Role of the electronically-active amorphous state in low-temperature processed In2O3 thin-film transistors
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99 GENERAL AND MISCELLANEOUS↗
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Long-term persistence of infectious Zika virus: Inflammation and behavioral sequela in mice
The neurodevelopmental defects associated with ZIKV infections early in pregnancy are well documented, however the potential defects and long-term consequences associated with milder infections in late pregnancy and perinatal period are less well understood. To model these, we challenged 1 day old (P1) immunocompetent C57BL/6 mice with ZIKV. The animals developed a transient neurological syndrome including unsteady gait, kinetic tremors, severe ataxia and seizures 10–15 days post-infection (dpi) but symptoms subsided after a week, and most animals survived. Despite apparent recovery, MRI of convalescent mice show reduced cerebellar volume that correlates with altered coordination and motor function as well as hyperactivity and impulsivity. Persistent mRNA levels of pro-inflammatory genes including Cd80 , Il-1α , and Ifn-γ together with Cd3 , Cd8 and perforin ( PrfA) , suggested persistence of low-grade inflammation. Surprisingly, the brain parenchyma of convalescent mice harbor multiple small discrete foci with viral antigen, active apoptotic processes in neurons, and cellular infiltrates, surrounded by activated astrocytes and microglia as late as 1-year post-infection. Detection of negative-sense strand viral RNA and isolation of infectious virus derived from these convalescent mice by blinded passage in Vero cells confirmed long-term persistence of replicating ZIKV in CNS of convalescent mice. Although the infection appears to persist in defined reservoirs within CNS, the resulting inflammation could increase the risk of neurodegenerative disorders. This raises concern regarding possible long-term effects in asymptomatic children exposed to the virus and suggests that long-term neurological and behavioral monitoring as well as anti-viral treatment to clear virus from the CNS may be useful in patients exposed to ZIKV at an early age.
59 BASIC BIOLOGICAL SCIENCES↗
DFT Investigation of Ammonia Formation via a Langmuir–Hinshelwood Mechanism on Mo-Terminated δ-MoN(0001)
In this work, we employed density functional theory to elucidate the energetics associated with elementary steps along a Langmuir-Hinshelwood mechanism for the Haber-Bosch synthesis of ammonia from N 2 and H 2 on a hexagonal, Mo-terminated molybdenum nitride surface. Using nudged elastic band calculations, we determined the energy barriers involved in the reaction processes. An active site consisting of four nearest-neighbor Mo atoms, previously identified as an active site on similar surfaces, was chosen to investigate the reaction processes. Using this approach, we calculate a barrier of ~0.5 eV for the dissociation of N 2 . The superior activity of the dissociation of the strong N 2 bonds is rationalized based on the unique geometric and electronic configurations present at these active sites. Despite the favorable energetics for nitrogen dissociation, the energy cost for hydrogenation of NH x (0 ≤ x ≤ 2) species is shown to be energetically limiting for the formation of ammonia through the Langmuir-Hinshelwood mechanism at these sites, with elementary step activation barriers calculated to be as large as ~2 eV. A comparison to Haber-Bosch results derived from a similar γ-Mo 2 N model system suggests the relative independence of surface chemistry and bulk stoichiometry for rhombic Mo 4 active sites present on molybdenum nitrides.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A tunable LIC1-adaptor interaction modulates dynein activity in a cargo-specific manner
Cytoplasmic dynein-1 (dynein) is the motor responsible for most retrograde transport of cargoes along microtubules in eukaryotic cells, including organelles, mRNA and viruses. Cargo selectivity and activation of processive motility depend on a group of so-called “activating adaptors” that link dynein to its general cofactor, dynactin, and cargoes. The mechanism by which these adaptors regulate dynein transport is poorly understood. Here, based on crystal structures, quantitative binding studies, and in vitro motility assays, we show that BICD2, CRACR2a, and HOOK3, representing three subfamilies of unrelated adaptors, interact with the same amphipathic helix of the dynein light intermediate chain-1 (LIC1). While the hydrophobic character of the interaction is conserved, the three adaptor subfamilies use different folds (coiled-coil, EF-hand, HOOK domain) and different surface contacts to bind the LIC1 helix with affinities ranging from 1.5 to 15.0 μM. We propose that a tunable LIC1-adaptor interaction modulates dynein’s motility in a cargo-specific manner.
59 BASIC BIOLOGICAL SCIENCES↗
MOOSE ProbML: Parallelized probabilistic machine learning and uncertainty quantification for computational energy applications
Here, this paper presents the development and demonstration of massively parallel probabilistic machine learning (ML) and uncertainty quantification (UQ) capabilities within the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source computational platform for parallel finite element and finite volume analyses. In addressing the computational expense and uncertainties inherent in complex multiphysics simulations, this paper integrates Gaussian process (GP) variants, active learning, Bayesian inverse UQ, adaptive forward UQ, Bayesian optimization, evolutionary optimization, and Markov chain Monte Carlo (MCMC) within MOOSE. It also elaborates on the interaction among key MOOSE systems — Sampler, MultiApp, Reporter, and Surrogate — in enabling these capabilities. The modularity offered by these systems enables development of a multitude of probabilistic ML and UQ algorithms in MOOSE. Example code demonstrations include parallel active learning and parallel Bayesian inference via active learning. The impact of these developments is illustrated through five applications relevant to computational energy applications: UQ of nuclear fuel fission product release, using parallel active learning Bayesian inference; very rare events analysis in nuclear microreactors using active learning; advanced manufacturing process modeling using multi-output GPs (MOGPs) and dimensionality reduction; fluid flow using deep GPs (DGPs); and tritium transport model parameter optimization for fusion energy, using batch Bayesian optimization. These capabilities are part of the MOOSE framework.
97 - MATHEMATICS AND COMPUTING↗
Advanced multi-layer active magnetic regenerator systems and processes for magnetocaloric liquefaction
A process for liquefying a process gas that includes introducing a heat transfer fluid into an active magnetic regenerative refrigerator apparatus that comprises a single stage comprising dual multilayer regenerators located axially opposite to each other.
Holladay, Jamie D.↗
Advanced multi-layer active magnetic regenerator systems and processes for magnetocaloric liquefaction
A process for liquefying a process gas that includes introducing a heat transfer fluid into an active magnetic regenerative refrigerator apparatus that comprises a single stage comprising dual multilayer regenerators located axially opposite to each other.
Holladay, Jamie D.↗
Creating and Preserving Nanoparticles during Co-Sintering of Solid Oxide Electrodes and Its Impact on Electrocatalytic Activity
A novel processing method that creates and preserves ceramic nanoparticles in solid oxide electrodes during co-sintering at traditional sintering temperatures is introduced. Specifically, carbon templated samarium-doped ceria nanoparticles (nSDC) were successfully integrated with commercial lanthanum strontium cobalt ferrite (LSCF) and commercial SDC powders, producing LSCF-SDC-nSDC cathodes upon processing. The effect of nSDC concentration on cathode electrocatalytic activity was investigated at low operational temperatures, 600 °C–700 °C, with symmetrical cells. Low nSDC loadings, ≤5 wt% nSDC, significantly decreased cell polarization resistance whereas higher loadings increased it. The best electrochemical performance was achieved with 5 wt% nSDC, lowering the polarization resistance by 41% at 600 °C. Fuel cell tests demonstrate that adding 5 wt% nSDC increased the maximum fuel cell power density by 38%. Electrochemical impedance spectra showed substantial improvements in both fuel cell polarization resistance and ohmic resistance, indicating that nSDC increased the electrocatalytically active area of the cathode. This work demonstrates a simple, novel method for effectively increasing electrocatalytic activity of solid oxide electrodes at low operational temperatures.
Muhoza, Sixbert P. (ORCID:0000000299864241)↗
Commercialization of High-Density High Assay Low Enriched Uranium Fuel Systems
The Office of Reactor Conversion and Uranium Supply (NA 231) at the National Nuclear Security Administration leads the conversion effort for the United States High Performance Research Reactors (USHPRR). These reactors are the final civilian reactors in the US to transition from High Enriched Uranium (HEU) to high assay low enriched uranium (HALEU). Each of these reactors represents unique capabilities and no currently available fuel system meets their needs for conversion. The Fuel Fabrication (FF) Pillar of the USHPRR project is responsible for the fabrication of experimental elements, conversion elements, and establishing a commercial economical production capability. FF is also responsible to share with the domestic and international community the theoretical knowledge gained. Other pillars within the USHPRR project provide the experimental and conversion fuel designs, assist the reactors with licensing activities, and ensure the entire fuel cycle is evaluated. Over the last decade, FF has worked with the production partners at Y-12 National Security Complex (Y-12) and BWXT Nuclear Operations Group, Research and Test Reactors (BWXT). Y-12 has begun processing the alloy feedstock for the conversion elements with a qualified process. BWXT has started the final fabrication of the experimental elements. Once the experimental elements are complete, BWXT will begin conversion element fabrication. The FF Pillar resides at Pacific Northwest National Laboratory (PNNL) and uses PNNL, universities, commercial vendors, and the DOE national laboratory system to evaluate process development activities to improve the process steps. FF supports the fabrication of two high density fuel systems, monolithic U-10Mo (Figure 1) and Uranium Silicide (Figure 2). The U-10Mo fuel system is further along the development process. FF assists in long term planning with the production partners. This includes ramping production of the elements from experimental quantities to annual steady state needs. As part of the ramp up, opportunities to improve yield and product quality are identified to ensure the fuel systems are cost effective.
Catalan, Michael A. [BATTELLE (PACIFIC NW LAB)]↗
Linear Quantum Coupler for Clean Bosonic Control
Quantum computing with superconducting circuits relies on high-fidelity driven nonlinear processes. An ideal quantum nonlinearity would selectively activate desired coherent processes at high strength, without activating parasitic mixing products or introducing additional decoherence. The wide bandwidth of the Josephson nonlinearity makes this difficult, with undesired drive-induced transitions and decoherence limiting qubit readout, gates, couplers, and amplifiers. Significant strides have recently been made into building better “quantum mixers,” with promise being shown by Kerr-free three-wave mixers that suppress driven frequency shifts, and balanced quantum mixers that explicitly forbid a significant fraction of parasitic processes. We propose a novel mixer that combines both these strengths, with engineered selection rules that make it essentially linear (not just Kerr-free) when idle, and activate clean parametric processes even when driven at high strength. Furthermore, its ideal Hamiltonian is simple to analyze analytically, and we show that this ideal behavior is first-order insensitive to dominant experimental imperfections. We expect this mixer to allow significant advances in high-𝑄 control, readout, and amplification.
Quantum circuits↗
Active Learning for Metamaterial Optimization on HPC and QC Integrated Systems
Active learning algorithms, integrating machine learning, quantum computing and optics simulation in an iterative loop, offer a promising approach to optimizing metamaterials. However, these algorithms can face difficulties in optimizing highly complex structures due to computational limitations. High-performance computing (HPC) and quantum computing (QC) integrated systems can address these issues by enabling parallel computing. In this study, we develop an active learning algorithm working on HPC-QC integrated systems. We evaluate the performance of optimization processes within active learning (i.e., training a machine learning model, problem-solving with quantum computing, and evaluating optical properties through wave-optics simulation) for highly complex metamaterial cases. Our results showcase that utilizing multiple cores on the integrated system can significantly reduce computational time, thereby enhancing the efficiency of optimization processes. Therefore, we expect that leveraging HPC-QC integrated systems helps effectively tackle large-scale optimization challenges in general.
Kim, Seongmin↗
High n -type conductivity and carrier concentration in Si-implanted homoepitaxial AlN
We demonstrate Si-implanted AlN with high conductivity (>1 Ω -1 cm -1 ) and high carrier concentration (5 × 101 8 cm -3 ). This was enabled by Si implantation into AlN with a low threading dislocation density (TDD) (<10 3 cm -2 ), a non-equilibrium damage recovery and dopant activation annealing process, and in situ suppression of self-compensation during the annealing. Low TDD and active suppression of V Al -nSi Al complexes via defect quasi Fermi level control enabled low compensation, while low-temperature, non-equilibrium annealing maintained the desired shallow donor state with an ionization energy of ~70 meV. The realized n-type conductivity and carrier concentration are over one order of magnitude higher than that reported thus far and present a major technological breakthrough in doping of AlN.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Composite media for non-oxidative ethane dehydrogenation, and related ethane activation systems and method of processing an ethane-containing stream
A composite media for non-oxidative C 2 H 6 dehydrogenation comprises an aluminosilicate zeolite matrix, and an EDH catalyst on one or more of an external surface of the aluminosilicate zeolite matrix and internal surfaces within pores of the aluminosilicate zeolite matrix. The EDH catalyst comprises one or more of Fe, Zn, Pt, Ga, alloys thereof, and oxides thereof. A C 2 H 6 activation system, and a method of processing a C 2 H 6 -containing stream are also described.
Wang, Lucun↗
Multielectron Bond Cleavage Processes Enabled by Redox-Responsive Phosphinimide Ligands
The activation of small molecules via multielectron redox processes offers promise in mediating difficult transformations related to energy conversion processes. While molecular systems that engage in one- and two-electron redox processes are widespread, those that participate in the direct transfer of four or more electrons to small molecules are very rare. To that end, we report a mononuclear Cr II complex competent for the 4-electron reduction of dioxygen (O 2 ) and nitrosoarenes. These systems additionally engage in facile two-electron group transfer reactivity, including O atom excision and nitrene transfer. Structural, spectroscopic, and computational studies support bond activation processes that intimately occur at a mononuclear chromium(phosphinimide) center and highlight the unusual structural responsiveness of the phosphinimides in stabilizing a range of metal redox states.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Integration of Omics into a New Comprehensive Rate Law for Competitive Terminal Electron-Accepting Processes in Reactive Transport Models: Application to N, Fe, S, and Contaminant Transformations in Stream and Wetland Sediments
Surface waters represent important sources of alternate energy and drinking water in the United States, and characterizing the biogeochemical processes that affect surface water quality is relevant to the DOE-BER mission. Sediment biogeochemical processes regulate the release of carbon (C), nutrients, and contaminants to surface waters and thus influence water quality. Sediment biogeochemical processes are dynamic and affected by the deposition and remobilization of solid material and changes in environmental conditions driven by water discharge variations. Wetlands are important natural filters of surface waters which may either trap, metabolize, or mobilize nutrients and contaminants. Despite their importance, biogeochemical processes regulating nutrient and contaminant release and C transformation in stream and wetland sediments cannot be predicted accurately by current mathematical models. These reactive transport models largely rely on detectable changes in geochemical conditions to activate metabolic processes, do not accurately account for the competition between microbial processes, and poorly constrain effects of hydrological perturbations on biogeochemical processes. In this BER-SBR exploratory project, metagenomic and geochemical signatures were combined to identify microbially-mediated redox processes in anaerobic stream and wetland sediments from the Savannah River Site (SRS, ANL SFA) and East Fork Poplar Creek (EFPC, ORNL SFA) that play important roles in C, uranium (U), and mercury (Hg) transformations. In addition, sediment incubations were conducted to examine the competition between anaerobic respiration processes Finally, new rate laws were developed for reactive transport models that rely on complementary metagenomic and geochemical signatures to identify the underlying anaerobic microbial processes in stream and wetland sediments, describe the competition between the dominant metabolic processes involved in nutrient release and U and Hg mobilization, and more accurately quantify carbon transformation and the response of microbial processes to changes in redox conditions associated with hydrological forcing. These rate laws were optimized in batch reactors with SRS wetland sediments, where iron and sulfate reduction dominate. Anaerobic carbon remineralization processes followed the expected thermodynamic sequence of microbial respiration with depth in both sediments, except that geochemical signals indicated that sulfate reduction was inactive in EFPC sediments and moderate in SRS wetland sediments. Estimates indicated that microbial iron reduction contributed to at least half of the production of reduced iron in these sediments. Incubations demonstrated that nitrate reduction, denitrification, and dissimilatory nitrate reduction to ammonium were active in the natural EFPC sediment and activated upon nitrate amendment in these nitrate-rich sediments. In turn, these processes were outcompeted by the addition of either iron oxides or sulfate as alternative terminal electron acceptors. Although geochemical products of sulfate reduction were not detected in the incubations, the abundance of sulfate reduction genes increased with depth in the sediment and was equally more pronounced in treatments amended with either iron oxides or sulfate. Simultaneously, anaerobic sulfide oxidizing bacteria coupling sulfide oxidation to DNRA (and not conventional denitrification) were apparently enriched over time, regardless of the treatments. These findings indicate that sulfate reduction is important in freshwater stream sediments and probably catalyzed by a cryptic sulfur cycle involving nitrogen species, in which the sulfur products from sulfate reduction are immediately removed by side reactions and not detectable by geochemical measurements alone. Similar experiments in SRS sediments, however, demonstrated little interaction between nitrogen and sulfur cycling microorganisms. Sulfate reduction was impacted by the addition of more thermodynamically favorable electron acceptors, suggesting either that iron-reducing microorganisms outcompeted sulfate-reducing microorganisms for organic substrate, depleting the stock of electron donor available for sulfate reduction, or that the cryptic sulfur cycle was shunted by the precipitation of FeS generated as a result of the abiotic reduction of iron oxides by dissolved sulfide. A diagnostic modeling exercise was conducted to further investigate the competition between terminal electron accepting processes. As conventional kinetic models typically do not account for cryptic cycles and used inaccurate formulations to describe competition between microbial communities, new metabolic rate laws were developed that explicitly express the electron acceptor-specific enzyme of each energetically favorable metabolic process based on gene abundance detected in the incubations. The model was tested with the sediment slurry incubation data to determine whether substrate competition could explain the decrease in sulfate reduction observed in the presence of iron oxide competitor. The model was able to reproduce geochemical concentrations really well in each treatment once the model was calibrated with the unamended control, suggesting that microbial competition was indeed driven by thermodynamic considerations. Overall, carbon remineralization processes and rates will be reproduced much more realistically with the new metabolic rate laws.
54 ENVIRONMENTAL SCIENCES↗
Understanding the Surface Structure and Catalytic Activity of SnO x /Au(111) Inverse Catalysts for CO 2 and H 2 Activation
Carbon dioxide hydrogenation is a promising approach for the reduction of greenhouse gas pollution via the production of fuels and high-value chemicals utilizing C1 chemistry. In this process, the activation of nonpolar molecules, CO 2 and H 2 , at mild conditions is challenging. In this study, we report a well-defined inverse SnO x /Au(111) catalyst that shows the ability to activate both CO 2 and H 2 at room temperature. Scanning tunneling microscopy (STM) and ambient pressure X-ray photoemission spectroscopy (AP-XPS) are combined to understand the surface structure, growth mode, chemical state, and activity of SnO x /Au(111) surfaces. Nanostructures of SnO x at the sub-monolayer level were prepared by depositing Sn on Au(111) followed by O 2 oxidation. For the as-prepared SnO x /Au(111), two-dimensionally formed SnO x thin films on a Au(111) substrate were observed with STM of two different moieties, discernible based on their height: clusters (~0.4 Å) and nanoparticles (NPs, 1–2.5 Å), which are assigned to Sn–Au alloys and SnO x , respectively, in corroboration with XPS analysis. Furthermore, SnO x /Au(111) was annealed under UHV to test its thermal stability. Upon annealing at 400–600 K, a disappearance of SnO x NPs and reappearance of highly dispersed Sn clusters were clearly noticeable from the STM and XPS results, identifying the thermal decomposition of SnO x and subsequent formation of Sn–Au alloys on the surface due to the recombination of Sn clusters with Au. We investigated the reactivity of the SnO x /Au(111) surfaces toward CH 4 , CO 2 , and H 2 . The SnO x /Au(111) surfaces have excellent CO 2 and H 2 activation abilities even at room temperature with negligible reactivity for methane activation. Our AP-XPS results show that H 2 can be activated on the SnO x NPs by the reduction to Sn. For CO 2 , the activation and further dissociation are identified by a reoxidation of Sn with newly formed Sn–O bonds and the formation of surface carbon. Therefore, we propose that SnO x is a potential catalyst or additive to achieve CO 2 hydrogenation under mild conditions.
36 MATERIALS SCIENCE↗
The case for digital twins in metal additive manufacturing
The digital twin (DT) is a relatively new concept that is finding increased acceptance in industry. A DT is generally considered as comprising a physical entity, its virtual replica, and two-way digital data communications in-between. Its primary purpose is to leverage the process intelligence captured within digital models—or usually their faster-solving surrogates—towards generating increased value from the physical entities. The surrogate models are created using machine learning based on data obtained from the field, experiments and digital models, which may be physics-based or statistics-based. Anomaly detection and correction, and diagnostic closed-loop process control are examples of how a process DT can be deployed. In the manufacturing industry, its use can achieve improvements in product quality and process productivity. Metal additive manufacturing (AM) stands to gain tremendously from the use of DTs. This is because the AM process is inherently chaotic, resulting in poor repeatability. However, a DT acting in a supervisory role can inject certainty into the process by actively keeping it within bounds through real-time control commands. Closed-loop feedforward control is achieved by observing the process through sensors that monitor critical parameters and, if there are any deviations from their respective optimal ranges, suitable corrective actions are triggered. The type of corrective action (e.g. a change in laser power or a modification to the scanning speed) and its magnitude are determined by interrogating the surrogate models. Because of their artificial intelligence (AI)-endowed predictive capabilities, which allow them to foresee a future state of the physical twin (e.g. the AM process), DTs proactively take context-sensitive preventative steps, whereas traditional closed-loop feedback control is usually reactive. Apart from assisting a build process in real-time, a DT can help with planning the build of a part by pinpointing the optimum processing window relevant to the desired outcome. Again, the surrogate models are consulted to obtain the required information. In this article, we explain how the application of DTs to the metal AM process can significantly widen its application space by making the process more repeatable (through quality assurance) and cheaper (by getting builds right the first time).
36 MATERIALS SCIENCE↗
Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida
Although synthetic biology can produce valuable chemicals in a renewable manner, its progress is still hindered by a lack of predictive capabilities. Media optimization is a critical, and often overlooked, process which is essential to obtain the titers, rates and yields needed for commercial viability. Here, we present a molecule- and host-agnostic active learning process for media optimization that is enabled by a fast and highly repeatable semi-automated pipeline. Its application yielded 60% and 70% increases in titer, and 350% increase in process yield in three different campaigns for flaviolin production in Pseudomonas putida KT2440. Explainable Artificial Intelligence techniques pinpointed that, surprisingly, common salt (NaCl) is the most important component influencing production. The optimal salt concentration is very high, comparable to seawater and close to the limits that P. putida can tolerate. The availability of fast Design-Build-Test-Learn (DBTL) cycles allowed us to show that performance improvements for active learning are rarely monotonous. This work illustrates how machine learning and automation can change the paradigm of current synthetic biology research to make it more effective and informative, and suggests a cost-effective and underexploited strategy to facilitate the high titers, rates and yields essential for commercial viability.
59 BASIC BIOLOGICAL SCIENCES↗