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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

End-to-End Automated Segmentation Framework for Four-Dimensional Scanning Transmission Electron Microscopy Data

Four-dimensional scanning transmission electron microscopy (4D-STEM) is powerful for rapidly characterizing arrays of nanoparticles produced via high-throughput synthesis. However, such 4D-STEM datasets typically contain thousands of nanoparticles, each characterized by thousands of diffraction patterns spatially distributed across the nanoparticle, necessitating efficient and comprehensive analysis. We propose an end-to-end segmentation framework to automatically segment each nanoparticle into regions with distinct composition/orientation of crystal grains, using only the 4D-STEM data. Bragg disk information is extracted in a physics-informed manner from the diffraction patterns at each spatial location and combined with the real space coordinates to form feature vectors. These feature vectors are then used as inputs to a Gaussian mixture model (GMM) to segment the nanoparticle into distinct regions. We also develop two visualization tools based on the GMM outputs to infer the interface transition and the degree of superposition. Our framework comprehensively integrates machine learning tools and physics knowledge, and provides a basis for substantially compressing enormous 4D-STEM datasets, e.g., by replacing the full 4D-STEM dataset for each nanoparticle with only a single set of Bragg disk features for each distinct crystal grain identified in the nanoparticle. In this article, we demonstrate the power of our framework by presenting results for real, complex datasets.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

The DESI DR1 Peculiar Velocity Survey: Growth Rate Measurements from the Galaxy Power Spectrum

The large-scale structure of the Universe and its evolution encapsulate a wealth of cosmological information. A powerful means of unlocking this knowledge lies in measuring the auto-power spectrum and/or the cross-power spectrum of the galaxy density and momentum fields, followed by the estimation of cosmological parameters based on these spectrum measurements. In this study, we generalize the cross-power spectrum model to accommodate scenarios where the density and momentum fields are derived from distinct galaxy surveys. The growth rate of the large-scale structures of the Universe, commonly represented as $fσ_8$, is extracted by jointly fitting the monopole and quadrupole moments of the auto-density power spectrum, the monopole of the auto-momentum power spectrum, and the dipole of the cross-power spectrum. Our estimators, theoretical models and parameter-fitting framework have been tested using mocks, confirming their robustness and accuracy in retrieving the fiducial growth rate from simulation. These techniques are then applied to analyze the power spectrum of the DESI Bright Galaxy Survey and Peculiar Velocity Survey, and the fit result of the growth rate is $fσ_8=0.440^{+0.080}_{-0.096}$ at effective redshift $z_{\rm eff}=0.07$. By synthesizing the fitting outcomes from correlation functions, maximum likelihood estimation and power spectrum, yields a consensus value of $fσ_8(z_{\rm eff}=0.07) = 0.450 ^{+0.055}_{-0.055}$, and correspondingly we obtain $γ=0.580^{+0.110}_{-0.110}$, $Ω_\mathrm{m}=0.301^{+0.011}_{-0.011}$ and $σ_8=0.834^{+0.032}_{-0.032}$. The measured $fσ_8$ and $γ$ are consistent with the prediction of the $Λ$ Cold Dark Matter Model and General Relativity.

Qin, F. [Marseille, CPPM] (ORCID:0000000179507864)↗
DOE OSTI2020

Oxidation of soil organic carbon during an anoxic-oxic transition

Redox reactions of iron (Fe) can play an important role in controlling the stability and transformation of organic carbon (OC) in soils. However, there is limited knowledge about the dynamics of Fe and OC in soils during anoxic-oxic transitions, which are common in temperate and tropical biomes. In this study, we investigated the fate and transformation of Fe and OC during a 5-day oxic incubation of four pre-reduced (8 days anoxic) forest soils. Up to 3.7% of total OC in the pre-reduced soils was oxidized to CO 2 , in conjunction with the oxidation of 31 –84% of the extractable Fe(II). OC oxidation in pre-reduced soils was lower than non-reducedcontrol soils, indicating that recently anoxically-incubated soils support lower OC metabolism upon re-oxidation than consistently oxic soils. Oxic OC oxidation was negatively correlated with the fraction of Fe-bound OC and also the fraction of short-range-ordered (SRO) Fe oxides. These results suggest that association with Fe oxides—especially SRO Fe oxides—can inhibit the availability of OC for oxidation. However, the difference between the oxidation of OC in pre40 reduced soils and consistently oxic controls could not be linked to the moderate changes in the Fe mineral composition during the anoxic-oxic transition, indicating the importance of longer term OC association with Fe oxides, as well as other processes, in regulating the oxidation of OC during the redox fluctuations. OC association with Fe oxides should be considered during simulations of biogeochemical cycling of OC in soils during the redox fluctuations, for which other processes such as the anoxic transformation of OC and response of the microbial community also need to be incorporated.

58 GEOSCIENCES↗
DOE OSTI2026

The DESI DR1 peculiar velocity survey: Growth rate measurements from the galaxy power spectrum

The large-scale structure of the Universe and its evolution encapsulate a wealth of cosmological information. A powerful means of unlocking this knowledge lies in measuring the auto-power spectrum and/or the cross-power spectrum of the galaxy density and momentum fields, followed by the estimation of cosmological parameters based on these spectrum measurements. In this study, we generalize the cross-power spectrum model to accommodate scenarios in which the density and momentum fields are derived from distinct galaxy surveys. The growth rate of the large-scale structures of the Universe, commonly represented as fσ 8 , was extracted by jointly fitting the monopole and quadrupole moments of the auto-density power spectrum, the monopole of the auto-momentum power spectrum, and the dipole of the cross-power spectrum. Our estimators, theoretical models, and parameter-fitting framework were tested using mocks, confirming their robustness and accuracy in retrieving the fiducial growth rate from simulation. These techniques were then applied to analyse the power spectrum of the DESI Bright Galaxy Survey and Peculiar Velocity Survey. The fit result of the growth rate is fσ8 = 0.440$^{+0.080}_{−0.096}$ at effective redshift zeff = 0.07. By synthesizing the fitting outcomes from correlation functions, maximum likelihood estimation, and the power spectrum, a consensus value is yielded of fσ 8 (z eff = 0.07) = 0.450$^{+0.055}_{−0.055}$, and correspondingly we obtain γ = 0.580$^{+0.110}_{−0.110}$, Ω m = 0.301$^{+0.011}_{−0.011}$, and σ 8 = 0.834$^{+0.032}_{−0.032}$. The measured fσ8 and γ are consistent with the prediction of the Λ cold dark matter model and general relativity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

An Automatic Processing Framework for In Situ Determination of Ecohydrological Root Water Content by Ground-Penetrating Radar

This report concerns Root water content (RWC) as a vital component in water flux in soil-plant-atmosphere continuum. Knowledge of RWC helps to better understand the root function and the soil-root interaction and improves water cycle modeling. However, due to the lack of appropriate methods, field monitoring of RWC is seriously constrained. In this study, we used ground-penetrating radar (GPR), a common geophysical technique, to characterize RWC of coarse roots noninvasively. An automatic GPR data processing framework was proposed to (1) identify hyperbolic root reflections and locate roots in GPR images and (2) extract waveform parameters from the reflected wave of identified roots. These waveform parameters were then used to establish an empirical model and a semiempirical model to determine RWC. We validated the developed models using GPR root data at three antenna center frequencies (500 MHz, 900 MHz, and 2 GHz) that were produced from simulation experiments (with RWC ranging from 70% to 150%) and field experiments in sandy soils (with RWC ranging from 66% to 144%). Our results show that both the empirical and the semiempirical models achieved a good performance in estimating RWC with similar accuracy, i.e., the prediction error [root-mean-square error (RMSE)] was less than 8% for the simulation data and 12% for the field data. For both models, the accuracy of RWC estimation was the highest when applied to 2-GHz data. This study renders a new opportunity to determine RWC under field conditions that enhances the application of GPR for root study and the understanding and modeling of ecohydrology in the rhizosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Iron and Phosphorus Geochemistry in High-Centered and Low-Centered Polygon Soils from the Barrow Environmental Observatory, Utqiagvik, Alaska, 2015

This dataset provides chemical characterization of soil cores obtained in triplicate from center and trough positions of a high-centered polygon and center, ridge, and trough positions of a low-centered polygon in the NGEE Arctic research area within the Barrow Environmental Observatory, Utqiagvik, Alaska. Photographs of the 15 collected cores are provided. Cores were collected to thaw depth in early October 2015 and subsequently divided into subsamples for analysis, which included bulk soil horizons (organic or mineral) and finer scale depth increments (minimum 4 cm thickness per increment). Bulk horizons and depth increments were characterized using sequential chemical extractions for Fe and P and with Fe K-edge x-ray absorption spectroscopy. Additional soil parameters such as loss-on-ignition, gravimetric water content, and carbon and nitrogen concentrations were also quantified for bulk horizons. This dataset includes four csv files, one pdf user guide, and one zip folder of *.jpg photos.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Probing oil recovery in shale nanopores with small-angle and ultra-small-angle neutron scattering

Increasing oil production from unconventional shale reservoirs is crucial to meet growing energy demands while achieving lower carbon emission than conventional crude oil. Enhanced oil recovery (EOR) has been proposed to improve hydrocarbon recovery rates through the injection of a fluid into the reservoir to facilitate residual oil release from the shale formation. However, economical and sustainable implementation of EOR requires advanced knowledge of fluid behavior in nano-sized pore spaces in shale. In this study, we utilize small-angle neutron scattering (SANS) and ultra-small-angle neutron scattering (USANS) as experimental probes to examine decane removal from a shale nano- to micro-porous matrix, utilizing methane as the injectant. The extent of decane saturation and recovery post-methane pressurization is quantified for clay-rich and carbonate-rich shale samples. A key finding is that extraction of decane by methane on depressurization is related to the methane-decane critical point. Furthermore, we found that although clay-rich shale had a much higher porosity of 5.6%, compared with 1.2% for carbonate-rich shale, decane was more easily removed from the carbonate-rich matrix, leading to similar hydrocarbon yields. These promising results demonstrate the ability of SANS and USANS to provide key insights into oil recovery from nano- to micron-sized pores in shale matrices. Combined with effects of various fractures on fluid behavior in shale, this experimental technique can be used to assess the viability of EOR injectants.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2022

Framework for the Evolution of Heuristics in Advanced Manufacturing

Here, in this study, we work toward addressing a knowledge gap in understanding how heuristics are developed, retrieved, employed, and modified by designers. Having a better awareness of one’s own set of heuristics can be beneficial for relaying to other team members, improving a team’s training processes, and aiding others on their path to design expertise. The ability to understand and justify the use of a heuristic should lead to more effective decision-making in systems design. To do this, the heuristics and their characteristics must be extracted using a repeatable scientific research methodology. This study describes a unique extraction and characterization process compared to prior literature. It includes some of the first work towards documenting heuristics for both designers and operators in a hybrid manufacturing setting. Eight participants performed a series of two design journals, two interviews, and one survey. Heuristics were extracted and refined between each method and then verified by participants in the survey. The surveys produced novel statistically significant findings in regard to heuristic characterizations, impacting how participants view how often a heuristic is used, the reliability of the heuristic, and the evolution of the heuristic. Lastly, an alternate perspective of heuristics as an error management bias is highlighted and discussed.

42 ENGINEERING↗
DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
DOE OSTI2020

Structure of the Neutron with Quasi-Elastic Scattering from 3H and 3HE

The magnetic form factor characterizes the magnetic distribution inside the neutron and knowledge of it is essential for the understanding of the neutron structure. The E12-11-112 experiment aimed to measure the neutron magnetic form factor (GnM) from the 3H/3He cross sections ratio, in the Q2 range of 0.5 - 3 GeV2. Quasi-elastic (QE) electron scattering was used to probe nucleons on the nucleus of the mirror 3H and 3He nuclei, in Hall A at the Thomas Jefferson National Accelerator Facility (JLab). The use of the mirror nuclei ratio is a novel extraction that cancels several systematic uncertainties from the cross section effects in the ratio. The motivation of this measurement is the disagreement between the precision measurements among the Q2 range of 0.5 - 1 GeV2 in the worldwide data. A total of five Q2 points were taken between this region and four more were taken with a Q2>1 GeV2 in order to estimate the accuracy of the method. Ideally, the 3H/3He ratio should be enough to calculate the magnetic form factor, but due to nuclear effects, there will be a correction of up to 8 % depending on the Q2 value that has to be evaluated before the form factor extraction. The cross sections were measured with a point-to-point uncertainty of 3 - 4.5 %, and the ratios were also evaluated with an uncertainty of 1.5 - 3 %. Therefore, the preliminary studies of the Gn M are summarized, and from the present analysis, it can be concluded that the precision of the measurement in the region of interest will be of the order of 1.5%.

Santiesteban, Sandra↗
DOE OSTI2022

Multilayer interface tracking model of pure tungsten oxidation

Here, we present a numerical model to predict oxide scale growth on tungsten surfaces under exposure to oxygen at high temperatures. The model captures the formation of four thermodynamically-compatible oxide sublayers, WO 2 , WO 2.72 , WO 2.9 , and WO 3 , on top of the metal substrate. Oxide layer growth is simulated by tracking the oxide/oxide and oxide/metal interfaces using a sharp-interface Stefan model coupled to diffusion kinetics. The model is parameterized using selected experimental measurements and electronic structure calculations of the diffusivities of all the oxide subphases involved. We simulate oxide growth at temperatures of 600°C and above, extracting the power law growth exponents in each case, which we find to deviate from classical parabolic growth in several cases. We conduct a comparison of the model predictions with an extensive experimental data set, with reasonable agreement at most temperatures. While many gaps in our understanding still exist, this work is a first attempt at embedding the thermodynamic and kinetic complexity of tungsten oxide growth into a comprehensive mesoscale kinetic model that attempts to capture the essential features of tungsten oxidation to fill existing knowledge gaps and guide and enhance future tungsten oxidation models.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Circularization of 23S rRNA but not 16S rRNA within archaeal ribosomes

Background Processing of archaeal 16S and 23S rRNAs is believed to involve excision of individual rRNAs from polycistronic precursors, circularization of excised rRNAs, and re-linearization before the incorporation into ribosomes. However, all the knowledge is derived from several isolated species, leaving open the possibility that different processes may occur in other archaeal groups. Results Here, we investigate rRNAs from diverse and mostly uncultivated archaea. Sequencing of total cellular RNA from eight phylum-level lineages indicates that archaeal circular 23S rRNA transcript abundances vastly exceed those of linear counterparts, and linear versions are often undetectable. As the majority of rRNAs derive from mature ribosomes, the data suggest that ribosomes contain circular 23S rRNAs. Thus, we directly sequence RNA extracted from isolated ribosomes of a model archaeon, Methanosarcina acetivorans, and confirm that the 23S rRNAs in the ribosomes are circular. Structural modeling places the 5′ and 3′ ends of the linear precursors of archaeal 23S rRNAs in close proximity to form a GNRA tetraloop (in which N is A, C, G, or U and R is A or G), consistent with their existence as circular molecules. We also confirm the existence of circular 16S rRNA intermediates in transcriptomes of most archaea, yet a circular form is not evident in some distinct archaeal groups, suggesting that certain archaea do not circularize 16S rRNA during processing. Conclusions Our findings uncover unexpected variations in the processing required to generate mature rRNAs and the conformation of functional molecules in archaeal ribosomes.

Archaea↗
DOE OSTI2022

A meta-analysis of semantic classification of citations

The aim of this literature review is to examine the current state of the art in the area of citation classification. In particular, we investigate the approaches for characterizing citations based on their semantic type. We conduct this literature review as a meta-analysis covering 60 scholarly articles in this domain. Although we included some of the manual pioneering works in this review, more emphasis is placed on the later automated methods, which use Machine Learning and Natural Language Processing (NLP) for analyzing the fine-grained linguistic features in the surrounding text of citations. The sections are organized based on the steps involved in the pipeline for citation classification. Specifically, we explore the existing classification schemes, data sets, pre-processing methods, extraction of contextual and non-contextual features, and the different types of classifiers and evaluation approaches. The review highlights the importance of identifying the citation types for research evaluation, the challenges faced by the researchers in the process, and the existing research gaps in this field.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Statistical Complexity of Quantum Learning

Abstract Learning problems involve settings in which an algorithm has to make decisions based on data, and possibly side information such as expert knowledge. This study has two main goals. First, it reviews and generalizes different results on the data and model complexity of quantum learning, where the data and/or the algorithm can be quantum, focusing on information‐theoretic techniques. Second, it introduces the notion of copy complexity, which quantifies the number of copies of a quantum state required to achieve a target accuracy level. Copy complexity arises from the destructive nature of quantum measurements, which irreversibly alter the state to be processed, limiting the information that can be extracted about quantum data. As a result, empirical risk minimization is generally inapplicable. The paper presents novel results on the copy complexity for both training and testing. To make the paper self‐contained and approachable by different research communities, an extensive background material is provided on classical results from statistical learning theory, as well as on the distinguishability of quantum states. Throughout, the differences between quantum and classical learning are highlighted by addressing both supervised and unsupervised learning, and extensive pointers are provided to the literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

SNAD transient miner: Finding missed transient events in ZTF DR4 using k-D trees

Here we report the automatic detection of 11 transients (7 possible supernovae and 4 active galactic nuclei candidates) within the Zwicky Transient Facility fourth data release (ZTF DR4), all of them observed in 2018 and absent from public catalogs. Among these, three were not part of the ZTF alert stream. Our transient mining strategy employs 41 physically motivated features extracted from both real light curves and four simulated light curve models (SN Ia, SN II, TDE, SLSN-I). These features are input to a k-D tree algorithm, from which we calculate the 15 nearest neighbors. After pre-processing and selection cuts, our dataset contained approximately a million objects among which we visually inspected the 105 closest neighbors from seven of our brightest, most well-sampled simulations, comprising 89 unique ZTF DR4 sources. Our result illustrates the potential of coherently incorporating domain knowledge and automatic learning algorithms, which is one of the guiding principles directing the SNAD team. It also demonstrates that the ZTF DR is a suitable testing ground for data mining algorithms aiming to prepare for the next generation of astronomical data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Uncovering the linear boron environment in Na 3 BP 2 through solid-state 11 B NMR spectroscopy

Boron-based compounds exhibit a wide range of structural diversity, with potential applications spanning organic and inorganic chemistry. Herein, we focus on the characterization of the linear boron-phosphorus unit P═B═P in Na 3 BP 2 using solid-state nuclear magnetic resonance (ssNMR) spectroscopy and density functional theory (DFT) calculations. High-resolution 11 B ssNMR spectra were recorded at two fields, and key parameters such as chemical shift anisotropy (CSA), quadrupolar coupling constants (C Q ), and electric field gradient (EFG) tensors were extracted. The 11 B NMR results revealed a distinct chemical environment for the two-coordinate boron atom, with a CSA span (Ω) of 280 ppm and a C Q of 3.0 MHz. These values were further validated through periodic plane-wave DFT calculations, which showed good agreement with experimental results. The obtained spectral parameters are compared to other linear boron units, such as the BO 2 motif, providing a broader context for understanding boron coordination in inorganic compounds. This work expands the body of NMR knowledge on boron-containing materials, particularly for linear boron motifs. The findings contribute to the growing field of boron chemistry and its potential applications in advanced materials.

Porter, Andrew P. [Ames Laboratory (AMES), Ames, I↗
DOE OSTI2024

Precision Measurements of the Neutron Magnetic Form Factor to High Momentum Transfer using Durand's Method

Protons and neutrons, collectively known as nucleons, along with electrons, constitute the funda- mental building blocks of the visible universe. Understanding their internal structure is crucial for addressing key scientific questions about our origin and existence. Elastic electron-nucleon scatter- ing provides insights into the spatial distributions of charge and current within nucleons through their electromagnetic form factors. Accurate knowledge of these form factors over a broad range of Q2, the squared four-momentum transfer in the scattering process, reveals details about the nucleon’s internal structure. However, high-Q2 data of the nucleon electromagnetic form factor is scarce due to the challenges associated with such measurements. This thesis reports preliminary results from high-precision measurements of the neutron magnetic form factor (Gn M ) to unprecedented Q2 using Durand’s method, also known as the “ratio” method. Systematic errors are greatly reduced by extracting Gn M from the ratio of neutron-coincident (D(e, e'n)) to proton-coincident (D(e, e'p)) quasi-elastic electron scattering from deuteron. The scattered electrons were detected in the BigBite spectrometer, which features multiple Gas Elec- tron Multiplier (GEM) layers with large active area for high-precision tracking at very high rates. Simultaneous nucleon detection was performed by the Super BigBite spectrometer, which utilizes a dipole magnet with large solid angle acceptance at forward angles and a novel hadron calorimeter with very high and comparable detection efficiencies for both protons and neutrons. This setup could handle very high luminosity, making high-Q2 measurements feasible. Data were collected at five Q2 points: 3, 4.5, 7.4, 9.9, and 13.6 (GeV/c)2. Preliminary results are reported for all, with the lowest two Q2 points in good agreement with existing world data, while the higher points significantly extend the Q2 range in which Gn M is known accurately. The precision of the highest Q2 point is expected to remain unmatched for years to come.

Datta, Provakar↗