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At least 253 records · Page 14

DNA-assembled superconducting 3D nanoscale architectures

Studies of nanoscale superconducting structures have revealed various physical phenomena and led to the development of a wide range of applications. Most of these studies concentrated on one- and two-dimensional structures due to the lack of approaches for creation of fully engineered three-dimensional (3D) nanostructures. Here, we present a ‘bottom-up’ method to create 3D superconducting nanostructures with prescribed multiscale organization using DNA-based self-assembly methods. We assemble 3D DNA superlattices from octahedral DNA frames with incorporated nanoparticles, through connecting frames at their vertices, which result in cubic superlattices with a 48 nm unit cell. The superconductive superlattice is formed by converting a DNA superlattice first into highly-structured 3D silica scaffold, to turn it from a soft and liquid-environment dependent macromolecular construction into a solid structure, following by its coating with superconducting niobium (Nb). Through low-temperature electrical characterization we demonstrate that this process creates 3D arrays of Josephson junctions. This approach may be utilized in development of a variety of applications such as 3D Superconducting Quantum interference Devices (SQUIDs) for measurement of the magnetic field vector, highly sensitive Superconducting Quantum Interference Filters (SQIFs), and parametric amplifiers for quantum information systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Innovative Approaches to the Decommissioning of a Redundant Plutonium Processing Facility - 20147

At Sellafield, in the North West of the United Kingdom (UK), there are a number of redundant plutonium processing facilities. Over the past three decades several decommissioning strategies have been deployed to clean up these redundant facilities. Whilst there have been many successes previously reported, there remain many challenges. As previously presented at WM2017 (Paper Ref 17081) some of these challenges have recently been addressed through a new approach which introduced a number of innovations. The purpose of these innovations is to seek ways to significantly reduce risks to both decommissioning personnel and the environment by minimising the extent of physical 'hands on' decommissioning activities and by simplifying the waste production process. This paper provides an update on the progress of these innovations since 2017. Additionally, the paper also provides details of the new decommissioning technologies which have been introduced over the past 2 years to enable the most challenging aspects of the redundant plutonium processing facility to be decommissioned. These new decommissioning technologies address challenges associated with the preparation of process vessels and pipework to enable their safe in-situ size reduction by remote means: - New decommissioning techniques have been introduced to allow penetrations to be remotely cut into process vessels and pipework to provide a means of accessing and controlling the venting of any potential hydrogen gas present. - Additionally, a new large scale bespoke remote diamond wire cutting machine has been designed and manufactured to enable the process vessels to be size reduced in situ. Both of these new cutting technologies have involved collaborative working with specialist equipment suppliers including extensive development, trialling demonstration and commissioning prior to bringing the equipment into operational service. Several years of work have now culminated in reaching the final stages of decommissioning of the redundant plutonium processing facility with decommissioning of the final process vessels now underway and completion expected by the end of 2019. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Acid Etching‐Driven Self‐Assembly of Mn‐Shell Inducing Rock‐Salt Phase for Enhanced Single‐Crystal Ni‐Rich Cathodes

With the wide adoption of Li‐ion batteries, Ni‐rich cathode is considered as one of the most promising candidates of cathodes due to its high energy density and low cost. However, stability decreased with increasing Ni content in the Ni‐rich cathode. To solve this bottleneck, many strategies, such as coating, doping, surface modification, and special morphologies, have been developed. Herein, we introduce a groundbreaking approach for enhancing Ni‐rich cathode through an innovative acid etching process that promotes Mn shell self‐assembly, inducing a rock‐salt phase on the surface. This method not only simplifies the Ni‐rich cathode modification process, but also significantly improves the structural stability and electrochemical performance of Ni‐rich cathode. Our findings demonstrate that developed single‐crystal Ni‐rich cathode shows 3–34 % better stability compared to both commercial modified Ni‐rich cathode and unmodified counterparts. The unique Mn shell effectively mitigates reversible phase shifts during cycling, contributing to a remarkable enhancement in cycling stability. Additionally, this novel fabrication technique paves the way for cost‐effective production of high‐performance cathode materials, offering substantial benefits for lithium‐ion battery technology. And this study proves the potential of this method in advancing the design and development of durable, high‐capacity cathode materials for next‐generation batteries.

Ni-rich cathode↗

A general quantum algorithm for open quantum dynamics demonstrated with the Fenna-Matthews-Olson complex

Using quantum algorithms to simulate complex physical processes and correlations in quantum matter has been a major direction of quantum computing research, towards the promise of a quantum advantage over classical approaches. In this work we develop a generalized quantum algorithm to simulate any dynamical process represented by either the operator sum representation or the Lindblad master equation. We then demonstrate the quantum algorithm by simulating the dynamics of the Fenna-Matthews-Olson (FMO) complex on the IBM QASM quantum simulator. This work represents a first demonstration of a quantum algorithm for open quantum dynamics with a moderately sophisticated dynamical process involving a realistic biological structure. We discuss the complexity of the quantum algorithm relative to the classical method for the same purpose, presenting a decisive query complexity advantage of the quantum approach based on the unique property of quantum measurement.

97 MATHEMATICS AND COMPUTING↗

Implementation of the Glycolate Destruction Process with Sodium Permanganate in the Defense Waste Processing Facility (DWPF) - 20141

The Savannah River Site (SRS) seeks to replace formic acid with glycolic acid as a chemical reductant in the Defense Waste Processing Facility (DWPF), where borosilicate glass is mixed with high-level radioactive waste, melted at high temperatures, then poured into stainless steel canisters for safe storage. Prior research and development have demonstrated the feasibility and advantages of the new nitric-glycolic acid flowsheet over the current nitric-formic acid flowsheet to chemically adjust the radioactive sludge slurry waste in the Sludge Receipt and Adjustment Tank (SRAT) prior to vitrification. Use of glycolic acid reduces hydrogen and ammonia generation during the reduction process, thus reducing flammability hazards and purge requirements in the DWPF processing vessels. In addition, glycolic acid will support the new Salt Waste Processing Facility (SWPF) by allowing for receipt of higher volumes of waste due to reduction of flammable gases, increased boil-up rates in the vessels, and easing the processing of future sludge batches containing high levels of mercury. However, during DWPF operations, trace amounts of glycolate are anticipated to be entrained in the recycle stream and sent to the Concentration, Storage, and Transfer Facilities (CSTF) via the Recycle Collection Tank (RCT). The RCT receives condensate from the Slurry Mix Evaporator Condensate Tank (SMECT) and Off-gas Condensate Tank (OGCT) that may contain unreacted glycolate. A literature review found the potential for hydrogen generation due to thermolysis of glycolate in caustic CSTF conditions. Savannah River National Laboratory (SRNL) conducted several tests on simulant and radioactive waste from various tanks at SRS and confirmed that glycolate added to waste at concentrations of approximately 1000 mg/L and higher generates hydrogen through thermolysis. There are potential operating scenarios in the future where glycolate concentration could exceed 1000 mg/L in the 2H Evaporator Drop Tank, the highest accumulation point for DWPF organics received via recycle. These findings presented a need to develop another process to mitigate the impact of glycolic acid to the CSTF. Several DWPF compatible options were evaluated for their ability to destroy glycolate in the DWPF recycle stream under various processing conditions, thereby mitigating its introduction to the CSTF. Downselection testing identified sodium permanganate as the most promising and simplest option to oxidize glycolate. It has been demonstrated to be effective in various RCT simulant compositions, operating temperatures, oxidant addition rates, solution pH, and initial glycolate concentrations. A feasibility study identified existing tanks within the facility that could be used for receiving and storing the commercially available sodium permanganate, as well as feeding it to the RCT for processing. The use of existing equipment as well as the ability to purchase the pre-mixed chemical reduces implementation and operational complexity. The downstream impacts of the sodium permanganate reaction were also evaluated and demonstrated to have minimal impact on other chemical species present in the RCT and CSTF, as well as on the materials of construction of the RCT and CSTF vessels and associated piping and instrumentation. Future work is in progress to validate the glycolate destruction process with testing in actual radioactive RCT waste before implementation in DWPF. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

CRISPR/Cas9 Directed Reprogramming of iPSC for Accelerated Motor Neuron Differentiation Leads to Dysregulation of Neuronal Fate Patterning and Function

Neurodegeneration causes a significant disease burden and there are few therapeutic interventions available for reversing or slowing the disease progression. Induced pluripotent stem cells (iPSCs) hold significant potential since they are sourced from adult tissue and have the capacity to be differentiated into numerous cell lineages, including motor neurons. This differentiation process traditionally relies on cell lineage patterning factors to be supplied in the differentiation media. Genetic engineering of iPSC with the introduction of recombinant master regulators of motor neuron (MN) differentiation has the potential to shorten and streamline cell developmental programs. We have established stable iPSC cell lines with transient induction of exogenous LHX3 and ISL1 from the Tet-activator regulatory region and have demonstrated that induction of the transgenes is not sufficient for the development of mature MNs in the absence of neuron patterning factors. Comparative global transcriptome analysis of MN development from native and Lhx-ISL1 modified iPSC cultures demonstrated that the genetic manipulation helped to streamline the neuronal patterning process. However, leaky gene expression of the exogenous MN master regulators in iPSC resulted in the premature activation of genetic pathways characteristic of the mature MN function. Dysregulation of metabolic and regulatory pathways within the developmental process affected the MN electrophysiological responses.

59 BASIC BIOLOGICAL SCIENCES↗

Considerations regarding the Use of Computer Vision Machine Learning in Safety-Related or Risk-Significant Applications in Nuclear Power Plants

With the advancements made to date in the field of artificial intelligence (AI), significant potential exists to utilize AI capabilities for nuclear power plant (NPP) applications. AI can replicate human decision making and it is usually faster and more accurate than humans. For implementations that impact critical NPP applications (e.g., safety-related or non-safety systems that potentially affect overall plant risk), a deeper safety analysis of the AI methods is necessary. AI applied to NPP operations could resemble the use of digital I&C (DI&C) because such applications involve digital computer hardware and custom-designed software that input plant data, execute complex software algorithms, and output the results to a system or licensed human operator to potentially provoke an action. For AI methods to be compliant with current safety requirements for DI&C, AI compatibility must be evaluated, and AI-related gaps may exist that prevent the prompt deployment of AI in NPPs. This effort aims to evaluate how example AI technologies align with the DI&C safety framework, and discusses how they could be analyzed, modeled, tested, and validated in a manner similar to typical DI&C technologies. Because AI is a broad field that encompasses areas such as machine learning (ML), natural language processing, and computer vision, this research focused on a subset of methods categorized as the computer vision ML (CVML) methods. This report explores two CVML use cases, gauge reading and fire watch, considered relevant to the DI&C standards, as they could play a safety-critical role. For the gauge reading use case, a CVML-enabled technology that can read gauges at oblique angles is utilized. For the fire watch use case, a CVML-enabled technology is utilized that migrates fire watch from a manual (human) approach to automated fire detection. These use cases are mainly intended to give context to the CVML system discussion. This effort assumes the worst-case scenario, with the CVML system being used to replace a safety-related or risk-significant system, thus requiring evaluation. Evaluating CVML against most of the relevant safety requirements for DI&C yielded several CVML-specific considerations due to the uniqueness of its characteristics in comparison with typical DI&C systems. For example, CVML models often employ commonly used (open-source) datasets, and it is not always possible to determine the level of overlap among open-source datasets. Therefore, the independence of the developed CVML models when demonstrating diversity is questionable, therefore creating vulnerability to common cause failure (CCF). The design verification process is also impacted since the data overlap could result in overestimation of the software validation and verification (V&V) performance results. Section 2 of this report evaluates a list of the identified CVML-specific characteristics and discusses the resulting considerations and potential solutions in the context of each referenced requirement. A summation is provided in Section 3. This report is not to be used as a guideline. It was developed to identify and consider issues in the implementation of ML technologies used to augment activities that may have a bearing on plant operation. The report draws parallels to the use of DI&C technologies, for which many standards are available to guide their use in nuclear plant operation. It considers the technologies and some of the potential implications of their use in safety-related applications but is not intended to address regulatory or licensing related issues.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

3D Characterization of the Columnar-to-Equiaxed Transition in Additively Manufactured Inconel 718

Additive manufacturing (AM) provides enormous processing flexibility, enabling novel part geometries and optimized designs. Access to a local heat source further permits the potential for local microstructure control on the scale of individual melt pools, which can enable local control of part properties. In order to design tailored processing strategies for target microstructures, models predicting the columnar-to-equiaxed transition must be extended to the high solidification velocities and complex thermal histories present in AM. Here, we combine 3D characterization with advanced modeling techniques to develop a more complete understanding of the solidification process and evolution of microstructure during electron beam melting (EBM) of Inconel 718. Full calibration of existing microstructure prediction models demonstrates the differences between AM processes and more conventional welding techniques, underlying the need for accurate determination of key parameters that can only be measured directly in 3D. The ability to combine multisensor data in a consistent 3D framework via data fusion algorithms is essential to fully leverage these advanced characterization approaches. Thermal modeling provides insight on microstructure development within isolated solidification events and demonstrates the role of Marangoni effects on controlling solidification behavior.

Polonsky, Andrew↗

Development of a Castable High Strength Secondary Aluminum Alloy from Recycled Wrought Aluminum Scrap

A new process for recycling scrap AA7075 aerospace alloys directly into a high strength castable secondary alloy is demonstrated. The process involves enhancing the castability of the utilized primary wrought aluminum scrap feedstock by scrap alloy chemistry optimization and zirconium additions in order to mitigate the inherent hot tearing tendency of 7075 alloys. By utilizing the new process, the hot tearing index of scrap AA7075 was reduced from 26 to 4. Defect-free castings were obtained in pilot-scale castings trials. The developed secondary 7075 alloy also satisfied preset target mechanical properties with strength levels exceeding 250 MPa and elongation levels higher than 3%. The validity of the new approach and its readiness for transition to commercial practice was demonstrated by casting cylinder heads directly from the modified 7075 scrap.

36 MATERIALS SCIENCE↗

Stochastic fracture generation and thermo-hydro-mechanical modeling in an equivalent continuum framework for enhanced geothermal systems

Enhanced geothermal systems (EGS) involve fracturing low permeability material to establish well connectivity and then injecting and circulating fluid into the fractured subsurface for geothermal power production. Changes in fracture aperture from contraction of the cooling matrix rock may alter network connectivity and risk thermal short-circuiting. Thermo-hydro-mechanical (THM) models are a useful tool to study these processes. However, as fracture networks are complex, and data may be limited, fracture networks in THM models are often stochastically generated. Given reliance on stochastic fracture networks and THM modeling to represent the subsurface and assess productivity of EGS, increased understanding of the influence of such statistically derived fracture networks on flow and heat transport in THM models is needed. Here, a new fracture process model is developed in the reactive transport code PFLOTRAN to stochastically generate fracture families and simulate changes in fracture aperture over time due to temperature changes of the rock matrix. Sixty-four different fracture networks ranging from well to poorly-connected, are modeled in PFLOTRAN with and without mechanical processes (THM vs TH). Results indicate that for well-connected fracture networks, thermal short-circuiting is less of a concern due to the abundance of available alternative flowpaths. For poorly-connected fracture networks, inclusion of mechanical processes showed steep thermal drawdown coincident with increase in fracture aperture along developing colder flowpaths, demonstrating the risk of thermal short-circuiting. Simulations with additional, larger fractures engineered to establish connectivity in a poorly-fractured subsurface, indicate that while stochastic variation of fracture orientation of the background network had limited influence, such variation in the engineered fractures significantly affected flow and heat transport.

Discrete fracture networks (DFN)↗

A systematic study and framework of fringe projection profilometry with improved measurement performance for in-situ LPBF process monitoring

Fringe Projection Profilometry (FPP) is a cost-effective and non-invasive technology that has been shown to measure finer features. Here, in this work, we developed an in-situ FPP method to measure the dynamic topography of powder bed and printed layer during Laser Powder Bed Fusion (LPBF) additive manufacturing (AM) process. A systematic study towards developing a comprehensive framework of LPBF-specific FPP is demonstrated to enhance and evaluate the performance of applying FPP for in-situ LPBF monitoring, including 1) a modified sensor model with localized correction; 2) improved phase unwrapping with FFT filtering 3) quantitative uncertainty analysis; and 4) experimental validation with ex-situ characterization. The developed LPBF-specific FPP system and methods are implemented on a commercial LPBF-AM machine, achieving better accuracy, more robustness, and increased field of view while maintaining sufficient measurement range and decent resolution, in contrast to literature methods. The established FPP framework will facilitate the development of closed-loop control strategies for advancing LPBF based AM.

42 ENGINEERING↗

Renormalization of excitonic properties by polar phonons

Here we employ quasiparticle path integral molecular dynamics to study how the excitonic properties of model semiconductors are altered by electron–phonon coupling. We describe ways within a path integral representation of the system to evaluate the renormalized mass, binding energy, and radiative recombination rate of excitons in the presence of a fluctuating lattice. To illustrate this approach, we consider Fröhlich-type electron–phonon interactions and employ an imaginary time influence functional to incorporate phonon-induced effects nonperturbatively. The effective mass and binding energies are compared with perturbative and variational approaches, which provide qualitatively consistent trends. We evaluate electron-hole recombination rates as mediated through both trap-assisted and bimolecular processes, developing a consistent statistical mechanical approach valid in the reaction limited regime. These calculations demonstrate how phonons screen electron–hole interactions, generically reducing exciton binding energies and increasing their radiative lifetimes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

SPOTTED-LEAF7 targets the gene encoding β-galactosidase9, which functions in rice growth and stress responses

Abstract β-Galactosidases (Bgals) remove terminal β-D-galactosyl residues from the nonreducing ends of β-D-galactosidases and oligosaccharides. Bgals are present in bacteria, fungi, animals, and plants and have various functions. Despite the many studies on the evolution of BGALs in plants, their functions remain obscure. Here, we identified rice (Oryza sativa) β-galactosidase9 (OsBGAL9) as a direct target of the heat stress-induced transcription factor SPOTTED-LEAF7 (OsSPL7), as demonstrated by protoplast transactivation analysis and yeast 1-hybrid and electrophoretic mobility shift assays. Knockout plants for OsBGAL9 (Osbgal9) showed short stature and growth retardation. Histochemical β-glucuronidase (GUS) analysis of transgenic lines harboring an OsBGAL9pro:GUS reporter construct revealed that OsBGAL9 is mainly expressed in internodes at the mature stage. OsBGAL9 expression was barely detectable in seedlings under normal conditions but increased in response to biotic and abiotic stresses. Ectopic expression of OsBGAL9 enhanced resistance to the rice pathogens Magnaporthe oryzae and Xanthomonas oryzae pv. oryzae, as well as tolerance to cold and heat stress, while Osbgal9 mutant plants showed the opposite phenotypes. OsBGAL9 localized to the cell wall, suggesting that OsBGAL9 and its plant putative orthologs likely evolved functions distinct from those of its closely related animal enzymes. Enzyme activity assays and analysis of the cell wall composition of OsBGAL9 overexpression and mutant plants indicated that OsBGAL9 has activity toward galactose residues of arabinogalactan proteins (AGPs). Our study clearly demonstrates a role for a member of the BGAL family in AGP processing during plant development and stress responses.

54 ENVIRONMENTAL SCIENCES↗

Parametric Testing and Evaluation of a Novel Biphasic Absorption Process for Post-Combustion Carbon Capture

A novel biphasic CO2 absorption process (BiCAP) is being developed at the University of Illinois. BiCAP is enabled by using a new class of biphasic solvents developed based on multi-property criteria for postcombustion carbon capture. The process was previously demonstrated at the lab-scale, including in 10 kWe absorption and desorption column systems. In the current study, the process has been scaled up by building and testing a 40 kWe integrated capture skid at a power plant. Parametric tests for two biphasic solvents were conducted with the synthetic flue gas made of bottle CO2 gas and air to validate the performance and operational flexibility of the unit with respect to varying process or operating conditions. Operation of the phase separator, which is unique equipment to BiCAP, was verified to be stable and efficient. Two liquid phases could be separated via static settlement based on a density difference and >90% of the CO2 absorbed was concentrated in the separated CO2-rich phase. Results of parametric tests showed that the minimum heat duty appeared at the optimal stripping pressure. A higher stripping temperature was more favourable to improve overall energy performance. Introducing a secondary cold solvent feed stream to the top of the stripper via bypassing the cross heat exchanger helped reduce the heat loss carried with water vapour. Test results also revealed that the heat duty requirement was comparable when the CO2 concentration in flue gas decreased from ~11 to 4 vol.% as the phase separation decoupled the absorption and desorption steps in BiCAP. Under typical steady-state conditions, both biphasic solvents lowered the process heat duty by >40% compared with the reference 30 wt.% monoethanolamine solvent.

Absorption↗

Nanostructures for Electrical Energy Storage (NEES) (2020 Final Technical Report)

Nanostructures for Electrical Energy Storage (NEES, www.efrc.umd.edu) was an Energy Frontier Research Center supported by the DOE Office of Science, Basic Energy Sciences, from 8/1/2009 to 7/31/2020. Led by the University of Maryland, NEES enjoyed extensive collaborations with its funded partners, including two DOE Laboratories and six universities. The NEES vision has been to reveal a set of scientific insights and design principles that can underpin a next-generation electrical energy storage approach, building on advances in nanoscale science and technology to achieve simultaneous high power and high energy over extended charge/discharge cycling. The vision is motivated by the recognition that scaling into the nano regime opens the door to new physical phenomena and that the tools enlisted in nanoscale research provide major new opportunities for the synthesis not only of materials at molecular scale but for structures at nano scale and above. NEES has translated this vision into its research program based on two observations. First, while the behavior of ions and electrons in electrolytes and in electrode materials is crucial to electrical energy storage (or more appropriately electrochemical energy storage), it is the transport of ion and electron charge between different structural components of a storage device that ultimately determine its performance. With it well recognized that the choice of electrode materials typically constrain ion transport kinetics as well as maximum ion concentration, the search for better electrode materials has been a primary driver of battery research. At the same time the synthesis of electrodes is typically based on aggregation of particles with varying size, shape, and orientation in the electrode. Together with the presence of additional materials to impart electrical conductivity and cohesion to the composite electrode, change in electrode materials is necessarily accompanied by structural changes at the nano/micro scale that are difficult to categorize and manage. From the beginning, NEES’ vision has been to create and study simpler, highly controlled spatial arrangements of known materials as battery components (electrodes, current collectors, and electrolyte) and to understand how design and structure above the molecular scale determines the energy storage performance available from known materials. Second, advances in nanoscience dramatically expanded the portfolio of synthesis methods, structural motifs, and new phenomena available for research. Some of these gave rapid access to new building blocks at the deep nanoscale (e.g., carbon nanotubes grown by self-assembly, nanoscale arrays formed by electrochemical self-alignment, monolayer films controlled by self-limiting reaction). Such advances served as the enabler for the NEES vision to be pursued experimentally through study of 3D structures created and controlled at the nano, micro, and meso scales. Here, we use meso as in the BES MESO Report, implying not only intermediate or varying length scales, but very much the way behavior is influenced by other factors including aggregation of nanocomponents at different densities and spatial configurations, statistical variations in the aggregates, hierarchical architectures in which they can be assembled, or local 3D configurations that result from the architectures. Over its life cycle, NEES has pursued two overarching goals: (1) to understand the scientific fundamentals of electrochemical storage from the nanoscale to the mesoscale; and (2) to create and learn from innovative, controlled, heterogeneous nanostructures, where such nanostructures can enable the first goal and serve as models for future paradigms in energy storage. Specific goals have included: Synthesize heterogeneous nanostructures comprised of multiple materials arranged in controlled fashion and characterize their behavior; Demonstrate and elucidate design principles for achieving simultaneous high power and high energy; Develop materials processes which enable precision control of thin layers and 3D structures; Investigate the impact of artificial interphases on electrode stability during ion insertion/deinsertion; Create dense arrays of nanostructures to understand how the architecture of these assemblies, along with nanostructure design, influences energy storage behavior at the mesoscale; Identify and understand the consequences of nanoconfinement and local inhomogeneities in 3D mesoscale arrays; Develop and apply computational models to stimulate, guide and interpret experiments.

25 ENERGY STORAGE↗

PixelStorm: A Remote Display for Remote Sensing Ground Stations

PixelStorm is a software application for displaying native high-performance applications from remote cloud environments. It is tailored for remote sensing missions that require high framerates, high resolutions, and minimal loss of quality. PixelStorm utilizes hardware-accelerated video compression on graphics processing units and a Sandia-developed streaming network protocol. Using our architecture, we can demonstrate interactive native applications running across two 4K monitors at 60 frames per second while maintaining the visual fidelity required by our missions. This technology allows for the migration of mission critical desktop applications to cloud environments.

97 MATHEMATICS AND COMPUTING↗

Workshop on Manufacturing and Integration Challenges for Analog and Neuromorphic Computing

The U.S. Department of Energy’s (DOE’s) Advanced Manufacturing Office (AMO) held its third virtual workshop on Semiconductor R&D for Energy Efficiency on August 11-13, 2021. This public workshop – titled “Manufacturing and Integration Challenges for Analog and Neuromorphic Computing” – brought together more than 180 leading scientific and technical experts to identify challenges and opportunities to improve manufacturing capabilities for analog and neuromorphic computing architectures to significantly increase energy efficiency of microelectronic devices. The workshop featured perspectives of researchers from national laboratories, universities, government agencies, and industry with expertise in novel and emerging devices, processes, and metrology and characterization. This workshop is intended to inform an AMO research, development, demonstration, and deployment (RDD&D) plan to significantly reduce semiconductor energy use by 2030. It was co-sponsored by the Semiconductor Research Corporation (SRC) and the DOE Office of Science’s Advanced Scientific Computing Research (ASCR).

97 MATHEMATICS AND COMPUTING↗

Novel Non-Atomizing Process for Producing Powder for Additive Manufacturing

Oak Ridge National Laboratory (ORNL) worked with Metal Powder Works (MPW) to demonstrate feasibility of MPW proprietary DirectPowder™ process to fabricate powder from DuAlumin-3D (ORNL developed high temperature additively manufactured - AM - aluminum) alloy ingots. Adoption of DirectPowder™ process powder has significant potential to reduce cost, energy expenditure, eliminate CO 2 emissions, and provide a higher quality product.

36 MATERIALS SCIENCE↗