow in confined environments that exists without any external bulk-imposed fields or gradients. It provides a theoretical framework for understanding the combined effect of active and charged boundaries that are relevant in biological or soft matter systems, and can be utilized in electrofluidic and iontronic applications.

active matter↗

DOE OSTI2020

Hydrodynamics of countercurrent flows in a structured packed column: effects of initial wetting and dynamic contact angle

The structured packed column has gained considerable attention as a prospective technology to mitigate carbon dioxide emissions from thermal power plants. The computational flow investigation of such systems is a multiscale problem, and local hydrodynamics plays a key role in overall column efficiency. Accordingly, multiphase flow studies in the representative elementary unit (REU) of a packed column can offer insights into the local flow behavior such as flow pattern, interfacial area, etc. The interfacial area plays a key role in the overall interphase mass transfer and thereby in separation efficiency. The predicted effective area matches well with corresponding ones in experiments for Mellapak 250.Y packings. The contact angle (i.e., solid substrate behavior) is one of the critical factors that dictates wettings, and thereby the interfacial area. The dynamic contact angle (DCA) was considered in order to explore the effects of contact angle hysteresis on the interfacial area. DCA has more pronounced impact on the interfacial area for a solvent possessing a higher value of surface tension and equilibrium contact angle. The interfacial area shows temporal undulation and does not achieve a pseudo steady state due to the capillary force. In contrary, the interfacial area gets a net value for a solvent having low surface tension value. Further, specification of the static contact angle at the solid substrate shows the least value of the interfacial area. We also extensively studied the effect of the initial sheet condition (dry vs wet) on the interfacial area. The initially wetted sheets show slightly higher value of the interfacial area as compared to the initially dry sheets at a fixed flow rate for a solvent having high surface tension value. Wetting hysteresis decreases with increasing liquid loads. On the other hand, wetting hysteresis does not occur for a solvent having value of low surface tension and equilibrium contact angle.

structured packings, dynamic contact angle, interf↗

DOE OSTI2021

Quantum Light Emission from Coupled Defect States in DNA-Functionalized Carbon Nanotubes

Solid-state single-photon sources are essential building blocks for quantum photonics and quantum information technologies. This study demonstrates promising single-photon emission from quantum defects generated in single-wall carbon nanotubes (SWCNTs) by covalent reaction with guanine nucleotides in their single-stranded DNA coatings. Low-temperature photoluminescence spectroscopy and photon-correlation measurements on individual guanine-functionalized SWCNTs (GF-SWCNTs) indicate that multiple, closely spaced guanine defect sites within a single ssDNA strand collectively form an exciton trapping potential that supports a localized quantum state capable of room-temperature single-photon emission. In addition, exciton traps from adjacent ssDNA strands are weakly coupled to give cross-correlations between their separate photon emissions. Theoretical modeling identifies coupling mechanism as a capture of band-edge excitons. Finally, because the spatial pattern of nanotube functionalization sites can be readily controlled by selecting ssDNA base sequences, GF-SWCNTs should become a versatile family of quantum light emitters with engineered properties.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Data transmission by quantum matter wave modulation

Abstract Classical communication schemes exploiting wave modulation are the basis of our information era. Quantum information techniques with photons enable future secure data transfer in the dawn of decoding quantum computers. Here we demonstrate that also matter waves can be applied for secure data transfer. Our technique allows the transmission of a message by a quantum modulation of coherent electrons in a biprism interferometer. The data is encoded in the superposition state by a Wien filter introducing a longitudinal shift between separated matter wave packets. The transmission receiver is a delay line detector performing a dynamic contrast analysis of the fringe pattern. Our method relies on the Aharonov–Bohm effect but does not shift the phase. It is demonstrated that an eavesdropping attack will terminate the data transfer by disturbing the quantum state and introducing decoherence. Furthermore, we discuss the security limitations of the scheme due to the multi-particle aspect and propose the implementation of a key distribution protocol that can prevent active eavesdropping.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning

Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.

58 GEOSCIENCES↗

DOE OSTI2024

Entanglement of excitons by acoustic guiding

A system is described that exhibits the functionality of a beam-splitter, typically an optical device that splits a beam of light in two. In this case, the beams are acoustic pulses and can lead to the creation of a Wannier-Mott exciton: a bound state of an electron and an electron hole whose attraction to each other is maintained by the electrostatic Coulomb force. This exciton beam-splitter is lossy (i.e., involves the dissipation of electrical or electromagnetic energy). Half of the time the exciton is radiated away. Nevertheless, if the exciton is not lost, the exciton is now in a superposition of two states that can be well separated in position. Four such beam-splitters can be used to make an exciton interferometer that uses the interference patterns from the interacting acoustic pulses to extract information.

Yurke, Bernard↗

DOE OSTI2022

Computational approaches to model X-ray photon correlation spectroscopy from molecular dynamics

X-ray photon correlation spectroscopy (XPCS) allows for the resolution of dynamic processes within a material across a wide range of length and time scales. X-ray speckle visibility spectroscopy (XSVS) is a related method that uses a single diffraction pattern to probe ultrafast dynamics. Interpretation of the XPCS and XSVS data in terms of underlying physical processes is necessary to establish the connection between the macroscopic responses and the microstructural dynamics. To aid the interpretation of the XPCS and XSVS data, we present a computational framework to model these experiments by computing the X-ray scattering intensity directly from the atomic positions obtained from molecular dynamics simulations. We compare the efficiency and accuracy of two alternative computational methods: the direct method computing the intensity at each diffraction vector separately, and a method based on fast Fourier transform that computes the intensities at all diffraction vectors at once. Furthermore, the computed X-ray speckle patterns capture the density fluctuations over a range of length and time scales and are shown to reproduce the known properties and relations of experimental XPCS and XSVS for liquids.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Application of the Nonballistic Model to the Black Hole Candidate XTE J1752-223 and the Quasar NRAO 150

Optical and radio observations of the black hole candidate XTE J1752-223 have exhibited a slightly curved motion of the jet components, which is associated with its radio light curve. In addition, observations of the quasar NRAO 150 have revealed a core–jet structure wobbling with a high angular speed. In this paper, the phenomena displayed in these two different sources are interpreted as the precession of a bent jet. In such a scenario, hot spots reproduced at different separations from the core precess on the same precession cone, in which different components correspond to different propagation times to the observer. By fitting the kinematics of the components of XTE J1752-223 and its light curve with a curved pattern of precession period 314 days, we find that the propagation time can make an earlier event appear later, and the jet axis can oscillate during its precession. Simulating the quasar NRAO 150 with the same scenario reveals that the knots at larger separation from the core precess at a slower speed than those closer in. A possible mechanism relating to the cooling time of a component is proposed. These three new results are of importance in understanding the physics underlying the curved jet as well as the activity of the central engine of different black hole systems.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Understanding Differences in California Climate Projections Produced by Dynamical and Statistical Downscaling

Abstract We compare historical and end‐of‐century temperature and precipitation patterns over California from one dynamically downscaled simulation using the Weather Research and Forecast (WRF) model and two simulations statistically downscaled using Localized Constructed Analogs (LOCA). We uniquely separate causes of differences between dynamically and statistically based future climate projections into differences in historical climate (gridded observations versus regional climate model output) and differences in how these downscaling techniques explicitly handle future climate changes (numerical modeling versus analogs). In these methods, solutions between different downscaling techniques differ more in the future compared to the historical period. Changes projected by LOCA are insensitive to the choice of driving data. Only through dynamical downscaling can we simulate physically consistent regional springtime warming patterns across the Sierra Nevada, while the statistical simulations inherit an unphysical signal from their parent Global Climate Model (GCM) or gridded data. The results of our study clarify why these different techniques produce different outcomes and may also provide guidance on which downscaled products to use for certain impact analyses in California and perhaps other Mediterranean regimes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Electrochemistry-Induced Direct Deposition of Nanoscale Thin Zeolitic Imidazolate Framework-8 Films on Insulator Substrates

Electrochemical approaches have been explored as controlled means to prepare thin films of metal–organic frameworks (MOFs) on electrodes but have rarely been used to form insulator films on insulator surfaces. Herein, we report an electrochemistry-based approach to direct deposition of a thin film of zeolitic imidazolate framework-8 (ZIF-8) onto an insulator surface. The film deposition was induced by a cathodic reaction at an electrode that was placed above the insulator with a separation of ≈100 μm in a methanol solution containing ZnCl 2 and 2-methylimidizole. The effects of the electrode and insulator material, applied potential, electrode–substrate distance, deposition time, and the number of deposition cycles were systematically investigated to gain insight into the deposition mechanism. The results of these measurements were consistent with a hypothesized mechanism involving cathodic base generation at the working electrode for ligand deprotonation, formation of intermediate species, their diffusion toward the substrate, and the formation of ZIF-8 on the substrate. Interestingly, the size, shape, and position of the film on the substrate replicated those of the working electrode, showing the applicability of this approach to the patterned deposition of a ZIF-8 film. In addition, film thickness could be easily controlled in the range of tens to hundreds of nanometers by adjusting the potential application conditions. Furthermore, this electrochemistry-induced method will provide a simple means for the patterned formation of a MOF film of controlled thickness on an insulator without metal precoating and thus will open the possibility of designing unique devices for various applications including chemical sensing and separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

LANSCE Accelerator Modernization Project (LAMP) Overview [Slides]

LANSCE is based on a high-current 800 MeV proton accelerator capable of delivering 1 MW of beam power to the central target area. The LANSCE accelerator provides beam simultaneously to 5 separate experimental areas. LANSCE operates with 650 µs RF macro pulses at 120 Hz. Within this macro pulse several different beam timing patterns are utilized. LANSCE also accelerates H + and H – simultaneously. LANSCE celebrated 50 years of beam delivery in 2022.

43 PARTICLE ACCELERATORS↗

DOE OSTI2019

Operando FTIR Studies of Electrolyte Solution Structures and Dynamics at Electrode-Electrolyte Interfaces

The well-stabilized electrode-electrolyte interface (EEI) is critical to battery performance and safety, thus understanding the electrolyte solution structures (e.g., ion solvation and ionic association) and dynamics within EEI is an important area of study. Unfortunately, due to the sensitive nature of the electrodes, ex-situ characterization methods are limited and challenging to interpret as chemical and structural changes occur upon deconstructing the cell. In this work, we present operando characterization of near-surface electrolyte solution structures and dynamics by ATR-FTIR spectroscopy during battery cycling. We studied two selected model electrodes, a lithium nickel manganese cobalt oxide (NMC) or a planar silicon (Si), in a standard Gen2 electrolyte of 1.2 M LiPF6 in ethylene carbonate (EC):ethyl methyl carbonate (EMC) (3:7 wt%). In the former case, the composite cathode on aluminum foil is placed face-down on an ATR crystal. A ring of lithium metal serves as the counter/reference electrode and no separator is required. In the latter case, a lightly doped p-type silicon wafer is phosphorous-doped at the surface to increase the conductivity of the sample without compromising infrared transparency. The ATR crystal is then pressed on the back of the silicon with a minimal air gap to ensure sufficient optical contact between the two materials. The front of wafer is then placed in a modified coin-type cell geometry with a minimal electrolyte, a separator, and a lithium metal counter/reference electrode. In both experiments, we analyze the electrolyte solution structures within the EEI and find similar patterns as a function of potential. Firstly, we find that EMC decomposes faster than EC -suggesting that EMC plays a greater role in the electrochemical reactions on the electrode surface (e.g., formation of a protective layer to prevent further electrolyte decomposition). Secondly, we monitor the increase/decrease of the vibrational absorption peaks attributed to carbonates coordinated with Li+ ions relative to the decrease/increase of their uncoordinated counterparts, as expected when changing the local concentration of Li+ ions near the electrode surface during deintercalation/intercalation into the electrode. Finally, we observe that the features assigned to the Li+-PF6 -/-solvent molecules contact ion pair relative to the uncoordinated PF6 - anion. These observations give important information regarding the electrolyte solution structures and dynamics near the electrode surfaces. We look forward to applying these techniques to additional lithiumion battery systems with different cathode or anode materials and beyond lithium-ion systems to characterize surface chemistry/evolution (on-going research) and solution structures/dynamics (in particular, (de)solvation behaviors) within the EEI, which can be a bottleneck to efficient ion transfer from the electrolyte to the electrode.

14 SOLAR ENERGY↗

DOE OSTI2023

Light-activated microtubule-based two-dimensional active nematic

We assess the ability of two light responsive kinesin motor clusters to drive dynamics of microtubule-based active nematics: opto-K401, a processive motor, and opto-K365, a non-processive motor. Measurements reveal an order of magnitude improvement in the contrast of nematic flow speeds between maximally- and minimally-illuminated states for opto-K365 motors when compared to opto-K401 construct. For opto-K365 nematics, we characterize both the steady-state flow and defect density as a function of applied light. We also examine the transient behavior as the system switches between steady-states upon changes in light intensities. Although nematic flows reach a steady state within tens of seconds, the defect density exhibits transient behavior for up to 10 minutes, showing a separation between small-scale active flows and system-scale structural states. Furthermore, our work establishes an experimental platform that can exploit spatiotemporally-heterogeneous patterns of activity to generate targeted dynamical states.

42 ENGINEERING↗

DOE OSTI2020

STRUCTURAL STABILITY OF MAGNETIC TUNNEL JUNCTION BASED MOLECULAR SPINTRONICS DEVICES (MTJMSD)

Harnessing the exotic properties of molecular level nanostructures to produce novel sensors, metamaterials, and futuristic computer devices can be technologically transformative. In addition, connecting the molecular nanostructures to ferromagnetic electrodes bring the unprecedented opportunity of making spin property based molecular devices. We have demonstrated that magnetic tunnel junction based molecular spintronics device (MTJMSD) approach to address numerous technological hurdles that have been inhibiting this field for decades. MTJMSD approach is based on producing a capacitor like a testbed where two metal electrodes are separated by an ultrathin insulator and subsequently bridging the molecule nanostructure across the insulator to transform a capacitor into a molecular device. Our prior work showed that MTJMSDs produced extremely intriguing phenomenon such as room temperature current suppression by six orders, spin photovoltaic effect, and evolution of new forms of magnetic metamaterials arising due to the interaction of the magnetic a molecule with two ferromagnetic thin films. However, making robust and reproducible electrical connections with exotic molecules with ferromagnetic electrodes is full of challenges and requires attention to MTJMSD structural stability. This paper focuses on MTJMSD stability by describing the overall fabrication protocol and the associated potential threat to reliability. MTJMSD is based on microfabrication methods such as (a) photolithography for patterning the ferromagnetic electrodes, (b) sputtering of metallic thin films and insulator, and (c) at the end electrochemical process for bridging the molecules between two ferromagnetic films separated by ~ 2nm insulating gap.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Structure Perception in 3D Point Clouds

Understanding human perception is critical to the design of effective visualizations. The relative benefits of using 2D versus 3D techniques for data visualization is a complex decision space, with varying levels of uncertainty and disagreement in both the literature and in practice. This study aims to add easily reproducible, empirical evidence on the role of depth cues in perceiving structures or patterns in 3D point clouds. We describe a method to synthesize a 3D point cloud that contains a 3D structure, where 2D projections of the data strongly resemble a Gaussian distribution. We performed a within-subjects structure identification study with 128 participants that compared scatterplot matrices (canonical 2D projections) and 3D scatterplots under three types of motion: rotation, xy-translation, and z-translation. We found that users could consistently identify three separate hidden structures under rotation, while those structures remained hidden in the scatterplot matrices and under translation. This work contributes a set of 3D point clouds that provide definitive examples of 3D patterns perceptible in 3D scatterplots under rotation but imperceptible in 2D scatterplots.

data analysis↗

DOE OSTI2018

Performance of Advanced Signal Processing and Pattern Recognition Algorithms Using Raw Data from Ultrasonic Guided Waves and Fiber Optics Transducers

The Light Water Reactor Sustainability Program was initiated to evaluate technologies that could be used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real-time measurements, rather than at a pre-determined inspection interval. This transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for most nuclear power generation costs. This report describes the current state of research related to ultrasonic guided wave testing and its application to detecting defects in commercial nuclear power plants. The report analyzes the applicability of the guided wave technology to secondary piping systems, as well as studying the potential for expanding the range of guided wave technology to include bent piping and other piping components. The ultrasonic guided waves can inspect long stretches of straight piping; however, more complex geometries such as elbows, welds, and tees are causing spurious reflections and coherent noise, which significantly decreases the sensitivity of the technique. To deal with these limitations, high-definition fiber optic sensors are applied to complex piping geometries, and advanced machine learning algorithms are used to detect deviations from healthy states. This report also analyzes the performance of advanced signal processing and machine learning-based pattern recognition algorithms in detecting defects in secondary structures. It is demonstrated on guided wave data collected at nuclear power plants that the independent component analysis can separate different coherent noise components and segregate them from useful signals. It also demonstrates that advanced machine learning techniques, such as neural networks, support vector machines, and deep learning networks, can detect minor defects present in inspected structures. Recommendation about the applicability of advanced machine learning techniques to online piping monitoring are also given.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗