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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Soil contamination from lead battery manufacturing and recycling in seven African countries

Highlights: • Soil lead contamination was found at lead battery recycling facilities in Africa. • Mean lead concentrations were 2600 mg/kg in soil on the outside of plants. • Lead pollution at these sites poses significant health risks to the public. • Lead contamination at these plants suggest that extensive remediation is warranted. Lead battery recycling is a growing hazardous industry throughout Africa. We investigated potential soil contamination inside and outside formal sector recycling plants in seven countries. We collected 118 soil samples at 15 recycling plants and one battery manufacturing site and analyzed them for total lead. Lead levels in soils ranged from < 40–140,000 mg/kg. Overall mean lead concentrations were ~23,200 mg/kg but, average lead levels were 22-fold greater for soil samples from inside plant sites than from those collected outside these facilities. Arithmetic mean lead concentrations in soil samples from communities surrounding these plants were ~2600 mg/kg. As the lead battery industry in Africa continues to expand, it is expected that the number and size of lead battery recycling plants will grow to meet the forecasted demand. There is an immediate need to address ongoing exposures in surrounding communities, emissions from this industry and to regulate site closure financing procedures to ensure that we do not leave behind a legacy of lead contamination that will impact millions in communities throughout Africa.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multipole interaction in spectral line broadening by plasmas

A long-standing ambiguity in spectral line broadening theory related to the electric field divergence in the quadrupole term of the screened Coulomb radiator-plasma interaction is examined. This is accomplished with multipole expansions from a Taylor series as well as a partial wave analysis of the screened Coulomb interaction. Here, it is explicitly shown that the two approaches agree up to the octupole term for an effective radiator size small compared to the plasma screening length. Furthermore, the ambigous corrections are confirmed appearing naturally in both methods. For higher multipoles the agreement is demonstrated numerically with a computer program using exact arithmetic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Exposure of the Heart in Breast Cancer Radiation Therapy: A Systematic Review of Heart Doses Published During 2003 to 2013

Purpose: Breast cancer radiation therapy cures many women, but where the heart is exposed, it can cause heart disease. We report a systematic review of heart doses from breast cancer radiation therapy that were published during 2003 to 2013. Methods and Materials: Eligible studies were those reporting whole-heart dose (ie, dose averaged over the whole heart). Analyses considered the arithmetic mean of the whole-heart doses for the CT plans for each regimen in each study. We termed this “mean heart dose.” Results: In left-sided breast cancer, mean heart dose averaged over all 398 regimens reported in 149 studies from 28 countries was 5.4 Gy (range, <0.1-28.6 Gy). In regimens that did not include the internal mammary chain (IMC), average mean heart dose was 4.2 Gy and varied with the target tissues irradiated. The lowest average mean heart doses were from tangential radiation therapy with either breathing control (1.3 Gy; range, 0.4-2.5 Gy) or treatment in the lateral decubitus position (1.2 Gy; range, 0.8-1.7 Gy), or from proton radiation therapy (0.5 Gy; range, 0.1-0.8 Gy). For intensity modulated radiation therapy mean heart dose was 5.6 Gy (range, <0.1-23.0 Gy). Where the IMC was irradiated, average mean heart dose was around 8 Gy and varied little according to which other targets were irradiated. Proton radiation therapy delivered the lowest average mean heart dose (2.6 Gy, range, 1.0-6.0 Gy), and tangential radiation therapy with a separate IMC field the highest (9.2 Gy, range, 1.9-21.0 Gy). In right-sided breast cancer, the average mean heart dose was 3.3 Gy based on 45 regimens in 23 studies. Conclusions: Recent estimates of typical heart doses from left breast cancer radiation therapy vary widely between studies, even for apparently similar regimens. Maneuvers to reduce heart dose in left tangential radiation therapy were successful. Proton radiation therapy delivered the lowest doses. Inclusion of the IMC doubled typical heart dose.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Stable evaluation of Green's functions in cylindrically stratified regions with uniaxial anisotropic layers

We present a robust algorithm for the computation of electromagnetic fields radiated by point sources (Hertzian dipoles) in cylindrically stratified media where each layer may exhibit material properties (permittivity, permeability, and conductivity) with uniaxial anisotropy. Analytical expressions are obtained based on the spectral representation of the tensor Green's function based on cylindrical Bessel and Hankel eigenfunctions, and extended for layered uniaxial media. Due to the poor scaling of these eigenfunctions for extreme arguments and/or orders, direct numerical evaluation of such expressions can produce numerical instability, i.e., underflow, overflow, and/or round-off errors under finite precision arithmetic. To circumvent these problems, we develop a numerically stable formulation through suitable rescaling of various expressions involved in the computational chain, to yield a robust algorithm for all parameter ranges. Numerical results are presented to illustrate the robustness of the formulation including cases of practical interest.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

On the classification of weakly integral modular categories

In this paper we classify all modular categories of dimension 4m, where m is an odd square-free integer, and all rank 6 and rank 7 weakly integral modular categories. This completes the classification of weakly integral modular categories through rank 7. In particular, our results imply that all integral modular categories of rank at most 7 are pointed (that is, every simple object has dimension 1). All the non-integral (but weakly integral) modular categories of ranks 6 and 7 have dimension 4m, with m an odd square free integer, so their classification is an application of our main result. The classification of rank 7 integral modular categories is facilitated by an analysis of the two group actions on modular categories: the Galois group of the field generated by the entries of the S-matrix and the group of invertible isomorphism classes of objects. We derive some valuable arithmetic consequences from these actions.

modular categories↗
DOE OSTI2018

Computational framework for modeling membrane processes without process and solution property simplifications

Accurately modeling membrane processes is critical to evaluating novel process configurations, designing scalable membrane systems, informing process cost estimates, and directing future research. Most membrane process models trade accuracy for computational efficiency by employing simplified approximations of the process (i.e. no salt flux, no pressure drop) and solution properties (i.e. ideal solution, and constant density, viscosity, and diffusivity). Our report introduces a detailed one-dimensional finite difference model for evaluating membrane processes that avoids these common simplifications. We apply this model to quantify the error introduced by these simplifications for case studies of reverse osmosis, osmotically assisted reverse osmosis, forward osmosis, and pressure retarded osmosis. While the magnitude of error introduced by these simplifications is reliant on the case study parameters and specifications, we find that existing model formulations can underestimate or overestimate average water flux by nearly 50% for some membrane processes operating under standard conditions. Lastly we investigate the error introduced by simplified inlet-outlet models that do not solve the governing system of differential equations, and we assess the accuracy of novel inlet-outlet formulations that use a log and geometric mean, instead of the typical arithmetic mean, to represent non-linear water flux profiles.

42 ENGINEERING↗
DOE OSTI2022

Characterization of multi-material 316L-Hastelloy X fabricated via laser powder-bed fusion

The aim of this work is to provide firsthand knowledge of 316 L stainless steel (316 L) and Hastelloy X (HX) multi-material processing via laser powder bed fusion (PBF). Specifically, microstructure of the interface is studied using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD). Surface metrology is performed to characterize the resulting surface roughness at the interface followed by tensile and flexural testing of multi-material samples to characterize the mechanical properties of the interfaces. Results showed that use of proper process parameters for each individual material led to formation of a compositional gradient at the interface that stretched for 240 μm (10–12 layers) with no evidence of cracking or porosity. The interface exhibited higher surface roughness compared to 316 L or HX as measured by arithmetic mean height, Sa, and maximum valley depth, Sv, parameters. During tensile testing, samples failed in the 316 L region away from the interface, with comparable yield strength, ultimate strength, and ductility. Finally, it was concluded that the “naturally” formed interface created a compositional gradient which was defect free due to similar values of coefficient of thermal expansion, input energy density, and different Marangoni numbers of the materials.

316L stainless Steel↗
DOE OSTI2016

Sequence polymorphism in an insect RNA virus field population: A snapshot from a single point in space and time reveals stochastic differences among and within individual hosts

Population structure of Homalodisca coagulata Virus-1 (HoCV-1) among and within field-collected insects sampled from a single point in space and time was examined. Polymorphism in complete consensus sequences among single-insect isolates was dominated by synonymous substitutions. The mutant spectrum of the C2 helicase region within each single-insect isolate was unique and dominated by nonsynonymous singletons. Bootstrapping was used to correct the within-isolate nonsynonymous:synonymous arithmetic ratio (N:S) for RT-PCR error, yielding an N:S value ~one log-unit greater than that of consensus sequences. Probability of all possible single-base substitutions for the C2 region predicted N:S values within 95% confidence limits of the corrected within-isolate N:S when the only constraint imposed was viral polymerase error bias for transitions over transversions. These results indicate that bottlenecks coupled with strong negative/purifying selection drive consensus sequences toward neutral sequence space, and that most polymorphism within single-insect isolates is composed of newly-minted mutations sampled prior to selection. -- Highlights: •Sampling protocol minimized differential selection/history among isolates. •Polymorphism among consensus sequences dominated by negative/purifying selection. •Within-isolate N:S ratio corrected for RT-PCR error by bootstrapping. •Within-isolate mutant spectrum dominated by new mutations yet to undergo selection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Quantum Perturbation Theory Using Tensor Cores and a Deep Neural Network

In this work, time-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of a deep neural network. The main computational cost of each deep layer is dominated by tensor contractions, i.e., dense matrix–matrix multiplications, in mixed-precision arithmetics, which achieves close to peak performance. Quantum response calculations are demonstrated and analyzed using self-consistent charge density-functional tight-binding theory as well as coupled-perturbed Hartree–Fock theory. For linear response calculations, a novel parameter-free convergence criterion is presented that is well-suited for numerically noisy low-precision floating point operations and we demonstrate a peak performance of almost 200 Tflops using the Tensor cores of two Nvidia A100 GPUs.

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

Advanced Polymer Characterization: Modular Operations for Spectral Alignment by Iterative Compression (MOSAIC)

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry encodes structural information across diverse homo- and copolymer ensembles, yet decrypting these spectra requires a systematic analytical approach. We introduce Modular Operations for Spectral Alignment by Iterative Compression (MOSAIC)─a general cipher algorithm that applies modular arithmetic to filter monomer-derived mass contributions and cluster MALDI peaks by nonconstitutional repeating units (non-CRUs). MOSAIC performs sequential modular operations using monomer mass differences as base units to compress complex spectral data, revealing end-group distributions and comonomer incorporation. As a demonstration, we applied MOSAIC to five copolymers formed by two different polymerization mechanisms. Furthermore, the resulting remainder–mass plots clearly resolve polymer homologs with distinct non-CRUs into visually apparent clusters, enabling intuitive assignment of mass spectral features.

Wang, Hanlin M. [University of Illinois at Urbana−↗
DOE OSTI2024

Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs

Compact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two innovative data encoding techniques, known as QCrank and QBArt, which exhibit significant quantum parallelism via uniformly controlled rotation gates. The QCrank method encodes a series of real-valued data as rotations on data qubits, resulting in increased storage capacity. On the other hand, QBArt directly incorporates a binary representation of the data within the computational basis, requiring fewer quantum measurements and enabling well-established arithmetic operations on binary data. We showcase various applications of the proposed encoding methods for various data types. Notably, we demonstrate quantum algorithms for tasks such as DNA pattern matching, Hamming weight computation, complex value conjugation, and the retrieval of a binary image with 384 pixels, all executed on the Quantinuum trapped-ion QPU. Furthermore, we employ several cloud-accessible QPUs, including those from IBMQ and IonQ, to conduct supplementary benchmarking experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Synthetic-domain computing and neural networks using lithium niobate integrated nonlinear phononics

Analogue computing uses the physical behaviours of devices to provide energy-efficient arithmetic operations. However, scaling up analogue computing platforms by simply increasing the number of devices leads to challenges such as device-to-device variation. Here, in this study, we report scalable analogue computing and neural networks in the synthetic frequency domain using an integrated nonlinear phononic platform on lithium niobate. This synthetic-domain computing is robust to device variations, as vectors and matrices are concurrently encoded at different frequencies within a single device, achieving a high throughput per area. Leveraging inherent nonlinearities, our device-aware neural network can perform a four-class classification task with an accuracy of 98.2%. The nonlinear phononic computing hardware also maintains consistent performance over a wide operational temperature range (characterized up to 192 °C). Our synthetic-domain computing combines single-device parallelism, inherent nonlinearity and environmental stability, and could be of use in edge computing applications in which power efficiency and environmental resilience are crucial.

Ji, Jun [Virginia Polytechnic Inst. and State Univ↗
DOE OSTI2017

Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

Currently, Constraint-Based Reconstruction and Analysis (COBRA) is the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Data values also have greatly varying magnitudes. Furthermore, standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME models have 70,000 constraints and variables and will grow larger). We also developed a quadrupleprecision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging problems tested here. DQQ will enable extensive use of large linear and nonlinear models in systems biology and other applications involving multiscale data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Structural properties of a bone-ceramic composite as a promising material in spinal surgery

The paper describes the results of in vitro tests of composite bone-ceramic implants and procedures for modifying implant surfaces to enhance osteogenesis. Analysis of CBCI ESs demonstrated that they have a porous structure with the mean longitudinal pore size of 70 µm and the mean transverse pore size of 46 µm; surface pores are open, while inner pores are closed. Elemental analysis of the CBCI surface demonstrates that CBCIs are composed of aluminum and zirconium oxides and contain HA inclusions. Profilometry of the CBCI ES surface revealed the following deviations: the maximum deviation of the profile in the sample center is 15 µm and 16 µm on the periphery, while the arithmetical mean and mean square deviations of the profile are 2.65 and 3.4 µm, respectively. In addition, CBCI biodegradation products were pre-examined; a 0.9% NaCl solution was used as a comparison group. Potentially toxic and tissue accumulated elements, such as cadmium, cobalt, mercury, and lead, are present only in trace amounts and have no statistically significant differences with the comparison group, which precludes their potential toxic effects on the macroorganism. Ceramic-based CBCI may be effective and useful in medicine for restoration of the anatomic integrity and functions of the bone tissue.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Probing and exploiting the chaotic dynamics of a hydrodynamic photochemical oscillator to implement all the basic binary logic functions

The convective motions within a solution of a photochromic spiro-oxazine being irradiated by UV only on the bottom part of its volume, give rise to aperiodic spectrophotometric dynamics. In this paper, we study three nonlinear properties of the aperiodic time series: permutation entropy, short-term predictability and long-term unpredictability, and degree distribution of the visibility graph networks. After ascertaining the extracted chaotic features, we show how the aperiodic time series can be exploited to implement all the fundamental two-inputs binary logic functions (AND, OR, NAND, NOR, XOR, and XNOR) and some basic arithmetic operations (half-adder, full-adder, half-subtractor). This is possible due to the wide range of states a nonlinear system accesses in the course of its evolution. Therefore, the solution of the convective photochemical oscillator results in hardware for chaos-computing alternative to conventional complementary metal-oxide semiconductor-based integrated circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Delayed Slater determinant update algorithms for high efficiency quantum Monte Carlo

Within ab initio Quantum Monte Carlo simulations, the leading numerical cost for large systems is the computation of the values of the Slater determinants in the trial wavefunction. Each Monte Carlo step requires finding the determinant of a dense matrix. This is most commonly iteratively evaluated using a rank-1 Sherman-Morrison updating scheme to avoid repeated explicit calculation of the inverse. The overall computational cost is therefore formally cubic in the number of electrons or matrix size. To improve the numerical efficiency of this procedure, we propose a novel multiple rank delayed update scheme. This strategy enables probability evaluation with application of accepted moves to the matrices delayed until after a predetermined number of moves, K . The accepted events are then applied to the matrices en bloc with enhanced arithmetic intensity and computational efficiency via matrix-matrix operations instead of matrix-vector operations. Here this procedure does not change the underlying Monte Carlo sampling or its statistical efficiency. For calculations on large systems and algorithms such as diffusion Monte Carlo where the acceptance ratio is high, order of magnitude improvements in the update time can be obtained on both multi- core CPUs and GPUs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

DR-RINS: Digital real-time relative intensity noise suppressor for pump–probe spectroscopy and microscopy

Relative intensity noise (RIN) inherent in fiber lasers poses a serious obstacle to their use in pump–probe spectroscopy and imaging. RIN can be removed through an analog balanced detector, or, as we have previously shown, software adaptive noise cancellation (ANC) on digitized signals. One major drawback to software ANC is the added time required for digitizing and post-processing. In this article, we describe a design for ANC on a field-programmable gate array (FPGA), making use of high-level synthesis tools and fixed-point arithmetic to achieve real-time laser RIN suppression at 25 MHz sample rates. Unlike the software-ANC approach, the FPGA-ANC device can serve as a dedicated drop-in denoiser, placed between the detectors and a commercial lock-in amplifier. We demonstrate its application to transient absorption spectroscopy and microscopy, lowering the noise floor to ~17 dB above the shot noise limit. Furthermore, we demonstrate a dramatic improvement in data acquisition time from ~6 h to ~5 min in a real-time imaging scenario.

47 OTHER INSTRUMENTATION↗