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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Autonomous elemental characterization enabled by a low cost robotic platform built upon a generalized software architecture

Despite the rapidly growing applications of robots in industry, the use of robots to automate tasks in scientific laboratories is less prolific due to the lack of generalized methodologies and the high cost of hardware. This paper focuses on the automation of characterization tasks necessary for reducing cost while maintaining generalization and proposes a software architecture for building robotic systems in scientific laboratory environments. A dual-layer (Socket.IO and ROS) action server design is the basic building block, which facilitates the implementation of a web-based front end for user-friendly operation and the use of ROS Behavior Trees for convenient task planning and execution. A robotic platform for automating mineral and material sample characterization is built upon the architecture, with an open-source, low-cost three-axis computer numerical control gantry system serving as the main robot. A handheld laser induced breakdown spectroscopy (LIBS) analyzer is integrated with a 3D printed adapter, enabling (1) automated 2D chemical mapping and (2) autonomous sample measurement (with the support of an RGB-Depth camera). We demonstrate the utility of automated chemical mapping by scanning the surface of a spodumene-bearing pegmatite core sample with a 1071-point dense hyperspectral map acquired at a rate of 1520 bits per second. Furthermore, we showcase the autonomy of the platform in terms of perception, dynamic decision-making, and execution, through a case study of LIBS measurement of multiple mineral samples. The platform enables controlled and autonomous chemical quantification in the laboratory that complements field-based measurements acquired with the same handheld device, linking resource exploration and processing steps in the supply chain for lithium-based battery materials.

Cao, Xuan [Lawrence Berkeley National Laboratory (↗
DOE OSTI2026

Conformal hetero-electrolyte interface between soft oxyhalides and garnet enables low-pressure lithium-reservoir-free solid-state batteries

The operation of solid-state batteries with a lithium metal anode and a high voltage cathode requires solid electrolytes (SEs) that are chemically stable with lithium, have a wide electrochemical window, and accommodate volume changes in the electrodes. Unfortunately, no SE has exhibited satisfactory mechanical and electrochemical properties that fit these requirements to date. Dual solid electrolyte systems that use a different SE for the anolyte and catholyte present a viable solution. Here, we focus on oxyhalide SEs that demonstrate superior ionic conductivity and cathode compatibility, where their lithium metal reactivity and poor reduction stability can be resolved using a lithium garnet (Li 6.5 La 3 Zr 1.5 Ta 0.5 O 12 , LLZTO) separator. Nonetheless, this imposes a new hetero-electrolyte (H-E) interface at the anolyte|catholyte contact that defines the ion transport across the boundary. We report its promising properties, which are deconvoluted from the electrical measurements of bilayer symmetric cells, for three representative oxyhalide catholytes, LiNbOCl 4 , LiTaOCl 4 , and Li 3 Al 3 O 2 Cl 8 . Pressure-dependent measurements reveal that the relative softness of the oxyhalides (hardness ≤0.4 GPa) enables H-E resistances lower than 150 Ω cm 2 at 2–3 MPa. Mesoscale modelling reveals that the transfer-active contact area of oxyhalides with LLZTO is about 2–3-fold higher than that of argyrodite, Li 6 PS 5 Cl. The low H-E resistance of the LiNbOCl 4 |Li 6.5 La 3 Zr 1.5 Ta 0.5 O 12 dual electrolyte enables the cycling of a Li|LiNi 0.82 Mn 0.07 Co 0.11 O 2 full cell with a high discharge capacity (200 mA h g −1 ) at 60 °C and ∼7 MPa. Importantly, we demonstrate a Li-reservoir-free full cell with high Coulombic efficiency (>99.5%) and capacity at 1 MPa using this approach coupled with a garnet-silver interlayer.

Palmer, Max [University of California, Santa Barba↗
DOE OSTI2021

Deep-learning-enabled Bayesian inference of fuel magnetization in magnetized liner inertial fusion

Fuel magnetization in magneto-inertial fusion (MIF) experiments improves charged burn product confinement, reducing requirements on fuel areal density and pressure to achieve self-heating. By elongating the path length of 1.01 MeV tritons produced in a pure deuterium fusion plasma, magnetization enhances the probability for deuterium–tritium reactions producing 11.8-17.1 MeV neutrons. Nuclear diagnostics thus enable a sensitive probe of magnetization. Characterization of magnetization, including uncertainty quantification, is crucial for understanding the physics governing target performance in MIF platforms, such as magnetized liner inertial fusion (MagLIF) experiments conducted at Sandia National Laboratories, Z-facility. We demonstrate a deep-learned surrogate of a physics-based model of nuclear measurements. A single model evaluation is reduced from O ( 10 – 100 ) (10–100) CPU hours on a high-performance computing cluster down to O ( 10 – 100 ) (10) ms on a laptop. This enables a Bayesian inference of magnetization, rigorously accounting for uncertainties from surrogate modeling and noisy nuclear measurements. The approach is validated by testing on synthetic data and comparing with a previous study. We analyze a series of MagLIF experiments systematically varying preheat, resulting in the first ever systematic experimental study of magnetic confinement properties of the fuel plasma as a function of fundamental inputs on any neutron-producing MIF platform. We demonstrate that magnetization decreases from B R ∼ 0.2 ~0.5 to B R ∼ 0.2 ~0.2 MG cm as laser preheat energy deposited increases from E preheat ∼ 460 ~460 J to E preheat ∼ 460 ~1.4 kJ. This trend is consistent with 2D LASNEX simulations showing Nernst advection of the magnetic field out of the hot fuel and diffusion into the target liner.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

SQMBox: Interfacing a semiempirical integral library to modular ab initio electronic structure enables new semiempirical methods

Ab initio and semiempirical electronic structure methods are usually implemented in separate software packages or use entirely different code paths. As a result, it can be time-consuming to transfer an established ab initio electronic structure scheme to a semiempirical Hamiltonian. Here we present an approach to unify ab initio and semiempirical electronic structure code paths based on a separation of the wavefunction ansatz and the needed matrix representations of operators. With this separation, the Hamiltonian can refer to either an ab initio or semiempirical treatment of the resulting integrals. We built a semiempirical integral library and interfaced it to the GPU-accelerated electronic structure code TeraChem. Equivalency between ab initio and semiempirical tight-binding Hamiltonian terms is assigned according to their dependence on the one-electron density matrix. The new library provides semiempirical equivalents of the Hamiltonian matrix and gradient intermediates, corresponding to those provided by the ab initio integral library. This enables the straightforward combination of semiempirical Hamiltonians with the full pre-existing ground and excited state functionality of the ab initio electronic structure code. We demonstrate the capability of this approach by combining the extended tight-binding method GFN1-xTB with both spin-restricted ensemble-referenced Kohn–Sham and complete active space methods. We also present a highly efficient GPU implementation of the semiempirical Mulliken-approximated Fock exchange. The additional computational cost for this term becomes negligible even on consumer-grade GPUs, enabling Mulliken-approximated exchange in tight-binding methods for essentially no additional cost.

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

DIII-D's role as a national user facility in enabling the commercialization of fusion energy

The path to fusion in the United States requires partnership between public and private sector. While the private sector provides the vigor to take some of the major steps necessary, there is a depth of expertise and capability in the public sector that is vital to resolving feasible approaches. As an open national user facility, DIII-D provides a crucial testbed to develop the required new technologies and approaches in relevant conditions. It has unparalleled potential to meet this challenge, thanks to its extreme flexibility and world leading diagnostics. This provides a basis to rapidly develop solutions that project to future reactors with confidence. The program has thus been redeveloped to enable public and private sector engagement and testing of new concepts. A new technology program has been launched to resolve plasma interacting technologies. With modest heating upgrades, the facility can confront the crucial “Integrated Tokamak Exhaust and Performance” gap, to resolve core, exhaust and technology solutions together. The device is also being redeveloped as a training facility, with dedicated student run time, a mentorship program, and open access to all opportunity roles, part of wider efforts to diversify and open pathways through inclusion, access, and equity. This exciting agenda is enabling scientists and technology researchers to pioneer the solutions needed for a Fusion Pilot Plant (FPP) and ITER this decade. As a national user facility, DIII D has singular potential to provide the tools, teams, and insight necessary, to do its part in moving the United States rapidly toward the commercialization of fusion energy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. Here, we present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Orientation-tunable local crystallization of Si films enabled by atomic imprint crystallization

Here, in this paper, we demonstrate area-selective crystallization of amorphous Si into tunable crystal orientations enabled by atomic imprint crystallization (AIC), where an amorphous Si layer is crystallized by solid phase epitaxy (SPE) from an externally impressed single-crystalline Si template. Using micro-patterned single-crystalline Si templates, a limited area of an amorphous Si film, where the film surface and patterned template surface are in contact, is crystallized via SPE to create an array of crystallographically aligned dots embedded in amorphous matrix. Combining AIC from the top surface and conventional SPE from the substrate, we demonstrate the fabrication of an array of crystalline dots embedded in single-crystalline matrix with tunable in-plane rotation angle. The results indicate the high tunability of the crystallization process enabled by AIC, allowing precise control of crystallographic properties of thin films with area-selectivity; such capability opens opportunities for the design of new materials for a wide range of applications in materials science.

Amorphous↗
DOE OSTI2023

$\mathrm{EBR-II MOX}$ Fuel Characterization Enabling ARES Phase I Testing

Pretransient characterization was performed for the Experimental Breeder Reactor II (EBR-II) mixed-oxide (MOX) fuel pellets from the SPA-2/-2B Operational Reliability Testing collaboration between Japan and the United States. Continued collaboration under the Advanced Reactor Experiments for Sodium Fast Reactor Fuels project will investigate the transient performance of these rods in the Transient Reactor Test facility at Idaho National Laboratory in the MOXTOP-THOR experiment. The results will fill a gap in existing transient performance data for MOX as these rods have a peak burnup of 14.3 at. % (~134.4 GWd/t) in the EBR-II. Fuel pellet properties were gathered from available resources and their irradiation and decay history evaluated. Further reactor physics calculations were performed to support the experiment design, reactor operations, and safety analyses necessary to enable the programmatic success of this effort. Of the three irradiated fuel pins, two will undergo transient testing, and all three will undergo post-irradiation examination. The methodology development and analysis activities utilized we report enable current experiment design work and provide the pathway through which measured data of this type can be further evaluated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Enabling cooperative adaptive cruise control on strings of vehicles with heterogeneous dynamics and powertrains

Recent studies have shown that positive impact of Cooperative Adaptive Cruise Control (CACC) can only be guaranteed as market penetration rate increases. Removing the string homogeneity constraint is essential to encourage widespread adoption. In this work, a hierarchical architecture is proposed to enable CACC on vehicles with not only mixed dynamics but also different powertrain types. A low-level layer deals with the vehicle and powertrain dynamics to provide accurate and consistent reference speed tracking response. The high-level layer uses: (1) a Linear Parameter Varying feedback system to provide loop stability, robustness and enforce a variable time gap policy and (2) a feedforward system that processes Vehicle-to-Vehicle information to enhance string stability and response bandwidth, by dealing with the string heterogeneity. A gap management strategy is built on top of the CACC architecture to handle gap setting changes or cut-in/out situations, via a dynamics constrained time gap trajectory planning algorithm. We report the proposed work has been designed, developed and validated on three different real passenger vehicles on public highways and test tracks, showing the potential of the proposed algorithm to enable robust string stable CACC, despite the different dynamics and powertrains considered.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Advanced spectrometric methods for characterizing bio-oils to enable refineries to reduce fuel carbon intensity during co-processing

A promising approach for supplementing petroleum-derived fuels to support reductions in green-house gas emissions is to convert abundant biomass feedstocks into renewable carbon-rich oils using pyrolysis. However, the resultant bio-oils contain various oxygenated compounds that can impart acidity, chemical and thermal instability, and immiscibility with petroleum derived fuels, necessitating further upgrading to derive fuel blendstocks. Co-processing bio-oils and petroleum-derived liquids in existing refineries is a potentially near-term, cost-effective approach for upgrading bio-oils while reducing refinery carbon intensities. However, one cause for hesitation in co-processing bio-oils is limited comprehensive characterization and speciation of the bio-oil components. Advanced analytical techniques are currently under investigation to enable identification of elusive species in bio-oils, enabling researchers to develop strategies to mitigate catalyst deactivation agents and contaminants. This review provides a brief overview of several analytical methods commonly used to analyze bio-oils and their limitations. In addition, advanced techniques currently under development are discussed to further elucidate bio-oil components that may limit its end use. This will help inform the technical and economic feasibility of co-processing bio-oils with petroleum-derived liquids, therefore, improving the overall downstream processes for biofuels blendstock production.

09 BIOMASS FUELS↗
DOE OSTI2021

A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

The global nature of the ITER project along with its projected ~ petabyte per day data generation presents a unique challenge, but also an opportunity for the fusion community to rethink, optimize, and enhance our scientific discovery process. Recognizing this, collaborative research with computational scientists was undertaken over the past several years to create a framework for large-scale data movement across wide-area networks (WANs), to enable global near-real time analysis of fusion data. This would broaden the available computational resources for analysis/simulation, and increase the number of researchers actively participating in experiments. An official demonstration of this framework for fast, large data transfer and real-time analysis was carried out between the KSTAR tokamak in Daejeon, Korea and PPPL in Princeton, USA. Streaming large data transfer, with near real-time movie creation and analysis of the KSTAR Electron Cyclotron Emission imaging (ECEI) data, was performed using the I/O framework ADIOS, and comparisons made at PPPL with simulation results from the XGC1 code. These demonstrations were made possible utilizing an optimized network configuration at PPPL, which achieved over 8.8 Gbps (88% utilization) in throughput tests from NFRI to PPPL. This demonstration showed the feasibility for large-scale data analysis of KSTAR data, and provides a nascent framework to enable use of globally distributed computational and personnel resources in pursuit of scientific knowledge from the ITER experiment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

MCP-enabled agentic AI workflow for building energy modelling: framework and use cases

Traditional building energy modelling workflows remain labor-intensive and error-prone, requiring specialized expertise that limits broader adoption. This paper introduces a novel Model Context Protocol (MCP)-enabled framework that connects AI assistants to EnergyPlus through MCP, a standardized interface for tool invocation and context management. Two complementary integration paradigms are presented and compared: conversational integration, where users interact through natural language while an AI assistant orchestrates MCP tools on demand, and agentic workflow integration, where specialized agents coordinate autonomously to complete multi-step tasks. Using an experimental testbed for residential buildings, the end-to-end workflows are demonstrated. The conversational approach reduced typical inspection and modification tasks from 1-2 h to under 15 min, while maintaining full transparency through visible tool invocations. The agentic approach automated parametric analysis. These demonstrations establish MCP as a foundational layer for AI-assisted building energy modelling, enabling natural language interactions with simulation tools while preserving professional oversight and decision-making authority.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Phase-controlled direct laser acceleration enabled by longitudinal variation of the laser-driven quasi-static plasma magnetic field

Direct laser acceleration (DLA) enables energy transfer from an ultra-high-intensity laser to plasma electrons and underpins many laser-driven particle and radiation-source concepts. A laser-driven azimuthal plasma magnetic field is a key player in this process: it confines energetic electrons, induces betatron oscillations, and makes possible a resonant interaction between the betatron motion and the laser field. While this betatron resonance can enhance electron energy gain, the gain itself generally drives frequency detuning and promotes largely reversible energy exchange that limits net acceleration. Here we show, using a test-electron model with prescribed fields, that a slow longitudinal increase of the quasi-static plasma magnetic field qualitatively changes DLA by introducing hysteresis in the ratio of the betatron frequency to the laser frequency experienced by the electron, so that this ratio depends on the prior evolution of the electron even at the same energy. This hysteresis enables phase control of the electron-laser energy exchange and suppresses the usual reversibility of DLA, allowing electrons to retain the acquired energy and sustain energy gain without intermittent losses.

direct laser acceleration↗
DOE OSTI2020

Model predictive control of KSTAR equilibrium parameters enabled by TRANSP

Due to the complex behavior of tokamak plasmas and the importance of optimizing performance while avoiding instabilities and machine limits, plasma control algorithms are becoming increasingly dependent on sophisticated model-based control approaches. It is anticipated that the use of integrated modeling codes in the model-based control design process will reduce the amount of experimental time needed to implement new control algorithms by facilitating development of control-oriented models and enabling higher-fidelity closed-loop simulations. In this work, a reduced model is developed from a series of TRANSP simulations and is used to develop a model predictive control (MPC) algorithm for controlling important equilibrium parameters in KSTAR [1] discharges. The control algorithm uses the KSTAR neutral beam injection system and the target plasma current and plasma boundary as actuators, and optimizes the plasma stored energy, loop voltage, and internal inductance while avoiding constraints that could lead to disruptions. Higher fidelity testing of the control algorithm is performed using a flexible framework for enabling external processes to actively control plasma parameters in TRANSP simulations. Furthermore, closed-loop simulations demonstrate the ability of the control algorithm to respond to disturbances in density and confinement, handle actuator failures, and move the discharge to high non-inductive fraction conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Enabling robust offline active learning for machine learning potentials using simple physics-based priors

Abstract Machine learning surrogate models for quantum mechanical simulations have enabled the field to efficiently and accurately study material and molecular systems. Developed models typically rely on a substantial amount of data to make reliable predictions of the potential energy landscape or careful active learning (AL) and uncertainty estimates. When starting with small datasets, convergence of AL approaches is a major outstanding challenge which has limited most demonstrations to online AL. In this work we demonstrate a Δ-machine learning (ML) approach that enables stable convergence in offline AL strategies by avoiding unphysical configurations with initial datasets as little as a single data point. We demonstrate our framework’s capabilities on a structural relaxation, transition state calculation, and molecular dynamics simulation, with the number of first principle calculations being cut down anywhere from 70%–90%. The approach is incorporated and developed alongside AMP torch , an open-source ML potential package, along with interactive Google Colab notebook examples.

Shuaibi, Muhammed↗
DOE OSTI2026

Expedited desalination permitting enables adaptive planning and water system cost reduction

Seawater desalination is a drought-proof water supply for coastal cities, but widespread development of desalination plants in the U.S. has been limited by both cost and the complexity of permitting processes designed to minimize environmental impact . This work estimates the value of accelerating the permitting timeline without changing environmental or social standards. On average, faster permitting reduces the frequency of desalination plant construction and operation, the overall costs of robust water system operation, the environmental impacts of drought-tolerant water supplies due to shorter duration of plant operation. Expedited permitting allows fundamental changes in how water infrastructure is deployed, facilitating a transition from anticipatory construction and continuous operation of seawater desalination capacity as a redundant drought buffer to just-in-time (i.e. adaptive) deployment of seawater desalination capacity when critical drought thresholds are crossed. We demonstrate the value of expedited desalination permitting in enabling adaptive planning and reducing water system costs using a simple case study in Santa Barbara, CA. We discuss additional forms of adaptive water infrastructure planning as enabled by faster permitting and address their challenges and opportunities. Lastly, we identify synergies between innovation in adaptive planning, innovation in expedited permitting practices, and innovation in water technology.

adaptive planning↗
DOE OSTI2025

Mechanisms enabling reconfigurability and long-term retention in vanadium oxide electrochemical memory

Phase coexistence in nanoscale electrochemical random-access memory (ECRAM) has recently been demonstrated to enable both information storage and extraordinary reconfigurability. These proof-of-principle demonstrations have left the mechanistic details of such a process unresolved. Particularly, the mechanisms that stabilize the multiple phases, and the underlying processes behind sustained memory retention, remain unclear, and are necessary to design such devices. Here we report microscale ECRAM devices composed of V⁢O𝑥, which enables us to directly probe the active region in an operando fashion using optical techniques. Using Raman mapping, we show the phase coexistence driven by the electrochemical injection of O vacancies to be spatially uniform (i.e., with no filaments). The stability was observed to be unusually long, with 1% loss over 14 years in ambient conditions. First-principles calculations of the oxygen vacancy formation energies in V⁢O 𝑥 further support the thermodynamic coexistence of multiple V⁢O 𝑥 phases and clarify the origin of the observed long-term retention in the ECRAM devices. Further, we demonstrate single devices that can be voltage programmed to exhibit synaptic, neuronal, and reconfigurable logic gate functionalities. Furthermore, we not only uncover the phase coexistence mechanism that may help device design, but also demonstrate the circuit-level applications of reconfigurability.

Electrical conductivity↗
DOE OSTI2021

Flat-Band-Enabled Triplet Excitonic Insulator in a Diatomic Kagome Lattice

The excitonic insulator (EI) state is a strongly correlated many-body ground state, arising from an instability in the band structure of narrow-gap semiconductors towards exciton formation. Here we show that the flat valence and conduction bands of a Yin-Yang Kagome lattice, as exemplified in a superatomic graphene lattice, conspire to enable an interesting state of triplet EI, based on first-principles Density Functional Theory (DFT) calculations combined with many-body GW and Bethe-Salpeter Equation (BSE). As an intrinsic property of topological flat bands, highly localized electron and hole wavefunctions significantly reduce the screening and enhance the exchange interaction, leading to an unusually high triplet exciton binding energy (~1.2 eV) exceeding the GW band gap by ~0.2 eV and a huge singlet-triplet splitting of ~0.4 eV. Here, the flat-bands-enabled triplet EI state also points to the possibility of complete population inversion between the two topological flat bands for the realization of excited quantum spin Hall effect. Our findings enrich once again the intriguing physics of flat bands, which has drawn broad interest.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗