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At least 73 records · Page 4

Tetracene Functionalized Si(111) Achieves Enhanced Solar-to-Chemical Energy Conversion via Molecular Acceptor States

The properties of semiconductor|liquid interfaces play a critical role in determining the efficiency of solar-to-hydrogen (STH) conversion. Here, we investigate how molecular functionalization of Si(111) and Si(111)|TiO 2 surfaces impacts photoelectrochemical (PEC) hydrogen production efficiency. We find that functionalization of ∼3% of the atop sites of Si(111) with either 9-anthracene (Anth) or 5-tetracene (Tet), with the remaining sites passivated by methyl groups, provides substrates with high electronic quality and low surface oxide densities, as determined by X-ray photoelectron spectroscopy (XPS) measurements. Surface photovoltage (SPV) spectroscopy shows that surfaces modified with Anth or Tet exhibit an increased photovoltage, with Tet-functionalized surfaces yielding an additional 192 meV relative to methyl-terminated Si(111), indicating improved charge separation for Si-Tet. Further improvement in onset potential was achieved by replacing a nitrogen-containing TiO 2 atomic layer deposition (ALD) precursor (TDMAT) with a precursor lacking nitrogen (TTIP), which eliminates the parasitic defect band in the TiO 2 overlayer (p-Si(111)-Tet|TTIP-TiO 2 |Pt: V OC = +0.283 ± 0.041 V vs RHE). Density functional theory (DFT) analysis demonstrates that compared with Anth-modified Si(111), the Tet-modified surface exhibits more hybridized Si(111)-Tet states closer to the silicon band edges. Mercury contact current–voltage (I–V, dark) measurements quantified the relative interfacial density of states of Si-Tet, Si-Anth and Si-Me surfaces─revealing that the interfacial state density was highest for Si-Tet. This suggests that such hybridized interfaces serve to capture better photoexcited charge, which enables facile electron transfer to molecular acceptors in solution. Altogether, the data indicate that beneficial hybrid molecular LUMO surface states interacting with the Si conduction band edge results in improved hydrogen evolution (HER) performance for p-Si devices.

Group theory↗

Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs, especially where protective candidates can be rapidly identified or developed for emerging biothreats and escape variants. Current cutting-edge technologies for this purpose still rely on pathogen-exposed convalescent volunteers and a large screening effort to find a proverbial needle in a haystack. Computational design of protective antibodies based on pre-existing templates skips those requirements and allows for greater control over the breadth and target epitope, while also co-optimizing for potency and developability or other biophysical characteristics. We approached this problem by building and expanding an in vitro experimental rapid antibody production and characterization pipeline to support development of an autonomous, closed loop, active learning software system based on structural simulation and ground truth experimental data to design and evaluate antibody antigen interactions. Starting from early in the pandemic, we targeted SARS-CoV-2, the causative agent of COVID-19, by re-purposing neutralizing antibodies against SARS-CoV-1 that had been identified in the wake of that outbreak in the early 2000’s. We successfully re-targeted three different anti-SARS-CoV-1 antibodies to neutralize SARS-CoV-2 in vitro, where the antibodies were generated externally and tested through conventional binding and neutralization assays internally or with collaborators. As antibodies were identified from the blood of humans infected with SARS-CoV-2, we shifted to collaborate with academic partners to develop improved versions of their human-derived antibodies. This work reached its most important stage in rapid response to the emergence of the Omicron variant of concern (VOC) in late 2021. In a matter of weeks, enabled by on demand innovation to our screening pipeline, we computationally designed and experimentally characterized derivative antibodies of COV2-2130, one of two antibodies from Vanderbilt that form the basis of the AstraZeneca Evusheld prophylactic drug product. This drug product suffers a serious loss of efficacy against Omicron BA.1 and BA.1.1, the first Omicron strains. Due to tight integration of computational design and experimental evaluation, we were able to identify a pair of designs with potent neutralization of the main targets Omicron BA.1 and BA.1.1; but also the earlier Delta variant, and subsequent Omicron strains including BA.2, BA.4, BA.5, and BA.2.75, demonstrating that our multi-target design process can, by its nature, produce robust antibody designs that strictly improve over the parental antibody. These results, recognized by a 2022 Director’s Science and Technology award, have enabled the follow-on GUIDE program, to commence in FY23. While earlier design campaigns were substantially outsourced, we have engineered better and faster processes internally to better compliment, calibrate, and speed computational designs. As part of the follow-on GUIDE program, we will stand up a rapid and high-throughput antibody production and characterization facility staffed with the expertise and capabilities to foster our current collaboration across PLS and ENG as well as other partnerships toward computational design of biologics.

59 BASIC BIOLOGICAL SCIENCES↗

Colloquium : Quantum anomalous Hall effect

The quantum Hall (QH) effect, quantized Hall resistance combined with zero longitudinal resistance, is the characteristic experimental fingerprint of Chern insulators—topologically nontrivial states of two-dimensional matter with broken time-reversal symmetry. In Chern insulators, nontrivial bulk band topology is expressed by chiral states that carry current along sample edges without dissipation. Here, the quantum anomalous Hall (QAH) effect refers to QH effects that occur in the absence of external magnetic fields due to spontaneously broken time-reversal symmetry. The QAH effect has now been realized in four different classes of two-dimensional materials: (i) thin films of magnetically (Cr- and/or V-) doped topological insulators in the (Bi,Sb) 2 T 3 family, (ii) thin films of the intrinsic magnetic topological insulator MnBi 2 Te 4 , (iii) moiré materials formed from graphene, and (iv) moiré materials formed from transition-metal dichalcogenides. In this Colloquium, the physical mechanisms responsible for each class of QAH insulator are reviewed, with both differences and commonalities highlighted, and potential applications of the QAH effect are commented upon.

36 MATERIALS SCIENCE↗

Cosimulating Integrated Energy Systems With Heterogeneous Digital Twins: Matching a Connected World

Energy system integration promises in-creased resiliency and the unlocking of synergies, while also contributing to our goal of decarbonization. It is enabled by both old and new technologies, glued together with data and digital services. Hydrolyzers, heat pumps, distributed renewable generation, smart buildings, and the digital grid edge are all currently the subject of integration with the power system and the energy sector at large. To plan and operate such a multidisciplinary and multisectoral system properly, insight, tools, and expertise are all needed. Further, this is exactly where the state of the art fails to deliver: tools for integrated energy systems (IESs) are still in their infancy, and many times, even academia treats these sectors separately, producing experts in each of them but not across.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Providing Geospatial Intelligence through a Scalable Imagery Pipeline

This chapter describes ORNL’s (Oak Ridge National Laboratory’s) contributions to imagery preprocessing for geospatial intelligence research and development (R&D) in four sections. First, we discuss challenges involved in building an effective imagery preprocessing workflow and the world-class high-performance computing (HPC) resources at ORNL available to process petabytes of imagery data. Second, we highlight how we developed imagery preprocessing tools over three decades while paving the way for our current cutting-edge machine learning and computer vision algorithms that are impacting humanitarian and disaster response efforts. Third, we discuss how PIPE modules work together to turn raw images into analysis-ready datasets. Fourth, we look toward the future and discuss planned advancements to PIPE and computing trends that will affect geospatial intelligence R&D.

Reith, Andrew↗

Los Alamos National Laboratory R&D Intern

This report details the responsibilities, outcomes, and project details of a summer R&D internship at Los Alamos National Laboratory (LANL). LANL is a multidisciplinary laboratory that focuses on current cutting-edge research in many fields such as national security, engineering, materials science, computational modeling, and advanced manufacturing. The goal of the internship project was to work with lab engineers and resources to develop an energy absorbing structure for high-velocity impact applications. The successful development of this technology and methodology would not only positively impact future project funding but also contribute to the laboratory's commitment to solve national security challenges through simultaneous excellence. Such devices would also support efforts surrounding the research and development of energy absorbing structures and would provide new vital information backed by experimentation. Different computational and modeling methods were used to design these structures, in addition to qualitative background information provided by past literature. The resultant designs were successfully tested, and the test results were successfully quantified. From these results, new computational methods were developed through python programming and modeling to predict ideal materialistic properties for these structures given a sufficiently defined application.

36 MATERIALS SCIENCE↗

High voltage MOSFET devices and methods of making the devices

A SiC MOSFET device having low specific on resistance is described. The device has N+, P-well and JFET regions extended in one direction (Y-direction) and P+ and source contacts extended in an orthogonal direction (X-direction). The polysilicon gate of the device covers the JFET region and is terminated over the P-well region to minimize electric field at the polysilicon gate edge. In use, current flows vertically from the drain contact at the bottom of the structure into the JFET region and then laterally in the X direction through the accumulation region and through the MOSFET channels into the adjacent N+ region. The current flowing out of the channel then flows along the N+ region in the Y-direction and is collected by the source contacts and the final metal. Methods of making the device are also described.

42 ENGINEERING↗

Simple, general, realistic, robust, analytic tokamak equilibria. Part 2. Pedestals and flow

Part 1 described a wide range of analytic tokamak equilibria modelling smooth limiter surfaces, double- and single-null divertor surfaces, arbitrary aspect ratio, elongation, triangularity and beta. Part 2 generalizes the analysis to further include edge pedestals and toroidal flow. Specifically, edge pedestals are allowed in the pressure, pressure gradient and toroidal current density. Also, an edge-localized contribution to the bootstrap current is treated. In terms of flow, analytic solutions are obtained for two cases: a $\gamma = 2$ adiabatic and a $\gamma = \infty $ incompressible energy conservation relation.

Physics↗

Time-dependent experimental identification of inter-ELM microtearing modes in the tokamak edge on DIII-D

In a series of discharges on the DIII-D tokamak, fast vertical plasma jogs are used to induce current perturbations in the steep gradient region of the H-mode edge. These current perturbations directly influence the edge q profile, decoupling the resonant location and instability drive of pedestal-localized microtearing modes (MTMs.) By exploiting this effect, we develop and apply a new experimental technique to track the dynamical frequency evolution of MTMs in the pedestal region, providing a compelling validation of the MTM model. The frequency of potential MTMs is calculated as the Doppler-shifted electron diamagnetic frequency at rational q=m/n surfaces, showing remarkable agreement with chirped frequency behavior of n=3, 4 and 5 modes detected with fast magnetics. Data is collected throughout multiple ELM cycles in order to build robust statistics describing the time-dependent frequency evolution of MTMs, which can be explained by examining the recovery of pedestal gradients after an ELM event. MTMs have a dominant transport contribution in the electron thermal channel, so the presented results indicate that reduced models of pedestal transport must be electromagnetic in nature and constructed with accurate calculations of MTM stability; inclusion of this physics is essential for accurate predictions of the electron temperature pedestal profile. Supporting measurements of mode saturation, propagation direction and transport fingerprints are made to support the dynamic frequency determination, unambiguously and experimentally identifying MTMs in the pedestal region of DIII-D.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

L-H transition trigger physics in ITER-similar plasmas with applied n = 3 magnetic perturbations

The L–H transition power threshold PLH is observed to increase with applied n = 3 resonant magnetic perturbations (RMP) in ITER-similar-shape plasmas with balanced neutral beam torque injection in DIII-D. The increase is most pronounced with added electron–cyclotron heating: PLH increases with decreasing edge plasma collisionality as PLH/PLH-08 ~ (ν*)−0.5, where PLH-08 is the 2008 ITPA multi-machine power threshold scaling. This result raises concerns for H-mode access at low edge collisionality in ITER, where RMP may have to be applied before the L–H transition to safely suppress the first edge-localized mode. Non-axisymmetric modifications with RMP include a simultaneous reduction of the radial electric field (Er) well depth and E × B shear. This can be attributed to increasing edge toroidal co-current rotation, and is consistent with substantially increased local long-wavelength turbulence (measured via beam emission spectroscopy). At high RMP perturbation strength the edge electric field Er reverses sign locally (becomes positive), with changes in dominant turbulence modes. Edge magnetic stochasticity provides an attractive explanation of the observed modifications, and the observed changes in toroidal rotation and Er are consistent with a simple fluid model describing radial electron current flow along stochastic fieldlines. The observed collisionality dependence of the L-mode edge electric field with applied RMP is also qualitatively consistent with this model. Reflectometry data indicate a significant reduction of the normalized L-mode radial density gradient a/Ln at high RMP field with simultaneous increase in radial particle flux and electron thermal flux from power balance analysis. We conjecture that the increase of PLH with RMP results from the combined effects of reduced E × B flow shear (increasing turbulent transport levels) and toroidal/poloidal flow modulation due to edge stochasticity. Initial experiments indicate that non-resonant n = 3 magnetic perturbations lead only to relatively small changes in Er, E × B shear and fluctuation characteristics, and have less impact on the L–H transition power threshold. This motivates further exploration of the RMP spectrum dependence of PLH for possible mitigation of the observed threshold increase.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Generalized Hall currents in topological insulators and superconductors

We generalize the idea of the quantized Hall current to count gapless edge states in topological materials, applying equally well to theories in different dimensions, with or without continuous symmetries in the bulk or chiral anomalies on the boundaries. This current is related to the index of the Euclidean fermion operator and can be calculated via one-loop Feynman diagrams. Quantization of the current is shown to be governed by topology in phase space, and the procedure can be applied to topological classes governed by either Z or Z 2 invariants. We analyze several explicit examples of free fermions in relativistic field theories. We speculate that it may be possible to extend the technique to interacting theories as well, such as the interesting cases where interactions gap the edge states.

79 ASTRONOMY AND ASTROPHYSICS↗

Elastic-plastic conductor damage evaluation at over 0.4% strain using a high-stress REBCO coil

Abstract Recent reports on screening current stress simulations of high-field REBCO magnets frequently present peak stresses over 1 GPa. However, this result is probably an unrealistic artifact of purely elastic calculations, considering the macroscopic yield and fracture stresses of approximately 900 MPa and less than 1.1 GPa for Hastelloy substrate-coated conductors. Here, we evaluate elastic-plastic conductor damage at over 0.4% strain using a high-stress REBCO coil exposed to a high field to explore this elastic-plastic regime. The coil was located off-center in a low-temperature superconductor magnet so as to induce a significant screening current in the enhanced radial field. Voltage taps, a Hall sensor, and two strain gauges were used for the instrumentation. We obtained strains exceeding 0.4% near the outward edge during the coil current charge from 350 A to 390 A, where the coil was exposed to external axial and radial magnetic fields of 13 T and 0.5 T. Post mortem results showed wavy plastic deformation, electrical damage, and REBCO defects. An elastic-plastic simulation reproduced the measured strains and predicted that ∼1 GPa stress is sufficient to induce ∼0.9% strain, thus validating our initial concerns with purely elastic models. This paper provides our experimental and simulation results.

Bang, Jeseok (ORCID:0000000247616334)↗

Compatibility of divertor detachment and ELM suppression in DIII-D high- β p plasmas with ITER-similar shape

Abstract Integration of transient and steady-state divertor heat fluxes control with a high-performance core is necessary for future fusion reactors. In recent DIII-D high- β p experiments, divertor detachment and simultaneous edge localized mode (ELM) suppression are demonstrated while the plasma confinement quality is maintained high in ITER-similar shape. By optimizing the neon injection in high- β p scenario with ITER-similar shape, deep detachment and ELM suppression are achieved with a high-performance core ( β N ∼ 2.8, β p ∼ 2.3) at q 95 ∼ 7.5. Partial divertor detachment and suppression of large ELMs are achieved at q 95 ∼ 6. The stability analyses suggest that with low neon injection, the density pedestal becomes higher and steeper and the T i profile also increases, therefore the increased edge pressure and higher current density destabilize the Peeling-Ballooning mode (PBM), which would lead to a large ELM collapse. With strong neon gas puffing, the significantly reduced pedestal pressure and current density, due to the degraded T e pedestal, lead to the stabilization of PBM and ELMs are suppressed. For both cases, the coupling between the large radius internal transport barrier (ITB) and edge pedestal is the key reason for maintaining high global performance. The formation of large radius ITB compensates for pedestal degradation. Such results could provide an attractive scenario to well control the transient and steady-state heat flux onto the divertor plates while maintaining good plasma performance, which is an important step toward the steady-state operation of future fusion reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Control of chiral orbital currents in a colossal magnetoresistance material

Colossal magnetoresistance (CMR) is an extraordinary enhancement of the electrical conductivity in the presence of a magnetic field. It is conventionally associated with a field-induced spin polarization that drastically reduces spin scattering and electric resistance. Ferrimagnetic Mn 3 Si 2 Te 6 is an intriguing exception to this rule: it exhibits a seven-order-of-magnitude reduction in ab plane resistivity that occurs only when a magnetic polarization is avoided 1,2 . Here, we report an exotic quantum state that is driven by ab plane chiral orbital currents (COC) flowing along edges of MnTe 6 octahedra. The c axis orbital moments of ab plane COC couple to the ferrimagnetic Mn spins to drastically increase the ab plane conductivity (CMR) when an external magnetic field is aligned along the magnetic hard c axis. Consequently, COC-driven CMR is highly susceptible to small direct currents exceeding a critical threshold, and can induce a time-dependent, bistable switching that mimics a first-order ‘melting transition’ that is a hallmark of the COC state. Further, the demonstrated current-control of COC-enabled CMR offers a new paradigm for quantum technologies.

36 MATERIALS SCIENCE↗

Topological Analysis of The SPOKE Graph

The SPOKE graph [2, 6] is a sparse decorated semantic graph representing a collection of knowledge collected in many scientific databases from the fields of healthcare, biochemistry, chemistry, biology, et cetera. This knowledge graph is stored as a relational dataset decorated with metadata on each constituent vertex and edge. Formally, the graph is G(V, E, D), where V is a set of n vertices V := {1, ..., n} and edges of the form (i, j) ϵ E for i, j ϵ V, and table D that for any item in V υ E stores unstructured data such as vertex/edge type, nature of a relationship, et cetera. D(i) = {data involving vertex i ϵ V}, and D(i, j) = {data involving edge (i, j) ϵ E}. Here, we treat the graph as undirected in the sense that a direct relationship for (i, j) causes a (possibly opposite) reverse direct relationship for (j, i). The SPOKE graph G(V, E, D) is formed by processing a collection of relational datasets from medicine, chemistry, and biology, connecting many entities. Here, we analyze an instance from 2019, Spoke-20190707, where a graph file contains 6.16M edges and associated metadata and a vertex file contains 2.15M vertices and the associated metadata. There are 12 different types of vertex entities; all edge types used are implicit (see §2). There is other metadata in D on edges and vertices, but we just use the topology and the vertex labels in this report. SPOKE is growing as more knowledge is gained and more datasets are added. SPOKE is likely to grow 10x during the next phase of this project, and we therefore would like to consider topoligical analysis techniques that are scalable to several orders of magnitude larger than the current dataset (say >1B edges).

59 BASIC BIOLOGICAL SCIENCES↗

Near-edge beta induced Alfven eigenmode in DIII-D high bootstrap current fraction plasmas

In DIII-D high bootstrap current fraction plasmas heated solely by neutral beam injection, a fast ion-driven Beta-Induced Alfven Eigenmode (BAE) is frequently observed. The BAE peaks near the edge, suggesting the possibility of enhanced fast ion transport there and raising a potential concern for the first wall. Theoretical analysis and simulations show that the mode location is not fully determined by the fast-ion drive intensity, i.e. fast ion pressure gradient but is strongly related to the background unfavorable magnetic curvature drive near the edge, which is an intrinsic property of advanced scenarios that rely on a large radius internal transport barrier that exists for all the kinetic channels for performance improvement.

beta induced Alfven eigenmode↗

Accelerating particle-in-cell kinetic plasma simulations via reduced-order modeling of space-charge dynamics using dynamic mode decomposition

We present a data-driven reduced-order modeling of the space-charge dynamics for electromagnetic particle-in-cell (EMPIC) plasma simulations based on dynamic mode decomposition (DMD). The dynamics of the charged particles in kinetic plasma simulations such as EMPIC is manifested through the plasma current density defined along the edges of the spatial mesh. We showcase the efficacy of DMD in modeling the time evolution of current density through a low-dimensional feature space. Not only do such DMD based predictive reduced-order models help accelerate EMPIC simulations, they also have the potential to facilitate investigative analysis and control applications. Here, we demonstrate the proposed DMD-EMPIC scheme for reduced-order modeling of current density and speedup in EMPIC simulations involving electron beam under the influence of magnetic field, virtual cathode oscillations, and backward wave oscillator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Machine-Learning Accelerated Studies of Materials with High Performance and Edge Computing

In the studies of materials, experimental measurements often serve as the reference to verify physics theory and modeling; while theory and modeling provide a fundamental understanding of the physics and principles behind. However, the interactions and cross validation between them have long been a challenge even to-date. Not only that inferring a physics model from experimental data is itself a difficult inverse problem, another major challenge is the orders-of-magnitude longer wall-clock time required to carry out high-fidelity computer modeling to match the timescale of experiments. We envisage that by combining high performance computing, data science, and edge computing technology, the current predicament can be alleviated, and a new paradigm of data-driven physics research will open up. For example, we can accelerate computer simulations by first performing the large-scale modeling on high performance computers and train a machine-learned surrogate model. This computationally inexpensive surrogate model can then be transferred to the computing units residing closely to the experimental facilities to perform high-fidelity simulations at a much higher throughout. The model will also be more amenable to analyzing and validating experimental observations in comparable time scales at a much lower computational cost. Further integration of these accelerated computer simulations with an outer machine learning loop can also inform and direct future experiments, while making the inverse problem of physics model inference more tractable. We will demonstrate a proof-of-concept by using a quantum Monte Carlo application, Dynamical Cluster Approximation (DCA++), to machine-learn a surrogate model and accelerate the study of quantum correlated materials.

Li, Ying Wai↗