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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Data Deposition Platform for Sharing Nuclear Magnetic Resonance Data

Nuclear magnetic resonance (NMR) data are rarely deposited in open databases, leading to loss of critical scientific knowledge. Existing data reporting methods (images, tables, lists of values) contain less information than raw data, and are poorly standardized. Together, these issues limit FAIR (findable, accessible, interoperable, reusable) access to these data, which in turn creates barriers for compound dereplication and the development of new data-driven discovery tools. Existing NMR databases are either not designed for natural products data, or employ complex deposition interfaces that disincentivize deposition. Journals, including the Journal of Natural Products (JNP), are now requiring data submission as part of the publication process, creating the need for a streamlined, user-friendly mechanism to deposit and distribute NMR data. Recently, our team reported the development of the Natural Products Magnetic Resonance Database (NP-MRD; www.np-mrd.org). Here in this paper we present a new data deposition platform for the NP-MRD project that is designed to enable users to deposit NMR data for published or submitted manuscripts in under five minutes. This platform includes a suite of automated data extraction and standardization tools, together with a simple-to-use web-based interface and detailed error reporting to simplify the data deposition process and is available at www.np-mrd.org/submissions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Neural Network Analysis of Nuclear Magnetic Resonance and Infrared Spectra

Nuclear magnetic resonance (NMR) spectroscopy and infrared (IR) spectroscopy are powerful chemical characterization techniques with broad general usage. However, the manual evaluation of the resulting spectra is time-consuming and requires significant expertise, preventing insights from being used in real-time applications. With recent advances in computation and artificial intelligence (AI), new tools are available for automating spectral interpretation. In this work, machine learning (ML) algorithms using 1-dimensional convolutional neural networks (CNNs) were applied to identify common functional groups from spectral information. Raw spectra were collected virtually from the Human Metabolome Database (HMDB) and National Institute of Standards and Technology (NIST) Chemistry WebBook and processed into a suitable standard. Algorithm design was tailored to best fit the nature of the problem, with built-in flexibility to accommodate relevant parameters beyond the raw spectral input, specifically solvent identity and magnetic frequency for NMR. The predictive capability of the algorithm in identifying functional groups is displayed in several examples. This methodology has been compiled into a code repository and could easily be modified to adapt alternative data sources, including other spectrum types. To mitigate overfitting, a common problem in mathematical modeling where overfamiliarity with training data produces trends that are not representative of the general data, a novel metric was developed, referred to as Accufit. Accufit includes a parameter that penalizes substantial differences in the training accuracy and the accuracy of an independent validation set. Examples are presented showing the effectiveness of Accufit in maintaining the model’s predictive capability while controlling the overfitting when used as a custom metric for hyperparameter tuning.

Sturgill, James↗
DOE OSTI2026

Opportunities and Limitations of Nuclear Magnetic Resonance Spectroscopy in Astrobiology

For decades, Nuclear Magnetic Resonance (NMR) spectroscopy has been utilized as a powerful tool in various scientific disciplines, most prominently in chemistry, to determine molecular structures or monitor reactions. While well established in various fields, NMR applications in astrobiology are still unclear. This work aims to explore the potential of NMR in astrobiology, highlighting strengths but also weaknesses. We illustrate capabilities of NMR with two applications: (1) recently developed methods for position-specific carbon isotope analysis of complex organics; and (2) well-established tools for performing quantitative compositional analysis of complex organic mixtures. By utilizing samples relevant to astrobiology, specifically the amino acid valine and analogue mixtures of organics, we showcase that molecules retain a source dependent and distinct intramolecular carbon isotope fingerprint. We demonstrate that compositional sample analysis provides an independent and complementary line of evidence pointing towards the origin of a molecule or mixture. Together, these NMR tools have the potential to support life detection efforts, and aid in distinguishing between biotic and abiotic samples. Finally, we discuss sensitivity, detection limits, the portability of NMR, and propose how integration with mass-spectrometry techniques will be imperative to enable more targeted and comprehensive analyses relevant to astrobiology, including in-situ analysis but also sample return missions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Polarized target nuclear magnetic resonance measurements with deep neural networks

Continuous-wave Nuclear Magnetic Resonance (CW-NMR) operated in constant-current mode has served as a foundational technique for polarization measurement in solid-state dynamically polarized targets within nuclear and high-energy physics experiments for several decades, and it remains an essential tool. Conventional Q-meter-based phase-sensitive detection is critical for precise real-time determination of target polarization during scattering runs. However, the accuracy and reliability of these measurements are frequently compromised by elevated noise levels, baseline drift, and systematic uncertainties arising from signal isolation and fitting, ultimately degrading the overall experimental figure of merit. In this work, we report the first successful application of neural network architectures to continuous-wave NMR polarization metrology. By leveraging advanced machine learning techniques for signal extraction and denoising, we achieve a substantial reduction of fitting uncertainties under a variety of realistic simulated and experimental conditions. These improvements translate directly into more robust real-time (online) polarization monitoring and higher precision in subsequent offline analysis. By reducing analysis-induced uncertainty, the resulting methodology can improve the effective figure of merit for scattering experiments employing dynamically polarized targets and provides a new toolset for NMR-based polarimetry in high-energy and nuclear physics.

Metrology↗
DOE OSTI2024

Measurements of nematic susceptibility with phase sensitive nuclear magnetic resonance in pulsed strain fields

Here, we present nuclear magnetic resonance data in BaFe 2 As 2 in the presence of pulsed strain fields that are interleaved in time with the radio frequency excitation pulses. In this approach, the preceding nuclear magnetization acquires a phase shift that is proportional to the strain and pulse time. The sensitivity of this approach is limited by the homogeneous decoherence time, T 2 , rather than the inhomogeneous linewidth. We measure the nematic susceptibility as a function of temperature and demonstrate a three orders of magnitude improvement in sensitivity. This approach will enable studies of the strain response in a broad range of materials that previously were inaccessible due to inhomogeneous broadening.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Calibration of the Solid‐State Nuclear Magnetic Resonance Search for Axion‐Like Dark Matter

Abstract Calibration of nuclear‐magnetic‐resonance‐based searches for axion‐like dark matter can be performed by free induction decay (FID) measurements. This manu‐ script describes FID experiments on several solid materials, motivated by the Cosmic Axion Spin Precession Experiment (CASPEr) program. Experiments with 207 Pb nuclear spins in ferroelectrics, lead magnesium niobate‐lead titanate (PbMg 1/3 Nb 2/3 O 3 ) (PbTiO 3 ) 1/3 (PMN‐PT) and lead zirconium titante PbZr 0.52 Ti 0.48 O 3 (PZT) are directly relevant to the CASPEr‐electric search for the electric dipole moment interaction of axion‐like dark matter. Experiments with 31 P nuclear spins in gadolinium‐doped hydroxypyromorphite Pb 4.95 Gd 0.05 (PO 4 ) 3 OH (HPM:Gd) are used for apparatus calibration. The measurements characterized the nuclear spin ensemble coherence time and the magnetic resonance detection sensitivity for these samples. Calibration is performed using small tip‐angle pulses.

Winter, Andrew J.↗
DOE OSTI2024

Molecular Modes Elucidate the Nuclear Magnetic Resonance Relaxation of Viscous Fluids

The Bloembergen, Purcell, and Pound (BPP) theory of nuclear magnetic resonance (NMR) relaxation in fluids dating back to 1948 continues to be the linchpin in interpreting NMR relaxation data in applications ranging from characterizing fluids in porous media to medical imaging (MRI). The BPP theory is founded on assuming molecules are hard spheres with 1 H– 1 H dipole pairs reorienting randomly; assumptions that are severe in light of modern understanding of liquids. Nevertheless, it is intriguing to this day that the BPP theory was consistent with the original experimental data for glycerol, a hydrogen-bonding molecular fluid for which the hard-sphere-rigid-dipole assumption is inapplicable. To better understand this incongruity, atomistic molecular simulations are used to compute 1 H NMR T 1 relaxation dispersion (i.e., frequency dependence) in two contrasting cases: glycerol, and a (non hydrogen-bonding) viscosity standard. At high viscosities, simulations predict distinct functional forms of T 1 for glycerol compared to the viscosity standard, in agreement with modern measurements, yet both in contrast to BPP theory. The cause of these departures from BPP theory is elucidated, without assuming any relaxation models and without any free parameters, by decomposing the simulated T 1 response into dynamic molecular modes for both intramolecular and intermolecular interactions. The decomposition into dynamic molecular modes provides an alternative framework to understand the physics of NMR relaxation for viscous fluids.

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

Nuclear Magnetic Resonance Dynamics of LiTFSI–Pyrazole Eutectic Solvents

Deep Eutectic Solvents (DESs) have emerged as promising candidates to replace conventional organic solvents in various technological applications due to their low vapor pressure, non-flammability, and ease of preparation at low costs. In particular, Type IV DESs, which are composed of metal salts and hydrogen bond donors, are possible replacements for lithium-ion battery electrolytes. In this study, we investigate the molecular dynamics of solvents of lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) and pyrazole (PYR) at varying LiTFSI:PYR molar ratios (1:2, 1:3, 1:4, 1:5) using Nuclear Magnetic Resonance Dispersion (NMRD) and Pulsed Field Gradient (PFG) Nuclear Magnetic Resonance (NMR). PFG NMR reveals composition-dependent diffusion trends, while NMRD provides molecular-level insights into the longitudinal relaxation rate (R 1 = 1/T 1 ). Notably, the LiTFSI:PYR (1:2) sample shows distinct behavior across both techniques, exhibiting enhanced relaxation rates and lower self-diffusion for 1 H compared to the other nuclei ( 19 F and 7 Li), suggestive of stronger and more efficient Li + –pyrazole interactions, as confirmed by the modeling of the relaxation profiles. Our study advances understanding of ion dynamics in azole-based eutectic solvents, supporting their potential use in safer battery electrolytes.

FFC-NMR↗
DOE OSTI2026

Nuclear magnetic resonance dark-matter searches are sensitive to dark photons and the axion-photon coupling

We demonstrate that nuclear magnetic resonance based searches for dark matter (DM) have intrinsic and powerful sensitivity to dark photons and the axion-photon coupling. The reason is conceptually straightforward. An instrument such as CASPEr-Gradient begins with a large sample of nuclear spins polarized in a background magnetic field. In the presence of axion DM coupled to nucleons, the spin ensemble feels an effective magnetic field 𝐁 ∝ ∇𝑎 that tilts the spins, generating a potentially observable precession. If the magnetic field is real rather than effective, the system responds identically. A real field can be generated by a kinetically mixed dark photon within the shielded region the sample is placed or an axion coupled to photons through its interaction with the background magnetic field. We show that all three signals are detectable and distinguishable. If CASPEr-Gradient were to reach the QCD axion prediction of the axion-nucleon coupling, it would simultaneously be sensitive to kinetic mixings of 𝜀 ≃ 3 × 10 −16 and axion-photon couplings of 𝑔 𝑎⁢𝛾⁢𝛾 ≃ 2 × 10 −16 GeV −1 for 𝑚 ≃ 1 𝜇⁢eV. Our analysis demonstrates that CASPEr-Gradient has the tantalizing possibility of simultaneously measuring two separate couplings of axion dark matter, thereby providing a unique window into the axion’s UV completion.

Beadle, Carl [University of Geneva (Switzerland)] ↗
DOE OSTI2024

Evaluation of hydrogen storage in sandstone reservoirs using 1 H nuclear magnetic resonance spectroscopy

Evaluation of the hydrogen storage capacity of porous rocks is crucial for underground hydrogen storage. Using 1 H nuclear magnetic resonance (NMR) spectroscopy, we successfully characterized the hydrogen responses and identified storage mechanisms in Berea sandstone under varying water saturation. Here, the results indicate that the injected hydrogen behaves as a free gas phase and is capable of occupying the empty pore volume regardless of the saturation state. No hysteresis was observed during injection and production cycles.

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

Permethylation as a strategy for high–molecular–weight polysaccharide structure analysis by nuclear magnetic resonance—Case study of Xylella fastidiosa extracellular polysaccharide

Current practices for structural analysis of extremely large-molecular-weight polysaccharides via solution-state nuclear magnetic resonance (NMR) spectroscopy incorporate partial depolymerization protocols that enable polysaccharide solubilization in suitable solvents. Non-specific depolymerization techniques utilized for glycosidic bond cleavage, such as chemical degradation or ultrasonication, potentially generate structural fragments that can complicate complete and accurate characterization of polysaccharide structures. Utilization of appropriate enzymes for polysaccharide degradation, on the other hand, requires prior structural knowledge and optimal enzyme activity conditions that are not available to an analyst working with novel or unknown compounds. Herein, we describe an application of a permethylation strategy that allows the complete dissolution of intact polysaccharides for NMR structural characterization. This approach is utilized for NMR analysis of Xylella fastidiosa extracellular polysaccharide (EPS), which is essential for the virulence of the plant pathogen that affects multiple commercial crops and is responsible for multibillion dollar losses each year.

13C↗
DOE OSTI2025

Determining hexavalent chromium transport properties in alkaline nuclear waste using nuclear magnetic resonance spectroscopy

This study focuses on the transport properties of hexavalent chromium, specifically the chromate anion, to improve predictive models and environmental remediation strategies for Cr(VI) migration. Using 53 Cr Nuclear Magnetic Resonance (NMR) spectroscopy, the research quantifies chromate in multicomponent electrolytes replicating nuclear waste conditions at the Hanford Site in Washington State. The consistency of the 53 Cr NMR signal integral with chromate concentration, despite varying matrix compositions, establishes it as a reliable concentration indicator. The transport properties of chromate in an alkaline solution were assessed using relaxation-based measurements via saturation recovery and Carr-Purcell-Meiboom-Gill experiments, determining spin-lattice and spin-spin relaxation times. These measurements, combined with the Bloembergen-Purcell-Pound equation, helped estimate the rotational correlation time and the 53 Cr self-diffusion coefficient using Stokes-Einstein-Debye and Stokes-Einstein equations. Direct measurements were obtained through pulsed field gradient stimulated echo 53 Cr NMR spectroscopy. Monte Carlo simulations further estimated uncertainty propagation. The results enhance comprehension of chromate transport and highlight prospects for identifying transport properties of NMR-active nuclei, traditionally considered unreachable.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Nuclear magnetic resonance investigation of superconducting and normal state Nb 3 Sn

The superconductor Nb3Sn has important applications for construction of very high-field superconducting magnets. In this work we investigate its microscopic electronic structure with 93 Nb nuclear magnetic resonance (NMR). The high-quality Nb 3 Sn powder sample was studied in both 3.2 T and 7 T magnetic fields in the temperature range from 4 K to 300 K. From measurement of the spectrum and its theoretical analysis, we find evidence for anisotropy despite its cubic crystal structure. Magnetic alignment of the powder grains in the superconducting state was also observed. The Knight shift and spin-lattice relaxation rate, T 1 -1 , were measured and the latter compared with BCS theory for the energy gap Δ(0) = 2.7 ± 0.3k B T c at 3.2 T and Δ(0) = 2.33 ± 0.07 k B T c at 7 T, indicating suppression of the order parameter by magnetic field.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Surface Terminations of LaAlO 3 Perovskite Nanoparticles as Viewed by Solid-State Nuclear Magnetic Resonance

Nanocrystal surfaces generally undergo reconstructions that differentiate them from the bulk structures, often in nontrivial ways. Understanding these terminations is critical across diverse fields, from heterogeneous catalysis to the formation of topological states and the synthesis of semiconductor nanomaterials. Determining surface structures is currently an interdisciplinary task, most often involving high-resolution electron microscopy and surface electron diffraction. These methods, however, do not provide a global view of the ensemble of structures present in a sample. Here, we show how surface-sensitive solid-state nuclear magnetic resonance (SSNMR) spectroscopy methods can bridge this gap. In this context, we investigated the surface structure of lanthanum aluminate (LaAlO 3 ) perovskite nanoparticles. Four distinct surface terminations have previously been observed for this material, but their relative abundances were unknown. Using an array of double- and triple-resonance SSNMR methods probing the relative proximities of surface 1 H, 27 Al, 17 O, and 139 La nuclei, we conclude the surface to be majority terminated (80%) by AlO x with substantial (20%) LaO x terminated regions.

Materials↗
DOE OSTI2024

Structural Studies of Alloyed and Nanoparticle Transition Metal Dichalcogenides by Selenium-77 Solid-State Nuclear Magnetic Resonance Spectroscopy

Layered transition metal dichalcogenides (TMDCs) such as MoS 2 , MoSe 2 , and WSe 2 are under intense investigation because they are atomically thin semiconductors with photophysical properties that can be tuned by changing their composition or morphology. Mechanochemical processing has been proposed as a method to obtain alloyed TMDCs in the series Mo 1–x W x S y Se 2–y (x = 0–1; y = 0–2). However, elucidating the chemical transformations occurring at the atomic scale following mechanochemical processing can be challenging because the products are often amorphous or microcrystalline. To address this challenge, we probe TMDC mixing and alloying by using a combination of powder X-ray diffraction, Raman spectroscopy, diffuse reflectance spectroscopy, 77 Se solid-state nuclear magnetic resonance (SSNMR) spectroscopy, and planewave density functional theory (DFT) calculations. The nature of the milling material and reaction atmosphere are shown to be essential factors in limiting the formation of undesired oxide byproducts. We demonstrate acquisition of 77 Se SSNMR spectra using different combinations of Carr-Purcell Meiboom-Gill acquisition (CPMG) pulse sequences, magic angle spinning (MAS), and MAS dynamic nuclear polarization. Further, the combination of SSNMR with the other characterization methods clearly demonstrates that high energy impact ball milling induces molecular level alloying of Mo, W and chalcogen atoms in the family Mo 1–x W x S y Se 2–y . Gauge including projector augmented wave DFT calculations yield accurate 77 Se chemical shift (CS) tensor components. 77 Se SSNMR spectroscopy was also applied to study the structure of WSe 2 nanocrystals intercalated with ethylenediamine. The intercalated WSe 2 nanocrystals exhibit a more positive isotropic 77 Se CS as compared to bulk WSe 2 , however, the 77 Se CS anisotropy is the same, confirming the WSe 2 layers have a similar structure as in their bulk counterparts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dynamics of fluorinated imide-based ionic liquids using nuclear magnetic resonance techniques

There is increasing interest in studying molecular motions in ionic liquids to gain better insights into their transport properties and to expand their applications. Here, in this study, we have employed the fast field cycling relaxometry and pulsed field gradient nuclear magnetic resonance techniques to investigate the rotational and translational dynamics of fluorinated imide-based ionic liquids (ILs) at different temperatures. We have studied a total of six ILs composed of the 1-butyl-3-methylimidazolium cation ([BMIM] + ) combined with chemically modified analogs of the bis((trifluoromethyl)sulfonyl)imide anion ([NTf 2 ] − or [TFSI] − ). The primary objective of this paper is to broaden the understanding of how the anion's conformational flexibility, fluorination, and mass affect the molecular dynamics of cations and anions. Our results indicate that flexibility has the most significant impact on the rotational and translational motions of ions. Meanwhile, the effect of fluorination and mass is only relevant when conformational flexibility does not change significantly between the ILs being compared.

25 ENERGY STORAGE↗
DOE OSTI2025

Role of Nematic Fluctuations on Superconductivity in FeSe 0.47 ⁢Te 0.53 Revealed by Nuclear Magnetic Resonance under Pressure

The relationship between antiferromagnetic (AFM) spin fluctuations (SF), nematic fluctuations, and superconductivity (SC) has been central to understanding the pairing mechanism in iron-based superconductors (IBSCs). Iron chalcogenides, which hold the simplest crystal structure in IBSCs, provide a good platform to investigate the relationship. Here, we report 77 Se and 125 Te nuclear magnetic resonance studies of FeSe 0.47⁢ Te 0.53 , which is located close to a nematic quantum critical point (QCP), under pressures up to 1.35 GPa. Further, both the superconducting critical temperature and AFMSF were found to be enhanced under pressure, which suggests a correlation between SC and AFMSF in FeSe 0.47 ⁢Te 0.53 . However, the contribution of AFMSF to SC in FeSe 0.47 ⁢Te 0.53 was found to be much less compared to that in FeSe 1−𝑥 ⁢S 𝑥 , suggesting that nematic fluctuations play a dominant role in the SC in FeSe 1−𝑥⁢ Te 𝑥 around the nematic QCP.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

A simple and highly efficient protocol for 13 C-labeling of plant cell wall for structural and quantitative analyses via solid-state nuclear magnetic resonance

Plant cell walls are made of a complex network of interacting polymers that play a critical role in plant development and responses to environmental changes. Thus, improving plant biomass and fitness requires the elucidation of the structural organization of plant cell walls in their native environment. The 13 C-based multi-dimensional solid-state nuclear magnetic resonance (ssNMR) has been instrumental in revealing the structural information of plant cell walls through 2D and 3D correlation spectral analyses. However, the requirement of enriching plants with 13 C limits the applicability of this method. To our knowledge, there is only a very limited set of methods currently available that achieve high levels of 13 C-labeling of plant materials using 13 CO 2 , and most of them require large amounts of 13 CO 2 in larger growth chambers. In this study, a simplified protocol for 13C-labeling of plant materials is introduced that allows ca 60% labeling of the cell walls, as quantified by comparison with commercially labeled samples. This level of 13 C-enrichment is sufficient for all conventional 2D and 3D correlation ssNMR experiments for detailed analysis of plant cell wall structure. The protocol is based on a convenient and easy setup to supply both 13 C-labeled glucose and 13 CO 2 using a vacuum-desiccator. The protocol does not require large amounts of 13 CO 2 . This study shows that our 13 C-labeling of plant materials can make the accessibility to ssNMR technique easy and affordable. The derived high-resolution 2D and 3D correlation spectra are used to extract structural information of plant cell walls. This helps to better understand the influence of polysaccharide-polysaccharide interaction on plant performance and allows for a more precise parametrization of plant cell wall models.

09 BIOMASS FUELS↗