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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Direct Exciton Harvesting from a Bound Triplet Pair

Abstract Singlet fission is commonly defined as the generation of two triplet excitons from a single absorbed photon. However, ambiguities within this definition arise due to the complexity of the various double triplet states that exist in SF chromophores and the corresponding interconversion processes. To clarify this process, singlet fission is frequently depicted as sequential two‐step conversion in which a singlet exciton decays into a bound triplet‐pair biexciton state that dissociates into two “free” triplet excitons. However, this model discounts the potential for direct harvesting from the coupled biexciton state. Here, it is demonstrated that individual triplet excitons can be extracted directly from a bound triplet pair. It is demonstrated that due to the requirement for geminate triplet–triplet annihilation in intramolecular singlet fission compounds, unique spectral and kinetic signatures can be used to quantify triplet‐pair harvesting yields. An internal quantum efficiency for triplet exciton transfer from the triplet pair of >50%, limited only by the solubility of the compounds is achieved. The harvesting process is not dependent on the net multiplicity of the triplet‐pair state, suggesting that an explicit, independent dissociation step is not a requirement for using triplet pairs to do chemical or electrical work.

Chemistry↗
NASA NTRS1978

Dynamic model verification of large structural systems

The objective of the present methodology is two-fold: (1) to process test data obtained from either modal survey tests, or slow sine-sweep tests, to extract a set of orthogonal modes best matching the test data while being commensurate with the dynamic model, and (2) to modify submatrices of the dynamic model mass and stiffness matrices to adjust the model to best fit the test data. The method has been implemented using a linear statistical sequential estimator for computation on a CDC computer. Demonstration problems involving Space Shuttle quarter-scale vibration test data and dynamic models have been run. This paper will discuss the general methodology and experience to date.

Lee, L. T.↗
DOE OSTI2024

Quantum tensor network algorithms for evaluation of spectral functions on quantum computers

We investigate quantum algorithms derived from tensor networks to simulate the static and dynamic properties of quantum many-body systems. Using a sequentially prepared quantum circuit representation of a matrix product state (MPS) that we call a quantum tensor network (QTN), we demonstrate algorithms to prepare ground and excited states on a quantum computer and apply them to molecular nanomagnets (MNMs) as a paradigmatic example. In this setting, we develop two approaches for extracting the spectral correlation functions measured in neutron-scattering experiments: (a) a generalization of the SWAP test for computing wave function overlaps and, (b) a generalization of the notion of matrix product operators to the QTN setting which generates a linear combination of unitaries. The latter method is discussed in detail for translationally invariant spin-half systems, where it is shown to reduce the qubit resource requirements compared with the SWAP method and may be generalized to other systems. We demonstrate the versatility of our approaches by simulating spin-1/2 and spin-3/2 MNMs, with the latter being an experimentally relevant model of a Cr$^{3+}_{8}$ ring. Here, our approach has qubit requirements that are independent of the number of constituents of the many-body system and scale only logarithmically with the bond dimension of the MPS representation, making them appealing for implementation on near-term quantum hardware with mid-circuit measurement and reset.

Neutron scattering↗
DOE OSTI2025

Spotlight: efficient automated global optimization in rietveld analysis of diffraction data

Performing reliable Rietveld analysis on tens or hundreds of powder diffraction datasets from parametric or time-resolved experiments often poses a bottleneck in extracting meaningful results from the data. While automated analysis of data has recently been demonstrated, high temperature annealing studies, during which phase transformations occur and lattice parameters may change due to repartitioning of elements, are prime examples where automation by a simple phase identification from a database of room temperature structures or automation by sequential refinements is likely to fail. To enable reliable, efficient, automated Rietveld analysis, we present a Python package named Spotlight , building on established Rietveld packages such as MAUD, GSAS , or GSAS-II , which extends the refinement of best fit parameters to a global optimization using an ensemble of optimizers leveraging hierarchical parallel execution on high-performance computing clusters. Spotlight further enables the efficient design of refinement plans through the iterative automated machine-learning of a surrogate for the refinement on which the global optimizations are performed until results from the surrogate converge to the response surface data. We demonstrate Spotlight with the analysis of uranium molybdenum and Ti–6Al–4V datasets, as well as in two open-source tutorials analyzing aluminium oxide and lead sulphate.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Operational and geological controls of coupled poroelastic stressing and pore-pressure accumulation along faults: Induced earthquakes in Pohang, South Korea

Coupled poroelastic stressing and pore-pressure accumulation along pre-existing faults in deep basement contribute to recent occurrence of seismic events at subsurface energy exploration sites. Our coupled fluid-flow and geomechanical model describes the physical processes inducing seismicity corresponding to the sequential stimulation operations in Pohang, South Korea. Simulation results show that prolonged accumulation of poroelastic energy and pore pressure along a fault can nucleate seismic events larger than M w 3 even after terminating well operations. In particular the possibility of large seismic events can be increased by multiple-well operations with alternate injection and extraction that can enhance the degree of pore-pressure diffusion and subsequent stress transfer through a rigid and low-permeability rock to the fault. This study demonstrates that the proper mechanistic model and optimal well operations need to be accounted for to mitigate unexpected seismic hazards in the presence of the site-specific uncertainty such as hidden/undetected faults and stress regime.

58 GEOSCIENCES↗
DOE OSTI2022

Nanoscale covariance magnetometry with diamond quantum sensors

Nitrogen vacancy (NV) centers in diamond are atom-scale defects that can be used to sense magnetic fields with high sensitivity and spatial resolution. Typically, the magnetic field is measured by averaging sequential measurements of single NV centers, or by spatial averaging over ensembles of many NV centers, which provides mean values that contain no nonlocal information about the relationship between two points separated in space or time. In this work, we propose and implement a sensing modality whereby two or more NV centers are measured simultaneously, and we extract temporal and spatial correlations in their signals that would otherwise be inaccessible. We demonstrate measurements of correlated applied noise using spin-to-charge readout of two NV centers and implement a spectral reconstruction protocol for disentangling local and nonlocal noise sources.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Learning epistatic polygenic phenotypes with Boolean interactions

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1 , a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Flavour-dependent chemical freeze-out of light nuclei in relativistic heavy-ion collisions

We study the production of light nuclei in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7–200 GeV and Pb+Pb collisions at $\sqrt{s_{NN}}$ = 2.76 and 5.02 TeV within a flavour-dependent chemical freeze-out scenario, assuming different flavoured hadrons undergo separate chemical freeze-out. Using the Thermal-FIST package, thermal parameters extracted from fits to various sets of hadron yields, including and excluding light nuclei, are used to calculate the ratios of the yields of light nuclei, namely, d/p, $\overline{d}$/$\overline{p}$, t/p, t/d, 4 He/ 3 He, and 3 H Λ / 3 He. A comparison with experimental data from the STAR and ALICE collaborations shows that a sequential freeze-out scenario provides a better description of light nuclei yield ratios than the traditional single freeze-out approach. These results suggest the flavour-dependent chemical freeze-out for final state light nuclei production persists in heavy-ion collisions at both RHIC and LHC energies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Atomic autoionization in the photo-dissociation of super-excited deuterated water molecules fragmenting into D + + O + + D

We present the relaxation dynamics of deuterated water molecules via autoionization, initiated by the absorption of a 61 eV photon, producing the very rare D + + O + + D breakup channel. We employ the COLd target recoil ion momentum spectroscopy method to measure the 3D momenta of the ionic fragments and emitted electrons from the dissociating molecule in coincidence. We interpret the results using the potential energy surfaces extracted from multi-reference configuration interaction calculations. The measured particle energy distributions can be related to a super-excited monocationic state located above the double ionization threshold of D 2 O. The autoionized electron energy shows a sharp distribution centered around 0.5 eV, which is a signature of the atomic oxygen autoionization occurring in the direct and sequential dissociation processes of D 2 O + * at a large internuclear distance. In this way, an O + radical fragment and a low-energy electron are created, both of which can trigger secondary reactions in their environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1972

The simultaneous quantitation of ten amino acids in soil extracts by mass fragmentography

A specific and sensitive method for the identification and simultaneous quantitation by mass fragmentography of ten of the amino acids present in soil was developed. The technique uses a computer driven quadrupole mass spectrometer and a commercial preparation of deuterated amino acids is used as internal standards for purposes of quantitation. The results obtained are comparable with those from an amino acid analyzer. In the quadrupole mass spectrometer-computer system up to 25 pre-selected ions may be monitored sequentially. This allows a maximum of 12 different amino acids (one specific ion in each of the undeuterated and deuterated amino acid spectra) to be quantitated. The method is relatively rapid (analysis time of approximately one hour) and is capable of the quantitation of nanogram quantities of amino acids.

Pereira, W. E.↗
NASA NTRS1973

Assessment of Southern California environment from ERTS-1

ERTS-1 imagery is a useful source of data for evaluation of earth resources in Southern California. The improving quality of ERTS-1 imagery, and our increasing ability to enhance the imagery has resulted in studies of a variety of phenomena in several Southern California environments. These investigations have produced several significant results of varying detail. They include the detection and identification of macro-scale tectonic and vegetational patterns, as well as detailed analysis of urban and agricultural processes. The sequential nature of ERTS-1 imagery has allowed these studies to monitor significant changes in the environment. In addiation, some preliminary work has begun directed toward assessing the impact of expanding recreation, agriculture and urbanization into the fragile desert environment. Refinement of enhancement and mapping techniques and more intensive analysis of ERTS-1 imagery should lead to a greater capability to extract detailed information for more precise evaluations and more accurate monitoring of earth resources in Southern California.

Bowden, L. W.↗
DOE OSTI2022

A Generalized Transformer-Based Pulse Detection Algorithm

Pulse-like signals are ubiquitous in the field of single molecule analysis, e.g., electrical or optical pulses caused by analyte translocations in nanopores. The primary challenge in processing pulse-like signals is to capture the pulses in noisy backgrounds, but current methods are subjectively based on a user-defined threshold for pulse recognition. Here, we propose a generalized machine-learning based method, named pulse detection transformer (PETR), for pulse detection. PETR determines the start and end time points of individual pulses, thereby singling out pulse segments in a time-sequential trace. It is objective without needing to specify any threshold. It provides a generalized interface for downstream algorithms for specific application scenarios. PETR is validated using both simulated and experimental nanopore translocation data. It returns a competitive performance in detecting pulses through assessing them with several standard metrics. Finally, the generalization nature of the PETR output is demonstrated using two representative algorithms for feature extraction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Technical Lignin Fractionation: A Powerful Tool for Lignin Structure Homogenization and Its Application

Extracted lignin as a by-product of pulping and bio-refining processes is the main available bio-phenolic polymer. The structural complexity, polydispersity, and black color of lignin are the main limiting factors for its application. Preparation of lignin with lower structural complexity and polydispersity through lignin fractionation is one of the primary solutions to engineer lignin into a value-added material. Sequential lignin fractionation was developed based on pH reduction from alkaline to acidic using mineral acids and was recently optimized using carbon dioxide as a pH controller. The partial solubility of lignin in organic solvents is another promising method for lignin fractionation. Organic solvents with different polarity and solubility factors are able to fractionate lignin, yielding a more homogeneous chemical structure. As a modification of the lignin fractionation process, the use of solvents, water/organic solvent mixtures, such as alcohols, and acetic acid from room to high temperature has been proposed as a greener method for lignin fractionation. Using membrane technology is another promising method and current results indicate its good potential for lignin recovery and fractionation.

Sadeghifar, Hasan↗
NASA NTRS2017

The Origin of Sequential Chromospheric Brightenings

Sequential chromospheric brightenings (SCBs) are often observed in the immediate vicinity of erupting flares and are associated with coronal mass ejections. Since their initial discovery in 2005, there have been several subsequent investigations of SCBs. These studies have used differing detection and analysis techniques, making it difficult to compare results between studies. This work employs the automated detection algorithm of Kirk et al. (Solar Phys. 283, 97, 2013) to extract the physical characteristics of SCBs in 11 flares of varying size and intensity. We demonstrate that the magnetic substructure within the SCB appears to have a significantly smaller area than the corresponding H(alpha) emission. We conclude that SCBs originate in the lower corona around 0.1 R above the photosphere, propagate away from the flare center at speeds of 35-85 km/s, and have peak photosphere magnetic intensities of 148+/- 2.9 G. In light of these measurements, we infer SCBs to be distinctive chromospheric signatures of erupting coronal mass ejections.

Kirk, M. S.↗
DOE OSTI2024

Transfer learning for probabilistic localization of hidden cracks in concrete structures

Abstract The utility of discriminative supervised learning models built using multiple training-data sources is investigated for hidden crack localization in concrete. Feed-forward neural network (FFNN) is chosen as the model architecture, and transfer learning is used to assimilate the information obtained from different sources (computational physics simulations and laboratory experiments). The labeled training data consists of values of a damage index and the known locations of hidden cracks. The classification models need to learn how the presence of damage (hidden cracks) affects the damage index at different sensors for different test conditions. To this end, diagnostic FFNN models are built by sequentially adding and training new hidden layers to assimilate labeled information from computer models (different model geometries, test conditions, crack lengths, crack locations) and laboratory experiments on a plain cement slab. These transfer learning-based models are then used to localize damage in concrete specimens that reflect real-world conditions (i.e., specimens with steel reinforcement and randomly distributed aggregate). The actual damage state in these specimens is determined by extracting cores and performing petrographic studies on the extracted cores. The damage probability estimated by transfer learning-based models is compared with the petrographic damage rating index (DRI) to identify the most suitable approach to train the diagnostic models. The transfer learning-based diagnostic methodology shows promise and could be used in various structural health monitoring applications, where sufficient labeled data are typically not available from a single data source.

Miele, S.↗
DOE OSTI2022

Restoration Insights Gained from a Field Deployment of Dithionite and Acetate at a Uranium In Situ Recovery Mine

Mining uranium by in situ recovery (ISR) typically involves injecting an oxidant and a complexing agent to mobilize and extract uranium in a saturated ore zone. This strategy involves less infrastructure and invasive techniques than traditional mining, but ISR often results in persistently elevated concentrations of U and other contaminants of concern in groundwater after mining. These concentrations may remain elevated for an extended period without remediation. Here, we describe a field experiment at an ISR facility in which both a chemical reductant (sodium dithionite) and a biostimulant (sodium acetate) were sequentially introduced into a previously mined ore zone in an attempt to establish reducing geochemical conditions that, in principle, should decrease and stabilize aqueous U concentrations. While several lines of evidence indicated that reducing conditions were established, U concentrations did not decrease, and in fact increased after the amendment deployments. We discuss likely reasons for this behavior, and we also discuss how the results provide insights into improvements that could be made to the restoration process to benefit from the seemingly detrimental behavior.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning

Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyse this dependence using multiple machine learning methods and a dataset of >200 000 nonlinear gyrokinetic simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric geometries. The dataset is generated using a large collection of both optimised and randomly generated stellarator equilibria. At fixed gradients and other input parameters, the turbulent heat flux varies between geometries by several orders of magnitude. Trends are apparent among the configurations with particularly high or particularly low heat flux. Regression and classification techniques from machine learning are then applied to extract patterns in the dataset. Due to a symmetry of the gyrokinetic equation, the heat flux and regressions thereof should be invariant to translations of the raw features in the parallel coordinate, similar to translation invariance in computer vision applications. Multiple regression models including convolutional neural networks (CNNs) and decision trees can achieve reasonable predictive power for the heat flux in held-out test configurations, with highest accuracy for the CNNs. Using Spearman correlation, sequential feature selection and Shapley values to measure feature importance, it is consistently found that the most important geometric lever on the heat flux is the flux surface compression in regions of bad curvature. The second most important geometric feature relates to the magnitude of geodesic curvature. These two features align remarkably with surrogates that have been proposed based on theory, while the methods here allow a natural extension to more features for increased accuracy. The dataset, released with this publication, may also be used to test other proposed surrogates, and we find that many previously published proxies do correlate well with both the heat flux and stability boundary.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Sustainable production of 5-hydroxymethyl furfural from glucose for process integration with high fructose corn syrup infrastructure

5-Hydroxymethyl furfural (HMF) is a platform chemical, which can be derived from lignocellulosic biomass, and used for production of liquid fuels and polymers. We demonstrate a process for production of HMF using sequential enzymatic and catalytic reactions of glucose to synthesize HMF, and simulated-moving-bed (SMB) separation to purify HMF. The adsorption thermodynamic parameters of glucose, fructose, and HMF on a commercial chromatography resin are experimentally determined for modeling the SMB-based HMF production process. The experimental data are used to develop a rigorous process model and then estimate the cost of production. Chromatographic separation of HMF has 16% lower operating costs compared to an extraction-based process and has a minimum selling price of approximately $1478 per ton. We demonstrate that the HMF process can be integrated with the high fructose corn syrup (HFCS) process, and we performed analyses considering two systems including construction of a new integrated facility and retrofitting an existing HFCS facility to produce HMF. Our analyses suggest that the latter approach is a promising short-term low-risk strategy to advance the HMF production technology to commercial scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗