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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Computational studies reveal Fluorine based quinolines to be potent inhibitors for proteins involved in SARS-CoV-2 assembly

World is witnessing one of the worst pandemics of this century caused by SARS-CoV-2 virus which has affected millions of individuals. Despite rapid efforts to develop vaccines and drugs for COVID-19, the disease is still not under control. Chloroquine (CQ) and Hydroxychloroquine (HCQ) are two very promising inhibitors which have shown positive effect in combating the disease in preliminary experimental studies, but their use was reduced due to severe side-effects. Here, we performed a theoretical investigation of the same by studying the binding of the molecules with SARS-COV-2 Spike protein, the complex formed by Spike and ACE2 human receptor and a human serine protease TMPRSS2 which aids in cleavage of the Spike protein to initiate the viral activation in the body. Both the molecules had shown very good docking energies in the range of -6kcal/mol. Subsequently, we did a high throughput screening for other potential quinoline candidates which could be used as inhibitors. From the large pool of ligand candidates, we shortlisted the top three ligands (binding energy -8kcal/mol). We tested the stability of the docked complexes by running Molecular Dynamics (MD) simulations where we observed the stability of the quinoline analogues with the Spike-ACE2 and TMPRSS2 nevertheless the quinolines were not stable with the Spike protein alone. Thus, although the inhibitors bond very well with the protein molecules their intrinsic binding affinity depends on the protein dynamics. Moreover, the quinolines were stable when bound to electronegative pockets of Spike-ACE2 or TMPRSS2 but not with Viral Spike protein. We also observed that a Fluoride based compound: 3-[3-(Trifluoromethyl)phenyl]quinoline helps the inhibitor to bind with both Spike-ACE2 and TMPRSS2 with equal probability. The molecular details presented in this study would be very useful for developing quinoline based drugs for COVID-19 treatment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

UO2 microstructural evolutions induced by Ni, Mo, and W dopants for intentional forensics

The concept of tagging nuclear fuel with a chemical barcode to enable forensics analysis across the nuclear fuel cycle is an area of active investigation, particularly to ensure fabrication viability without disrupting current fuel performance. This study explored the feasibility of using Ni, Mo, and W isotopic double-spikes as dopants in UO2 fuel from the perspective of fuel fabrication. Doped UO2 pellets were produced using conventional fuel fabrication processes, including powder mixing, sieving, pressing, and sintering in a reductive atmosphere. Two composition levels, 100 and 1000 ppm, were evaluated for each dopant element with isotopic double-spike configurations. For the Ni system, additional dopant concentrations of 250 and 500 ppm were produced with nonperturbed isotopic ratios. The results demonstrated that successful incorporation of Ni, Mo, and W double-spikes into UO2 pellets occurred with minimal shift in final density or dopant loss during pellet fabrication. Isotopic analysis confirmed the presence of the double-spike signature even when diluted with natural isotopic material in ratio of 1:5 in the fabrication process. Microstructural examinations revealed different impacts on grain size compared with undoped UO2. This study showed that Ni incorporation up to ∼500 ppm promoted moderate grain growth, whereas the Mo and W systems caused grain size reduction at all concentrations. Changes in the UO2 lattice parameter as a function of composition were detected exclusively for Ni up to 500 ppm, indicating that the Ni solid solution was the main factor for the observed grain growth. Insoluble (Mo and W) or supersaturated (Ni > 500 ppm) conditions produced grain size reduction. The Ni-doped pellets in the solution range resulted in a final microstructure within fuel specifications, demonstrating its potential benefits of employing complex dopant systems for potential nuclear forensic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Hardware and Software Co-design Framework for Energy Efficient Neuromorphic Systems

Neuromorphic systems can be realized by a variety of algorithms and architectures. A common understanding is that spiking neuromorphic designs, which encode information into spatio-temporal spiking events, are both a biologically-accurate and efficient way of processing information. However, representing the information through timing relationships induces sophisticated circuit designs in traditional CMOS-based implementations. In recent years, high-capacity resistive memory (RRAM, aka, memristor) has demonstrated great potential in mimicking synaptic behaviors. Several RRAM-based spiking neuromorphic designs exist, most of which focus on rate coding schemes. These designs simplify circuit implementations of neuron models and explore challenges such as unsatisfactory speed, resolution, and performance. As an alternative, we will explore temporal coding spiking neuromorphic systems that encode information as the relative timing of neuron activations (spikes), which have been proven to be more adaptive and energy-efficient. Developing a neuromorphic system for spiking neural network (SNN) inference and online training, however, faces some major technical challenges: (1) It lacks circuit implementation support for temporal-coding SNN to achieve satisfying power efficiency and accuracy; (2) Although existing research works have investigated memristive synapse and neuron designs for spike-timing-dependent plasticity, the non-ideal conditions in implementation, such as device variations and signal degradation, degrade online learning accuracy of large scale systems; and (3) Non-optimized, inter-layer data traffic in SNNs, leads to unnecessary data communication costs. In this project, we plan to address these challenges by a hardware and software co-design framework that incorporates solutions at the circuit, architecture, and algorithm levels. At the circuit-level, we will elaborate on the in-situ SNN processing element designs for supporting both inference and online training modes. Variation-aware schemes will be studied to improve reliability. At the architecture level, we propose a pipelined, asynchronous architecture to retain the timing resolution of spikes. At the algorithm level, we will investigate an innovative SNN training algorithm for enabling activation sparsification and reducing unnecessary data communication costs. This neuromorphic system will provide an effective solution to real-life energy-constrained applications and significantly contribute to the exploration of next-generation high-performance computing systems under the DOE context.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Nonlinear ablative Rayleigh-Taylor instability: Increased growth due to self-generated magnetic fields

The growth rate of the nonlinear ablative Rayleigh-Taylor (RT) instability is enhanced by magnetic fields self-generated by the Biermann battery mechanism; a scaling for this effect with perturbation height and wavelength is proposed and validated with extended-magnetohydrodynamic simulations. The magnetic flux generation rate around a single RT spike is found to scale with the spike height. The Hall parameter, which quantifies electron magnetization, is found to be strongly enhanced for short-wavelength spikes due to Nernst compression of the magnetic field at the spike tip. Furthermore, the impact of the magnetic field on spike growth is through both the suppressed thermal conduction into the unstable spike and the Righi-Leduc heat flow deflecting heat from the spike tip to the base. Righi-Leduc is found to be the dominant effect for small Hall parameters, while suppressed thermal conduction dominates for large Hall parameters. These results demonstrate the importance of considering magnetic fields in all perturbed inertial confinement fusion hot spots.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Dictionary Learning with Accumulator Neurons

The Locally Competitive Algorithm (LCA) uses local competition between non-spiking leaky integrator neurons to infer sparse representations, allowing for potentially real-time execution on massively parallel neuromorphic architectures such as Intel's Loihi processor. Here, we focus on the problem of inferring sparse representations from streaming video using dictionaries of spatiotemporal features optimized in an unsupervised manner for sparse reconstruction. Non-spiking LCA has previously been used to achieve unsupervised learning of spatiotemporal dictionaries composed of convolutional kernels from raw, unlabeled video. We demonstrate how unsupervised dictionary learning with spiking LCA (\hbox{S-LCA}) can be efficiently implemented using accumulator neurons, which combine a conventional leaky-integrate-and-fire (\hbox{LIF}) spike generator with an additional state variable that is used to minimize the difference between the integrated input and the spiking output. We demonstrate dictionary learning across a wide range of dynamical regimes, from graded to intermittent spiking, for inferring sparse representations of both static images drawn from the CIFAR database as well as video frames captured from a DVS camera. On a classification task that requires identification of the suite from a deck of cards being rapidly flipped through as viewed by a DVS camera, we find essentially no degradation in performance as the LCA model used to infer sparse spatiotemporal representations migrates from graded to spiking. We conclude that accumulator neurons are likely to provide a powerful enabling component of future neuromorphic hardware for implementing online unsupervised learning of spatiotemporal dictionaries optimized for sparse reconstruction of streaming video from event based DVS cameras.

artificial intelligence↗
DOE OSTI2019

Impacts of intentional mycoplasma contamination on CHO cell bioreactor cultures

Mycoplasma contamination events in biomanufacturing facilities can result in loss of production and costly cleanups. Mycoplasma may survive in mammalian cell cultures with only subtle changes to the culture and may penetrate the 0.2 µm filters often used in the primary clarification of harvested cell culture fluid. Culture cell-based and indicator cell-based assays that are used to detect mycoplasma are highly sensitive but can take up to 28 days to complete and cannot be used for real-time decision making during the biomanufacturing process. To support real-time measurements of mycoplasma contamination, there is a push to explore nucleic acid testing. However, cell-based methods measure growth or colony forming units and nucleic acid testing measures genome copy number; this has led to ambiguity regarding how to compare the sensitivity of the methods. In addition, the high risk of conducting experiments wherein one deliberately spikes mycoplasma into bioreactors has dissuaded commercial groups from performing studies to explore the multiple variables associated with the upstream effects of a mycoplasma contamination in a manufacturing setting. Here we studied the ability of Mycoplasma arginini to persist in a single-use, perfusion rocking bioreactor system containing a Chinese hamster ovary (CHO) DG44 cell line expressing a model monoclonal immunoglobulin G1 (IgG1) antibody. We examined M. arginini growth and detection by culture methods, as well as the effects of M. arginini on mammalian cell health, metabolism, and productivity. We compared process parameters and controls normally measured in bioreactors including dissolved oxygen, gas mix, and base addition to maintain pH, to examine parameter changes as potential indicators of contamination. Our work showed that M. arginini affects CHO cell growth profile, viability, nutrient consumption, oxygen use, and waste production at varying timepoints after M. arginini introduction to the culture. Importantly, how the M. arginini contamination impacts the CHO cells is influenced by the concentration of CHO cells and rate of perfusion at the time of M. arginini spike. Careful evaluation of dissolved oxygen, pH control parameters, ammonia, and arginine over time may be used to indicate Mycoplasma contamination events in biomanufacturing facilities can result in loss of production and costly cleanups. Mycoplasma may survive in mammalian cell cultures with only subtle changes to the culture and may penetrate the 0.2 µm filters often used in the primary clarification of harvested cell culture fluid. Culture cell-based and indicator cell-based assays that are used to detect mycoplasma are highly sensitive but can take up to 28 days to complete and cannot be used for real-time decision making during the biomanufacturing process. To support real-time measurements of mycoplasma contamination, there is a push to explore nucleic acid testing. However, cell-based methods measure growth or colony forming units and nucleic acid testing measures genome copy number; this has led to ambiguity regarding how to compare the sensitivity of the methods. In addition, the high risk of conducting experiments wherein one deliberately spikes mycoplasma into bioreactors has dissuaded commercial groups from performing studies to explore the multiple variables associated with the upstream effects of a mycoplasma contamination in a manufacturing setting. Here we studied the ability of Mycoplasma arginini to persist in a single-use, perfusion rocking bioreactor system containing a Chinese hamster ovary (CHO) DG44 cell line expressing a model monoclonal immunoglobulin G1 (IgG1) antibody. We examined M. arginini growth and detection by culture methods, as well as the effects of M. arginini on mammalian cell health, metabolism, and productivity. We compared process parameters and controls normally measured in bioreactors including dissolved oxygen, gas mix, and base addition to maintain pH, to examine parameter changes as potential indicators of contamination. Our work showed that M. arginini affects CHO cell growth profile, viability, nutrient consumption, oxygen use, and waste production at varying timepoints after M. arginini introduction to the culture. Importantly, how the M. arginini contamination impacts the CHO cells is influenced by the concentration of CHO cells and rate of perfusion at the time of M. arginini spike. Careful evaluation of dissolved oxygen, pH control parameters, ammonia, and arginine over time may be used to indicate Mycoplasma contamination events in biomanufacturing facilities can result in loss of production and costly cleanups. Mycoplasma may survive in mammalian cell cultures with only subtle changes to the culture and may penetrate the 0.2 µm filters often used in the primary clarification of harvested cell culture fluid. Culture cell-based and indicator cell-based assays that are used to detect mycoplasma are highly sensitive but can take up to 28 days to complete and cannot be used for real-time decision making during the biomanufacturing process. To support real-time measurements of mycoplasma contamination, there is a push to explore nucleic acid testing. However, cell-based methods measure growth or colony forming units and nucleic acid testing measures genome copy number; this has led to ambiguity regarding how to compare the sensitivity of the methods. In addition, the high risk of conducting experiments wherein one deliberately spikes mycoplasma into bioreactors has dissuaded commercial groups from performing studies to explore the multiple variables associated with the upstream effects of a mycoplasma contamination in a manufacturing setting. Here we studied the ability of Mycoplasma arginini to persist in a single-use, perfusion rocking bioreactor system containing a Chinese hamster ovary (CHO) DG44 cell line expressing a model monoclonal immunoglobulin G1 (IgG1) antibody. We examined M. arginini growth and detection by culture methods, as well as the effects of M. arginini on mammalian cell health, metabolism, and productivity. We compared process parameters and controls normally measured in bioreactors including dissolved oxygen, gas mix, and base addition to maintain pH, to examine parameter changes as potential indicators of contamination. Our work showed that M. arginini affects CHO cell growth profile, viability, nutrient consumption, oxygen use, and waste production at varying timepoints after M. arginini introduction to the culture. Importantly, how the M. arginini contamination impacts the CHO cells is influenced by the concentration of CHO cells and rate of perfusion at the time of M. arginini spike. Careful evaluation of dissolved oxygen, pH control parameters, ammonia, and arginine over time may be used to indicate mycoplasma contamination in CHO cell cultures in a bioreactor before a read-out from a traditional method.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Simultaneous Determination of Halogens and Metals in Waste Plastic Pyrolysis Oil by Inductively Coupled Plasma Mass Spectrometry

A major barrier to integrating pyrolysis-derived oil into conventional refinery technology is the presence of impurities, particularly halogens and metals, that can deactivate catalysts. This study presents a novel, cost-effective approach for the simultaneous analysis of a subset of halogens, metals, nonmetals, and metalloids in complex, industrially relevant distilled pyrolysis oil samples, this was previously achievable only through multiple techniques. Excellent results were obtained using a widely accessible inductively coupled plasma mass spectrometry (ICP-MS) method with helium gas mode and standard laboratory consumables, enabling high-throughput analysis and efficient evaluation of adsorbent performance. Sample preparation is straightforward, requiring only dilution in a compatible matrix, and provides accurate and precise quantification, with 75%–137% spike recovery for Be, Ti, V, Cr, Fe, Ni, Co, Cu, As, Se, Mo, Cd, Sb, Tl, and Pb, and 62%–65% spike recovery for Cl and 109%–133% spike recovery for Br. Additionally, an enhanced version of the method using ICP-MS/MS and hydrogen gas is described, which has higher accuracy with 69%–112% spike recovery for Be, B, Ti, V, Fe, Co, Ni, Cu, As, Se, Mo, Cd, Sb, Tl, and Pb, with 85%–102% spike recovery for Cl and 63%–93% spike recovery for Br. Helium mode detection limits for industrially relevant elements (V, Fe, Ni, Cu, As, and Pb) are less than 1.7 µg/kg, and less than 0.2 mg/kg for Br and Cl. Furthermore, this methodology facilitates rapid systematic evaluation of adsorption capacities of materials under time-on-stream conditions and supports robust comparisons across diverse operating environments.

Lazarcik, James [University of Wisconsin-Madison, ↗
DOE OSTI2022

Quantitative Study of Plasmonic Gold Nanostar Geometry Toward Optimal SERS Detection

Abstract Complex shapes of gold nanostars (GNS) have been the major obstacle to the comprehensive understanding of the rich plasmonic phenomena of GNS. We conducted extensive, systematic numerical study of far-field and near-field spectral responses of GNS for varying geometrical parameters (e.g., number of spikes, spike tip angle, and spike-to-core ratio) to elucidate the relationship between the optical property and the geometry of GNS. We found that symmetric configurations of GNS spikes generated both dipole and quadrupole localized surface plasmon (LSP) modes, their hybridization resulted in the final near-field intensity at the spike tips, and there existed a specific GNS geometry that optimized the hybridization and thus the E-field intensity for any given wavelength. From these results, we further identified the shapes of GNS that generated the strongest Surface Enhanced Raman Scattering (SERS) enhancement factors in the visible and NIR spectral range. Hence, our results provide guiding principles in determining the optimal geometry of GNS for SERS applications with any choice of excitation wavelength.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Isotope Exchange between Mercuric [Hg(II)] Chloride and Hg(II) Bound to Minerals and Thiolate Ligands: Implications for Enriched Isotope Tracer Studies

Enriched mercury (Hg) stable isotopes have been widely used as tracers in field and laboratory investigations of Hg(II) biogeochemical transformations such as methylation and demethylation. Few studies, however, have considered concurrent isotope exchange reactions between newly spiked and pre-existing Hg(II) in environmental matrices, which may alter redistribution and thus transformation of the spiked and pre-existing Hg(II). Using enriched 198 Hg [as mercuric Hg(II) or HgCl n species], this study investigated isotope exchange between 198 Hg and pre-existing Hg(II) bound to metacinnabar (β-HgS), sediments, low-molecular-weight (LMW) thiols, and dissolved organic matter (DOM). The impact of isotope exchange on methylmercury production in the presence of organic ligands was also evaluated with an iron-reducing bacterium Geobacter sulfurreducens PCA in a phosphate buffered solution (pH 7.4). Here, we found that spiked 198 Hg readily exchanged with mineral-bound ambient Hg(II) despite concurrent Hg(II) adsorption and immobilization on the solids. Rapid exchange (<3 min) was also observed between spiked 198 Hg and 200 Hg pre-equilibrated with LMW thiols and DOM in solution. While the exchange did not cause net changes in Hg(II) chemical speciation, it resulted in redistribution of Hg(II) isotopes bound to the ligands and thus an apparently similar methylation rate and magnitude of the spiked 198 Hg and pre-existing 200 Hg by PCA cells when 198 Hg and 200 Hg were added at 1:1 ratio. These observations underscore the importance of isotope exchange when an enriched Hg isotope is applied in environmental matrices, as the exchange could potentially lead to biased rate calculations of Hg(II) transformation and bioaccumulation and thus risk assessments of new Hg(II) input to the natural ecosystems.

58 GEOSCIENCES↗
DOE OSTI2019

Non-targeted discovery-based analysis for gas chromatography with mass spectrometry: A comparison of peak table, tile, and pixel-based Fisher ratio analysis

The ability to discover minute differences between samples or sample classes for gas chromatography coupled to mass spectrometry (GC-MS) can be a challenging endeavor, especially when those differences are not a priori . Fisher ratio (F-ratio) analysis is an apt technique to probe the differences between GC-MS chromatograms. F-ratio analysis is a supervised, non-targeted, discovery-based method that compares two different samples (or sample classes) to reduce the GC-MS dataset into a hit list composed of class distinguishing compounds. Three different F-ratio techniques, peak table, tile, and pixel-based were used to “discover” nine non-native analytes that were spiked into gasoline at four different nominal concentrations of 250, 85, 25, 5 parts-per-million (ppm). For the tile and pixel-based F-ratio calculations, a novel methodology is introduced to improve the sensitivity of the F-ratio calculations while reducing false positives. Furthermore, we use a combinatorial technique using null class comparisons, termed null distribution analysis, to determine a statistical F-ratio cutoff for analysis of the hit lists. The pixel-based algorithm was the most sensitive method and was able to “discover” all nine spiked analytes at a nominal concentration of 250 ppm albeit with one false positive interspersed towards the bottom of the hit list. The pixel-based software was also able to “discover” more of the spiked analytes at the lower concentrations with seven of the spiked analytes “discovered” at 85 ppm, four of the spiked analytes “discovered” at 25 ppm, and one analyte “discovered” at 5 ppm.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Cooperative multivalent receptor binding promotes exposure of the SARS-CoV-2 fusion machinery core

The molecular events that permit the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to bind and enter cells are important to understand for both fundamental and therapeutic reasons. Spike proteins consist of S1 and S2 domains, which recognize angiotensin-converting enzyme 2 (ACE2) receptors and contain the viral fusion machinery, respectively. Ostensibly, the binding of spike trimers to ACE2 receptors promotes dissociation of the S1 domains and exposure of the fusion machinery, although the molecular details of this process have yet to be observed. We report the development of bottom-up coarse-grained (CG) models consistent with cryo-electron tomography data, and the use of CG molecular dynamics simulations to investigate viral binding and S2 core exposure. We show that spike trimers cooperatively bind to multiple ACE2 dimers at virion-cell interfaces in a manner distinct from binding between soluble proteins, which processively induces S1 dissociation. We also simulate possible variant behavior using perturbed CG models, and find that ACE2-induced S1 dissociation is primarily sensitive to conformational state populations and the extent of S1/S2 cleavage, rather than ACE2 binding affinity. These simulations reveal an important concerted interaction between spike trimers and ACE2 dimers that primes the virus for membrane fusion and entry.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Intricate structure of the plasma Rayleigh–Taylor instability in shock tubes

Spikes and bubbles grow on unstable interfaces that are accelerated in high-energy-density conditions. If a shock propagates ahead of the interface, the plasma can be heated to extreme conditions where conduction and radiation fluxes influence the hydrodynamics. For example, a National Ignition Facility experiment found reduced single-mode nonlinear mixed-width growth in conditions scaled from a supernova explosion. We present high-resolution two-dimensional radiation hydrodynamic simulations with the Flash code that quantitatively reproduce the experiment. Radiative fluxes are primarily responsible for ablating the spike and removing the mushroom caps. The ablated plasma increases the mixed mass and forms a low-density halo with spikes forming in both directions. This is considerably more complex than classical instability. The halo is sensitive to ablative physics, so radiographing it may aid in the verification of energy transport modeling. Although ablation changes the spike shape, it has little effect on the overall mixed width for these parameters. This is because ablation enhances the bubble velocity but it has the opposite effect on the spike. The radiation transport instead suppresses the growth via increasing the shocked foam density, thus decreasing the Atwood number. Furthermore, a terminal velocity model including the rarefaction expansion agrees with the experimental mixed-width growth.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Material‐Driven Neuronal Oscillators and Filters via Active Reactance in CC‐NDR and VC‐NDR Electro‐Thermal Memristors

The continued scaling of artificial intelligence and telecommunications hardware is increasingly constrained by the power, bandwidth, and area limitations of transistor-based circuits. Neuromorphic processor units, analog oscillators, and active inductors and capacitors rely on complex multi-transistor architectures restricting material choices and incurring energy and footprint overhead. Here, we show that active reactance in electro-thermal memristors provides an intrinsic, material driven route to neuronal oscillator dynamics and signal processing. Using a physics-based compact modeling framework, we bridge negative differential resistance (NDR) and bias-tunable reactance, which underlies spiking dynamics in electro-thermal memristors. Memristors with negative temperature coefficients of resistance (TCR) manifest current-controlled (CC-) NDR and act as active inductors, thus generating spiking above a critical circuit capacitance; whereas memristors with positive TCR manifest voltage-controlled (VC-) NDR and active capacitance, leading to spiking above a critical inductance. By creating a compact model for La 0.7 Ca 0.3 MnO 3 as a representative VC-NDR material and comparing it with LaCoO 3 manifesting CC-NDR, we explain the physical origins of their distinct current-voltage characteristics, reactive phase shifts and consequent spiking behaviors. Finally, we demonstrate tunable filtering enabled by the active reactance of electro-thermal memristors, establishing them as a compact hardware platform for neuronal oscillator functionality and integrated filtering beyond conventional CMOS.

active reactance↗
DOE OSTI2020

Work function measurements of clean and modified carbon nanospikes

Carbon surfaces incorporating densely packed spikes with nanometer sharpness have shown exceptional electrocatalytic properties attributed to the enhanced electric field from the topography of the spikes. In this article, the composition of nanospike surfaces is identified by x-ray photoelectron spectroscopy (XPS) and their absolute work function determined using ultraviolet photoemission spectroscopy (UPS). Low temperature annealing of as-grown samples above 275 °C is required to produce a clean surface which has a 4.13 eV work function, a half volt lower than that of flat graphite. Treatments that contaminate the spiked surface, including exposure to air, oxidation at elevated temperature, or immersion in water or hydrocarbons, all increase the work function. Processing that blunts the spikes, including exposure to an oxygen plasma, argon sputtering, or high temperature annealing (800 °C) result in a work function close to that of flat graphite. An unusual double onset in the UPS secondary electron intensity is observed on as-grown nanospike samples and is reproduced by absorbing hydrocarbons on the surface. In conclusion, this double onset appears to be unique to carbon substrates and may originate in inelastic scattering of photoelectrons.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Profiling B cell immunodominance after SARS-CoV-2infection reveals antibody evolution to non-neutralizing viral targets

Dissecting the evolution of memory B cells (MBCs) against SARS-CoV-2 is critical for understanding antibody recall upon secondary exposure. Here, we used single-cell sequencing to profile SARS-CoV-2-reactive B cells in 38 COVID-19 patients. Using oligo-tagged antigen baits, we isolated B cells specific to the SARS-CoV-2 spike, nucleoprotein (NP), open reading frame 8 (ORF8), and endemic human coronavirus (HCoV) spike proteins. SARS-CoV-2 spike-specific cells were enriched in the memory compartment of acutely infected and convalescent patients several months post symptom onset. With severe acute infection, substantial populations of endemic HCoV-reactive antibody-secreting cells were identified and possessed highly mutated variable genes, signifying preexisting immunity. Finally, MBCs exhibited pronounced maturation to NP and ORF8 over time, especially in older patients. Monoclonal antibodies against these targets were non-neutralizing and non-protective in vivo. These findings reveal antibody adaptation to non-neutralizing intracellular antigens during infection, emphasizing the importance of vaccination for inducing neutralizing spike-specific MBCs.

antibody↗
DOE OSTI2021

A new highly enriched 233 U reference material for improved simultaneous determination of uranium amount and isotope amount ratios in trace level samples

A highly-enriched 233 U reference material (>0.99987 n( 233 U)/n(U)) has been prepared and characterized for use as an isotope dilution mass spectrometry spike. An ion exchange separation was performed on 1 g of high purity 233 U to further reduce trace amounts of contaminant Pu in the material. The purified 233 U was then prepared as a master solution which was analyzed for molality of uranium by modified Davies and Gray titration. A portion of the master solution was quantitatively diluted and dispensed for reference material units. Selected units were analyzed for verification of uranium amount and to characterize uranium isotope amount ratios by multi-collector inductively couple plasma mass spectrometry. Furthermore, modelling of spike-corrected isotopic data show that the new spike will enable simultaneous measurements of uranium amount and isotope amount ratios with resulting uncertainties that are substantially less sensitive to over spiking than widely used 233 U certified reference materials.

233U↗
DOE OSTI2025

Solving sparse finite element problems on neuromorphic hardware

The finite element method (FEM) is one of the most important and ubiquitous numerical methods for solving partial differential equations (PDEs) on computers for scientific and engineering discovery. Applying the FEM to larger and more detailed scientific models has driven advances in high-performance computing for decades. Here we demonstrate that scalable spiking neuromorphic hardware can directly implement the FEM by constructing a spiking neural network that solves the large, sparse, linear systems of equations at the core of the FEM. We show that for the Poisson equation, a fundamental PDE in science and engineering, our neural circuit achieves meaningful levels of numerical accuracy and close to ideal scaling on modern, inherently parallel and energy-efficient neuromorphic hardware, specifically Intel’s Loihi 2 neuromorphic platform. We illustrate extensions to irregular mesh geometries in both two and three dimensions as well as other PDEs such as linear elasticity. Our spiking neural network is constructed from a recurrent network model of the brain’s motor cortex and, in contrast to black-box deep artificial neural network-based methods for PDEs, directly translates the well-understood and trusted mathematics of the FEM to a natively spiking neuromorphic algorithm.

Applied mathematics↗
DOE OSTI2022

Design optimization for Richtmyer–Meshkov instability suppression at shock-compressed material interfaces

We report the Richtmyer–Meshkov instability (RMI) is a phenomenon that occurs at the interface of two substances of different densities due to an impulsive acceleration, such as a shock wave passing through this interface. Under these conditions, the instability can be seen as interface perturbations begin to grow into narrow jets or spikes of one substance that propagate into the other. In some cases, this interface may involve an elastic–plastic material, which can play a significant role in the development and behavior of the RMI. The ability to effectively control RMI jetting and spike growth is one major limiting factor in technological challenges, such as inertial confinement fusion, that involve using high-pressure shock waves to implode a fuel target. The propagation of RMI growth can lead to increased asymmetry in this implosion process and significantly reduce the obtained energy yield. We use hydrodynamics simulations of impactor shock-compression experiments and methods based in design optimization to suppress RMI spike growth by altering the geometry and other properties of a shock-compressed elastic–plastic material target that shares an interface with atmospheric air. These hydrodynamics simulations use an arbitrary Lagrangian–Eulerian method with a high-order finite element approach. Our results demonstrate that RMI suppression can be achieved by intentionally creating a separate upstream interface instability to counteract the growth of long narrow RMI spikes at an interface with initial perturbations.

36 MATERIALS SCIENCE↗