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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reduced-order model to approximate response matrices for filter stack spectrometers

We present a reduced-order model to calculate response matrices rapidly for filter stack spectrometers (FSSs). The reduced-order model allows response matrices to be built modularly from a set of pre-computed photon and electron transport and scattering calculations through various filter and detector materials. While these modular response matrices are not appropriate for high-fidelity analysis of experimental data, they encode sufficient physics to be used as a forward model in design optimization studies of FSSs, particularly for machine learning approaches that require sampling and testing a large number of FSS designs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Maximum a posteriori Ly α estimator (MAPLE): band power and covariance estimation of the 3D Ly α forest power spectrum

We present a novel maximum a posteriori estimator to jointly estimate band powers and the covariance of the three-dimensional power spectrum (P3D) of Ly $\alpha$ forest flux fluctuations, called MAPLE. Our Wiener-filter based algorithm reconstructs a window-deconvolved P3D in the presence of complex survey geometries typical for Ly $\alpha$ surveys that are sparsely sampled transverse to and densely sampled along the line of sight. We demonstrate our method on idealized Gaussian random fields with two selection functions: (i) a sparse sampling of 30 background sources per square degree designed to emulate the current Dark Energy Spectroscopic Instrument; (ii) a dense sampling of 900 background sources per square degree emulating the upcoming Prime Focus Spectrograph Galaxy Evolution Survey. Our proof-of-principle shows promise, especially since the algorithm can be extended to marginalize jointly over nuisance parameters and contaminants, i.e. offsets introduced by continuum fitting. Our code is implemented in JAX and is publicly available on GitHub.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A Portable Miniature Cryogenic Environment for In Situ Neutron Diffraction

Neutron diffraction instruments offer a platform for materials science and engineering studies at extended temperature ranges far from ambient. As one of the widely used neutron sample environment types, cryogenic furnaces are usually bulky and complex, and they may need hours of beamtime overhead for installation, configuration, cooling, and sample change, etc. To reduce the overhead time and expedite experiments at the state-of-the-art high-flux neutron source, we developed a low-cost, miniature, and easy-to-use cryogenic environment (77–473 K) for in situ neutron diffraction. A travel-size mug serves for the environment where the samples sit inside. Immediate cooling and an isothermal dwell at 77 K are realized on the sample by direct contact with liquid N 2 in the mug. The designed Al inserts serve as the holder of samples and heating elements, alleviate the thermal gradient, and clear neutron pathways. Both a single-sample continuous measurement and multi-sample high-throughput measurements are demonstrated in this environment. High-quality and refinable in situ neutron diffraction patterns are acquired on model materials. The results quantify the orthorhombic-to-cubic phase transformation process in LiMn 2 O 4 and differentiate the anisotropic lattice thermal expansions and bond length evolutions between rhombohedral perovskite oxides with composition variation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Ensuring thermodynamic consistency with invertible coarse-graining

Coarse-grained models are a core computational tool in theoretical chemistry and biophysics. A judicious choice of a coarse-grained model can yield physical insights by isolating the essential degrees of freedom that dictate the thermodynamic properties of a complex, condensed-phase system. The reduced complexity of the model typically leads to lower computational costs and more efficient sampling compared with atomistic models. Designing “good” coarse-grained models is an art. Generally, the mapping from fine-grained configurations to coarse-grained configurations itself is not optimized in any way; instead, the energy function associated with the mapped configurations is. In this work, we explore the consequences of optimizing the coarse-grained representation alongside its potential energy function. We use a graph machine learning framework to embed atomic configurations into a low-dimensional space to produce efficient representations of the original molecular system. Because the representation we obtain is no longer directly interpretable as a real-space representation of the atomic coordinates, we also introduce an inversion process and an associated thermodynamic consistency relation that allows us to rigorously sample fine-grained configurations conditioned on the coarse-grained sampling. We illustrate that this technique is robust, recovering the first two moments of the distribution of several observables in proteins such as chignolin and alanine dipeptide.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Transboundary determinants of avian zoonotic infectious diseases: challenges for strengthening research capacity and connecting surveillance networks

As the climate changes, global systems have become increasingly unstable and unpredictable. This is particularly true for many disease systems, including subtypes of highly pathogenic avian influenzas (HPAIs) that are circulating the world. Ecological patterns once thought stable are changing, bringing new populations and organisms into contact with one another. Wild birds continue to be hosts and reservoirs for numerous zoonotic pathogens, and strains of HPAI and other pathogens have been introduced into new regions via migrating birds and transboundary trade of wild birds. With these expanding environmental changes, it is even more crucial that regions or counties that previously did not have surveillance programs develop the appropriate skills to sample wild birds and add to the understanding of pathogens in migratory and breeding birds through research. For example, little is known about wild bird infectious diseases and migration along the Mediterranean and Black Sea Flyway (MBSF), which connects Europe, Asia, and Africa. Focusing on avian influenza and the microbiome in migratory wild birds along the MBSF, this project seeks to understand the determinants of transboundary disease propagation and coinfection in regions that are connected by this flyway. Through the creation of a threat reduction network for avian diseases (Avian Zoonotic Disease Network, AZDN) in three countries along the MBSF (Georgia, Ukraine, and Jordan), this project is strengthening capacities for disease diagnostics; microbiomes; ecoimmunology; field biosafety; proper wildlife capture and handling; experimental design; statistical analysis; and vector sampling and biology. Here, we cover what is required to build a wild bird infectious disease research and surveillance program, which includes learning skills in proper bird capture and handling; biosafety and biosecurity; permits; next generation sequencing; leading-edge bioinformatics and statistical analyses; and vector and environmental sampling. Creating connected networks for avian influenzas and other pathogen surveillance will increase coordination and strengthen biosurveillance globally in wild birds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Leveraging visible and near-infrared spectroelectrochemistry to calibrate a robust model for Vanadium(IV/V) in varying nitric acid and temperature levels

Spectroelectrochemistry and optimal design of experiments can be used to rapidly build accurate models for species quantification and enable a greater level of process awareness. Optical spectroscopy can provide vital elemental and molecular information, but several hurdles must be overcome before it can become a widely adopted analytical method for remote analysis in the nuclear field. Analytes with varying oxidation state, acid concentration, and fluctuating temperature must be efficiently accounted for to minimize time and resources in restrictive hot cell environments. The classic one-factor-at-a-time approach is not suitable for frequent calibration/maintenance operations in this setting. Therefore, a novel alternative was developed to characterize a system containing vanadium(IV/V) (0.01–0.1 M), nitric acid (0.1–4 M), and varying temperatures (20–45 °C). Here, spectroelectrochemistry methods were used to acquire a sample set selected by optimal design of experiments. This new approach allows for the accurate analysis of vanadium and HNO 3 concentration by leveraging UV–Vis–NIR absorption spectroscopy with robust and accurate chemometric models. The top model's root mean squared error of prediction percent values were 3.47%, 4.06%, 3.40%, and 10.9% for V(IV), V(V), HNO 3 , and temperature, respectively. These models, efficiently developed using the designed approach, exhibited strong predictive accuracy for vanadium and acid with varying oxidation states and temperature using only spectrophotometry, which advances current technology for real-world hot cell applications. Additionally, Nernstian analysis of the V(IV/V) standard potential was performed using traditional absorbance methods and multivariate curve resolution (MCR). The successful tests demonstrated that MCR Nernst tests may be valuable in highly convoluted spectral systems to better understand the redox processes' behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A Gaussian process based surrogate approach for the optimization of cylindrical targets

Simulating direct-drive inertial confinement experiments presents significant computational challenges, both due to the complexity of the codes required for such simulations and the substantial computational expense associated with target design studies. Machine learning models, and in particular, surrogate models, offer a solution by replacing simulation results with a simplified approximation. In this study, we apply surrogate modeling and optimization techniques that are well established in the existing literature to one-dimensional simulation data of a new cylindrical target design containing deuterium–tritium fuel. These models predict yields without the need for expensive simulations. We find that Bayesian optimization with Gaussian process surrogates enhances sampling efficiency in low-dimensional design spaces but becomes less efficient as dimensionality increases. Nonetheless, optimization routines within two-dimensional and five-dimensional design spaces can identify designs that maximize yield, while also aligning with established physical intuition. Optimization routines, which ignore constraints on hydrodynamic instability growth, are shown to lead to unstable designs in 2D, resulting in yield loss. However, routines that utilize 1D simulations and impose constraints on the in-flight aspect ratio converge on novel cylindrical target designs that are stable against hydrodynamic instability growth in 2D and achieve high yield.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

The DAQ system of the 12,000 channel CMS high granularity calorimeter prototype

The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC). Concomitant with this increase will be an increase in the number of interactions in each bunch crossing and a significant increase in the total ionising dose and fluence. One part of this upgrade is the replacement of the current endcap calorimeters with a high granularity sampling calorimeter equipped with silicon sensors, designed to manage the high collision rates. As part of the development of this calorimeter, a series of beam tests have been conducted with different sampling configurations using prototype segmented silicon detectors. In the most recent of these tests, conducted in late 2018 at the CERN SPS, the performance of a prototype calorimeter equipped with ≈12,000 channels of silicon sensors was studied with beams of high-energy electrons, pions and muons. This paper describes the custom-built scalable data acquisition system that was built with readily available FPGA mezzanines and low-cost Raspberry Pi computers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Fast and Accurate Core Analysis by the Full-Immersion Pressure-Pulse Decay: Part 2—Practice and Demonstration

Summary This study continues the work of presenting a novel approach for making petrophysical assessments of tight core samples. This method, the full-immersion pressure-pulse decay (Hannon 2019), involves applying a rapid increase in pressure in a chamber surrounding the entire outer surface area of a cylindrical sample, shutting the system in, and monitoring the pressure decay in the chamber as it reaches a new equilibrium. A precursor article covered the numerical simulator designed to model flow through the sample, demonstrating its performance and accuracy in addition to providing a first-order comparison between the speed and shape of the pressure-decay responses of the full-immersion method with those of other similar transient methods. This study covers the parameter-estimation procedure and experimental verification through a proof-of-concept laboratory investigation. The investigations provided here demonstrate that under appropriate, achievable experimental conditions, the pressure data can be analyzed in such a way that returns an estimate of the porosity and apparent permeabilities both parallel and perpendicular to bedding from a single test performed on a single cylindrical sample. After determining these experimental conditions (the uniqueness window), this report outlines a data-inversion strategy to estimate the petrophysical properties (porosity, horizontal permeability, and vertical permeability) from each test. This strategy is put to the test through comparisons with measurements performed by a commercial core laboratory. A common set of samples recovered from an outcrop of a tight-gas sandstone formation were investigated using the full-immersion method, and their results are compared with those from conventional steady-state measurement procedures performed by the commercial laboratory. Comparisons between petrophysical characterizations of these samples, which had permeabilities between 25 nd and 2.3 μd, demonstrated close agreement in most cases. However, whereas steady-state measurements performed at the professional laboratory required 4 to 5 hours of testing time per measurement of a single permeability, similar assessments using the full-immersion technique, requiring approximately 5 to 10 minutes to complete, returned estimates of the horizontal and vertical permeability simultaneously. Additional analyses are provided to determine principal reasons of discrepancies in instances where agreement was not as strong. Based on lessons learned from these experiences, the report closes with suggestions on areas of improvement in the experimental approach. Once complete, these developments should propel this technology to fill a critical need to determine petrophysical properties (porosity and permeability) of tight rocks in a time-efficient manner and in a way that does not compromise their accuracy.

Energy & Fuels↗
DOE OSTI2023

Standard experimental paradigm designs and data exclusion practices in cognitive psychology can inadvertently introduce systematic “shadow” biases in participant samples

Abstract Standard cognitive psychology research practices can introduce inadvertent sampling biases that reduce the reliability and generalizability of the findings. Researchers commonly acknowledge and understand that any given study sample is not perfectly generalizable, especially when implementing typical experimental constraints (e.g., limiting recruitment to specific age ranges or to individuals with normal color vision). However, less obvious systematic sampling constraints, referred to here as “shadow” biases, can be unintentionally introduced and can easily go unnoticed. For example, many standard cognitive psychology study designs involve lengthy and tedious experiments with simple, repetitive stimuli. Such testing environments may 1) be aversive to some would-be participants (e.g., those high in certain neurodivergent symptoms) who may self-select not to enroll in such studies, or 2) contribute to participant attrition, both of which reduce the sample’s representativeness. Likewise, standard performance-based data exclusion efforts (e.g., minimum accuracy or response time) or attention checks can systematically remove data from participants from subsets of the population (e.g., those low in conscientiousness). This commentary focuses on the theoretical and practical issues behind these non-obvious and often unacknowledged “shadow” biases, offers a simple illustration with real data as a proof of concept of how applying attention checks can systematically skew latent/hidden variables in the included population, and then discusses the broader implications with suggestions for how to manage and reduce, or at a minimum acknowledge, the problem.

Siritzky, Emma M.↗
DOE OSTI2024

HHL algorithm with mapping function and enhanced sampling for model predictive control in microgrids

Here, this paper presents a refined quantum Harrow Hassidim Lloyd (HHL) algorithm for microgrid control. The first novelty of the developed method is that a mapping shift function enables the original HHL algorithm to handle general linear equations with non-singular and indefinite matrix. Second, a method of Matrix Extension for Amplifying Sampling Probabilities of Intended Solution (ME-ASPI) is proposed to design the reformulated linear algebraic equations, allowing for improved sampling efficiency of the quantum tomography in the refined HHL algorithm. Then, we applied the method to solve the model predictive control (MPC) problem in nonlinear dynamical microgrids. Specifically, with the ME-ASPI method, the refined HHL algorithm can effectively obtain the intended partial optimal control inputs for MPC. The optimization of quadratic programming problem in each time step of MPC is transformed into a linear system problem, which is addressed by the proposed quantum solver through using only partial information, with the time complexity improved from $\mathscr{O}(\mathscr{N}^{2.37286})$ classically to $\mathscr{O}(\mathscr{N}^{2} log \mathscr{N}$ x $p$ log $p)$ in quantum. Numerical examples have validated the effectiveness of the refined HHL algorithm with the proposed mapping function and the ME-ASPI method. By leveraging quantum properties, the proposed method provides a hybrid quantum–classical framework for microgrid control. This generic method can also potentially tackle many other challenges in analyzing and controlling general complex engineered systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Surface science insight note: A linear algebraic approach to elucidate native films on Fe 3 O 4 surface

Standard materials are often used to obtain spectra that can be compared to those from unknown samples. Spectra measured from these known substances are also used as a means of computing sensitivity factors to allow quantification by X-ray photoelectron spectroscopy (XPS) of less well-defined materials. Spectra from known materials also provide line shapes suitable for inclusion in spectral models which, when fitted to spectra, permit the chemical state for a sample to be assessed. Both types of information depend on isolating photoemission signals from the inelastically scattered signal. In this Insight note, technical issues associated with the use of XPS of as received Fe 3 O 4 powder sample surface are discussed. The Insight note is designed to show how linear algebraic techniques applied to data collected from a sample marketed as pure Fe 3 O 4 powder are used to verify that XPS has been performed on chemistry representative of the sample. The methods described in this Insight note can further be utilized in elucidating complex XPS data obtained from thin films formed or evolved during cyclic/non-steady use of complex (electro)catalyst surfaces, especially in the presence of contaminants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Surface and bulk characterization of reservoir and cap-rocks: Past, present, and future

Geologic formations provide potentially some of the largest volume capacities for CO 2 storage or sequestration. Potential storage sites can be deep saline aquifers, depleted oil reservoirs, and coal seams, surrounded by sealing layers to prevent CO 2 from leaking. It is therefore critical to understand mechanisms contributing to CO 2 trapping and CO 2 leaks. Both phenomena are governed by reactions at the interfaces of the reservoir and cap-rocks and are controlled by the complex chemistry and pore structures of rocks. Mechanisms at the macroscale are affected by the processes occurring at the nanoscale. This review highlights the necessity of multitechnique, multiscale characterization of rocks and points to the importance of surface analysis and surface science studies. Two shale rocks (seals) from Niobrara and Agardhfjellet formations with complex surface chemistry are used as examples throughout the paper. Typically, evaluation of rocks with x-ray diffraction, thermogravimetric analysis, Rock-Eval pyrolysis, gas adsorption, and electron microscopy combined with energy dispersive x-ray spectroscopy is conducted to provide valuable information about the bulk mineralogy, elemental composition, pore volume, and adsorbed species on the sample surface. These studies are necessary prior to designing surface sensitive experiments with x-ray photoelectron spectroscopy (XPS), guiding both sample preparation and sample analysis. XPS has been widely used to study the surface composition of rocks during the investigations of their fine-scale wettability, and the main findings are highlighted here. This paper also reviews the existing literature on ambient-pressure XPS, which provides new opportunities to study in situ chemical alteration due to interactions with CO 2 and offers recommendations for adapting this technique to study rock-fluid interactions, allowing for the identification of fundamental interactions during CO 2 sequestration and guide selection of formation sites for improved storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Single-Molecule Fluorescence Microscopy Reveals Energy Transfer Active versus Inactive Nanocrystal/Dye Conjugate Pairs

Defect-mediated energy transfer (EnT) is a radiative process that occurs between donor defect states in the forbidden bandgap of semiconductor nanocrystals (NCs) and dye molecules bound to their surfaces. The EnT efficiency depends on the number of dye molecules attached to each NC, the donor–acceptor distance, and the dipole orientation factor between the donor and acceptor, all of which vary across all individual NCs in a sample. While ensemble-level fluorescence spectroscopy measurements have provided average values for donor–acceptor distances, dye-to-NC ratios, and EnT rate constants, questions remain about the impact of donor/acceptor heterogeneity on observed EnT efficiencies. Notably, ensemble-level measurements cannot distinguish between bare NCs and EnT-active versus inactive NC/dye pairs in the same sample batch, limiting the ability to design systems with 100% EnT efficiency. To address this, we studied defect-mediated EnT between AlexaFluor 555 dye acceptors chemically bound to ZnO NC donors at the level of single molecules and single NCs. Interestingly, 20% of bound NC/dye pairs are EnT-inactive, likely contributing to residual defect photoluminescence (PL) observed in ensemble-level measurements and reducing overall EnT efficiency. Single particle-level ZnO defect PL and acceptor fluorescence trajectories exhibited distinct microfluctuations, which are absent in bare ZnO NCs. We hypothesized that our observations can be explained with a competitive dye fluorescence quenching pathway, possibly due to charge transfer between the excited state dye and the ZnO NC. Numerical simulations of single-molecule PL traces for this scenario produced microfluctuations consistent with the experimental results. These findings highlight the impact of sample heterogeneity on EnT processes and provide insights for designing light-harvesting systems with optimized EnT efficiency.

25 ENERGY STORAGE↗
DOE OSTI2020

Particle Sensitivity Analysis

We propose to develop a computational sensitivity analysis capability for Monte Carlo sampling-based particle simulation relevant to Aleph, Cheetah-MC, Empire, Emphasis, ITS, SPARTA, and LAMMPS codes. These software tools model plasmas, radiation transport, low-density fluids, and molecular motion. Our report demonstrates how adjoint optimization methods can be combined with Monte Carlo sampling-based adjoint particle simulation. Our goal is to develop a sensitivity analysis to drive robust design-based optimization for Monte Carlo sampling-based particle simulation - a currently unavailable capability.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

High Throughput Argon-37 Field System

We report Pacific Northwest National Laboratory (PNNL) has developed a unique fieldable 37 Ar measurement system designed to measure 37 Ar activity concentrations from soil gas samples to detect above ground and underground nuclear explosions. The Argon-37 Field System is modular in design to accommodate both chemical processing and nuclear detection. The system can be packed into shipping crates and shipped to a location near where the sampling is taking place. The system can process six 2-m 3 whole-air samples in 24 hours and can measure the 37 Ar activity in each of the samples using six proportional counters. The proportional counters, designed and built at PNNL, are surrounded with both active and passive shielding to reduce background and can achieve a minimum detection concentration of 10 mBq/m 3 of 37 Ar in whole-air equivalent. The Argon-37 Field System has undergone extensive testing against rigorous requirements to assure the system meets the needs of the noble gas nuclear explosion monitoring community.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2026

Sub-Nanometer Nanoclusters of Copper Atop Single-Atom Copper Moieties toward Electrochemical CO 2 Hydrogenation to Methane

The electrochemical CO 2 reduction (eCO 2 R) offers a compelling route for converting CO 2 into value-added fuels and chemicals. Among CO 2 -derived products, methane (CH 4 ) occupies a distinct position, serving both as a key intermediate for emerging cascade electro-oxidation to oxygenates and as a strategically important extraterrestrial fuel that can be generated in situ from off-planet CO 2 resources. Although Cu-based catalysts capable of selectively producing CH 4 have been reported, they seldom sustain high selectivity at practically relevant current densities. Here, we created a single-step co-pyrolysis strategy toward generating and anchoring Cu sub-nanometer clusters (Cu SNC ) atop Cu-N x single-atom (SA) motifs embedded within N-doped carbon (NC), with controllable nanostructures through tuning of the synthesis parameters. Complementary spectroscopic analyses and density functional theory (DFT) calculations help reveal a structure−activity correlation that could guide the catalyst design. The Cu SNC @NC sample synthesized at 550 °C pyrolysis temperature (best described and modeled as Cu 3 -CuN 4 domains) represents the most effective combination of cluster size, metal-nitrogen coordination, and adsorption energetics needed to selectively promote CH 4 generation versus other eCO 2 R products. Incorporating pulsed electrolysis and hydrophobicity-modulated transport tuning at the triple-phase boundary (TPB) further enhanced CH 4 production achieving a partial CH 4 current density of ∼321 mA cm −2 , 53% Faradaic efficiency (FECH 4 ), and less than 4% combined FE for other eCO 2 R products, simplifying downstream CH 4 purification or upgrading. This work establishes generalizable principles for controlling Cu cluster atomicity and metal−nitrogen coordination, both of which are recognized determinants of CH 4 -efficient eCO 2 R.

CH4 production↗