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Results for “high-temperature refractory thermal energy storage”

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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1,262 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Complex-Concentrated Anion Doping Enables Ultra-Stable Lattice Oxygen and Structural Integrity in Lithium-Rich Layered Oxide Cathodes

Lithium- and manganese-rich layered oxides (LMR) stand out as next-generation lithium-ion cathode chemistries, which harness both transition-metal and lattice-oxygen redox processes to deliver exceptional capacity and energy density. However, their full potential is hindered by intrinsic oxygen instability and structural degradation, resulting in pronounced voltage fade and capacity decay. Here, we present a complex-concentrated anion-doping paradigm in which multiple anions, F, Br, and S, are incorporated into the oxygen sublattice to enhance oxygen-redox and structural stability. X-ray absorption spectroscopy and aberration-corrected scanning transmission electron microscopy confirm ultra-stable local oxygen coordination environments during long-term cycling, with detrimental phase transformations and oxygen-loss-induced cavitation dramatically inhibited. Notably, we show that the characteristic LiTM6 transition metal (TM) honeycomb ordering is preserved even after electrochemical cycling. Concurrently, this strategy yields an unprecedented volume change of only 0.63% upon charging to 4.8 V vs. Li+/Li, achieving the first zero-strain LMR cathode. The resulting LMR cathode delivers ultralow voltage fade (1 mV per cycle during the first 100 cycles and becomes negligible in subsequent cycles) and outstanding energy retention (93% after 200 cycles) in a pouch cell configuration. Our complex-concentrated anion-doping concept establishes a broadly applicable strategy for resolving chemo-mechanical failure mechanisms in ceramic intercalation electrodes for next-generation energy storage.

Li-ion batteries↗
DOE OSTI2026

Mechanical, Electrochemical & Thermal Models - Training (CRADA CRD-19-00798 Final Report)

Under the proposed effort in partnership with Hyundai Motor Company (HMC), the National Renewable Energy Laboratory (NLR) will host Dr. Jaeyoung Lim from HMC for a period of one year to jointly develop mathematical models for battery cells and modules subject to mechanical crush. NLR will assist with the development of mathematical models that Dr. Lim will incorporate into his research effort on new concepts of mobility with electric vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Flame retardant biogenic building insulation materials from hemp fiber

Biogenic thermal insulation materials are in high demand because of its carbon-sequestration nature. However, high flammability, moisture condensation, and relatively high thermal conductivity of biogenic material are major concerns for sustainable building applications. In this study, we report the fire-retardant cellulose xerogel insulation nanocomposites derived from hemp fiber recycling and silica xerogel, in which the boric acid treatment improves its fire retardancy. The as-prepared materials show a low thermal conductivity of 31.3 mW/m K, high flexural modulus of 665 MPa, hydrophobicity with the water contact angle of 115°, and fire retardancy with 30% weight loss over a period of burning time 10 min. Overall, this work provides an effective method for the synthesis of fire-retardant biogenic thermal insulation materials and shows a promising way for next-generation bio-based insulation materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Flame stabilization in DME spray flames under engine-relevant conditions characterized by OH* chemiluminescence and formaldehyde laser-induced fluorescence

The transient and quasi-steady flame structures of Dimethyl Ether (DME) fuel sprays, produced by a single-hole injector (Spray D), were investigated using Planar Laser-Induced Fluorescence (PLIF) and chemiluminescence imaging in a constant-volume chamber under Engine Combustion Network (ECN) Spray A conditions (900 K ambient temperature, 60 bar ambient pressure, 1500 bar injection pressure, and 22.8 kg/m 3 ambient density). Low-temperature chemical reaction zones were visualized using formaldehyde (CH 2 O) PLIF with 355 nm excitation, while high-temperature flame regions were captured via chemiluminescence imaging of excited-state hydroxyl radicals (OH*). Both transient and quasi-steady flame structures clearly show the transition from CH 2 O to OH*, highlighting the progression from low- to high-temperature combustion, while the position of the flame is displaced for DME compared to reference hydrocarbon n-dodecane. Homogeneous reactor calculations with detailed chemistry and using adiabatic mixing for initial temperature show that CH 2 O peaks are significantly higher for DME at the same equivalence ratio, with a higher heat-release during the cool-flame regime with respect to the fuel heating value. Thus, the cool-flame dynamic as a precursor to high-temperature combustion and flame stabilization exhibit distinct behavior for DME relative to conventional hydrocarbons, and these phenomena are effectively resolved through the soot-free nature of DME and the high-speed, time-resolved diagnostics.

CH2O laser-induced fluorescence↗
DOE OSTI2024

Phonon screening and dissociation of excitons at finite temperatures from first principles

The properties of excitons, or correlated electron–hole pairs, are of paramount importance to optoelectronic applications of materials. A central component of exciton physics is the electron–hole interaction, which is commonly treated as screened solely by electrons within a material. However, nuclear motion can screen this Coulomb interaction as well, with several recent studies developing model approaches for approximating the phonon screening of excitonic properties. While these model approaches tend to improve agreement with experiment, they rely on several approximations that restrict their applicability to a wide range of materials, and thus far they have neglected the effect of finite temperatures. Here, we develop a fully first-principles, parameter-free approach to compute the temperature-dependent effects of phonon screening within the ab initio GW -Bethe–Salpeter equation framework. We recover previously proposed models of phonon screening as well-defined limits of our general framework, and discuss their validity by comparing them against our first-principles results. We develop an efficient computational workflow and apply it to a diverse set of semiconductors, specifically AlN, CdS, GaN, MgO, and SrTiO 3 . We demonstrate under different physical scenarios how excitons may be screened by multiple polar optical or acoustic phonons, how their binding energies can exhibit strong temperature dependence, and the ultrafast timescales on which they dissociate into free electron–hole pairs.

Science & Technology - Other Topics↗
DOE OSTI2026

Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials

Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li + , by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energies and forces of nonequilibrium reactive PAN configurations, to unravel the kinetics of PAN cyclization initiated by a nucleophile (OH – dissociated from LiOH) attacking the terminal nitrile carbon. We find, based on the reaction free-energetics, rates, and charge analysis, that the nucleophile attack producing the first ring is the rate-limiting step, which subsequently triggers Li + -coupled electron transfer along the PAN backbone, causing ∼10 4 times faster sequential ring-formation of the remaining nitriles. PAN’s extended configurations, where dipolar and H-bonding interactions are minimal, enable such rapid kinetics. By validating our computational findings with IR and NMR experiments, we establish a pathway for designing reactive polymers with enhanced charge transport for energy applications.

Chahal-Crockett, Rajni [Oak Ridge National Laborat↗
DOE OSTI2025

Distinguishing Thermal Fluctuations from Polaron Formation in Halide Perovskites

Recent angle-resolved photoelectron spectroscopy (ARPES) measurements of the hole effective mass in CsPbBr$_3$ revealed an enhancement of $\sim$50 % compared to the bare mass computed from first principles for CsPbBr$_3$ at $T = 0 K$. This large enhancement was interpreted as evidence of polaron formation. Employing accurate finite-temperature first-principles calculations, we show that the calculated hole effective mass of CsPbBr$_3$ at $T = 300 K$ can explain experimental results without invoking polarons. Thermal fluctuations are particularly strong in halide perovskites compared to conventional semiconductors such as Si and GaAs, and cannot be ignored when comparing with experiment. We not only resolve the debate on polaron formation in halide perovskites, but also demonstrate the general importance of including thermal fluctuations in first-principles calculations for strongly anharmonic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-Fidelity Building Emulator for Integrated Comfort and Energy Analysis using EnergyPlus and Radiance

The growing need for smart, energy-efficient, and occupant-centric buildings has created a demand for advanced control systems that can optimize building operations to balance energy savings, demand flexibility, and comfort. However, current building energy simulation tools, such as EnergyPlus, have limitations that hinder the development and evaluation of these complex control systems. To address this challenge, we introduce a high-fidelity building emulator that dynamically couples EnergyPlus with Radiance for enhanced daylight modeling. The introduced workflow allows researchers and practitioners to rapidly develop and evaluate innovative control solutions. An example study looking at a south-facing office zone revealed up to 67% deviation in predicted light levels, which can significantly impact building assessment.

Yu, Tammie↗
DOE OSTI2026

Heavy Element Spectroscopy in the Gas Phase

Actinides are inherently unstable and undergo nuclear decay processes with a concurrent release of energy. Consequently, they are used for nuclear power generation, nuclear weapons, and nuclear medicine. However, the radioactive decay processes also pose significant technological problems for the safe treatment and storage of spent nuclear materials. Cost-effective extraction of the actinides is the key first step in the remediation of nuclear waste, but the appropriate chemical means have yet to be determined. Our present understanding of the chemistry of actinides is limited, with the role of the 5f electrons posing a set of particularly challenging questions. The work reported here is focused on the use of electronic spectroscopy to probe the bonding of small molecules in the gas phase that contains thorium or uranium. Analyses of these data, carried out within the framework of ligand field theory, reveal clear evidence that the 5f electrons are spectators that retain their atomic metal ion character.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2027

Low thermal budget dopant activation in shallow junctions of implanted Si via Tunable plasma enhanced annealing

Low thermal budget (LTB) annealing has become increasingly critical for shallow junctions as semiconductor devices are scaled down. Plasma Enhanced Annealing (PEA) has emerged as a promising LTB annealing process, however its mechanisms remain unclear. In this study, arsenic and boron implanted silicon wafers were subjected to helium or argon ion bombardment generated by annealing plasmas under controlled ion energies and doses in a home-built annealing reactor. The structural and electrical changes were characterized by four-point probe measurements, secondary ion mass spectrometry, Raman spectroscopy and spectroscopic ellipsometry. A pronounced reduction in sheet resistance, accompanied by a decrease in the damage layer thickness demonstrates the surface selective annealing capability of PEA process. Comparative studies using He and Ar plasmas and related ion bombardment reveal a dependence of activation efficacy on both ion species and the dopant implantation profile. Furthermore, a multi-step annealing mechanism is proposed, consisting of an initial (low dose) structural recovery stage followed by the generation of “extended” defects and concluded at large doses by an enhanced dopant activation. The proposed annealing kinetics are thought to be controlled by ion flux, dose and energy. Finally, these results highlight PEA as an effective, surface-selective, LTB approach for shallow-dopant activation and near surface annealing, and provide mechanistic insights into PEA processes.

dopant activation↗
DOE OSTI2026

The Development of Kinetic and Radiation Hydrodynamics Modeling of Thermonuclear Burn Propagation in Isochoric p - 11 B Through the Support of the INFUSE Program

The report summarizes DOE INFUSE-supported work between HB11 Energy and the University of Rochester’s TriForce Institute to improve computational modeling of advanced fusion fuels, especially proton–boron-11 (p- 11 B). The project extended the TriForce particle-in-cell/Monte Carlo collision code to include physics needed for dense, high-temperature p- 11 B burn studies, including p- 11 B fusion reactions, three-alpha-particle reaction products, relativistic Coulomb collisions, large-angle nuclear scattering, bremsstrahlung radiation, inverse bremsstrahlung absorption, and photon transport. The upgraded models were verified against focused physics tests and against known deuterium–tritium burn behavior. The study then used one-dimensional spherical simulations to estimate the conditions required for thermonuclear burn propagation in isochoric p- 11 B fuel. The calculations found that burn propagation is possible in the model, but only under very extreme hot-spot conditions, such as about 7000 g/cm 3 at 500 keV or 9000 g/cm 3 at 300 keV for a 20-micron hot spot. These conditions are much more demanding than current demonstrated inertial confinement fusion hot spots. The report concludes that the INFUSE collaboration successfully advanced kinetic and radiation modeling capabilities for p- 11 B fusion and provided useful estimates of ignition requirements. However, the simulated fuel gains remain below what would be needed for practical inertial fusion energy, and further work is needed to reconcile differences among kinetic, radiation-hydrodynamic, and analytic models and to identify more achievable target designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Design and Modeling of the Charge Readout of a SiMOS Quantum Dot with a Single Electron Transistor and CryoCMOS

Single electron spin qubits trapped in SiMOS quantum dots are a promising technology for scaling to thousand- or million-qubit systems due to their compatibility with mature CMOS manufacturing processes. A readout system that combines a single electron transistor with a custom cryogenic CMOS amplification and digitization chain offers key advantages by avoiding the use of bulky RF components or room-temperature interconnects. We present design techniques and simulation results for an optimized qubit-SET cryoCMOS interface, culminating in the design of the QNDR1 ASIC, the first cryogenic readout ASIC designed under the Quandarum project, which targets the development of a many-channel spin qubit based detector for use in high energy physics.

Quinn, Adam [Fermilab] (ORCID:0009000605056371)↗
DOE OSTI2025

Imprints of High-Density Nuclear Symmetry Energy on Crustal Fraction of Neutron Star Moment of Inertia

The density dependence of nuclear symmetry energy E sym (ρ) remains the most uncertain aspect of the equation of state (EOS) of supradense neutron-rich nucleonic matter. Utilizing an isospin-dependent parameterization of the nuclear EOS, we investigate the implications of the observational crustal fraction of the neutron star (NS) moment of inertia ΔI/I for the E sym (ρ). We find that symmetry energy parameters significantly influence the ΔI/I, while the EOS of symmetric nuclear matter has a negligible effect. In particular, an increase in the slope L and skewness J sym of symmetry energy results in a larger ΔI/I, whereas an increase in the curvature K sym leads to a reduction in ΔI/I. Moreover, the ΔI/I is shown to have the potential for setting a lower limit of symmetry energy at densities exceeding 3 ρ 0 , particularly when L is constrained to values less than 60 MeV, thereby enhancing our understanding of supradense NS matter.

equation of state↗
DOE OSTI2024

Fluorophilic Sigma(σ)‐Lock Self‐Healable Copolymers

Although F-Containing molecules and macromolecules are often used in molecular biology to increase the binding with Lewis acidic groups by introducing favorable C−F dipoles, there is virtually no experimental evidence and limited understanding of the nature of these interactions, especially their role in synthetic polymeric materials. These studies elucidate the molecular origin of inter- and intra-Chain interactions responsible for self-healing of F-Containing copolymers composed of pentafluorostyrene and n-butyl acrylate units (p(PFS/nBA). Guided by dynamic surface oscillating force (SOF) and spectroscopic measurements supported by molecular dynamics (MD) simulations, these studies show that the reformation of σ-σ orbitals in −C−F of PFS and CH 3 CH 2 − of nBA units enables the recovery of entropic energy via fluorophilic-σ-lock van der Waals forces when PFS/nBA molar ratios are ~50/50. The strength of these interactions determined experimentally for self-healable PFS/nBA compositions is in the order ~0.3 kcal/mol which primarily comes from fluorophilic-σ-lock (~70 %) contributions. These interactions are significantly diminished for non-self-healable counterparts. Strongly polarized −C−F σ orbitals create lateral dipolar forces enhancing the affinity towards −C−H orbitals, facilitating energetically favorable interactions. Entropic recovery driven by non-Covalent bonding offers a valuable tool in designing materials with unique functionalities, particularly self-healable batteries and energy storage devices.

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

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler [National Renewable Energy Lab. (NR↗
DOE OSTI2026

SAM Finite Volume Method Development Status Update: GCR Application, Restart, and MultiApp

The System Analysis Module (SAM) is being developed as a modern system analysis code for advanced non-light-water-reactor safety analysis under the U.S. DOE NEAMS program. Previous feasibility studies have demonstrated that a staggered-grid finite volume method (SG-FVM), implemented under the MOOSE framework, can deliver more than an order of magnitude speedup over the existing continuous Galerkin finite element method (CG-FEM) solver for liquid-cooled, incompressible but thermally expandable flow systems. This work extends the previous effort to compressible, gas-cooled reactor applications, where pressure couples directly into the mass equation adding additional nonlinearity into the equation system. New code capabilities are implemented for pebble bed high-temperature gas-cooled reactor (PB-HTGR) analysis, including a pebble bed CoreChannel component, built-in pebble bed effective thermal conductivity model and channel-to-channel crossflow model. The capabilities are tested, benchmarked, and demonstrated for problems with increased level of model and physical complexities, including the HTTU effective thermal conductivity test, the SANA passive cooling test, and a demonstration case using the GPBR200 reactor design covering steady-state operation, DLOFC and PLOFC transients. Across all cases, the SG-FVM solver demonstrated strong robustness and efficiency, and the solutions agree well with reference results and data. The finding of this work proves that SG-FVM is a viable and efficient solver pathway for compressible, gas-cooled reactor system analysis in SAM. In addition, work has been done to successfully support SAM-FVM recover/restart code feature that is essential to reactor safety analysis applications, and MultiApp code feature that is essential to multi-scale and multi-physics simulations. In summary, this work continued from previous feasibility studies, and further demonstrated that the SG-FVM will serve as a strong foundation for SAM’s advanced solver algorithm for future deployment.

Zou, Ling↗
DOE OSTI2026

Development of in-situ polymerized intrinsically conductive resin and low-cost carbon pigments offering high conductivity for sensing, EMI shielding and lighting protection

Electrically conductive composites are emerging across diverse industries such as electronic, automotive, aerospace, advanced air mobility, biomedical, infrastructure, defense and security offering static charge dissipation, electromagnetic interference shielding, lighting protection, sensing, dicing, corrosion monitoring, etc. Conductivity enhanced composites provide several advantages compared to conventional metals including weight reduction, corrosion resistance, energy efficient processability, tunable properties and multifunctionality. Polymers are typically insulating in nature and require conducting filler for electron transport. However, dispersion and polymer-filler interphases are critical and often disrupt conducting pathways. Besides, conductive fillers such as graphene, carbon nanotube, MXene, silver nanowire, etc. are expensive, limiting their wide adoption in composite industry. On the other hand, a limited number of intrinsically conductive polymers are available among which polyaniline (PANI) has been widely studied due to its high conductivity, thermal and chemical stability. However, PANI is difficult to process and exhibits weak mechanical properties. In brief, there is a significant demand for electrically conductive polymer formulation with cost-effective conducting fillers that offer processability in scale to expand the market of conductivity enhanced materials.

Kumar, Vipin [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2026

Integrating AI Data Centers with the Power Grid

The rapid expansion of artificial intelligence (AI) has triggered an unprecedented surge in electricity demand, with US data center energy use projected to double or triple 2023 levels by 2028. This exponential growth places strain on grid infrastructure, which can hinder timely construction of desired computing capacity. To bridge this supply-demand gap, utilities and AI developers are increasingly turning to demand flexibility, a strategy that incentivizes shifting or reducing power use during peak periods of grid stress. Data centers are uniquely equipped for flexible operations due to their digital workloads, built-in redundancy, and onsite energy assets. This article outlines four primary mechanisms to enable data center flexibility: computational load flexibility (shifting tasks temporally or geographically), flexible use of core facility infrastructure adjustments, energy storage utilization, and onsite electricity generation. To encourage adoption, utilities are deploying new tariff designs, including voluntary interruptible service riders, mandated flexibility requirements, and streamlined interconnection processes for flexible loads. For the highly capitalized and rapidly growing AI industry, the primary motivators for embracing these strategies are expediting facility interconnection, satisfying emerging regulatory mandates, and mitigating community resistance. While demand flexibility cannot substitute the long-term need for new bulk power generation, it serves as an essential, immediate solution for enabling near-term deployment. By transforming data centers from grid stressors into stabilizing assets, flexible operations can ensure reliable grid integration, ease market pressures, and support a resilient power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗