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At least 19 records

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↗

MEMS-Based Force-Detected Nuclear Magnetic Resonance (FDNMR) Spectrometer

Nuclear Magnetic Resonance (NMR) spectroscopy allows assignment of molecular structure by acquiring the energy spectrum of nuclear spins in a molecule, and by interpreting the symmetry and positions of resonance lines in the spectrum. As such, NMR has become one of the most versatile and ubiquitous spectroscopic methods. Despite these tremendous successes, NMR experiments suffer from inherent low sensitivity due to the relatively low energy of photons in the radio frequency (rt) region of the electromagnetic spectrum. Here, we describe a high-resolution spectroscopy in samples with diameters in the micron range and below. We have reported design and fabrication of force-detected nuclear magnetic resonance (FDNMR).

force detected nuclear magnetic resonance (FDNMR)↗

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↗

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.↗

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↗

Measuring surface-area-to-volume ratios in soft porous materials using laser-polarized xenon interphase exchange nuclear magnetic resonance

We demonstrate a minimally invasive nuclear magnetic resonance (NMR) technique that enables determination of the surface-area-to-volume ratio (S/V) of soft porous materials from measurements of the diffusive exchange of laser-polarized 129Xe between gas in the pore space and 129Xe dissolved in the solid phase. We apply this NMR technique to porous polymer samples and find approximate agreement with destructive stereological measurements of S/V obtained with optical confocal microscopy. Potential applications of laser-polarized xenon interphase exchange NMR include measurements of in vivo lung function in humans and characterization of gas chromatography columns.

Non-NASA Center↗

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.↗

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↗

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↗

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.↗

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.↗