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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Lanczos Algorithm, the Transfer Matrix, and the Signal-to-Noise Problem

This Letter introduces a method for determining the energy spectrum of lattice quantum chromodynamics by applying the Lanczos algorithm to the transfer matrix and using a bootstrap generalization of the Cullum-Willoughby method to filter out spurious eigenvalues. Proof-of-principle analyses of the simple harmonic oscillator and the lattice quantum chromodynamics proton mass demonstrate that this method provides faster ground-state convergence than the “effective mass,” which is related to the power-iteration algorithm. Lanczos provides more accurate energy estimates than multistate fits to correlation functions with small imaginary times while achieving comparable statistical precision. Two-sided error bounds are computed for Lanczos results and guarantee that excited-state effects cannot shift Lanczos results far outside their statistical uncertainties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Two-electron interference in two-photon attosecond double ionization of neon

The pump-probe experiments enabled by x-ray free-electron lasers (XFEL) will allow us to directly observe correlated electronic motion with attosecond time resolution by detecting photoelectron pairs in coincidence. In helium, the transition between the nonsequential and sequential regime in two-photon double ionization (TPDI) is well explained by a virtual-sequential model. Much less is known, however, about the TPDI process in more complex atoms. Recently, we extended the virtual-sequential model to arbitrary light pulses [Chattopadhyay Phys. Rev. A 108, 013114 (2023)]. This extension employs multichannel scattering states for the single ionization of both the neutral and the ionized target, which we initially applied to helium. In the present study, we show that our extended virtual-sequential model reproduces the qualitative features of the angularly integrated observables with available experimental results for neon, a considerably more complex target. We observe an intriguing feature of inverted two-particle interference in the joint-energy distribution of Ne compared to He. Finally, this phenomenon, attributable to the presence of a final doubly ionized state with triplet symmetry coupled to the two photoelectrons, should be observable with current experimental technologies.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Characteristic terahertz emissions induced by optically excited collective orbital modes

Here, we study the generation of collective orbital modes, their evolution, and the characteristic nonlinear optical response induced by them in a photoinduced orbital-ordered correlated oxide using real-time simulations based on an interacting multiband tight-binding (TB) model. The d-d optical transitions under femtoseconds light-pulse in an orbital-ordered state excite collective orbital modes, also known as “orbitons”. Consistently incorporating electronic interactions and the interplay between charge, spin, and lattice degrees of freedom in the TB-model provides a clearer understanding of how these factors influence the generation and evolution of collective orbital modes. The dynamics of Jahn-Teller vibrational modes in the photoinduced state modify the intersite orbital interaction, which further amplifies these orbital modes. In the presence of weak ferroelectricity, the excitation of collective orbital modes induces a strong THz oscillatory photocurrent, which is long-lived. This suggests an alternative way to experimentally detect low-energy collective modes through THz-emission studies in the photoinduced state. Our study also elucidates that quasiparticle dynamics in improper ferroelectric oxides can be exploited to achieve highly interesting and nontrivial optoelectronic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Joint Resource Modeling and Assessment for Hybrid Distributed Solar and Wind Systems

The inherent variability and uncertainty in distributed energy resources can presents myriad challenges to the planning and operations of power systems. These risks are poised to become larger as the penetration of renewable energy sources rises in the power generation mix. Hybrid solar-wind energy systems are able to mitigate some of these risks by their complementary resource availability. Surface solar and wind fields are coupled and correlated in both space and time. Appropriately estimating the hybrid solar wind energy system requires simulating the spatio-temporal structure of these fields that can be produced for each time horizon. We introduce a novel joint spatio-temporal stochastic differential equation (SPDE) approach that captures the spatio-temporal dynamics of solar and wind fields and their joint dependency over a domain for each time step. In the case study on Colorado, we consider nonstationary three-level hierarchical spatio temporal models for both hourly solar irradiance data and wind speed data in Colorado. Dependence between the solar irradiance data and wind speed data is captured by a shared spatio-temporal random effect. Our approach performs well in terms of the prediction score criterion.

joint modeling↗
DOE OSTI2024

Toward quantum networking with frequency-bin qudits

Quantum networking holds tremendous promise in transforming computation and communication. Entangled-photon sources are critical for quantum repeaters and networking, while photonic integrated circuits are vital for miniaturization and scalability. In this talk, we focus on generating and manipulating frequency-bin entangled states within integrated platforms. We encode quantum information as a coherent superposition of multiple optical frequencies; this approach is favorable due to its amenability to high-dimensional entanglement and compatibility with fiber transmission. We successfully generate and measure the density matrix of biphoton frequency combs from integrated silicon nitride microrings, fully reconstructing the state in an 8 × 8 two-qudit Hilbert space, the highest so far for frequency bins. Moreover, we employ Vernier electro-optic phase modulation methods to perform time-resolved measurements of biphoton correlation functions. Currently, we are exploring bidirectional pumping of microrings to generate indistinguishable entangled pairs in both directions, aiming to demonstrate key networking operations such as entanglement swapping and Greenberger–Horne–Zeilinger state generation in the frequency domain.

Myilswamy, Karthik V.↗
DOE OSTI2020

Probabilistic Assessment of Damage from Same Shock Response Spectra due to Variations in Damping

Interpretation of field data from shock tests and subsequent assessment of product safety margins via laboratory testing are based on the shock response spectra (SRS). The SRS capture how a single degree of freedom (SDOF) structure responds to the shock at differing frequencies and, therefore, no longer contain the duration or other temporal parameters pertaining to the shock. A single duration can often be included in the technical specification or in the recreation of acceleration vs. time history from the specified SRS; however, there is little basis for that beyond technical judgment. The loss of such temporal information can result in the recreated SRS being the same while its effect on a system or component can be different. This paper attempts to quantify this deficiency as well as propose a simple method of capturing damping from shock waves that can allow the original waveform to be more accurately reconstructed from the SRS. In this study the decay rate associated with various frequencies that comprise the overall shock was varied. This variation in the decay rate leads to a variation in the acceleration vs. time history, which can be correlated to a “Damage Index” that captures the fatigue damage imparted to the object under shock. Several waveforms that have the same SRS but varying rates of decay for either high- or low-frequency components of the shock were investigated. The resulting variation in stress cycles and Damage Index is discussed in the context of the lognormal distribution of fatigue failure data. It is proposed that, along with the SRS, the decay rate is also captured to minimize the discrepancy between field data and representative laboratory tests.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Baycal

Bayesian Model Calibration (BayCal) toolkit is a software plugin for Risk Analysis Virtual Environment (RAVEN) framework, arming at inversely quantifying the uncertainties associated with simulation model parameters based on available experiment data. BayCal seeks statistical inference of the uncertain input parameters that are consistent with the available measurement data or observed data. The unique feature of BayCal is the capability to be linked with RAVEN to build corresponding calibration workflows for complex multi-physics simulations. In addition to be able to use the machine learning capability of RAVEN to significantly reduce the computational cost of expensive simulation models, another distinctive feature of BayCal is the capability to deal with high-dimensional correlated model outputs, such as time series observations at multiple locations, via principal component analysis (PCA) technique.

Wang, Congjian↗
DOE OSTI2026

Stochastic equilibrium Raman spectroscopy (STERS)

In this manuscript, we propose a new method for cavity- and surface-enhanced Raman spectroscopy (SERS) with improved temporal resolution in the measurement of stochastic Raman spectral fluctuations. Our approach combines Fourier spectroscopy and photon correlation to decouple the integration time from the temporal resolution. Using statistical optics Monte Carlo simulations, we establish the relationship between time resolution and Raman signal strength, revealing that typical Raman spectral fluctuations, commensurate with molecular conformational dynamics, can theoretically be resolved on micro- to millisecond timescales. The method can further extract average single-molecule dynamics from small sub-ensembles, thereby potentially mitigating challenges in achieving strictly single-molecule isolation on SERS substrates.

Cobb-Bruno, Colburn [University of California, Ber↗
DOE OSTI2022

Spectrally separable photon-pair generation in dispersion engineered thin-film lithium niobate

Existing nonlinear-optic implementations of pure, unfiltered heralded single-photon sources do not offer the scalability required for densely integrated quantum networks. Additionally, lithium niobate has hitherto been unsuitable for such use due to its material dispersion. We engineer the dispersion and the quasi-phasematching conditions of a waveguide in the rapidly emerging thin-film lithium niobate platform to generate spectrally separable photon pairs in the telecommunications band. Such photon pairs can be used as spectrally pure heralded single-photon sources in quantum networks. We estimate a heralded-state spectral purity of >94% based on joint spectral intensity measurements. Further, a joint spectral phase-sensitive measurement of the unheralded time-integrated second-order correlation function yields a heralded-state purity of (86 ± 5)%.

36 MATERIALS SCIENCE↗
DOE OSTI2022

DCIF (Directional Cooling-Induced Fracturing) Westerly Granite AE Borehole Damage Effect Test (Task 3-0)

Directional Cooling-Induced Fracturing (DCIF) experiments were conducted on three rectangular Westerly granite blocks (width=depth=4.0", height=2.0") which were preheated to 200, 400, and 600 degree C to induce damage (microcracks) with varying degrees. Liquid nitrogen was poured in a small, 1"-diameter copper cup attached to the top of the sample, and the resulting acoustic emissions (AEs) and temperature changes on the surface of the sample were monitored. The experiments were conducted under one selected biaxial stress (5.8MPa). The obtained AEs were used to determine the microcracking source locations and amplitude, and the associated moment tensors. The onset time of the AEs was correlated with the cooling temperature, which was used to show that the temperature at the onset of microcracking is not affected significantly by the preexisting damage, compared to the impact of the stress in the sample. Included in this submission are the animations of the AE locations and graphics displaying the measured temperature-AE activity changes for samples with different degrees of microcrack damage.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Crowdsourcing Felt Reports Using the MyShake Smartphone App

MyShake is a free citizen science smartphone app that provides a range of features related to earthquakes. Features available globally include rapid postearthquake notifications, live maps of earthquake damage as reported by MyShake users, safety tips, and various educational features. The app also uses the accelerometer in the mobile device to detect earthquake shaking, and to record and submit waveforms to a central archive. In addition, MyShake delivers earthquake early warning alerts in California, Oregon, and Washington. Here, in this study, we compare the felt shaking reports provided by MyShake users in California with the U.S. Geological Survey’s (USGSs) “Did You Feel It?” intensity reports. The MyShake app simply asks, “What strength of shaking did you feel?” and users report on a five-level scale. When the MyShake reports are averaged in spatial or time bins, we find strong correlation with the Modified Mercalli Intensity scale values reported by the USGS based on the DYFI surveys. The MyShake felt reports can therefore contribute to the creation of shaking intensity maps.

58 GEOSCIENCES↗
DOE OSTI2023

Testing Paired Neural Network Models for Aftershock Identification

Aftershock sequences are a burden to real-time seismic monitoring. Cross-correlation can be used because aftershocks exhibit similar waveforms, but the method is computationally expensive. Deep learning may be an alternative, as it is computationally efficient, but great attention to training and testing is required in order to trust that the model can generalize to new aftershock sequences. This is problematic for aftershock sequences, because large-magnitude earthquakes are unpredictable and are globally widespread. Here, we test several paired neural network (PNN) models trained on a augmented (noise-added) earthquake dataset, to determine whether they can be generalized to process real aftershock sequences. Two aftershock datasets that were originally detected by cross-correlation and subsequently validated by an expert analyst were used. We found that current PNN models struggle to generalize to aftershock sequences. However, we identify approaches to improve training future PNN models and believe that improvements may be achieved by transfer learning.

58 GEOSCIENCES↗
DOE OSTI2022

Effect of the Nuclear Equation of State on Relativistic Turbulence-induced Core-collapse Supernovae

Abstract The nuclear equation of state (EOS) is an important component in the evolution of core-collapse supernovae. In this paper we make a survey of various EOSs in the literature and analyze their effect on spherical core-collapse models in which the effects of three-dimensional turbulence is modeled by a general relativistic formulation of Supernova Turbulence In Reduced-dimensionality (STIR). We show that the viability of the explosion is quite EOS dependent and that it best correlates with the early-time interior entropy density of the proto–neutron star. We check that this result is not progenitor dependent, although the lowest-mass progenitors show different explosion properties, due to the different pre-collapse nuclear composition. Larger central entropies also induce more vigorous proto–neutron star convection in our one-dimensional turbulence model, as well as a wider convective layer.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Degradation Mechanisms in TOPCon/POLO Solar Cells

This presentation shows how Poly-Si/SiO2 contacts show a post firing degradation/regeneration cycle. The following conclusions were made: 1) cycle changes surface passivation (Jo), but not the bulk lifetime; 2) cycle time depends on anneal temperature in light or dark (higher T, faster cycle); 3) 400 degrees Celsius anneal eliminates the cycle - only generation occurs; 4) cycle time and magnitude is not correlated with Tfire; 5) contact passivation is stable after cycling; 6) fundamental nature of defect(s) responsible is not known; 7) TOPCon cells/modules, if constructed well, seem to show minimal degradation issues (PVEL, Jolywood, Jinko, ET Solar), and 8) NREL/ASU are studying TOPCon cells and UC Davis has a molecular dynamics model for TOPCon.

degradation↗
DOE OSTI2021

Vectors of Efficiency in Hybrid Poplar Genotype Testing

Abstract The Natural Resources Research Institute Hybrid Poplar Program breeds and tests genetically improved clones for bio-mass production and environmental services. The testing process progresses from Nursery Progeny Tests (NPT) to Family Field Trials (FFT) to Clone Trials (CT) to Yield Blocks (YB), with limited replication of many clones in FFT and CT and a limited number of highly selected clones set out in monoclonal blocks (YB) to approximate the conditions of commercial plantations. We used correlation vectors, R 2 (coefficient of determination) and r s (Spearman’s Coefficient) for growth (DBH 2 ) and McFadden’s Pseudo R 2 for canker severity score, to determine where testing times could be altered (age – age correlations) and whole testing steps eliminated. FFT can be shortened from 5 years to 4 years. In CT, rank correlations between age 5 (half-rotation) and age 9/10 (full rotation) were significant (R 2 = 0.39 – 0.72), but age 5 selection missed 44 % of the top ten clones at age 9/10. Clone rank in CT at full, but not half, rotation was correlated with rank at full rotation in YB. Choosing clones at 9 years in CT adds 4 years but allows possible elimination of YB for clone selection. Both FFT and CT are necessary. Canker abundance and severity in CT at full rotation cannot be determined at earlier ages. An aggressive strategy saves 6 years of testing.

Forestry↗
DOE OSTI2025

Mesoporous Thin Film Architectures: Addressing Material Demands through Molecular Self-Assembly

Mesoporous thin films spark interest across a wide range of disciplines due to their tunable nanostructures, large internal surface areas, and strong compatibility with planar optical, electronic, and microfluidic devices. While attention in the porous materials community has shifted toward macroporous or disordered nanoporous systems, a resurgence in mesoporous thin film research is underway, driven by new molecular self-assembly methods, advanced materials chemistry, and improved characterization techniques. The integration of high-χN block copolymer design, kinetically persistent micelle templating, and postdeposition processing protocols now allows control over structural parameters such as pore size, wall thickness, porosity, and connectivity. These advances have overcome many of the thermodynamic and processing constraints that previously limited widespread adoption. Rather than serving only as high-surface-area supports, mesoporous thin films are engineered as active interfaces where responsive chemistries and nanoscale confinement act in tandem. Embedding switchable ligands, thermoresponsive polymers, redox mediators, or ion-selective groups directly within the pore walls enables real-time control over transport, optical, and electrochemical properties. These capabilities open up new directions in adaptive coatings, gated membranes, and fast-response biosensors. To further expand their functional scope, mesoporous films are integrated into hierarchical and multicomponent architectures. Techniques such as triblock terpolymer templating, crack-directed assembly, and nanoimprint lithography allow for control over spatial organization on the micron and submicron scale and pore system orientation. This enables programmable anisotropy, enhanced molecular diffusion, and wavelength-selective photonic behavior, essential for next-generation sensing, catalysis, and energy applications. Such structural and functional complexity requires equally sophisticated characterization. Multimodal and in situ techniques can track material dynamics under operational conditions. Recent progress includes extended-range ellipsometric porosimetry (EP) for hierarchical architectures, vacuum EP for interface energetics, time-resolved EP for diffusion kinetics, and correlative AFM-SAXS mapping. The introduction of advanced neutron-based spectroscopies, particularly quasielastic neutron scattering (QENS), promises to provide real-time access to ion transport dynamics and segmental motion under nanoscale confinement, offering a path toward deeper mechanistic understanding of structure-performance correlations in mesoporous systems. This Account reflects the technical advances made and the interdisciplinary collaborations that have shaped our collective vision. The particular dimensions of mesopores enable us to subtly tune interactions at the molecular, interfacial, and mesoscopic levels that permit us to harness nanoconfinement. What emerges is a versatile, modular platform capable of chemical gating, energy transduction, and sensing with a level of tunability unmatched by other porous materials. We highlight critical challenges including the need for more robust large-area processing, a deeper understanding of dynamic behavior under cycling, and better integration with device-level architectures. Our strategies support the transition of mesoporous thin films into active high-performance components in next-generation energy, environmental, and biomedical systems.

oxides↗
DOE OSTI2022

Prospects and Limitations of Predicting Fuel Ignition Properties from Low-Temperature Speciation Data

Using chemical kinetic modeling and statistical analysis, we investigate the possibility of correlating key chemical “markers”–typically small molecules–formed during very lean (φ ~ 0.001) oxidation experiments with near-stoichiometric (φ ~ 1) fuel ignition properties. One goal of this work is to evaluate the feasibility of designing a fuel-screening platform, based on small laboratory reactors that operate at low temperatures and use minimal fuel volume. Buras et al. [Combust. Flame2020,216, 472–484] have shown that convolutional neural net (CNN) fitting can be used to correlate first-stage ignition delay times (IDTs) with OH/HO2 measurements during very lean oxidation in low-T flow reactors with better than factor-of-2 accuracy. In this work, we test the limits of applying this correlation-based approach to predict the low-temperature heat release (LTHR) and total IDT, including the sensitivity of total IDT to the equivalence ratio, φ. We demonstrate that first-stage IDT can be reliably correlated with very lean oxidation measurements using compressed sensing (CS), which is simpler to implement than CNN fitting. LTHR can also be predicted via CS analysis, although the correlation quality is somewhat lower than for first-stage IDT. In contrast, the accuracy of total IDT prediction at φ = 1 is significantly lower (within a factor of 4 or worse). Furthermore, these results can be rationalized by the fact that the first-stage IDT and LTHR are primarily determined by low-temperature chemistry, whereas total IDT depends on low-, intermediate-, and high-temperature chemistry. Oxidation reactions are most important at low temperatures, and therefore, measurements of universal molecular markers of oxidation do not capture the full chemical complexity required to accurately predict the total IDT even at a single equivalence ratio. As a result, we find that φ-sensitivity of ignition delay cannot be predicted at all using solely correlation with lean low-T chemical speciation measurements.

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

Fast quantum ghost imaging with a single-photon-sensitive time-stamping camera

Quantum ghost imaging (QGI) leverages correlations between entangled photon pairs to reconstruct an image using light that has never physically interacted with an object. Despite extensive research interest, this technique has long been hindered by slow acquisition speeds, due to the use of raster-scanned detectors or the slow response of intensified cameras. Here, we utilize a single-photon-sensitive time-stamping camera to perform QGI at ultra-low-light levels with rapid data acquisition and processing times, achieving high-resolution and high-contrast images in under 1 min. Our work addresses the trade-off between image quality, optical power, data acquisition time, and data processing time in QGI, paving the way for practical applications in biomedical and quantum-secured imaging.

Mavian, Alex (ORCID:0000000279448830)↗