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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 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↗
NASA NTRS1972

Nuclear magnetic resonance properties of lunar samples.

Nuclear magnetic resonance spectra of Na-23, Al-27, and P-31 in fines samples 10084,60 and 14163,168 and in crystalline rock samples 12021,55 and 14321,166, have been recorded over a range of frequencies up to 20 MHz. A shift in the field at which maximum absorption occurs for all of the spectra relative to the field at which maximum absorption occurs for terrestrial analogues is attributed to a sample-dependent magnetic field at the Na, Al, and P sites opposing the laboratory field. The magnitude of these fields internal to the samples is sample dependent and varies from 5 to 10 G. These fields do not correlate with the iron content of the samples. However, the presence of single-domain particles of iron distributed throughout the plagioclase fraction that contains the principal fraction of Na and Al is inferred from electron magnetic resonance spectra shapes.

Kline, D.↗
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↗
NASA NTRS1972

Wide-range nuclear magnetic resonance detector

Compact and easy to use solid state nuclear magnetic resonance detector is designed for measuring field strength to 20 teslas in cryogenically cooled magnets. Extremely low noise and high sensitivity make detector applicable to nearly all types of analytical nuclear magnetic resonance measurements and can be used in high temperature and radiation environments.

Sturman, J. C.↗
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↗
NASA NTRS1972

C-13 nuclear magnetic resonance in organic geochemistry.

Study of C-13 nuclear magnetic resonance (NMR) spectra of polycyclic fused systems. The fingerprint qualities of the natural abundance in C-13 NMR spectra permitting unequivocal identification of these compounds is discussed. The principle of structural additivity of C-13 NMR information is exemplified on alpha and beta androstanes, alpha and beta cholestanes, ergostanes, sitostanes, and isodecanes.

Balogh, B.↗
NASA NTRS1967

An improved nuclear magnetic resonance spectrometer

Cylindrical sample container provides a high degree of nuclear stabilization to a nuclear magnetic resonance /nmr/ spectrometer. It is placed coaxially about the nmr insert and contains reference sample that gives a signal suitable for locking the field and frequency of an nmr spectrometer with a simple audio modulation system.

Elleman, D. D.↗