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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Efficient phase-factor evaluation in quantum signal processing

Quantum signal processing (QSP) is a powerful quantum algorithm to exactly implement matrix polynomials on quantum computers. Asymptotic analysis of quantum algorithms based on QSP has shown that asymptotically optimal results can in principle be obtained for a range of tasks, such as Hamiltonian simulation and the quantum linear system problem. A further benefit of QSP is that it uses a minimal number of ancilla qubits, which facilitates its implementation on near-to-intermediate term quantum architectures. However, there is so far no classically stable algorithm allowing computation of the phase factors that are needed to build QSP circuits. Existing methods require the use of variable precision arithmetic and can only be applied to polynomials of a relatively low degree. We present here an optimization-based method that can accurately compute the phase factors using standard double precision arithmetic operations. We demonstrate the performance of this approach with applications to Hamiltonian simulation, eigenvalue filtering, and quantum linear system problems. Furthermore, our numerical results show that the optimization algorithm can find phase factors to accurately approximate polynomials of a degree larger than 10000 with errors below 10 -12 .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Hanford Site Composite Analysis Data Package: Exposure Scenarios and Radionuclide Specific Dose Conversion Factors.

This data package summarizes the exposure assumptions, equations, and methods used to calculate radionuclide-specific unit dose factors and the radiological doses for both groundwater and atmospheric pathways as a part of the revised Hanford Site Composite Analysis. An All-Pathways Representative Person exposure scenario is considered to evaluate exposure via both groundwater and atmospheric transport pathways. The radiological dose assessments for both groundwater and atmospheric pathways are included in the performance assessments for various Waste Management Areas at the Hanford Site. This data package calculates exposure route-specific and total unit dose factors for composite-analysis-specific radionuclides of concern based on the exposure assumptions used in the composite analysis and performance assessments. This data package presents the results and comparison of the radionuclide-specific unit dose factors based on the exposure assumptions used in the revised composite analysis and various performance assessments.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Cognitive aging outcomes are related to both tau pathology and maintenance of cingulate cortex structure

Abstract INTRODUCTION Successful cognitive aging is related to both maintaining brain structure and avoiding Alzheimer's disease (AD) pathology, but how these factors interplay is unclear. METHODS A total of 109 cognitively normal older adults (70+ years old) underwent amyloid beta (Aβ) and tau positron emission tomography (PET) imaging, structural magnetic resonance imaging (MRI), and cognitive testing. Cognitive aging was quantified using the cognitive age gap (CAG), subtracting chronological age from predicted cognitive age. RESULTS Lower CAG (younger cognitive age) was related to slower decline in episodic memory, multi‐domain cognition, and atrophy of the midcingulate cortex (MCC). Lower entorhinal cortical tau was linked to slower decline in episodic memory, multi‐domain cognition, and hippocampal atrophy. DISCUSSION These results suggest that aging outcomes may be influenced by two independent pathways: one associated with tau accumulation, affecting primarily memory and hippocampal atrophy, and another involving tau‐independent structural preservation of the MCC, benefiting multi‐domain cognition over time. Highlights Younger cognitive age (lower cognitive age gap [CAG]) is related to slower cognitive decline. Lower CAG is linked to slower midcingulate cortex (MCC) atrophy. Reduced tau in the entorhinal cortex is related to less hippocampal atrophy and cognitive decline. Structural preservation of the MCC benefits multi‐domain cognition over time. Two independent pathways influence cognitive aging: tau accumulation and MCC preservation.

Neurosciences & Neurology↗
DOE OSTI2024

Chelator‐Assisted Precipitation‐Based Separation of the Rare Earth Elements Neodymium and Dysprosium from Aqueous Solutions

The rare earth elements (REEs) are critical resources for many clean energy technologies, but are difficult to obtain in their elementally pure forms because of their nearly identical chemical properties. Here, an analogue of macropa, G-macropa, was synthesized and employed for an aqueous precipitation-based separation of Nd 3+ and Dy 3+ . G-macropa maintains the same thermodynamic preference for the large REEs as macropa, but shows smaller thermodynamic stability constants. Molecular dynamics studies demonstrate that the binding affinity differences of these chelators for Nd 3+ and Dy 3+ is a consequence of the presence or absence of an inner-sphere water molecule, which alters the donor strength of the macrocyclic ethers. Leveraging the small REE affinity of G-macropa, we demonstrate that within aqueous solutions of Nd 3+ , Dy 3+ , and G-macropa, the addition of HCO 3 − selectively precipitates Dy 2 (CO 3 ) 3 , leaving the Nd 3+ −G-macropa complex in solution. With this method, remarkably high separation factors of 841 and 741 are achieved for 50 : 50 and 75 : 25 mixtures. Further studies involving Nd 3+ :Dy 3+ ratios of 95 : 5 in authentic magnet waste also afford an efficient separation as well. Finally, G-macropa is recovered via crystallization with HCl and used for subsequent extractions, demonstrating its good recyclability.

Gao, Yangyang↗
DOE OSTI2021

Recent advancements in the genetic engineering of microalgae

The development of more sustainable food, feed, and bio-products is critical to mitigating the environmental stresses facing our world today. Algae, which includes seaweeds, eukaryotic microalgae, and cyanobacteria, are a promising platform to achieving this, as they have low energy and space requirements, are safe for human and animal consumption, and can be manipulated to produce a diversity of valuable bioproducts. This review focuses on microalgae, both eukaryotic and cyanobacteria. In the past, addressing the major challenges of bringing microalgal production systems to an economically viable scale only had a relatively small genetic toolset to work with, in comparison to other microbial systems such as bacteria and yeast. Expanding the molecular tools available for genetic engineering of microalgae will lead to higher product yields, and accelerate the development of new microalgal bioproducts for commercial applications, thereby supporting the shift towards more environmentally friendly products. In this review, we highlight significant advances from recent years on the design of microalgal expression vectors, discovery of genetic regulatory elements (promoters and transcription factors), optimization of transformation methods, and development of new strain improvement techniques, all aimed at advancing microalgae to become a more efficient biomanufacturing platform. We then discuss how these tools have been applied to improving recombinant protein production, and to enhance metabolic pathway engineering.

09 BIOMASS FUELS↗
DOE OSTI2023

Evaluation of riparian enhancement actions in the Columbia River Basin

Riparian enhancement is a common restoration technique in the Columbia River Basin (CRB) and the Pacific Northwest. However, relatively few studies have evaluated its success and even fewer studies include long-term monitoring. Forty-one riparian planting projects located in the CRB, each with a paired treatment and control reach, were evaluated in the summer of 2018 and 2019 using an extensive post-treatment (EPT) design. At each reach, we quantified woody plant abundance, richness, diversity, and vegetation and canopy cover as riparian response variables, and measured terrace height and documented individuals with bud browse, deceased, and with predator protection as potential explanatory variables. Species richness, woody plant abundance (all height classes combined and within shrub height class), and the proportion of woody plants with bud browse or deceased were all higher in treatment than control reaches. However, no significant improvements were observed for any other riparian response or explanatory variables. Implementation techniques, including invasive species removal efforts, follow-up planting, watering, and the type of restoration project (floodplain restoration and planting, in-stream restoration and planting, or solely planting) were also considered as potential explanatory variables and were found to have significant impacts on restoration response for abundance and cover variables. Further, our results suggest that planting implementation methods, site level physical factors, and time since planting all influence the success of riparian planting projects and if not addressed in restoration design and implementation can contribute to a lack of detectable response.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

New determination of the branching ratio of the structure dependent radiative K + → e + ν e γ

The branching ratio of the structure dependent (SD) radiative K + → e + ν e γ decay relative to that of the K + → e + ν e (γ) decay including the internal bremsstrahlung (IB) process (K e2 (γ) ) has been measured in the J-PARC E36 experiment using plastic scintillator/lead sandwich detectors, in contrast to the previous E36 measurement, which used a CsI(Tl) calorimeter. In the analysis, the effect of IB was also taken into account in the SD radiative decay as $K$$^{SD}_{e2γ(γ)}$. By combining the new data with the previous E36 result after revision for the IB correction for $K$$^{SD}_{e2γ(γ)}$, a new value Br($K$$^{SD}_{e2γ(γ)}$)/Br(Ke 2(γ) ) = 1.20 ± 0.07 has e2γ(γ) e2γ(γ) been determined. This is consistent with a recent lattice QCD calculation, but larger than the expectation of Chiral Perturbation Theory (ChPT) at order O(p 4 ) and the previous KLOE value. Using the method to relate form factor and branching ratio described in the KLOE paper, the present result is also consistent with the form factor prediction based on a gauged nonlocal chiral quark model, but larger than that from ChPT at order O(p 6 ).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Electrochemical properties of poly(ethylene oxide) electrolytes above the entanglement threshold

The ion transport in electrolytes depends on three transport coefficients, conductivity (κ), salt diffusion coefficient (D), and the cation transference number with respect to the solvent velocity ($t_+^0$), and the thermodynamic factor ($T_f$). Current methods for determining these parameters involve four separate experiments, and the coupled nature of the equations used to determine them generally results in large experimental uncertainty. We present data obtained from 64 independent polymer electrolytes comprising poly(ethylene oxide) (PEO) and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) salt. The molecular weights of PEO ranged from 5 to 275 kg mol -1 ; these samples are all above the entanglement threshold. We minimize the experimental uncertainty in transport and thermodynamic measurements by exploiting the fact that ion transport in entangled polymer electrolytes should be independent of molecular weight. The dependence of $κ, D, t_+^0$, and $T_f$ as a function of salt concentration in the range 0.035 ≤ r ≤ 0.30 are presented with a 95% confidence interval, where r is the molar ratio of lithium ions to ethylene oxide monomer units. While κ, D, and $T_f$ are all positive as required by thermodynamic constraints, there is no constraint on the sign of $t_+^0$. We find that $t_+^0$ is negative in the salt concentration range of 0.093 ≤ r ≤ 0.189.

25 ENERGY STORAGE↗
DOE OSTI2024

High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning

Ionic liquids (ILs) are a novel group of green solvents with great promise for various industrial applications, including carbon capture and lignocellulosic biomass deconstruction. However, the use of ILs at the industrial scale remains challenging due to their high viscosities at ambient temperatures. To develop ILs with lower viscosities, a systematic study of their quantitative structure–property relationship (QSPR) is desirable. Here, we developed four machine learning (ML) models to predict viscosity at various temperature and pressure ranges, trained over a wide range of ILs consisting of various cationic and anionic families. ML methods including two-factor polynomial regression (two-factor PR), support vector regression (SVR), feed-forward neural networks (FFNN), and categorical boosting (CATBoost) were developed based on features that have proven useful in previous ML studies: COSMO-RS (conductor-like screening model for real solvents)-derived surface screening charge densities (sigma profiles). FFNN and CATBoost were the most accurate in predicting IL viscosities with lower average absolute relative deviation and higher R2 values on the test set. Tanimoto similarity scores were calculated to characterize the chemical space and structural similarity of the investigated ions. Furthermore, SHapley Additive exPlanation (SHAP) analysis was employed to interpret the ML results. Temperature, the polar area of ILs, and the nonpolar regions of ions are key features that influence the viscosity predictions. Importantly, the IL viscosity prediction here is the most accurate reported to date.

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

Adsorption separation of heavier isotope gases in subnanometer carbon pores

Isotopes of heavier gases including carbon ( 13 C/ 14 C), nitrogen ( 13 N), and oxygen ( 18 O) are highly important because they can be substituted for naturally occurring atoms without significantly perturbing the biochemical properties of the radiolabelled parent molecules. These labelled molecules are employed in clinical radiopharmaceuticals, in studies of brain disease and as imaging probes for advanced medical imaging techniques such as positron-emission tomography (PET). Established distillation-based isotope gas separation methods have a separation factor ( S ) below 1.05 and incur very high operating costs due to high energy consumption and long processing times, highlighting the need for new separation technologies. Here, we show a rapid and highly selective adsorption-based separation of 18 O 2 from 16 O 2 with S above 60 using nanoporous adsorbents operating near the boiling point of methane (112 K), which is accessible through cryogenic liquefied-natural-gas technology. A collective-nuclear-quantum effect difference between the ordered 18 O 2 and 16 O 2 molecular assemblies confined in subnanometer pores can explain the observed equilibrium separation and is applicable to other isotopic gases.

36 MATERIALS SCIENCE↗
DOE OSTI2024

LibERI—A portable and performant multi-GPU accelerated library for electron repulsion integrals via OpenMP offloading and standard language parallelism

A portable and performant graphics processing unit (GPU)-accelerated library for electron repulsion integral (ERI) evaluation, named LibERI, has been developed and implemented via directive-based (e.g., OpenMP and OpenACC) and standard language parallelism (e.g., Fortran DO CONCURRENT). Offloaded ERIs consist of integrals over low and high contraction s, p, and d functions using the rotated-axis and Rys quadrature methods. GPU codes are factorized based on previous developments with two layers of integral screening and quartet presorting. In this work, the density screening is moved to the GPU to enhance the computational efficacy for large molecular systems. Here, the L-shells in the Pople basis set are also separated into pure S and P shells to increase the ERI homogeneity and reduce atomic operations and the memory footprint. LibERI is compatible with any quantum chemistry drivers supporting the MolSSI Driver Interface. Benchmark calculations of LibERI interfaced with the GAMESS software package were carried out on various GPU architectures and molecular systems. The results show that the LibERI performance is comparable to other state-of-the-art GPU-accelerated codes (e.g., TeraChem and GMSHPC) and, in some cases, outperforms conventionally developed ERI CUDA kernels (e.g., QUICK) while fully maintaining portability.

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

Statistical inference of collision frequencies from x-ray Thomson scattering spectra

Thomson scattering spectra measure the response of plasma particles to incident radiation. In warm dense matter, which is opaque to visible light, x-ray Thomson scattering (XRTS) enables a detailed probe of the electron distribution and has been used as a diagnostic for electron temperature, density, and plasma ionization. In this work, we examine the sensitivities of inelastic XRTS signatures to modeling details, including the dynamic collision frequency and the electronic density of states. Applying verified Monte Carlo inversion methods to dynamic structure factors obtained from time-dependent density functional theory, we assess the utility of XRTS signals as a way to inform the dynamic collision frequency, especially its direct-current limit, which is directly related to the electrical conductivity.

Collision frequency↗
DOE OSTI2023

Canalization of Phenotypes—When the Transcriptome is Constantly but Weakly Perturbed

Abstract Recent studies have increasingly pointed to microRNAs (miRNAs) as the agent of gene regulatory network (GRN) stabilization as well as developmental canalization against constant but small environmental perturbations. To analyze mild perturbations, we construct a Dicer-1 knockdown line (dcr-1 KD) in Drosophila that modestly reduces all miRNAs by, on average, ∼20%. The defining characteristic of stabilizers is that, when their capacity is compromised, GRNs do not change their short-term behaviors. Indeed, even with such broad reductions across all miRNAs, the changes in the transcriptome are very modest during development in stable environment. By comparison, broad knockdowns of other regulatory genes (esp. transcription factors) by the same method should lead to drastic changes in the GRNs. The consequence of destabilization may thus be in long-term development as postulated by the theory of canalization. Flies with modest miRNA reductions may gradually deviate from the developmental norm, resulting in late-stage failures such as shortened longevity. In the optimal culture condition, the survival to adulthood is indeed normal in the dcr-1 KD line but, importantly, adult longevity is reduced by ∼90%. When flies are stressed by high temperature, dcr-1 KD induces lethality earlier in late pupation and, as the perturbations are shifted earlier, the affected stages are shifted correspondingly. Hence, in late stages of development with deviations piling up, GRN would be increasingly in need of stabilization. In conclusion, miRNAs appear to be a solution to weak but constant environmental perturbations.

Lu, Guang-An↗
DOE OSTI2026

Two-pion contribution to the hadronic vacuum polarization with staggered quarks

We present results from the first lattice QCD calculation of the two-pion contributions to the light-quark connected vector-current correlation function obtained from staggered-quark operators. We employ the MILC Collaboration’s gauge-field ensemble with 2 + 1 + 1 flavors of highly improved staggered sea quarks at a lattice spacing of a ≈ 0.15 fm with a light sea-quark mass at its physical value. The two-pion contributions allow for a refined determination of the noisy long-distance tail of the vector-current correlation function, which we use to compute the light-quark connected contribution to hadronic vacuum polarization (HVP) with improved statistical precision. We compare our results with traditional noise-reduction techniques used in lattice QCD calculations of the light-quark connected HVP, namely, the so-called fit and bounding methods. We observe a factor of roughly 3 improvement in the statistical precision in the determination of the HVP contribution to the muon’s anomalous magnetic moment over these approaches. We also lay the group theoretical groundwork for extending this calculation to finer lattice spacings with increased numbers of staggered two-pion taste states.

Lahert, Shaun [Utah U.; Illinois U., Urbana] (ORCI↗
DOE OSTI2024

Calculating the two-photon exchange contribution to K L → μ + μ − decay

We present a theoretical framework within which both the real and imaginary parts of the complex, two-photon exchange amplitude contributing to K L → μ + μ − decay can be calculated using lattice quantum chromodynamics. The real part of this two-photon amplitude is of approximately the same size as that coming from a second-order weak strangeness-changing neutral-current process. Thus a test of the standard model prediction for this second-order weak process depends on an accurate result of this two-photon amplitude. A limiting factor of our proposed method comes from low-energy three-particle π π γ states. The contribution from these states will be significantly distorted by the finite volume of our calculation—a distortion for which there is no available correction. However, a simple estimate of the contribution of these three-particle states suggests their contribution to be at most a few percent allowing their neglect in a lattice calculation with a 10% target accuracy. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2020

Effective Missing Value Imputation Methods for Building Monitoring Data

To understand behaviors of natural and man-made events, such as energy consumption of buildings, which accounts for 40% of energy uses in the US, we deploy automated monitoring devices to record periodic observations. However, such experimental and observation data often contains problems and irregularities that have to be cleaned up before analyses. Due to various conditions affecting sensor operations, the communication channels, recording steps, or the recording media, the recorded data might have missing values, errors, or anomalous values. An effective way to clean up these problems is to replace these missing values, errors and anomalous values with expected values, a process generally known as imputation. In this work, we survey commonly used missing value imputation techniques and compare their performance on a set of building monitoring data. To compare the different types of sensor measurements with widely varying characteristics, we use normalized root mean squared error (NRMSE) as the key metric for the effectiveness of the imputation methods. We additionally consider periodicity and run time when considering comparing methods. Through extensive testing, we find that for small gap sizes, up to 8 consecutive missing values, linear interpolation performs the best; for larger gaps stretching up to 48 consecutive missing values, K-nearest neighbors provides the most accurate imputations; for even larger gaps, more computational intensive methods, such as matrix factorization, achieve the smallest NRMSE. Additionally, we observe that these computationally intensive algorithms not only provide accurate imputations for large gaps, but are also more robust across all types of sensors.

Cho, B↗
DOE OSTI2023

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decom- pose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high- level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

deep learning↗