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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Designing complex concentrated alloys with quantum machine learning and language modeling

Designing novel complex concentrated alloys (CCAs) is an essential topic in materials science. However, due to the complicated high-dimensional component-property relationship, tuning material properties by researchers’ experience is challenging, even when guided by physical or empirical rules. Here, we adopt quantum computing (QC) technology and machine learning models to provide a proof-of-concept application of QC in physical metallurgy. We propose a quantum support vector machine (QSVM) model to predict single-phase CCAs. We show that fine-tuned quantum kernels with entanglement deliver promising performance, with a maximum accuracy of 89.4%. The QSVM model is then used to identify 1,741 lightweight CCAs jointly with a new text-mining-based method. Meanwhile, we devise a controllable approach to study the effect of noise on model performance and find that the noise level needs to be minimized for high-performance QSVM models. Finally, this study provides a practical and general approach to designing CCAs based on quantum technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning

The goal of this study is to develop a general strategy for bacterial engineering using an integrated synthetic biology and machine learning (ML) approach. This strategy was developed in the context of increasing L-threonine production in Escherichia coli ATCC 21277. A set of 16 genes was initially selected based on metabolic pathway relevance to threonine biosynthesis and used for combinatorial cloning to construct a set of 385 strains to generate training data (i.e., a range of L-threonine titers linked to each of the specific gene combinations). Hybrid (regression/classification) deep learning (DL) models were developed and used to predict additional gene combinations in subsequent rounds of combinatorial cloning for increased L-threonine production based on the training data. As a result, E. coli strains built after just three rounds of iterative combinatorial cloning and model prediction generated higher L-threonine titers (from 2.7 g/L to 8.4 g/L) than those of patented L-threonine strains being used as controls (4-5 g/L). Interesting combinations of genes in L-threonine production included deletions of the tdh, metL, dapA, and dhaM genes as well as overexpression of the pntAB, ppc, and aspC genes. Mechanistic analysis of the metabolic system constraints for the best performing constructs offers ways to improve the models by adjusting weights for specific gene combinations. Graph theory analysis of pairwise gene modifications and corresponding levels of L-threonine production also suggests additional rules that can be incorporated into future ML models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Boric acid removal with polyol-functionalized polyether membranes

Poor selectivity of conventional desalination membranes for boron often necessitates multi-stage treatment trains to achieve desired boron removal for end uses such as irrigation. One approach to membrane design for improved single-pass boric acid removal is via incorporation of chelating ligands that selectively sorb boron. In this study, membranes based on epoxy-amine cross-linked poly(glycidyl glycidyl ether) (PGGE) were synthesized and functionalized with N-methyl-d-glucamine (NMDG), a polyol known to interact selectively with boron. PGGE and PGGE-NMDG membranes exhibited boron sorption isotherms that were well-described by dual mode isotherms. PGGE-NMDG sorbed 2.5 mmol B/g dry polymer from a neutral aqueous solution containing 100 mmol B/L, which was almost three times the adsorption density of a commercial boron selective resin, Amberlite IRA743. Further, the membranes were regenerated in acid without a significant loss of boron sorption capacity over four cycles. Interactions between boron and NMDG and PGGE sites (e.g., epoxides) impacted boron diffusion in both membranes. The use of ligand functionalized membranes to capture target solutes such as boron requires high loading of interacting sites to maximize uptake capacity. Establishing fundamental structure/property rules for boron selectivity could lead to new material designs with improved boron separation properties for water purification.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effect of Sn oxides on the thermal conductivity of polycrystalline SnSe

SnSe is a promising thermoelectric material, with intrinsically low lattice thermal conductivity, κL. Surprisingly, in several reports, polycrystalline samples are found to have a higher thermal conductivity than single crystals. This disparity has been attributed to trace amounts of thermally conductive Sn oxides at the grain boundaries of polycrystalline samples. The same culprit was recently proposed to explain the reduction of κL in purified, oxide-free, SnSe polycrystals. Here, we test this hypothesis by: (i) tuning the type of oxide in SnSe by exploiting thermodynamic stability regions, since Sn-rich or Sn-poor compositions favour the formation of SnO or SnO 2 , respectively; and (ii) varying the quantity of SnO 2 by intentionally oxidizing SnSe powder before consolidation, to obtain samples with quantifiable amounts - up to 15% - of SnO 2 . We find that the κL of SnSe is impervious to changes in the type or the amount of Sn oxide present in the samples. Our results show that a simple “rule of mixtures” cannot be used to estimate the effect of grain boundary oxides on the thermal conductivity of SnSe. These results call for an improved understanding of the intriguing thermal transport mechanisms in SnSe and numerous other systems where a two-phase transport is presumed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets

This work introduces the EXSCLAIM! toolkit for the automatic extraction, separation, and caption-based natural language annotation of images from scientific literature. EXSCLAIM! is used to show how rule-based natural language processing and image recognition can be leveraged to construct an electron microscopy data set containing thousands of keyword-annotated nanostructure images. Moreover, it is demonstrated how a combination of statistical topic modeling and semantic word similarity comparisons can be used to increase the number and variety of keyword annotations on top of the standard annotations from EXSCLAIM! With large-scale imaging datasets constructed from scientific literature, users are well positioned to train neural networks for classification and recognition tasks specific to microscopy-tasks often otherwise inhibited by a lack of sufficient annotated training data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms

This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs alone can have inherent limitations, we propose a hybrid architecture combining LMs and GANs to efficiently generate new molecules, demonstrating superior performance over standalone masked LMs, particularly for smaller population sizes. This hybrid LM-GAN architecture enhances efficiency in optimizing properties and generating novel samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The structure of low-lying 1 - states in 90,94 Zr from (α,α'γ) and (p,p'γ) reactions

The low-lying dipole strength in the 90,94 Zr nuclei was investigated via (ρ,ρ'γ) at 80 MeV and (α, α', γ) at 130 MeV. The experiments, made at RCNP, used the magnetic spectrometer Grand Raiden for the scattered particles and the array CAGRA with HPGe detectors for the γ-decay. For 94 Zr these are the first data for both reactions and for 90 Zr these are the first data with (ρ,ρ'γ) and the first ones at high resolution for (α, α', γ). The comparison of the present results for the two nuclei with existing (γ, γ') data shows that both nuclear probes produce an excitation pattern different than that of the electromagnetic probes. DWBA calculations were made using form factors deduced from transition densities, based on RPA calculations, characterized by a strong neutron component at the nuclear surface. A combined analysis of the two reactions was performed for the first time to investigate the isoscalar character of the 1 - states in 90,94 Zr. The (ρ,ρ'γ) cross section was calculated using values for the isoscalar electric dipole energy-weighted sum rule (E1 ISEWSR) obtained from the (α, α', γ) data. The isoscalar strength for 90 Zr was found to exhaust 20 ± 2.5% of the EWSR in the energy range up to 12 MeV. In case of 94 Zr, a strength of 9 ± 1.1% of the EWSR was found in the range up to 8.5 MeV. Although an overall general description was obtained in the studied energy intervals, not all proton cross sections were well reproduced using the isoscalar strength from (α, α', γ). This might suggest mixing of isoscalar and isovector components and that this mixing and the degree of collectivity are not the same for all the 1 - states below the particle binding energy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Transverse structure of the pion beyond leading twist with basis light-front quantization

We investigate the twist-3transverse-momentum-dependent parton distribution functions (TMDs) of the pion with basis light-front quantization. The twist-3 TMDs are not independent and can be decomposed into twist-2and genuine twist-3 terms from the equations of motion (EOM). We compute the TMDs from the resulting light-front wave functions obtained by diagonalizing the light-front QCD Hamiltonian, determined for pion’s constituent quark-antiquark and quark-antiquark-gluon Fock sectors, with three-dimensional confinement. We also obtain the twist-3 parton distribution functions (PDFs) and show that they preserve the sum rule, which affirms the robustness of our approach. This is the first time that theoretical predictions are made for subleading twist structures of the pion containing interference terms between two light-front Fock sectors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Observation of three-proton-decaying resonant states in 21 Al

The structure of the proton-unbound nucleus 21 Al has been studied via proton transfer from a 9 Be target to a 20 Mg beam at E/A = 103 MeV. Three resonant states in 21 Al are identified by the invariant-mass reconstruction of 18 Ne + 3p events. The decay-energy spectra for the two subsystems of 21 Al , i.e. 18 Ne+ 1 p and 18 Ne+ 2 p , indicate that the observed resonant state with the lowest energy has decay branches consisting of three sequential steps of one-proton decay. Based on the ab initio and the distorted-wave Born approximation (DWBA) calculations, this resonant state at Ε ⁎ = 6.09(11) MeV likely has a spin-parity of (1/2 + - 7/2 + ). Meanwhile, the possibility that the observed resonant peak represents an admixture of several states cannot be ruled out. The good isospin-symmetry of the low-lying states with d 5/2 , d 3/2 configurations in the 21 Al- 21 O pair and the ab initio calculations indicate weak configuration mixing between the s - and d -orbital components. The systematics of experimental energy differences between the 5/2 + ground state and lowest-lying 3/2 + excited state for isotopes (isotones) of Z(N) = 13 suggest a likely semimagic nature of 21 Al.

21Al↗
DOE OSTI2020

Transverse spin effects in hard semi-inclusive collisions

The nucleons (protons and neutrons) are by far the most abundant form of matter in our visible Universe; they are composite particles made of quarks and gluons, the fundamental quanta of Quantum Chromo Dynamics (QCD). The usual interpretation of the nucleon dynamics in high energy interactions is often limited to a simple one-dimensional picture of a fast moving nucleon as a collection of co-linearly moving quarks and gluons (partons), interacting accordingly to perturbative QCD rules. However, massive experimental evidence shows that, in particular when transverse spin dependent observables are involved, such a simple picture is not adequate. The intrinsic transverse motion of partons has to be taken into account; this opens the way to a new, truly 3-dimensional (3D) study of the nucleon structure. A review of the main experimental data, their interpretation and understanding in terms of new transverse momentum dependent partonic distributions, and the progress in building a 3D imaging of the nucleon is presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Experimental and kinetic modeling study of tetralin: A naphtheno-aromatic fuel for gasoline, jet and diesel surrogates

Distillate fuels contain significant proportions of naphtheno-aromatic components and tetralin is a suitable surrogate component to represent this molecular moiety. The presence of aromatic and naphthyl rings makes kinetic modeling of tetralin very challenging. Primary radicals formed during the oxidation of tetralin can be aryl, benzylic or paraffinic in nature. Using available information on reaction paths and rate constants of naphthenes and alkyl-aromatics, in this study a kinetic model of tetralin has been developed with emphasis on low-temperature chemistry and high-pressure conditions. Due to the lack of high-level quantum chemical calculations on reaction pathways of tetralin, analogous rates from ab-initio studies on benzylic and paraffinic radicals have been adopted here. Some modifications to the reaction rate rules are incorporated to account for the unique characteristics of tetralin's molecular structure. Important reaction channels have been identified using reaction path and brute force sensitivity analyses. In order to investigate the model performance at low temperatures, new experiments are carried out in a rapid compression machine on blends of tetralin and 3-methylpentane. Blending of low-reactivity tetralin with a high-reactivity alkane allowed the investigation of tetralin ignition at very low temperatures (665 – 856 K). The kinetic model developed in the current study is found to predict the current experiments and literature data adequately. The new model will aid in high-fidelity surrogate predictions at engine-relevant conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Experimental and kinetic modeling study of the low- temperature and high-pressure combustion chemistry of straight chain pentanol isomers: 1-, 2- and 3-Pentanol

Pentanols have received significant attention as a potential alternative fuel or fuel additive owing to their high energy densities and low vapor pressure. The development of robust chemical kinetic models for alternative fuels which can provide accurate and efficient predictions of combustion performance across a wide range of engine relevant conditions is important in developing cleaner, more efficient combustors. Although the high temperature oxidation kinetics of pentanol isomers has been researched considerably, their low temperature combustion chemistry needs further investigation. While previously proposed low temperature mechanisms for 1-pentanol based on analogy and rate rules need further refinement, the low temperature oxidation kinetics of 2-pentanol and 3-pentanol has not been studied previously by any means, experimentally or theoretically. A newly developed kinetic mechanism is presented in this work for the three straight chain pentanol isomers: 1-, 2- and 3-pentanol. Low temperature kinetics is based on a recent study by Lockwood et al., 2022 [20] involving theoretical calculations at the CCSD(T)/cc-pV∞Z level of theory for the oxidation pathways involving alcohol peroxy radicals. Rate of production analyses performed in this study highlight the importance of the newly added pressure-dependent reactions of the α-alcohol peroxy radical forming an RO 2 adduct. While the α-alcohol fuel radical reacts with O 2 to directly decompose via a chemically activated pathway at low pressures, the formation of the RO 2 adduct is favored at high pressures. Furthermore, the detailed model is comprehensively validated against new ignition experiments at low temperature and high pressure, together with the wide range of data available in the literature. Both qualitative and quantitative predictions of the experimental data using the proposed kinetic model are satisfactory for all three pentanol isomers studied here.

1-pentanol↗
DOE OSTI2024

A deep learning-based battery sizing optimization tool for hybridizing generation plants

Hybrid generation and energy storage systems offer the ability to increase flexibility of the combined asset. This flexibility can be used to increase provision of services already provided by the generation asset, such as timing sale of electricity to the energy market during high price periods, and also enable provision of additional services, such as ancillary services or contribute to resource adequacy. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that, among other factors, maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary (i.e., based on “rules of thumb”) or too complex to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run). This work presents a novel deep learning-based battery sizing optimization tool that is designed to help generation asset owners easily assess preliminary sizing considerations for potential battery investments to hybridize their generation facility. The tool uses deep learning to predict revenue over a broad search space of potential battery sizes, estimates capital and operating costs (including accounting for battery degradation), and computes financial performance of each potential battery system investment, recommending a system with maximum financial performance. The tool is tested and validated for hydropower assets. Finally, this tool will help a greater cross-section of industry consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.

13 HYDRO ENERGY↗
DOE OSTI2020

State-of-the-art short-term electricity market operation with solar generation: A review

With the accelerating penetration of solar energy in energy systems, market operations concerning the solar-associated intermittency are widely discussed. How to correctly model the solar generation in the market and solve the uncertainty-based operation problems call for solutions. Unlike other renewable resources, solar power can be more easily applied to the demand side through behind-the-meter installations in a distributed manner with significant future extensibility. The corresponding operational rules in the markets are largely being revised. Over the past two decades, with the development of deregulated power markets, numerous studies related to uncertainty-based market operations tend to transform the market from a deterministic and centralized framework to a stochastic and decentralized one. As the relevant literature is most recent, this paper reviews their contributions to electricity market operations with solar integration and underlines the research directions of this topic and ongoing industry practices. A thorough review of the electricity markets worldwide with solar energy is provided. A variety of proposed mathematical solutions to the problem are also addressed.

14 SOLAR ENERGY↗
DOE OSTI2022

An agent-based modeling tool supporting bioenergy and bio-product community communication regarding cellulosic bioeconomy development

The emergence of the cellulosic bioeconomy requires not only adequate technological, economic, and policy advances, but also effective communication and coordination among the multiple stakeholders in the bioeconomy community. Aiming to facilitate stakeholder communication and collaboration for cellulosic bioeconomy growth, an agent-based model is developed as a community communication tool in this study. The tool simulates the behaviors and interactions of key stakeholders, as well as the responses and interactions between the stakeholders and biophysical, socioeconomic, and environmental conditions. The agent behavior rules are developed and validated based on behavior and economic theories and information attained from a stakeholder focus group study and a land use survey. A pilot study in a Central Illinois watershed shows stakeholder synergies, i.e., positive feedback loops across multiple stakeholders. The modeling tool's role in facilitating community communication is demonstrated via the focus group study. A comparison of policy scenarios suggests that the cellulosic bioeconomy development is fastest under a portfolio of policies combining the views of the multiple stakeholders. Critically, subsidy for small-scale bio-facilities is essential as it rapidly initializes an early market for cellulosic feedstocks and the adoption of Miscanthus. As a result, the communication tool is applied to bringing stakeholders and researchers together to develop shared visions, assess barriers, and explore solution portfolios to initialize and speed up the regional cellulosic bioeconomy.

09 BIOMASS FUELS↗
DOE OSTI2022

Unusual photophysical properties of a new tricyclic derivative of thiopurines in terms of potential applications

The thio analogues of purine bases have been found to possess notable biological and pharmacological capabilities and have an important role to play as anticancer and immunosuppressive drugs. In this work a new tricyclic analogue of guanosine containing sulfur was synthesized, in particular, DTEG (2',3',5'-tri-O-acetyl-6,9-dithioethanoguanosine). Although there is promise for thiopurine derivatives for biomedical applications, there are some liabilities in regard to their exposure to light. As a preliminary survey for such difficulties with DTEG, this work looks into spectral and photophysical processes of DTEG using time-resolved and steady-state optical excitation. In contrast to other thiopurines, which have long-lived triplets, DTEG is shown to have a short-lived triplet making it less dangerous for singlet-oxygen sensitization. Even in anaerobic solutions, its photoreactivity is negligible. These various unusual photochemical properties of DTEG are consistent with DTEG being very promising as an alternative drug to the currently used 6-thiopurines. DTEG also has some interesting photophysical behavior that is distinct from other thioketones. Although thioketones have an unusual fluorescence violating Kasha’s Rule and emitting from the second excited singlet state, DTEG does this also, but, in addition, it shows dual fluorescence by emitting from its first excited singlet as well. The assignments of the nature of these excited states are supported by DFT results. This theory and associated kinetic analysis show quantitatively that the dual fluorescence is, in part, tied to the relatively fast S 2 to S 1 internal conversion compared to other S 2 decays and, in part, tied to the relatively slow nonradiative decay of S 1 itself.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Superconductivity in a strange metal

In a gas of charged particles with the density n, mass m, and charge e, the electrical conductivity σ is given by the Drude formula: σ = ne 2 /mΓ, where Γ is the scattering rate. Standard metals are well described by Landau's Fermi Liquid (FL) theory, in which the electric current is carried by quasi-particles, low-energy excitations of the FL that resemble electrons with some effective mass m*. The scattering rate Γ= 1/τ= v F /l, where t is the (momentum) relaxation) time, l is the mean-free path, and v F is the Fermi velocity, can be expressed ("Matthiessen's Rule") as a sum of contributions from various scattering channels: Γ = Γ 0 + Γ el-el + Γ el-ph + …, where Γ 0 describes scattering on lattice imperfections, Γ el-el the electron-electron scattering, Γ el-ph the electron-phonon scattering, etc. Of these, Γ 0 = v F /l 0 , where l 0 is the average distance between the defects, is temperature-independent. Γ el-el should scale as T 2 because of Fermi statistics; for two electrons to scatter on one another, both must come from the "Debye shell" of the width k B T/E F , where k B is the Boltzmann constant and E F is the Fermi energy. Γ el-ph typically grows as T 5 , so we expect this to overwhelm the other channels at a high enough T. However, since l cannot be shorter than the distance between the atoms, the total Γ saturates at Mott-Ioffe-Regel (MIR) limit, roughly v F /a 0 , where v F is the Fermi velocity and a 0 is the lattice constant. The resistivity should also saturate at low T, at ρ 0 = m*v F /ne 2 l 0 T. FL theory also describes other electronic properties; e.g., it predicts that in the magnetic field B, the resistivity of the metal should increase with B 2 , because σ(B) = σ(B=0)/(1 + (ω c /Γ) 2 ), where ω c = eB/m*.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

A heat flux sensor leveraging the transverse Seebeck effect in elemental antimony

Certain configurations of anisotropic single crystal materials can generate a thermoelectric voltage orthogonal to an induced temperature gradient. This phenomenon is known as the Transverse Seebeck Effect (TSE) and can be leveraged to fabricate simple and robust heat flux sensors. Only a small number of materials have been considered as TSE-based transducers and, among these, few have been developed into sensors with ruggedization against chemical and mechanical degradation. Here, we report on the fabrication and characterization of a rugged TSE-based heat flux sensor using prismatic antimony single crystals. The heat flux sensor was tested under static and dynamic heating scenarios. The sensor has a linear responsivity of 16.8 µV/(W/cm2) to heat fluxes spanning more than two orders of magnitude and a time constant of 4.4 s. The sensor’s response to localized heating, probed with a laser scanning technique, validated that the transduction mechanism is primarily the TSE by ruling out a sizable contribution from the conventional Seebeck effect. Finite element analysis corroborated that components used in the sensor package are the primary determinants of the time constant and the decrement of the responsivity from its theoretical maximum. Design principles that may be applied to elicit a faster transient response or higher responsivity are proposed. Furthermore, the results establish single crystal antimony as a promising transducer material for heat flux measurement systems and demonstrate potential effects of ruggedization on sensor performance.

36 MATERIALS SCIENCE↗