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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Semiconducting Small Molecules as Active Materials for p-Type Accumulation Mode Organic Electrochemical Transistors

A series of semiconducting small molecules with bithiophene or bis-3,4-ethylenedioxythiophene cores are designed and synthesized. The molecules display stable reversible oxidation in solution and can be reversibly oxidized in the solid state with aqueous electrolyte when functionalized with polar triethylene glycol side chains. Evidence of promising ion injection properties observed with cyclic voltammetry is complemented by strong electrochromism probed by spectroelectrochemistry. Blending these molecules with high molecular weight polyethylene oxide (PEO) is found to improve both ion injection and thin film stability. The molecules and their corresponding PEO blends are investigated as active layers in organic electrochemical transistors (OECTs). For the most promising molecule:polymer blend (P4E4:PEO), p-type accumulation mode OECTs with µA drain currents, μS peak transconductances, and a µC* figure-of-merit value of 0.81 F V -1 cm -1 s -1 are obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2023

How to Decarbonize Our Energy Systems: Process‐Informed Design of New Materials for Carbon Capture

Decarbonisation from a variety of industrial and power emission sectors highlights a marked need for capture technologies that can be optimized for different CO 2 sources and integrated into an equally diverse range of applications of captured CO 2 as a feedstock. Some capture technologies are already operated at an industrial scale but may not be optimal for all required applications. Advanced tailored sorbent-based technologies allow flexible operation and reduced costs as they offer higher capture capacities and significantly lower energy penalties than the state-of-the-art systems. To accelerate the discovery, development, and deployment of novel advanced materials, it is critically important that efforts between experimentalists, theoreticians, and process engineers are coordinated. The PrISMa project addresses this challenge by integrating materials design with process design and environmental considerations to allow for tailor-making carbon capture solutions optimally tuned for local sources and sinks. In this article, we highlight some of the recent results obtained with the PrISMa platform.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The Peridigm Meshfree Peridynamics Code

Abstract Peridigm is a meshfree peridynamics code written in C++ for use on large-scale parallel computers. It was originally developed at Sandia National Laboratories and is currently managed as an open-source, community driven software project. Its primary features include bond-based, state-based, and non-ordinary state-based constitutive models, bond failure laws, contact, and support for explicit and implicit time integration. To date, Peridigm has been used primarily by methods developers focused on solid mechanics and material failure. Peridigm utilizes foundational software components from Sandia’s Trilinos project and was designed for extensibility. This paper provides an overview of the solution methods implemented in Peridigm , a discussion of its software infrastructure, and demonstrates the use of Peridigm for the solution of several example problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

End-to-end online performance data capture and analysis for scientific workflows

With the increased prevalence of employing workflows for scientific computing and a push towards exascale computing, it has become paramount that we are able to analyze characteristics of scientific applications to better understand their impact on the underlying infrastructure and vice-versa. Such analysis can help drive the design, development, and optimization of these next generation systems and solutions. Here, we present the architecture, integrated with existing well-established and newly developed tools, to collect online performance statistics of workflow executions from various, heterogeneous sources and publish them in a distributed database (Elasticsearch). Using this architecture, we are able to correlate online workflow performance data, with data from the underlying infrastructure, and present them in a useful and intuitive way via an online dashboard. We have validated our approach by executing two classes of real-world workflows, both under normal and anomalous conditions. The first is an I/O-intensive genome analysis workflow; the second, a CPU- and memory-intensive material science workflow. Based on the data collected in Elasticsearch, we are able to demonstrate that we can correctly identify anomalies that we injected. The resulting end-to-end data collection of workflow performance data is an important resource of training data for automated machine learning analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Performance analysis of hybrid power configurations: Impact on primary energy intensity, carbon dioxide emissions, and life cycle costs

In this work, utilization of Natural gas and Hydrogen to support current and future building energy needs to offset the total electric demand while improving the grid resiliency and energy efficiency was investigated. Demand side energy management will play an important role in efficiently managing the available energy resources. Performance assessment of different power generation and energy management configurations is presented in this paper. Development of solutions in addressing grid resiliency by providing the ability to design suitable configurations for meeting individual building energy needs is discussed. Primary movers (PM) such as internal combustion engines (ICE) and fuel cells (FC) along with small scale auxiliary renewable energy source such as photovoltaics (PV) were considered. Key attributes of total carbon foot-print, life cycle costs including capital and operational expenditure, electric grid offset or peak shaving capability, thermal energy availability and its further potential to offset total electric demand, and primary energy intensity are analyzed and discussed in detail.

08 HYDROGEN↗
DOE OSTI2022

A multi-product landscape life-cycle assessment approach for evaluating local climate mitigation potential

Increasing demand for land-based climate mitigation requires more efficient management of agricultural landscapes for competing objectives. Here we develop methods for assessing trade-offs and synergies between intensification and carbon-sequestering conservation measures in annual crop production landscapes using the DayCent ecosystem model and the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) life-cycle assessment (LCA) model. We compiled county-scaled crop yields, fertilizer application rates, and tillage intensity for a corn–soybean farming case study landscape in the US state of Iowa. Using DayCent, we estimated a baseline soil organic carbon (SOC) accrual rate of 0.29 Mg C ha -1 y -1 driven by historical increases in crop productivity and reductions in tillage intensity. We then simulated the effects of management interventions targeted toward intensification (stover removal) and SOC sequestration (tillage intensity reduction and winter cover crop addition) individually and in combination. We propose a new multi-product landscape–LCA approach that analyzes marginal changes in corn grain, corn stover, and soybean production from the landscape in terms of their value for biofuel production (corn ethanol, soy biodiesel, and cellulosic ethanol from stover) and associated net displacement of conventional fossil-derived fuel use. This enables us to evaluate both intensification and sequestration effects in common CO 2 -equivalent mitigation units. We also used DayCent-simulated yields under the different land management scenarios to estimate farm-level costs and revenues. Our results show that intensification via collecting 30% of corn stover for biofuel production would increase the total greenhouse gas (GHG) mitigation potential of this landscape by 0.93 Mg CO 2 e ha -1 y -1 and provide $49 ha -1 y -1 of additional net revenue from biomass sales, but would reduce the baseline SOC accumulation rate by approximately 40%. In contrast, integrated approaches that include co-adoption of winter cover cropping and/or tillage intensity reduction would result in increased rates of SOC accumulation above the baseline, achieving simultaneous improvements in both farm profits and the overall GHG mitigation potential of the landscape.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Dynamic tunability of phase-change material transition temperatures using ions for thermal energy storage

Thermal energy storage (TES) based on phase-change materials (PCMs) has many current and potential applications, such as climate control in buildings, thermal management for batteries and electronics, thermal textiles, and transportation of pharmaceuticals. Despite its promise, the adoption of TES has been limited, in part due to limited tunability of the transition temperature, which hinders TES performance for varying use temperatures. Transition temperature tuning of a material using an external stimulus, such as pressure or an electric field, typically requires very large stimuli. To circumvent this problem, here, we report on the dynamic transition temperature tunability of a PCM using ions. We achieve a transition temperature tunability up to 6°C in polyethylene glycol (PEG) by using the salt lithium oxalatodifluoroborate at a low voltage of 2.5 V, which may enable simpler and safer devices/system designs. We also explain the thermal properties of the salt/PCM solution using the Flory-Huggins theory.

25 ENERGY STORAGE↗
DOE OSTI2026

Integrated millimeter-wave cavity electro-optic transduction

Emerging communications and computing technologies will rely ever-more on expanding the useful radio frequency spectrum into the millimeter-wave and terahertz frequency range. Both classical and quantum applications would benefit from advancing integration and incorporation of millimeter-wave and electro-optic technologies into common devices, such as modulators. Here we demonstrate an integrated triply-resonant, superconducting electro-optic transducer. Our design incorporates an on-chip 107 GHz niobium titanium nitride superconducting resonator, modulating a thin-film lithium niobate optical racetrack resonator operating at telecom wavelengths. We observe a maximum photon transduction efficiency of η OE ≈ 0.82 × 10 −6 and an average single-photon electro-optic interaction rate of g 0 /2π ≈ 0.7 kHz. We also present a study and analysis of the challenges associated with the design of integrated millimeter-wave resonators and propose possible solutions to these challenges. Our work paves the way for further advancements in resonant electro-optic technologies operating at millimeter-wave frequencies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Current status and future directions of multivalent metal-ion batteries

Batteries based on multivalent metals have the potential to meet the future needs of large-scale energy storage, due to the relatively high abundance of elements such as magnesium, calcium, aluminium and zinc in the Earth’s crust. However, the com- plexity of multivalent metal-ion chemistries has led to rampant confusions, technical challenges, and eventually doubts and uncertainties about the future of these technologies. In this Review, we clarify the key strengths as well as common misconceptions of multivalent metal-based batteries. We then examine the growth behaviour of metal anodes, which is crucial for their safety promises but hitherto unestablished. We further discuss scrutiny of anode efficiency and cathode storage mechanism pertaining to complications arising from electrolyte solutions. Lastly, we critically review existing cathode materials and discuss design strategies to enable genuine multivalent metal-ion-based energy storage materials with competitive performance.

25 ENERGY STORAGE↗
DOE OSTI2024

Massively scalable workflows for quantum chemistry: BigChem and ChemCloud

Electronic structure theory, i.e., quantum chemistry, is the fundamental building block for many problems in computational chemistry. Here we present a new distributed computing framework (BigChem), which allows for an efficient solution of many quantum chemistry problems in parallel. BigChem is designed to be easily composable and leverages industry-standard middleware (e.g., Celery, RabbitMQ, and Redis) for distributed approaches to large scale problems. BigChem can harness any collection of worker nodes, including ones on cloud providers (such as AWS or Azure), local clusters, or supercomputer centers (and any mixture of these). BigChem builds upon MolSSI packages, such as QCEngine to standardize the operation of numerous computational chemistry programs, demonstrated here with Psi4, xtb, geomeTRIC, and TeraChem. BigChem delivers full utilization of compute resources at scale, offers a programable canvas for designing sophisticated quantum chemistry workflows, and is fault tolerant to node failures and network disruptions. We demonstrate linear scalability of BigChem running computational chemistry workloads on up to 125 GPUs. Finally, we present ChemCloud, a web API to BigChem and successor to TeraChem Cloud. ChemCloud delivers scalable and secure access to BigChem over the Internet.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Neural network accelerator for quantum control

Efficient quantum control is necessary for practical quantum computing implementations with current technologies. Conventional algorithms for determining optimal control parameters are computationally expensive, largely excluding them from use outside of the simulation. Existing hardware solutions structured as lookup tables are imprecise and costly. By designing a machine learning model to approximate the results of traditional tools, a more efficient method can be produced. Such a model can then be synthesized into a hardware accelerator for use in quantum systems. In this study, we demonstrate a machine learning algorithm for predicting optimal pulse parameters. This algorithm is lightweight enough to fit on a low-resource FPGA and perform inference with a latency of 175 ns and pipeline interval of 5 ns with > 0.99 gate fidelity. In the long term, such an accelerator could be used near quantum computing hardware where traditional computers cannot operate, enabling quantum control at a reasonable cost at low latencies without incurring large data bandwidths outside of the cryogenic environment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Selective formation of metastable polymorphs in solid-state synthesis

Metastable polymorphs often result from the interplay between thermodynamics and kinetics. Despite advances in predictive synthesis for solution-based techniques, there remains a lack of methods to design solid-state reactions targeting metastable materials. Here, we introduce a theoretical framework to predict and control polymorph selectivity in solid-state reactions. This framework presents reaction energy as a rarely used handle for polymorph selection, which influences the role of surface energy in promoting the nucleation of metastable phases. Through in situ characterization and density functional theory calculations on two distinct synthesis pathways targeting LiTiOPO 4 , we demonstrate how precursor selection and its effect on reaction energy can effectively be used to control which polymorph is obtained from solid-state synthesis. A general approach is outlined to quantify the conditions under which metastable polymorphs are experimentally accessible. With comparison to historical data, this approach suggests that using appropriate precursors could enable targeted materials synthesis across diverse chemistries through selective polymorph nucleation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

IR challenges and the machine detector interface at FCC-ee

The FCC-ee, with its unprecedented luminosity goal and high energy reach, creates challenges and requires solutions to many issues in order to produce a realistic design for the complex machine detector interface. The interaction region design for the FCC-ee adopts the crab-waist collision scheme and proposes an elegant local chromaticity correction system. An asymmetric layout of nearby dipoles suppresses the critical energy of synchrotron radiation incoming to the detector at the interaction point to a maximum value of 100 keV. The main challenge of the FCC-ee machine detector interface design is to combine the many conflicting accelerator and 2 T detector constraints, aiming for the optimal trade-off choices that simultaneously allow for a best machine performance in terms of integrated luminosity and data taking efficiency. Much of the success of the FCC-ee will be related to the interaction region design, as a result of the ingredients coming from areas of accelerator physics, mechanical engineering and detector optimization.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

QUANT-NET Control Plane Framework (QNCP) v1.0.0

The QUANT-NET Control Plane (QNCP) provides a software framework for expressing and managing quantum network resources. It may be used to orchestrate a physical quantum testbed with real device driver implementations, or it may be used as a proving ground when developing new protocols and management functions. In practice, both approaches may be useful when undertaking research and development in emerging quantum testbeds. While a number of control systems have been developed for specific quantum platform demonstrations, an openly available and general solution for operating quantum networks has not emerged. QNCP is designed to fill this gap. The framework has been designed to provide extensible, modular capabilities that include scheduling, routing, monitoring, and pluggable protocols. A number of reference implementations in each module category have been included in the installable packages; however, the intent is that each of these modules may be extended or re-implemented to meet the needs of the particular deployment or research need. The software is currently being used in the QUANT-NET testbed project, which spans resources between LBNL and UC Berkeley Physics.

Zhang, Liang [Lawrence Berkeley National Laborator↗
DOE OSTI2022

Advances in MINLP to Identify Energy-Efficient Distillation Configurations

Separation of mixtures of chemicals, ubiquitous in chemical and petrochemical industries, by distillation is energy intensive. Nearly 3% of the overall energy is used for distillation in the United States. Improving the distillation process is crucial for making chemical industries more sustainable. However, designing distillation sequences is challenging because the choice set is vast, and the equations governing the physical process are highly nonconvex. Traditional design practices rely on heuristics and often result in suboptimal solutions. Tumbalam Gooty et al. present the first approach that reliably identifies the distillation sequence that requires the least energy for a given separation. By embedding convex hulls of substructures and adapting the reformulation-linearization technique to fractions of polynomials, they demonstrated that their approach outperforms the state-of-the-art. Their work will help the chemical industry reduce greenhouse gas emissions associated with distillation.

Business & Economics↗
DOE OSTI2021

Phosgene Neutralization Chemistry for the Explosive Destruction System (EDS)

This report summarizes activities at Sandia National Laboratories as part of the Explosive Destruction System (EDS) Phase 3 (P3) System design. An exploration of chemical neutralization strategies of phosgene was conducted for safe disposal of recovered mortars and M79 1000 lb. bombs filled with carbonyl dichloride "phosgene" or "CG agent" (molecular formula = COCl 2 ). The incumbent strategy utilized aqueous sodium hydroxide was found to be the worst-case scenario, producing enough CO 2 gas that would cause an unacceptable pressure and temperature spike. Several chemical neutralization strategies were evaluated based on criteria set by the operating envelope of the P3 design. In the end, it was determined that a pure solution of N-methyl ethanolamine (MeEA) or 90% monoethanolamine (MEA)(aqueous) provided the best balance reaction profile, cost, and safety.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Versatile Heat Transfer Module

Development of micro-reactors that can be easily transported to remote areas for civilian or military applications is an immediate focus of U.S. nuclear industry and therefore is being pursued by multiple reactor vendors (e.g. HolosGen LLC, Westinghouse, Ultra Safe Nuclear Corporation, URENCO, etc.) and actively supported by DOE-NE, DOE-ARPA-E and by the Department Of Defense (DoD). Very-high-temperature gas-cooled (VHTR) and heat pipe cooled reactor technologies are regarded as promising concepts for microreactors applications since they enable compact power conversion systems and display beneficial inherent safety features. Development of microreactors with enhanced performance must be supported by innovations in nuclear technologies. Several technologies being developed by the microreactor industry are being revisited with material technologies developed at the Argonne National Laboratory (ANL). Innovative high-temperature compatible solutions are developed to enable advanced thermal-spectrum microreactor designs featuring improved neutronics performance at high-temperature operating conditions to enable high thermal efficiency and economic competitiveness. This report focuses on the Versatile Heat Transfer Module (VHTM) technology that is developed at ANL for heat-transfer in thermal-spectrum microreactors. This technology is built on a liquid alkali metal heat pipe concept while leveraging advanced nuclear material technologies for high-temperature applications at reduced neutronic penalty with potential improvement in inherent safety behavior. The VHTM can be applied to design high-temperature thermal-spectrum microreactor systems with reduced size, weight, uranium enrichment, or increased lifetime. The INFINIT technology deriving from the VHTM enables design of a highly compact long-life microreactor concept for fission surface power (FSP). Demonstration of multiphysics modeling capability was also completed to showcase the added inherent safety benefits obtained with this concept. The VHTM technology remain at an early development stage with low Technology Readiness Level and Manufacturing Readiness Level. The current demonstration of these technologies has focused on manufacturing capability, on performance of the hydrogen diffusion barrier under thermal cycling, while radiation tolerance demonstration is planned. The next step of the demonstration plan will be the assembly of miniature VHTM for high-temperature testing, which can be used as a prototype for future pilot scale demonstration. Such research is critical to pursue development of these technologies that have the clear potential to significantly improve the performance of currently investigated micro-reactors systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Modeling and Optimization of Zeolites for Contaminant Removal from Coal Combustion Impoundment Leachates

Coal Combustion Residual (CCR) landfills in the U.S. can lead to elevated contaminant concentrations in groundwater and runoff, particularly of arsenic, boron, and selenium. Zeolites can be readily synthesized from materials contained within the coal combustion ash impoundments and can be cation-exchanged to enhance adsorption capacity, selectivity, and reactivity. However, the optimization of zeolites becomes a daunting task when accounting for the variety of Si:Al ratios, the species of extra-framework cations present, and the zeolite pore structure. Molecular simulations provide methods to study and guide the design of zeolites for the sorption of contaminants from aqueous solution. In this work, models that predict the sorption of arsenic, boron, and selenium from water using cation-exchanged zeolites. Because of the lack of experimental adsorption data for these species of contaminants, models were parametrized to reproduce the results of first-principles calculations and then used to predict sorption for zeolites for a dataset containing 6000 combinations of zeolites and sorption conditions. Machine learning was used to train a model to predict sorption for materials in this database based on the results of the molecular simulations. Next, a genetic algorithm was used to optimize zeolites for the removal of each contaminant from aqueous solution for individual impoundment sites based on contaminant concentrations reported by the Electric Power Research Institute.

Findley, John↗