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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Urban Seismic Site Characterization by Fiber-Optic Seismology

ccurate ground motion prediction requires detailed site effect assessment, but in urban areas where such assessments are most important, geotechnical surveys are difficult to perform, limiting their availability. Distributed acoustic sensing (DAS) offers an appealing alternative by repurposing existing fiber-optic cables, normally employed for telecommunication, as an array of seismic sensors. We present a proof-of-concept demonstration by using DAS to produce high-resolution maps of the shallow subsurface with the Stanford DAS array, California. We describe new methods and their assumptions to assess H/V spectral ratio—a technique widely used to estimate the natural frequency of the soil—and to extract Rayleigh wave dispersion curves from ambient seismic field. These measurements are jointly inverted to provide models of shallow seismic velocities and sediment thicknesses above bedrock in central campus. The good agreement with an independent survey validates the methodology and demonstrates the power of DAS for microzonation.

Geochemistry & Geophysics↗
DOE OSTI2024

Electrically interfaced Brillouin-active waveguide for microwave photonic measurements

New strategies for converting signals between optical and microwave domains could play a pivotal role in advancing both classical and quantum technologies. Traditional approaches to optical-to-microwave transduction typically perturb or destroy the information encoded on intensity of the light field, eliminating the possibility for further processing or distribution of these signals. In this paper, we introduce an optical-to-microwave conversion method that allows for both detection and spectral analysis of microwave photonic signals without degradation of their information content. This functionality is demonstrated using an optomechanical waveguide integrated with a piezoelectric transducer. Efficient electromechanical and optomechanical coupling within this system permits bidirectional optical-to-microwave conversion with a quantum efficiency of up to -54.16 dB. Leveraging the preservation of the optical field envelope in intramodal Brillouin scattering, we demonstrate a multi-channel microwave photonic filter by transmitting an optical signal through a series of electro-optomechanical waveguide segments, each with distinct resonance frequencies. Such electro-optomechanical systems could offer flexible strategies for remote sensing, channelization, and spectrum analysis in microwave photonics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Accuracy of XAS theory for unraveling structural changes of adsorbates: CO on Ni(100)

Studying surface reactions using ultrafast optical pump and X-ray probe experiments relies on accurate calculations of X-ray spectra of adsorbates for correct identification of the spectral signatures and their dynamical evolution. We show that experimental XAS can be well reproduced for the different binding sites in a static prototype system CO/Ni(100) at a standard DFT (GGA) level of theory, using a plane-wave basis and pseudopotentials. This validates its utility in analyzing ultrafast X-ray probe experiments. The accuracy of computed relative core level binding energies is about 0.2 eV, representing a lower limit for which spectral features can be resolved with this method. We also show that the commonly used Z +1 approximation gives very good core binding energy shifts overall. However, we find a discrepancy for CO adsorbed in the hollow site, which we assign to the significantly stronger hybridization in hollow bonding than in on-top.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

XMM–Newton study of the Sculptor dwarf spheroidal galaxy

ABSTRACT We analysed observations with XMM–Newton in the field of the Sculptor dwarf spheroidal galaxy (dSph). The aim of the study was the classification of X-ray binaries and accreting white dwarfs belonging to the Sculptor dSph. Using different methods of X-ray timing and spectral analyses, together with an extensive multiwavelength study of the optical and infrared counterparts of the X-ray sources, we classified the sources detected with XMM–Newton in the field of the Sculptor dSph. The long-term variability of the sources has been studied over two XMM–Newton observations. None of the members of the Sculptor dSph show significant long-term variability over these two observations. We also searched for periodicity and pulsation using the Lomb–Scargle and Rayleigh Z$^{2}_{n}$ techniques. No signals of pulsation or periodicity have been found for the X-ray sources. The results show the presence of a noticeable number of background X-ray sources in the field of this galaxy. We classified 43 sources as active galactic nuclei, galaxies, and galaxy candidates. Three Galactic foreground stars have been identified in the field of the Sculptor dSph, and one of them is an M-dwarf candidate. Moreover, we classified four symbiotic-star candidates and three quiescent low-mass X-ray binary candidates in the Sculptor dSph. The luminosity of these X-ray sources is ∼1033−35 erg s−1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Improved measurements of the TeV-PeV extragalactic neutrino spectrum from joint analyses of IceCube tracks and cascades

The IceCube South Pole Neutrino Observatory has discovered the presence of a diffuse astrophysical neutrino flux at energies of TeV and beyond using neutrino induced muon tracks and cascade events from neutrino interactions. Here, we present two analyses sensitive to neutrino events in the energy range 1 TeV to 10 PeV, using more than 10 years of IceCube data. Both analyses consistently reject a neutrino spectrum following a single power-law with a significance of > 4⁢𝜎 in favor of a broken power law. We describe the methods implemented in the two analyses, the spectral constraints obtained, and the validation of the robustness of the results. Additionally, we report the detection of a muon neutrino in the medium energy starting events sample, or MESE, with an energy of 11.4$^{+2.46}_{−2.53}$ PeV, the highest energy neutrino observed by IceCube to date. The results presented here show insights into the spectral shape of astrophysical neutrinos, which has important implications for inferring their production processes in a multimessenger picture.

astroparticles↗
DOE OSTI2020

Spectroscopic signatures of next-nearest-neighbor hopping in the charge and spin dynamics of doped one-dimensional antiferromagnets

In this work, we study the impact of next-nearest-neighbor (nnn) hopping on the low-energy collective excitations of strongly correlated doped antiferromagnetic cuprate spin chains. Specifically, we use exact diagonalization and the density matrix renormalization group method to study the single-particle spectral function, the dynamical spin and charge structure factors, and the Cu L-edge resonant inelastic x-ray scattering (RIXS) intensity of the doped t-t'-J model for a set of t' values. We find evidence for the breakdown of spin-charge separation as |t'| increases and identify its fingerprints in the dynamical response functions. The inclusion of nnn hopping couples the spinon and holon excitations, resulting in the formation of a spin-polaron, where a ferromagnetic spin polarization cloud dresses the doped carrier. The spin-polaron manifests itself as additional spectral weight in the dynamical correlation functions, which appear simultaneously in the spin- and charge-sensitive channels. We also demonstrate that RIXS can provide a unique view of the spin-polaron, due to its sensitivity to both the spin and charge degrees of freedom.

1D spin chains↗
DOE OSTI2020

Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

One of the open problems in scientific computing is the long-time integration of nonlinear stochastic partial differential equations (SPDEs), especially with arbitrary initial data. We address this problem by taking advantage of recent advances in scientific machine learning and the spectral dynamically orthogonal (DO) and borthogonal (BO) methods for representing stochastic processes. The recently introduced DO/BO methods reduce the SPDE to solving a system of deterministic PDEs and a system of stochastic ordinary differential equations. Specifically, we propose two new physics-informed neural networks (PINNs) for solving time-dependent SPDEs, namely the neural network (NN)-DO/BO methods. The proposed methods incorporate the DO/BO constraints into the loss function (along with the modal decomposition of the SPDE) with an implicit form instead of generating explicit expressions for the temporal derivatives of the DO/BO modes. Hence, the NN-DO/BO methods can overcome some of the drawbacks of the original DO/BO methods. For example, we do not need the assumption that the covariance matrix of the random coefficients is invertible as in the original DO method, and we can remove the assumption of no eigenvalue crossing as in the original BO method. Moreover, the NN-DO/BO methods can be used to solve time-dependent stochastic inverse problems with the same formulation and same computational complexity as for forward problems. Furthermore, we demonstrate the capability of the proposed methods via several numerical examples, namely: (1) A linear stochastic advection equation with deterministic initial condition: we obtain good results with the proposed methods, while the original DO/BO methods cannot be applied directly in this case. (2) Long-time integration of the stochastic Burgers' equation: we show the good performance of NN-DO/BO methods, especially the effectiveness of the NN-BO approach for such problems with many eigenvalue crossings during the whole time evolution, while the original BO method fails. (3) Nonlinear reaction diffusion equation: we consider both the forward problem and the inverse problems, including very noisy initial point values, to investigate the flexibility of the NN-DO/BO methods in handling inverse and mixed type problems. Taken together, these simulation results demonstrate that the NN-DO/BO methods can be employed to effectively quantify uncertainty propagation in a wide range of physical problems, but future work should address the efficiency issue of PINNs for forward problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Spectroscopy of Degenerate One-Dimensional Electrons in Carbon Nanotubes (Final Report)

The goal of this research program was to understand the fundamental properties of degenerate one-dimensional (1-D) electrons in single-wall carbon nanotubes (SWCNTs). SWCNTs provide an ideal 1-D environment in which to study many-body physics. Semiconducting SWCNTs exhibit rich optical spectra dominated by extremely stable 1-D excitons, whereas metallic SWCNTs contain massless 1-D carriers with ultralong mean-free paths. Despite the large number of electrical, optical, and magnetic studies of SWCNTs during the last two decades, most of the predicted exotic properties of interacting 1-D electrons have yet to be observed, and some reported experimental results remain highly controversial. Here, we report on our research projects for the last 16 years summarizing spectroscopy on SWCNTs using an arsenal of spectroscopic methods from the terahertz to the visible spectral range, including ultrafast optical spectroscopy and ultrahigh magnetic fields. These studies provided a wealth of new insights into the nature of strongly correlated carriers in the ultimate 1-D limit that will lead to novel nanodevice concepts and implementations. Particular emphasis was placed on dynamical 1-D phenomena in the terahertz, infrared, and optical frequency ranges. Because SWCNTs are direct-band-gap materials, they are one of the leading candidates to unify electronic and optical functions in nanoscale circuits and elucidate how electron correlations can affect and control finite-frequency phenomena in 1-D systems. As a result, we have published 78 research articles (Web of Science), including ten reviews and book volumes/chapters. Below, we highlight and summarize several accomplishments that provided new insights into the fundamental properties of1-D carriers, excitons, and phonons of SWCNTs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Robust unfolding of MeV x-ray spectra from filter stack spectrometer data

Here, we present an inversion method capable of robustly unfolding MeV x-ray spectra from filter stack spectrometer (FSS) data without requiring an a priori specification of a spectral shape or arbitrary termination of the algorithm. Our inversion method is based upon the perturbative minimization (PM) algorithm, which has previously been shown to be capable of unfolding x-ray transmission data, albeit for a limited regime in which the x-ray mass attenuation coefficient of the filter material increases monotonically with x-ray energy. Our inversion method improves upon the PM algorithm through regular smoothing of the candidate spectrum and by adding stochasticity to the search. With these additions, the inversion method does not require a physics model for an initial guess, fitting, or user-selected termination of the search. Instead, the only assumption made by the inversion method is that the x-ray spectrum should be near a smooth curve. Testing with synthetic data shows that the inversion method can successfully recover the primary large-scale features of MeV x-ray spectra, including the number of x-rays in energy bins of several-MeV widths to within 10%. Fine-scale features, however, are more difficult to recover accurately. Examples of unfolding experimental FSS data obtained at the Texas Petawatt Laser Facility and the OMEGA EP laser facility are also presented.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Analysis of diagonal G and subspace W approximations within fully self-consistent GW calculations for bulk semiconducting systems

Fully self-consistent GW (sc-GW) methods are now available to evaluate quasiparticle and spectral properties of various molecular and bulk systems. However, such techniques based on the full matrix of G and W are computationally demanding. Additionally, the routinely used single-shot GW approximation (G 0 W 0 ) has an undesirable dependency on the choice of initial exchange-correlation functional. In the literature, many so-called self-consistent GW methods are based on diagonal approximation of G and low-ranking approximation of W. It is thus worth checking how good such approximations are in comparison with the full matrix method. In this work, we consider AlAs, AlP, GaP, and ZnS as the prototype systems to perform sc-GW calculations by expressing the full G matrix using a plane-wave basis set. We compared our sc-GW results with the diagonal G and subspace W approximated sc-GW results (sc-GW-diagG and sc-GW-subW methods). In the sc-GW-diagG method, interacting G is expanded in the eigenvectors of noninteracting G such that only diagonal elements are retained, whereas the number of eigenmodes is truncated in sc-GW-subW calculations. A systematic analysis of the results obtained from the above techniques is presented. The differences in the quasiparticle band gap between the approximated and the full matrix sc-GW approaches are mostly less than 1.7%, which validates such widely adopted approximations, and also shows how such low-ranking approximation can be used to include higher-order terms such as the vertex correction.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Band-mixing effects in one-dimensional charge transfer insulators

The low-energy properties of transition metal oxides (TMOs) are governed by the electrons occupying strongly correlated $d$-orbitals that are hybridized with surrounding ligand oxygen $p$ orbitals to varying degrees. Their physics is thus established by a complex interplay between the transition-metal (TM)-ligand hopping $t$, charge transfer energy $\Delta_\mathrm{CT}$, and on-site TM Hubbard repulsion $U$. Here, we study the spectral properties of a one-dimensional (1D) analog of such a $pd$ system, with alternating TM $d$ and ligand anion $p$ orbitals situated along a chain. Using the density matrix renormalization group method, we study the model's single-particle spectral function, x-ray absorption spectrum, and dynamical spin structure factor as a function of $\Delta_\mathrm{CT}$ and $U$. In particular, we present results spanning from the Mott insulating ($\Delta_\mathrm{CT} > U$) to negative charge transfer regime $\Delta_\mathrm{CT} < 0$ to gain a better understanding of the ground and momentum-resolved excited state properties of these different regimes. Our results can guide new studies on TMOs that seek to situate them within the Mott-Hubbard/charge transfer insulator classification scheme.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning

Detecting concealed chemicals and explosives remains a critical challenge in global security. Terahertz time-domain spectroscopy (THz-TDS) offers a promising non-invasive and stand-off detection technique owing to its ability to penetrate optically opaque materials without causing ionization damage. While many chemicals exhibit distinct spectral features in the terahertz range, conventional terahertz-based detection methods often struggle in real-world environments, where variations in sample geometry, thickness, and packaging can lead to inconsistent spectral responses. In this study, we present a chemical imaging system that integrates THz-TDS with deep learning to enable accurate pixel-level identification and classification of different explosives. Operating in reflection mode and enhanced with plasmonic nanoantenna arrays, our THz-TDS system achieves a peak dynamic range of 96 dB and a detection bandwidth of 4.5 THz, supporting practical, stand-off operation. By analyzing individual time-domain pulses with deep neural networks, the system exhibits strong resilience to environmental variations and sample inconsistencies. Blind testing across eight chemicals—including pharmaceutical excipients and explosive compounds—resulted in an average classification accuracy of 99.42% at the pixel level. Notably, the system maintained an average accuracy of 88.83% when detecting explosives concealed under opaque paper coverings, demonstrating its robust generalization capability. These results highlight the potential of combining advanced terahertz spectroscopy with neural networks for highly sensitive and specific chemical and explosive detection in diverse and operationally relevant scenarios.

Imaging and sensing↗
DOE OSTI2026

Efficient Measurement-Driven Eigenenergy Estimation with Classical Shadows

Quantum algorithms exploiting real-time evolution under a target Hamiltonian have demonstrated remarkable efficiency in extracting key spectral information. However, the broader potential of these methods, particularly beyond ground-state calculations, is underexplored. In this work, we introduce the framework of multiobservable dynamic mode decomposition (MODMD), which combines the observable dynamic mode decomposition (DMD), a measurement-driven eigensolver tailored for near-term implementation, with classical shadow tomography. MODMD leverages random scrambling in the classical shadow technique to construct, with exponentially reduced resource requirements, a signal subspace that encodes rich spectral information. Notably, we replace typical Hadamard-test circuits with a protocol designed to predict low-rank observables, thereby broadening the use of classical shadow tomography for predicting many low-rank observables. We establish theoretical guarantees on the spectral approximation from MODMD, taking into account distinct sources of error. In the ideal case, we prove that the spectral error scales as exp (−Δ⁢𝐸⁢𝑡 max ), where Δ⁢𝐸 is the Hamiltonian spectral gap and 𝑡 max is the maximal simulation time. This analysis provides a rigorous justification of the rapid convergence observed across simulations. To demonstrate the utility of our framework, we consider its application to fundamental tasks, such as determining the low-lying, i.e., ground or excited, energies of representative many-body systems. Our work paves the path for efficient designs of measurement-driven algorithms on near-term and early fault-tolerant quantum devices.

quantum algorithms & computation↗
DOE OSTI2023

Investigation of defects in melt and solution grown perovskite CsPbBr 3 single crystals

Recently, there has been considerable interest in x-ray and gamma ray detectors with large volume and high energy resolution that operate at room temperature. To improve detector energy resolution, the carrier mobility-lifetime product needs to be increased, and the electronic trap state concentration needs to be minimized. Defect concentrations in the part per billion range can alter the charge transport and carrier recombination lifetime. In this work, thermally stimulated current spectroscopy measurements were systematically carried out in bulk halide perovskite single crystals of CsPbBr 3 over a temperature range of 80–320 K. The origins and trap parameters of CsPbBr 3 crystals from the solution growth and melt growth procedures were determined and compared. Trap concentrations were ranged from 1 × 10 11 to 1 × 10 16 cm –3 . Appreciable detector performance was observed for CsPbBr3 crystals with trap concentrations less than 1 × 10 14 cm –3 . The comparison of spectral responses of crystal samples grown using two different methods shows that, after purification, solution-grown crystals are comparable to melt-grown crystals in terms of low defect concentration and improved detector performance. Finally, for an improved mobility-lifetime product and enhanced spectral response to high energy radiation from fissile materials, trap states in either type of a crystal ingot must be reduced closer to 10 11 cm –3 .

36 MATERIALS SCIENCE↗
DOE OSTI2021

Overcoming timestep limitations in boosted-frame particle-in-cell simulations of plasma-based acceleration

Explicit electromagnetic Particle-In-Cell (PIC) codes are typically limited by the Courant- Friedrichs-Lewy (CFL) condition, which implies that the timestep multiplied by the speed of light must be smaller than the smallest cell size. In the case of boosted-frame PIC simulations of plasma-based acceleration, this limitation can be a major hinderance as the cells are often very elongated along the longitudinal direction and the timestep is thus limited by the small, transverse cell size. This entails many small-timestep PIC iterations, and can limit the potential speed-up of the boosted-frame technique. Here, by using a CFL-free analytical spectral solver, and by mitigating additional numerical instabilities that arise at large timestep, we show that it is possible to overcome traditional limitations on the timestep and thereby realize the full potential of the boosted-frame technique over a much wider range of parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Neural Network Solver for Small Quantum Clusters

Machine learning approaches have recently been applied to the study of various problems in physics. Most of these studies are focused on interpreting the data generated by conventional numerical methods or the data on an existing experimental database. An interesting question is whether it is possible to use a machine learning approach, in particular a neural network, for solving the many-body problem. In this paper, we present a neural network solver for the single impurity Anderson model, the paradigm of an interacting quantum problem in small clusters. We demonstrate that the neural-network-based solver provides quantitative accurate results for the spectral function as compared to the exact diagonalization method. This opens the possibility of utilizing the neural network approach as an impurity solver for other many-body numerical approaches, such as the dynamical mean field theory.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Methods for the robust computation of the long-period seismic spectrum of broad-band arrays

SUMMARY We describe array methods to search for low signal-to-noise ratio (SNR) signals in long-period seismic data using Fourier analysis. This is motivated by published results that find evidence of solar free oscillations in the Earth's seismic hum. Previous work used data from only one station. In this paper, we describe methods for computing spectra from array data. Arrays reduce noise level through averaging and provide redundancy that we use to distinguish coherent signal from a random background. We describe two algorithms for calculating a robust spectrum from seismic arrays, an algorithm that automatically removes impulsive transient signals from data, a jackknife method for estimating the variance of the spectrum, and a method for assessing the significance of an entire spectral band. We show examples of their application to data recorded by the Homestake Mine 3-D array in Lead, SD and the Piñon Flats PY array. These are two of the quietest small aperture arrays ever deployed in North America. The underground Homestake data has exceptionally low noise, and the borehole sensors of the PY array also have very low noise, making these arrays well suited to finding very weak signals. We find that our methods remove transient signals effectively from the data so that even low-SNR signals in the seismic background can be found and tested. Additionally, we find that the jackknife variance estimate is comparable to the noise floor, and we present initial evidence for solar g-modes in our data through the T2 test, a multivariate generalization of Student's t-test.

Caton, Ross C.↗
DOE OSTI2024

Hyperspectral Detection of the Fluorescence Shift between Chirality-Sorted Empty and Water-Filled Single-Wall Carbon Nanotube Enantiomers

Single-wall carbon nanotubes (SWCNTs) have extraordinary electronic and optical properties that depend strongly on their exact chiral structure and their interaction with their inner and outer environment. The fluorescence (PL) of semiconducting SWCNTs, for instance, will shift depending on the molecules with which the SWCNT’s hollow core is filled. These interaction-induced shifts are challenging to resolve on the ensemble level in samples containing a mixture of different filling contents due to the relatively large inhomogeneous line width of the ensemble SWCNT PL compared to the size of these shifts. To circumvent this inhomogeneous broadening, single-tube spectroscopy and hyperspectral imaging are often applied, which until now required time-consuming statistical studies. Here, we present hyperspectral PL microscopy combined with automated SWCNT segmenting based on either principal component analysis or a convolutional neural network, capable of both spatially and spectrally resolving the PL along the length of many individual SWCNTs at the same time and automatically fitting peak positions and line widths of individual SWCNTs. The methodology is demonstrated by accurately determining the emission shifts and line widths of thousands of left- and right-handed empty and water-filled SWCNTs coated with a chiral surfactant, resulting in four statistical distributions which cannot be resolved in ensemble spectroscopy of unsorted samples. The results demonstrate a robust method to quickly probe ensemble properties with single-enantiomer spectral resolution. Moreover, it promises to be an absolute quantitative method to characterize the relative abundances of SWCNTs with different handedness or filling content in macroscopic samples, simply by counting individual species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗