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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Theoretical Calculation Guided Design of Single-Atom Catalysts toward Fast Kinetic and Long-Life Li–S Batteries

Lithium–sulfur (Li–S) batteries are enticing next-generation energy storage technologies due to their high theoretical energy density, environmental friendliness, and low cost. Yet, low conductivity of sulfur species, dissolution of polysulfides, poor conversion from sulfur reduction, and lithium sulfide (Li 2 S) oxidation reactions during discharge–charge processes hinder their practical applications. Herein, under the guidance of density functional theory calculations, we have successfully synthesized large-scale single atom vanadium catalysts seeded on graphene to achieve high sulfur content (80 wt % sulfur), fast kinetic (a capacity of 645 mAh g –1 at 3 C rate), and long-life Li–S batteries. Both forward (sulfur reduction) and reverse reactions (Li 2 S oxidation) are significantly improved by the single atom catalysts. This finding is confirmed by experimental results and consistent with theoretical calculations. The ability of single metal atoms to effectively trap the dissolved lithium polysulfides (LiPSs) and catalytically convert the LiPSs/Li 2 S during cycling significantly improved sulfur utilization, rate capability, and cycling life. Our work demonstrates an efficient design pathway for single atom catalysts and offers solutions for the development of high energy/power density Li–S batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Insights into the Electrochemical Oxidation and Reduction of Nickel Oxide Surfaces

Surface oxidation/reduction processes, driven by varying electrochemical potentials, can substantially impact catalyst effectiveness and, consequently, electrolyzer performance. Here, this study combines theoretical and experimental approaches to explore the surface redox behavior of nickel oxides, which are cost-effective and efficient catalysts for many electrochemical reactions. Surface Pourbaix diagrams for three different phases of nickel oxides, i.e., nickel hydroxide (Ni(OH) 2 ), nickel oxyhydroxide (NiOOH), and nickel dioxide (NiO 2 ), were constructed using density functional theory-based simulations. Various experimental methods, including cyclic voltammetry, in situ Raman spectroscopy, and electrochemical titration, were employed to probe the surface redox processes of nickel oxide thin films. Our findings indicate that the ABAB stacking sequence of Ni(OH) 2 lacks stability under oxidizing conditions to host the surface oxidation (deprotonation) events, while the AABBCC stacking sequence of NiOOH is energetically favorable due to the presence of interlayer hydrogen bonding. Rapid charge transfer facilitated by interlayer hydrogen bonding accounts for the higher reactivity of partially oxidized/reduced NiOOH (001) surfaces compared to Ni(OH) 2 (001) and NiO 2 (001) surfaces with the same stoichiometry, where interlayer hydrogen bonding is absent. Insights presented in this work can offer guidelines for optimizing operational conditions and tailoring the surface structures and oxidation states of nickel oxides to enhance performance in applications such as electrocatalysis and supercapacitors.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Nitrogenated Graphene Quantum Dot-Derived Copper Single-Atom Catalyst for Oxygen Reduction Reaction

Despite the promise of fuel cells as sustainable energy conversion technologies, their widespread adoption is hindered by the high cost of platinum group metal (PGM) catalysts, particularly for catalyzing the oxygen reduction reaction (ORR) at the cathode. Here, we report copper-based single-atom catalysts (Cu-SACs) as cost-effective non-PGM alternatives for ORR. The catalysts were synthesized via simple pyrolysis of pyrene-derived amine-terminated graphene quantum dots (GQDs) in the presence of nitrogen and metal precursors. The abundant nitrogen functionality in GQDs effectively stabilized isolated Cu atoms during their conversion to porous carbon. A subsequent acid-washing step yielded a high density of atomically dispersed Cu active sites. X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and high-resolution scanning transmission electron microscopy (HR-STEM) confirmed the effective incorporation of isolated Cu atoms into the carbon matrix through copper–nitrogen (Cu-N) coordination. Electrochemical measurements revealed excellent ORR activity with a dominant four-electron pathway. This synthetic strategy provides a practical route to developing economically viable, non-precious metal catalysts.

catalysts↗
DOE OSTI2026

Variance reduction in lattice QCD observables via normalizing flows

Normalizing flows can be used to construct unbiased, reduced-variance estimators for lattice field theory observables that are defined by a derivative with respect to action parameters. This work implements the approach for observables involving gluonic operator insertions in the SU(3) Yang-Mills theory and two-flavor QCD in four space-time dimensions. Variance reduction by factors of 10–60 is achieved in glueball correlation functions and in gluonic matrix elements related to hadron structure, with demonstrated computational advantages. The observed variance reduction is found to be approximately independent of the lattice volume, so volume transfer can be utilized to minimize training costs.

Abbott, Ryan [Columbia U.; MIT, Cambridge, CTP; IA↗
DOE OSTI2023

Phonon-assisted optical absorption of SiC polytypes from first principles

Silicon carbide (SiC) is an indirect-gap semiconductor material widely used in electronic and optoelectronic applications. While experimental measurements of the phonon-assisted absorption coefficient of SiC across its indirect gap have existed for more than 50 years, theoretical investigations of phonon-assisted absorption have been hampered by their excessive computational cost. Here, in this work, we calculate the phonon-assisted temperature-dependent optical absorption spectra of the commonly occurring SiC polytypes (3C, 2H, 4H, 6H, and 15R), using first-principles approaches based on density functional theory and related techniques. We show that our results agree with experimentally determined absorption coefficients in the spectral region between the direct and indirect band gaps. The temperature dependence of the spectra can be well predicted with taking the temperature dependence of the band gaps into account. Lastly, we compare the spectra obtained with second-order perturbation theory to those determined by the special displacement method and we show that the full consideration of the electronic energy renormalization due to temperature is important to further improve the prediction of the phonon-assisted absorption in SiC. Our insights can be applied to predict the optical spectra of the less common SiC polytypes and other indirect-gap semiconductors in general.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

PARETO UI 1.1.0 Release

PARETO is an open-source Python-based software package for oilfield produced water management and beneficiary reuse optimization. PARETO supports produced water industry by providing cost-effective water management solutions. This version introduced an updated User Interface (UI) which makes it easier to navigate and understand the solution for industry users. New Features: - Map files are added for visualization - Added output export function button - Water residual view added - Workflow was streamlined - File extension was expanded - Minor bugfix

AS↗
DOE OSTI2020

TA-55 Forensic Support Operations Cross-Training Exercise

As part of the annual FBI/LANL training schedule, a joint training exercise was conducted during the week of 10FEB2020 with FBI HEAT members and LANL Fissile Material Handlers (FMHs). The joint training exercise was conducted in a non-radiological area to reduce cost and ensure the test object would not be contaminated. The exercise test object was fabricated by LANL using surrogate materials to represent an object containing Special Nuclear Material (SNM). Working with Jim Blankenship and Kevin Swearingen, functional requirements of the test object included the following: unclassified, disassembly (to include screws), and moderately heavy. To meet the objectives, the test object was fabricated by welding a custom aluminum box (8” x 8” x 12”) with a lid which was secured by four (4) screws. The custom box housed a track and field shot put (16 lbs.) that was anchored by a hose clamp. The intent of the training was to disassemble the test object and conduct traditional forensic determinations on the parts. This lessons learned report documents all of the associated aspects related to the joint training effort.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

ERDE: The Earth-Field Resonance Detection and Evaluation Devices

Researchers at Los Alamos have developed the Earth-field Resonance Detection and Evaluation devices, or ERDE (German for “Earth”) which brings portable chemical analysis and identification into the field. ERDE uses the Earth’s magnetic field to perform nuclear magnetic resonance measurements without the need for sample preparation, costly cryogens, or calibration procedures, meaning it is extremely user-friendly. In fact, using the Earth’s magnetic field allows detection and identification of chemical species through non-magnetic pipes without sampling, reducing the likelihood of costly biofouling. This innovative new device brings together these outstanding benefits to improve processes in the detection of noxious chemicals in water supplies as well as the identification of toxic materials anywhere. There are two fully-functional ERDE prototypes and efforts are underway to miniaturize the system further for improved portability, increased sensitivity, and expanded capabilities. The Laboratory is seeking qualified licensing or CRADA partners to finalize development of this system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Formulation, Design and Experimental Verification of an Open Circuit Electro-Hydraulic Actuator

With the recent electrification trend in the fluid power area, more research has been incentivized to the proposal of cost-effective and energy-efficient solutions for hydraulic systems. Hence, electro-hydraulic actuator (EHA) architectures receive increasing attention. The paper proposes a novel open-circuit EHA architecture, with the goal to obtain a cost-effective mobile solution while maximizing the overall system efficiency. The proposed EHA is capable of meeting or exceeding traditional off-road machine performance, therefore enabling further electrification of off-road machines. Four-quadrant functionality, covering the full speed range, is achieved by a combination of a variable electro-hydraulic drive and valves with different functions and is validated by numerical as well as experimental methods. Here, a simulation model based on the Amesim environment and a dedicated test setup was developed to verify the performance. The good match between simulation and experimental results confirms the potential of the formulation approach of the proposed EHA for applications with different duty cycles and power levels.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Time-dependent density functional theory with the orthogonal projector augmented wave method

The projector augmented wave (PAW) method of Blöchl linearly maps smooth pseudo wavefunctions to the highly oscillatory all-electron DFT orbitals. Compared to norm-conserving pseudopotentials (NCPP), PAW has the advantage of lower kinetic energy cutoffs and larger grid spacing at the cost of having to solve for non-orthogonal wavefunctions. We earlier developed orthogonal PAW (OPAW) to allow the use of PAW when orthogonal wavefunctions are required. In OPAW, the pseudo wavefunctions are transformed through the efficient application of powers of the PAW overlap operator with essentially no extra cost compared to NCPP methods. Previously, we applied OPAW to DFT. Here, we take the first step to make OPAW viable for post-DFT methods by implementing it in real-time time-dependent (TD) DFT. Using fourth-order Runge–Kutta for the time-propagation, we compare calculations of absorption spectra for various organic and biological molecules and show that very large grid spacings are sufficient, 0.6–0.7 bohr in OPAW-TDDFT rather than the 0.4–0.5 bohr used in traditional NCPP-TDDFT calculations. This reduces the memory and propagation costs by around a factor of 3. Our method would be directly applicable to any post-DFT methods that require time-dependent propagations such as the GW approximation and the Bethe–Salpeter equation.

Chemistry↗
DOE OSTI2020

Thermal dissipation sensors enter a new age: Navigating frontiers in transpiration and hydrologic function

Thermal dissipation (TD) sensors have been used extensively for a wide array of sap flow applications, and yet recently several limitations of this approach have been identified, including a tendency to underestimate absolute flows, especially in small stems with very high flows. However, when used properly, the TD method remains the most cost effective and reliable approach for determining relative flows in comparison studies. Major advantages of this method include the ability to replicate maximally within trees, across trees of different sizes, and across multiple species to capture the full range of natural variation in the study system. From early studies, such variation has been deemed a major challenge for representing stand-level dynamics. In a series of recent comparison studies, we determined mean sap flux density dynamics within and among groups of trees. These studies were conducted within temperate coniferous forest (n=46), temperate oak savanna (n=36), temperate pine forest (n=49), temperate bottomland hardwood forest (n=23), tropical premontane rainforest (n=43), semiarid subtropical shrubland (n=25), and tropical dry forest (n=15), with a total of 237 sensors. Two study sites were also equipped with eddy covariance systems for determination of stand-level evapotranspiration. Sites subject to high background thermal gradients used the transient thermal dissipation method. Comparisons varied between studies but tended to focus on relative flow differences between species, between wetter and drier microsites, between understory and overstory components, or between wetter and drier periods of the growing season. Except for the shrubland sites, study trees tended to be at least 15-cm diameter and frequently exceeded 50-cm diameter. Results of this cross-site comparison highlight the inherent variation in natural stands and the importance of using large sample sizes. The TD approach remains a valuable and preferred method for the future determination of water use in trees. However, sensor replication can fundamentally impact a study's outcomes and should be more carefully considered in study design. Customized error mitigation strategies are best to address the sources of variation most problematic for a particular study. In conclusion, the development of “smart” calibration approaches that correct for fundamental effects of radial variation inherent in thermal dissipation studies is discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Physical bioenergetics: Energy fluxes, budgets, and constraints in cells

Cells are the basic units of all living matter which harness the flow of energy to drive the processes of life. While the biochemical networks involved in energy transduction are well-characterized, the energetic costs and constraints for specific cellular processes remain largely unknown. In particular, what are the energy budgets of cells? What are the constraints and limits energy flows impose on cellular processes? Do cells operate near these limits, and if so how do energetic constraints impact cellular functions? Physics has provided many tools to study nonequilibrium systems and to define physical limits, but applying these tools to cell biology remains a challenge. Physical bioenergetics, which resides at the interface of nonequilibrium physics, energy metabolism, and cell biology, seeks to understand how much energy cells are using, how they partition this energy between different cellular processes, and the associated energetic constraints. In this work, we review recent advances and discuss open questions and challenges in physical bioenergetics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Hamilton: Flexible, Open Source $10 Wireless Sensor System for Energy Efficient Building Operation

Sensors for improving building performance are rapidly populating the market, driven in part by the drive to reduce greenhouse gas emissions resulting from energy production as well as improve the interior environment for healthy and more productive spaces. UC Berkeley has led wireless sensor development over the past 25 years (e.g., Telos mote), with the Hamilton (named after Alexander Hamilton on the US $10 bill) as the most recent. The Hamilton sensor was designed as a low-cost high-performance sensor that is modular and interoperable. The objective of the Hamilton project was to create, evaluate and establish the technological foundations for secure and easy to deploy building energy efficiency applications utilizing pervasive, low-cost wireless sensors integrated with traditional Building Management Systems (BMS), consumer-sector building components, and powerful data analytics. The project included iterative hardware design, incorporating a high-performance database (BTrDb, http://btrdb.io/), creating and iterating the development of secure data middleware (BOSSwave, WAVE/WAVEMQ), working with and pushing the development of an open-source tiny operating system RiotOS, and implementing and improving protocols such as Thread/OpenThread and TCP/IP. The hardware benefited from careful design to drive down the cost; the design included a System-on-a-Chip (SoC), chip antenna, single crystal and five passive components. Careful design of the operating system created a low-power design to enable a long life with small batteries. The hardware included several sensors: temperature, radiant temperature, relative humidity, magnetometer, accelerometer, and light, with an optional occupancy (Passive InfraRed) sensor. The project was the basis of several applications, both internal to the research team and other researchers and professionals at other institutions. Several applications used the sensor hardware as the basis for other complex devices. Other applications used the sensors to improve building performance through interoperating with the building Heating Ventilation and Air-Conditioning (HVAC) system, such as using occupancy and/or distributed temperature sensing to reduce HVAC zone energy while still providing thermal comfort and to reduce peak loads in small commercial buildings. We demonstrated cloud-based energy analytics, implemented a schedule and a Model Predictive Controller in a small commercial building to optimize HVAC energy, occupancy and electricity price. Initial integration of these technological innovations was performed through the creation of execution containers containing the WAVE agent and various driver, proxy, or building system function logic. The research added to the understanding of efficient sensor hardware, secure middleware, time-series data management (high performance database), efficient communication protocols, and interoperating with applications and building systems. The project showed the technical effectiveness and economic feasibility of creating a low-cost, modular, and easy-to-deploy sensor. Through conversations with multiple end users, the research team discovered that many customers wanted data management and services in addition to the sensors. HamiltonIOT developed packages of sensors, border router, and data services to provide a seamless “plug-and-play” sensor deployment. Some customers were willing to pay for higher quality sensors (such as light); some customers wanted a robust enclosure (waterproof).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing

This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simu-lations. QoIs include species decay, product yield, and degree of mixing. The ROMs for QoIs are applied to quantify and understand how the chemical species evolve over time. First, high-resolution datasets for constructing ROMs are generated by solving anisotropic reaction-di?usion equations using a non-negative finite element formulation for di?erent input parameters. The reactive-mixing model input parameters are: time-scale associated with flipping of velocity, spatial-scale controlling small/large vortex structures of velocity, perturbation parameter of the vortex-based velocity, anisotropic dispersion strength/contrast, and molecular diffusion. Second, random forests, F-test, and mutual information criterion are used to evaluate the importance of model inputs/features with respect to QoIs. We observed that anisotropic dispersion strength/contrast is the most important feature and time-scale associated with flipping of velocity is the least important feature. Third, Support Vector Machines (SVM) and Support Vector Regression (SVR) are used to construct ROMs based on the model inputs. The constructed SVR-ROMs are then used to predict scaling of QoIs. We also present estimates and inequalities on the QoIs, which inform that the species decay, mix, and produce in an exponential fashion. These inequalities also inform that a radial basis function is the most suitable kernel for the SVM/SVR models for QoIs. It is observed that R2-score for SVR-ROMs on unseen data is greater than 0.9, implying that the SVR-ROMs are able to predict the reaction-diffusion system state reasonably well. Finally, in terms of the computational cost, the proposed SVM-ROMs are O(107) times faster than running a high-fidelity finite element simulation for evaluating QoIs. This makes the proposed ML-based ROMs attractive for reactive-transport sensing and real-time monitoring applications as they are significantly faster yet reasonably accurate.

Mudunuru, Maruti K.↗
DOE OSTI2022

Cost-effective carbon fiber precursor selections of polyacrylonitrile-derived blend polymers: carbonization chemistry and structural characterizations

Blending polyacrylonitrile (PAN) with plastic wastes and bio-based polymers provides a convenient and inexpensive method to realize cost-effective carbon fiber (CF) precursors. In this work, PAN-based blend precursors are investigated using ReaxFF reactive molecular dynamics simulations with respect to the formation of all-carbon rings, the evolutions of oxygen-containing and nitrogen-containing species, and the migration of carbon atoms to form turbostratic graphene. From these simulations, we identify that PAN/cellulose (CL) blend manifests the highest carbon yield and the most substantial all-carbon ring formation. This ReaxFF-based finding is confirmed by Raman and TEM experiments indicating high crystallinity for PAN/CL-derived blend CFs. We trace the pathway of gasification and carbonization of PAN/CL to elaborate the mechanism of the formation of all-carbon ring networks. We discover that the acetals of CL can catalyze the cyclization of the blend precursor, allowing for the search for CL derivatives or the other kinds of bio-based polymers with similar functionalities as alternative blends. In addition, we examine the structural characteristics using the carbon–carbon (C–C) radial distribution functions, C–C bond length distributions, and sp2 C atom ratios for the four representative precursors, i.e., PAN, oxidized PAN, PAN/nylon 6,6, and PAN/CL. Our simulation results show the most extensive all-carbon ring cluster and graphitic structure growths for PAN/CL. Here, we propose PAN/CL as a cost-effective alternative CF precursor, since (a) CL is naturally abundant and eco-friendly for production, (b) the blend precursor PAN/CL does not require oxidation treatment, (c) PAN/CL has a high carbon yield with substantial all-carbon ring formation, and (d) PAN/CL based CFs potentially provide a mechanical property enhancement.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Optimal carbon storage reservoir management through deep reinforcement learning

Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decision-making problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Finally, experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Benchmarking machine learning interatomic potentials via phonon anharmonicity

Abstract Machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, machine learning interatomic potentials (MLIPs) can accurately reproduce first-principles data at a cost similar to that of conventional interatomic potential approaches. While MLIPs have been extensively tested across various classes of materials and molecules, a clear characterization of the anharmonic terms encoded in the MLIPs is lacking. Here, we benchmark popular MLIPs using the anharmonic vibrational Hamiltonian of ThO 2 in the fluorite crystal structure, which was constructed from density functional theory (DFT) using our highly accurate and efficient irreducible derivative methods. The anharmonic Hamiltonian was used to generate molecular dynamics (MD) trajectories, which were used to train three classes of MLIPs: Gaussian approximation potentials, artificial neural networks (ANN), and graph neural networks (GNN). The results were assessed by directly comparing phonons and their interactions, as well as phonon linewidths, phonon lineshifts, and thermal conductivity. The models were also trained on a DFT MD dataset, demonstrating good agreement up to fifth-order for the ANN and GNN. Our analysis demonstrates that MLIPs have great potential for accurately characterizing anharmonicity in materials systems at a fraction of the cost of conventional first principles-based approaches.

interatomic potentials↗
DOE OSTI2022

Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation

The combination of neural networks and quantum Monte Carlo methods has arisen as a promising path forward for highly accurate electronic structure calculations. Previous proposals have combined equivariant neural network layers with a final antisymmetric layer in order to satisfy the antisymmetry requirements of the electronic wavefunction. However, to date it is unclear if one can represent antisymmetric functions of physical interest, and it is difficult to precisely measure the expressiveness of the antisymmetric layer. Here, this work attempts to address this problem by introducing explicitly antisymmetrized universal neural network layers. This approach has a computational cost which increases factorially with respect to the system size, but we are nonetheless able to apply it to small systems to better understand how the structure of the antisymmetric layer affects its performance. We first introduce a generic antisymmetric (GA) neural network layer, which we use to replace the entire antisymmetric layer of the highly accurate ansatz known as the FermiNet. We demonstrate that the resulting FermiNet-GA architecture can yield effectively the exact ground state energy for small atoms and molecules. We then consider a factorized antisymmetric (FA) layer which more directly generalizes the FermiNet by replacing the products of determinants with products of antisymmetrized neural networks. We find, interestingly, that the resulting FermiNet-FA architecture does not significantly outperform the FermiNet. This strongly suggests that the sum of products of antisymmetries is a key limiting aspect of the FermiNet architecture. To explore this further, we investigate a slight modification of the FermiNet, called the full determinant mode, which replaces each product of determinants with a single combined determinant. We find that the full single-determinant FermiNet closes a large part of the gap between the standard single-determinant FermiNet and FermiNet-GA on small atomic and molecular problems. Surprisingly, on the nitrogen molecule at a dissociating bond length of 4.0 Bohr, the full single-determinant FermiNet can outperform the largest standard FermiNet calculation with 64 determinants.

97 MATHEMATICS AND COMPUTING↗