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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An MLIR-based Compiler Flow for System-Level Design and Hardware Acceleration

The generation of custom hardware accelerators for applications implemented within high-level productive programming frameworks requires considerable manual effort. To automate this process, we introduce \sodaopt, a compiler tool that extends the MLIR infrastructure. \sodaopt automatically searches, outlines, tiles, and pre-optimizes relevant code regions to generate high-quality accelerators through high-level synthesis. \sodaopt can support any high-level programming framework and domain-specific language that interface with the MLIR infrastructure. By leveraging MLIR, \sodaopt solves compiler optimization problems with specialized abstractions. Backend synthesis tools connect to \sodaopt through progressive intermediate representation lowerings. \sodaopt interfaces to a design space exploration engine to identify the combination of compiler optimization passes and options that provides high-performance generated designs for different backends and targets. We demonstrate the practical applicability of the compilation flow by exploring the automatic generation of accelerators for deep neural networks operators outlined at arbitrary granularity and by combining outlining with tiling on large convolution layers. Experimental results with kernels from the PolyBench benchmark show that \sodaopt high-level optimizations improve execution delays of synthesized accelerators up to 60x. We also show that for the selected kernels, our solution outperforms the current of state-of-the art in more than 70% of the benchmarks and provides better average speedup in 55% of them.

Bohm Agostini, Nicolas↗
DOE OSTI2022

Bi-metallic Nanoparticle Synthesis for Advanced Manufactured Melt Wires

Science Undergraduate Laboratory Internship (SULI) Program Report: Additive manufacturing (AM) based on direct-write technologies has emerged as the predominant method for the fabrication of passive sensors for the harsh operating environments seen in a nuclear reactor. Through the modification of previous methods, Idaho National Laboratory and Villanova University have improved the synthesis process for AM feedstock, which will allow for the improvement of advanced nuclear sensors and instrumentation. A major part of this work includes the synthesis process of relevant AM compatible feedstock to support the development, fabrication, and testing of AM sensors for peak temperature detection. For this report, bismuth, bismuth/platinum, tin, tin/silver, tin/zinc, indium, and indium/silver bi-metallic nanoparticles were synthesized using the polyol method, which will enhance temperature sensitivity and allow for miniaturization. To characterize the synthesized nanoparticles, we used x-ray fluorescence to evaluate the elemental composition of the nanoparticles and differential scanning calorimetry and thermogravimetric analysis to determine the melting point and mass loss of the samples. Results show that bi-metallic nanoparticles are a viable option for the fabrication of high-resolution AM melt wires. The temperature sensitivity can be brought to within 5°C and melt wires can be fabricated that are in the micrometer scale. This will expand the range of irradiation experiments melt wires can be used for and the measured temperature will be significantly more accurate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Low Cost High Efficiency Photovoltaics Using Semiconductor Nanocrystals: Cooperative Research and Development CRADA Number CRD-15-00598 (Final Report)

Collaboration will occur between NREL and KIMM in the area of semiconductor nanocrystals for use in advanced solar photon energy conversion strategies. The project takes advantage of the unique capabilities and expertise regarding the incorporation of quantum dots (QD) into solar energy technologies that are available at NREL within the BES-funded programs. KIMM has unique expertise in the synthesis of new types of nanocrystals as well as advanced processes for solution processing.

14 SOLAR ENERGY↗
DOE OSTI2024

A Novel Integrated Fermentation Process with Engineered Microbial Consortia for Butanol Production from Lignocellulose Sugars without CO 2 Emission

The goal of this project was to develop a synthetic microbial consortium consisting of a lactic acid bacterium, a carboxydotrophic acetogen, and a solventogenic clostridia for the production of n butanol, an advanced biofuel and industrial chemical, from lignocellulose sugars (mainly glucose and xylose) and formate (produced from CO 2 by electrochemical reduction) in an integrated bioprocess (biorefinery), which can provide an effective solution to the technical challenges in developing energy and carbon optimized synthesis for the bioeconomy and achieve the program objectives of ARPA-E. The project focused on the design, modeling and construction of synthetic microbial consortia consisting of three bacterial species to maximize carbon conversion and butanol production with a 100% theoretical product yield from glucose and zero or negative CO 2 emission.

09 BIOMASS FUELS↗
DOE OSTI2022

Plasma-Catalyst Reactivity Control of Surface Nitrogen Species through Plasma-Temperature-Programmed Hydrogenation to Ammonia

Nonthermal plasma activation of N 2 can facilitate nitrogen adsorption on metal catalysts at low bulk temperatures and atmospheric pressure. Here, we apply a plasma-assisted temperature-programmed reaction (plasma-TPRxn) for ammonia (NH 3 ) synthesis using sequential exposure of a silica-supported metal catalyst to N 2 plasma followed by thermal hydrogen treatment while ramping the temperature to decouple the plasma activation of N 2 from surface catalyzed hydrogenation steps. This approach eliminates the effects from bulk plasma phase reactions, thereby allowing for direct interrogation of plasma activated nitrogen on the active metal surfaces. We confirm previously reported spectroscopic observations that show plasma-generated surface nitrogen can be converted to NH 3 through surface catalyzed pathways. Further, we demonstrate that the ammonia desorption peak temperature is sensitive to metal, with Pt desorbing NH 3 at the lowest temperature. Unsteady state microkinetic models of desorption kinetics as a function of initial N coverage and metal recover observed trends in NH 3 desorption temperatures and confirm that observed results reflect hydrogenation of plasma-induced N accommodation at each surface. In total, we show that the hydrogenation ability of the catalyst after plasma activation of N 2 is responsible for the reactivity trends observed in plasma-assisted NH 3 synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Chemical synthesis rewriting of a bacterial genome to achieve design flexibility and biological functionality

Understanding how to program biological functions into artificial DNA sequences remains a key challenge in synthetic genomics. Here, we report the chemical synthesis and testing of Caulobacter ethensis-2.0 ( C. eth-2.0 ), a rewritten bacterial genome composed of the most fundamental functions of a bacterial cell. We rebuilt the essential genome of Caulobacter crescentus through the process of chemical synthesis rewriting and studied the genetic information content at the level of its essential genes. Within the 785,701-bp genome, we used sequence rewriting to reduce the number of encoded genetic features from 6,290 to 799. Overall, we introduced 133,313 base substitutions, resulting in the rewriting of 123,562 codons. We tested the biological functionality of the genome design in C. crescentus by transposon mutagenesis. Our analysis revealed that 432 essential genes of C. eth-2.0 , corresponding to 81.5% of the design, are equal in functionality to natural genes. These findings suggest that neither changing mRNA structure nor changing the codon context have significant influence on biological functionality of synthetic genomes. Discovery of 98 genes that lost their function identified essential genes with incorrect annotation, including a limited set of 27 genes where we uncovered noncoding control features embedded within protein-coding sequences. In sum, our results highlight the promise of chemical synthesis rewriting to decode fundamental genome functions and its utility toward the design of improved organisms for industrial purposes and health benefits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Fluorine-programmed nanozipping to tailored nanographenes on rutile TiO 2 surfaces

The rational synthesis of nanographenes and carbon nanoribbons directly on nonmetallic surfaces has been an elusive goal for a long time. In this paper, we report that activation of the carbon (C)–fluorine (F) bond is a reliable and versatile tool enabling intramolecular aryl-aryl coupling directly on metal oxide surfaces. A challenging multistep transformation enabled by C–F bond activation led to a dominolike coupling that yielded tailored nanographenes directly on the rutile titania surface. Because of efficient regioselective zipping, we obtained the target nanographenes from flexible precursors. Fluorine positions in the precursor structure unambiguously dictated the running of the “zipping program,” resulting in the rolling up of oligophenylene chains. Finally, the high efficiency of the hydrogen fluoride zipping makes our approach attractive for the rational synthesis of nanographenes and nanoribbons directly on insulating and semiconducting surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Molecular Programming of Diorganyl Dichalcogenides for Rational Nanocrystal Design

Soft-chemistry nanocrystal synthesis leverages low-temperature, solution-phase reactions to access materials that can be kinetically stabilized rather than thermodynamically favored. Under such mild conditions, reaction pathways are governed not only by precursor composition but also by the molecular details that dictate how reactive atomic species are generated and delivered. Furthermore, harnessing this kinetic sensitivity offers a powerful opportunity: by deliberately programming precursor reactivity, nanocrystal composition, structure, and morphology can be rationally designed rather than empirically discovered.

Chalcogenides↗
DOE OSTI2019

Elucidating the composition of PtAg surface alloys with atomic-scale imaging and spectroscopy

Silver-based heterogeneous catalysts, modified with a range of elements, have found industrial application in several reactions in which selectivity is a challenge. Alloying small amounts of Pt into Ag has the potential to greatly enhance the somewhat low reactivity of Ag while maintaining high selectivity and resilience to poisoning. This single-atom alloy approach has had many successes for other alloy combinations but has yet to be investigated for PtAg. Using scanning tunneling microscopy (STM) and STM-based spectroscopy, we characterized the atomic-scale surface structure of a range of submonolayer amounts of Pt deposited on and in Ag(111) as a function of temperature. Near room temperature, intermixing of PtAg results in multiple metastable structures on the surface. Increasing the alloying temperature results in a higher concentration of isolated Pt atoms in the regions near Ag step edges as well as direct exchange of Pt atoms into Ag terraces. Furthermore, STM-based work function measurements allow us to identify Pt rich areas of the samples. Additionally, we use CO temperature programmed desorption to confirm our STM assignments and quantify CO binding strengths that are compared with theory. Importantly, we find that CO, a common catalyst poison, binds more weakly to Pt atoms in the Ag surface than extended Pt ensembles. Taken together, this atomic-scale characterization of model PtAg surface alloys provides a starting point to investigate how the size and structure of Pt ensembles affect reaction pathways on the alloy and can inform the design of alloy catalysts with improved catalytic properties and resilience to poisoning.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Integration of graphical approaches into optimization-based design of multistage liquid extraction

We propose two optimization models for designing two liquid extraction systems: simple multistage liquid extractors and extractors with extract reflux. Both models are motivated by the concepts of the modified McCabe-Thiele graphical method for multistage extractor design. The operating and equilibrium curves in the McCabe-Thiele method are represented by material balances and piece-wise linearized thermodynamics properties. The use of piece-wise approximations improves computational tractability of both optimization models. In addition, we consider some extensions such as dilute systems, insoluble solvents, and non-ideal stages. In conclusion, the applicability of the proposed models is demonstrated with four illustrative examples.

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

Steam-Assisted Ammonolysis of MoO2 as a Synthetic Pathway to Oxygenated δ-MoN

A common route for the synthesis of molybdenum nitrides is through the temperature-programmed reaction of molybdenum oxides with NH3, or ammonolysis. In this work, the role of precursor phase, gas phase chemistry (impact of H2O), and temperature profile on the reaction outcome (700 °C) was examined, which resulted in varying amounts of MoO2, H2MoO5, and the nitride phases—cubic γ (nominally Mo2N) and hexagonal δ (nominally MoN). The phase fraction of the δ phase increased with precursor in the sequence MoO2 > MoO3 > H2MoO5. Steam in the reaction gas also favored the production of δ over γ, but with too much steam, MoO2 was obtained in the product. Synthesis conditions for obtaining nearly phase-pure δ were identified: MoO2 as the precursor, 2% H2O in the gas stream, and a moderate heating rate (3 °C/min). In situ X-ray diffraction provided insights into the reaction pathway. Extensive physico-chemical analysis of the δ phase, including synchrotron X-ray and neutron diffraction, electron microscopy, thermogravimetric analysis, X-ray photoelectron spectroscopy, and prompt gamma activation analysis, revealed its stoichiometry to be MoO0.108(8)N0.892(8)H0.012(5), indicating non-trivial oxygen incorporation. The presence of N/O ordering and an impurity phase Mo5N6 were also revealed, detectable only by neutron diffraction. Notably, a computationally predicted MoON phase (doi: 10.1103/PhysRevLett.123.236402), of interest due to its potential to display a metal-insulator transition, did not appear under any reaction condition examined.

Pandey, Shobhit↗
DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗
DOE OSTI2023

GAHLS: an optimized graph analytics based high level synthesis framework

The urgent need for low latency, high-compute and low power on-board intelligence in autonomous systems, cyber-physical systems, robotics, edge computing, evolvable computing, and complex data science calls for determining the optimal amount and type of specialized hardware together with reconfigurability capabilities. With these goals in mind, we propose a novel comprehensive graph analytics based high level synthesis (GAHLS) framework that efficiently analyzes complex high level programs through a combined compiler-based approach and graph theoretic optimization and synthesizes them into message passing domain-specific accelerators. This GAHLS framework first constructs a compiler-assisted dependency graph (CaDG) from low level virtual machine (LLVM) intermediate representation (IR) of high level programs and converts it into a hardware friendly description representation. Next, the GAHLS framework performs a memory design space exploration while account for the identified computational properties from the CaDG and optimizing the system performance for higher bandwidth. The GAHLS framework also performs a robust optimization to identify the CaDG subgraphs with similar computational structures and aggregate them into intelligent processing clusters in order to optimize the usage of underlying hardware resources. Finally, the GAHLS framework synthesizes this compressed specialized CaDG into processing elements while optimizing the system performance and area metrics. Evaluations of the GAHLS framework on several real-life applications (e.g., deep learning, brain machine interfaces) demonstrate that it provides 14.27× performance improvements compared to state-of-the-art approaches such as LegUp 6.2.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Mix and match—a versatile equilibrium approach for hybrid colloidal synthesis

Colloidal synthesis is a powerful bottom-up approach for programmed self-assembly which holds promise for both research and industry. While diverse, each synthetic process is typically restricted to a specific chemistry. Many applications however require composite materials, whereas a chemical equilibrium can typically only match one material but not the other. Here, a scalable general approach is presented, alleviating the dependency on a specific chemical reaction, by resorting to a mechanical equilibrium; an isopycnic density-gradient-step is tailored to form clusters with prescribed composition. Valence control is demonstrated, making dimers, trimers, and tetramers with purity as high as 96%. The measured kinetics shows a scaleable throughput. The density gradient step plays a dual role of both filtering out undesired products and concentrating the target structures. The “Mix-and-Match” approach is general, and applies to a broad range of colloidal matter: diverse material compositions (plastics, glasses, and emulsions); a range of colloidal interactions (van der Waals, Coulomb, and DNA hybridization); and a spectrum of sizes (nanoscale to multiple micrometers). Lastly, the strength of the method is displayed by producing a monodisperse suspension from a highly polydisperse emulsion. The ability to combine colloids into architectures of hybrid materials has applications in pharmaceuticals, cosmetics, and photonics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Systematic Process Intensification involving Zeotropic Distillation

Distillation is commonly used for separating homogeneous fluid mixtures. While it is based on a simple thermally driven vapor-liquid equilibrium phenomenon at the two-phase region, different variants of distillation systems exist. These include simple distillation, dividing wall columns, pressure swing distillation, thermally coupled distillation and reactive distillation. Complex separation may also involve multiple distillation in a sequence to achieve the desired products. These sequences can be constructed as direct sequence, indirect sequence or thermally coupled columns. Even when a sequence is fixed, each column in the sequence can have a plethora of design alternatives to consider. All these make the design and intensification of distillation systems a challenging task. The large number of plausible design alternatives increases the combinatorial complexity for screening. Although significant efforts have been made in the past, we still lack a method for systematic identification of novel pathways for process intensification of distillation system. This is due to the lack of a generalized representation of all plausible alternatives in a single modeling and optimization framework. In this work, we first present a general representation of thermally driven vapor-equilibrium phenomenon using abstract building blocks. Specifically, each tray in a distillation column is represented by a liquid block and a vapor block with a shared semi-restricted boundary representing the interface. The arrangement of these building blocks in a two-dimensional grid gives rise to all plausible variants of distillation systems. These systems range from simple distillation, dividing wall columns to task-integrated columns. This enables us to systematically identify both classic and out-of-the-box intensified designs at the column and flowsheet levels. The overall distillation-based separation process synthesis problem is formulated as a single mixed-integer nonlinear programming (MINLP) model. In conclusion, the proposed approach is applied to multi-component zeotropic systems to show the capability of identifying promising intensified alternatives.

42 ENGINEERING↗
DOE OSTI2023

Methodology for physics-informed generation of synthetic neutron time-of-flight measurement data

Accurate neutron cross section data are a vital input to the simulation of nuclear systems for a wide range of applications from energy production to national security. The evaluation of experimental data is a key step in producing accurate cross sections. There is a widely recognized lack of reproducibility in the evaluation process due to its artisanal nature and therefore there is a call for improvement within the nuclear data community. This can be realized by automating/standardizing viable parts of the process, namely, parameter estimation by fitting theoretical models to experimental data. This automation effort could greatly benefit from a synthetic data resource. This work leverages problem-specific physics, Monte Carlo sampling, and a general methodology for data synthesis to generate unlimited, labelled experimental cross-section data that is statistically indistinguishable to the observed data. Heuristic and, where applicable, rigorous statistical comparisons to observed data support this claim. The demonstration is based on/limited to transmission measurements at Rensselaer Polytechnic Institute (RPI) and energy-differential cross sections in the resolved resonance region (RRR). An open-source software is published alongside this article that executes the complete methodology to produce high-utility synthetic datasets. The goal of this work is to provide an approach and corresponding tool that will allow the evaluation community to begin exploring more data-driven, ML-based solutions to long-standing challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Preliminary Design of Nuclear Reactor Heat Delivery Systems: Integration with Reference Oil Refinery, Methanol Synthesis, and Hydrogen Production

The Department of Energy’s (DOE) Integrated Energy Systems (IES) program is generating comprehensive analyses validating the opportunity for using nuclear energy in a variety of applications including future clean grids, providing heat for direct use, and providing heat to help reduce emissions in chemical commodities. This work focuses on the preliminary designs of thermal delivery systems that can integrate heat produced from a nuclear core to industrial processes. The key research question that needs to be answered is: what is the prospective method for integrating nuclear generated heat energy into non-electric applications that can facilitate combined heat and power operations by advanced nuclear reactor systems? This research is a composition of case studies showing preliminary conceptual designs for thermal delivery systems integrating advanced nuclear systems with a few industrial systems including high temperature steam electrolysis, a reference oil refinery, and potential future methanol systems that supplant some natural gas use with nuclear energy. Piping and instrumentation diagrams have been developed to show the conceptual integration of nuclear systems with representative industrial systems. Different features of the configurations are dependent on the specific integration requirements including energy source conditions, demand quantity, and require energy application conditions. Design concepts are validated using thermodynamic balance calculations to verify system performance including calculating system losses during transport. Key components: pumps and compressors, heat exchangers, and network piping are reported with key design information and sizing.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

CO Dissociation on model Co/SiO 2 catalysts – effect of adsorbed hydrogen

Here we found experimental evidence that shows the effect that adsorbed hydrogen can have on CO dissociation. For cobalt nanoparticles supported on SiO 2 , adsorbed hydrogen enhances CO dissociation. In contrast, adsorbed hydrogen inhibits CO dissociation on a cobalt film supported on SiO 2 . Considering the nature of cobalt deposited by physical vapor deposition, these results can be explained by a preference for CO dissociation to follow the hydrogen-assisted dissociation mechanism on FCC cobalt and step-edges, while the direct dissociation mechanism is preferred on HCP cobalt. These results are in agreement with previous theoretical results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗