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NASA NTRS1994

Rapid X-ray spectral variability in NGC 3227

We present preliminary results of an ASCA observation of the Seyfert 1 galaxy, NGC 3227. The source exhibits rapid X-ray amplitude and spectral variability, the flux below 2 keV varying by a factor of approximately 3 in approximately 10,000 s while the flux in the 2-10 keV band varies by a factor of approximately 2 in the same interval. The spectrum below approximately 1 keV shows complex structure compared to a simple power-law model. We argue that the simplest interpretation of the spectrum is in terms of a power-law continuum modified by absorption in a photoionized medium. Simple, static, ionized absorber models yield an ionization parameter of approximately 0.05 and column density approximately 3.6 x 10(exp 21)/sq cm. However, the data strongly indicate that the situation is much more complex than this. If the spectral variability is caused by a changing ionization state of the absorber, then both the ionization state and column density are required to decrease as the intrinsic source luminosity increases. This does not have a simple physical interpretation. On the other hand, the data are also consistent with the spectral variability being due to changes in the intrinsic power-law index with little change in the ionization state of the absorber. This case could correspond to an absorber which is always in some average ionization state and the continuum variability is too fast for the absorber to deviate significantly from that, or the absorber could be in the form of an X-ray-heated wind.

Ptak, A.↗

NASA NTRS1984

Evaluation of modal testing methods

Modal tests are playing an increasingly important role in structural dynamics efforts which are in need of analytical model verification or trouble shootings. In the meantime, the existing modal testing methods are undergoing great changes as well as new methods are being created. Although devoted advocates of each method can be found to argue the relative advantages and disadvantages, the general superiority, if any, of one or the other is not yet evident. The Galileo spacecraft, a realistic, complex structural system, will be used as a test article for performing modal tests by various methods. The results will be used to evaluate the relative merits of the various modal testing methods.

Chen, J.-C.↗

NASA NTRS2021

Recent Advancements in Modeling and Simulation of Entry Systems at NASA

This paper describes recent development of modeling and simulation technologies for entry systems in support of NASA’s exploration missions. Mission-tailored research and development in modeling of entry systems occurs across the Agency (e.g., within the Orion and Mars 2020 Programs), however the aim of this paper is to discuss the broad, cross-mission research conducted by NASA’s Entry Systems Modeling (ESM) Project, which serves as the Agency’s only concerted effort toward advancing entry systems across a range of technical disciplines. Technology development in ESM is organized and prioritized from a system-level perspective, resulting in four broad technical areas of investment: (1) Predictive material modeling, (2) Shock layer kinetics and radiation, (3) Computational and experimental aerosciences, and (4) Guidance, navigation, and control. Investments in thermal protection material modeling are geared toward high-fidelity, predictive models capable of handling complex structures, with an eye toward optimizing design performance and quantifying thermal protection system reliability. New computational tools have been developed to characterize material properties and behavior at the microstructural level, and experimental techniques (molecular beam scattering, micro-computed tomography, among others) have been developed to measure material kinetics, morphology, and other parameters needed to inform and validate detailed simulations. Advancements have also been made in macrostructural simulation capability to enable 3-D system-scale calculations of material response with complex topological features, including differential recession of tile gaps. Research and development in the area of shock layer kinetics has focused on air and CO2-based atmospheres. Capacity and capability of the NASA Ames Electric Arc Shock Tube (EAST) have been expanded in recent years and analysis of resulting data has led to several improvements in kinetic models, while simultaneously reducing uncertainties associated with radiative heat transfer predictions. First-principles calculations of fundamental kinetic, thermodynamic, and transport data, along with state-specificmodels for non-equilibrium flow regimes, have also yielded new insights and have the potential to vastly improve model fidelity. Aerosciences is a very broad area of interest in entry systems, yet a number of important challenges are being addressed: Coupled fluid-structure simulations of parachute inflation and dynamics; Experimental and computational studies of vehicle dynamics; Multi-phase flow with dust particles to simulate entry environments at Mars during dust storms; Studies of roughness-induced heating augmentation relevant to tiled and woven thermal protection systems; and Advanced numerical methods to optimize computational analyses for desired accuracy versus cost. Guidance and control in the context of entry systems has focused on development of methods for multi-axis control (i.e. pitch and yaw, rather than bank angle alone) of spacecraft during entry and descent. With precision landing requirements driven by Mars human exploration goals, recent efforts have yielded 6-DOF models of multi-axis control with propulsive descent of both inflatable and rigid ellipsled-like architectures. Results for both configurations have demonstrated the ability to land within the 50-meter precision requirement demanded by Mars human exploration missions, while also reducing propellant requirements by enabling more efficient control through entry and descent. Ongoing research in GN&C is developing mechanical specifications for the systems and establishing engineering feasibility.

aerothermodynamics↗

DOE OSTI2023

Operando studies reveal active Cu nanograins for CO 2 electroreduction

Carbon dioxide electroreduction facilitates the sustainable synthesis of fuels and chemicals. Although Cu enables CO 2 -to-multicarbon product (C 2+ ) conversion, the nature of the active sites under operating conditions remains elusive. Importantly, identifying active sites of high-performance Cu nanocatalysts necessitates nanoscale, time-resolved operando techniques. Here, we present a comprehensive investigation of the structural dynamics during the life cycle of Cu nanocatalysts. A 7 nm Cu nanoparticle ensemble evolves into metallic Cu nanograins during electrolysis before complete oxidation to single-crystal Cu 2 O nanocubes following post-electrolysis air exposure. Operando analytical and four-dimensional electrochemical liquid-cell scanning transmission electron microscopy shows the presence of metallic Cu nanograins under CO 2 reduction conditions. Correlated high-energy-resolution time-resolved X-ray spectroscopy suggests that metallic Cu, rich in nanograin boundaries, supports undercoordinated active sites for C–C coupling. Quantitative structure–activity correlation shows that a higher fraction of metallic Cu nanograins leads to higher C 2+ selectivity. A 7 nm Cu nanoparticle ensemble, with a unity fraction of active Cu nanograins, exhibits sixfold higher C 2+ selectivity than the 18 nm counterpart with one-third of active Cu nanograins. Importantly, the correlation of multimodal operando techniques serves as a powerful platform to advance our fundamental understanding of the complex structural evolution of nanocatalysts under electrochemical conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

STILGAR: Subsurface Models for Graymont Pleasant Gap Mine

The detection, location, and monitoring of underground structures are of great importance to national and global security. Tunnels and voids generate seismic signatures detectable at the surface, but using non-invasive seismic data to image near-surface presents several challenges in real-world applications. In this report, we describe the use of a dense surface seismic deployment to generate subsurface models of the Graymont Pleasant Gap mine - a single-layer mine with a complex structure embedded in a high-velocity P-wave limestone bedrock. Our approach consists of three key methods. We use P-wave arrival times from local blast events to perform a tomography inversion with the tomoTD method, constructing a P-wave velocity model of the subsurface. We model the layer above the mine using Rayleigh wave ellipticity and inversion techniques. We leverage ongoing anthropogenic activities to identify and locate noise sources both on the surface and within the subsurface. With this integrated approach we aim to overcome the challenges and enhance our ability to non-invasively characterize underground structures, contributing to improved seismic monitoring techniques.

58 GEOSCIENCES↗

NASA NTRS2019

Recent Advancements in Modeling and Simulation of Entry Systems at NASA

This paper describes recent development of modeling and simulation technologies for entry systems in support of NASA's exploration missions. Mission-tailored research and development in modeling of entry systems occurs across the Agency (e.g., within the Orion and Mars 2020 Programs), however the aim of this paper is to discuss the broad, cross-mission research conducted by NASA's Entry Systems Modeling (ESM) Project, which serves as the Agency's only concerted effort toward advancing entry systems across a range of technical disciplines. Technology development in ESM is organized and prioritized from a system-level perspective, resulting in four broad technical areas of investment: (1) Predictive material modeling, (2) Shock layer kinetics and radiation, (3) Computational and experimental aerosciences, and (4) Guidance, navigation, and control. Investments in thermal protection material modeling are geared toward high-fidelity, predictive models capable of handling complex structures, with an eye toward optimizing design performance and quantifying thermal protection system reliability. New computational tools have been developed to characterize material properties and behavior at the microstructural level, and experimental techniques (molecular beam scattering, micro-computed tomography, among others) have been developed to measure material kinetics, morphology, and other parameters needed to inform and validate detailed simulations. Advancements have also been made in macrostructural simulation capability to enable 3-D system-scale calculations of material response with complex topological features, including differential recession of tile gaps. Research and development in the area of shock layer kinetics has focused on air and CO2-based atmospheres. Capacity and capability of the NASA Ames Electric Arc Shock Tube (EAST) have been expanded in recent years and analysis of resulting data has led to several improvements in kinetic models, while simultaneously reducing uncertainties associated with radiative heat transfer predictions. First-principles calculations of fundamental kinetic, thermodynamic, and transport data, along with state-specific models for non-equilibrium flow regimes, have also yielded new insights and have the potential to vastly improve model fidelity. Aerosciences is a very broad area of interest in entry systems, yet a number of important challenges are being addressed: Coupled fluid-structure simulations of parachute inflation and dynamics; Experimental and computational studies of vehicle dynamics; Multi-phase flow with dust particles to simulate entry environments at Mars during dust storms; Studies of roughness-induced heating augmentation relevant to tiled and woven thermal protection systems; and Advanced numerical methods to optimize computational analyses for desired accuracy versus cost. Guidance and control in the context of entry systems has focused on development of methods for multi-axis control (i.e. pitch and yaw, rather than bank angle alone) of spacecraft during entry and descent. With precision landing requirements driven by Mars human exploration goals, recent efforts have yielded 6-DOF models of multi-axis control with propulsive descent of both inflatable and rigid ellipsled-like architectures.

Barnhardt, Michael D.↗

DOE OSTI2014

Anomalous wave structure in magnetized materials described by non-convex equations of state

We analyze the anomalous wave structure appearing in flow dynamics under the influence of magnetic field in materials described by non-ideal equations of state. We consider the system of magnetohydrodynamics equations closed by a general equation of state (EOS) and propose a complete spectral decomposition of the fluxes that allows us to derive an expression of the nonlinearity factor as the mathematical tool to determine the nature of the wave phenomena. We prove that the possible formation of non-classical wave structure is determined by both the thermodynamic properties of the material and the magnetic field as well as its possible rotation. We demonstrate that phase transitions induced by material properties do not necessarily imply the loss of genuine nonlinearity of the wavefields as is the case in classical hydrodynamics. The analytical expression of the nonlinearity factor allows us to determine the specific amount of magnetic field necessary to prevent formation of complex structure induced by phase transition in the material. We illustrate our analytical approach by considering two non-convex EOS that exhibit phase transitions and anomalous behavior in the evolution. We present numerical experiments validating the analysis performed through a set of one-dimensional Riemann problems. In the examples we show how to determine the appropriate amount of magnetic field in the initial conditions of the Riemann problem to transform a thermodynamic composite wave into a simple nonlinear wave.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2011

Exploring the Fate of Nitrogen Heterocycles in Complex Prebiotic Mixtures

A long standing question in the field of prebiotic chemistry is the origin of the genetic macromolecules DNA and RNA. DNA and RNA have very complex structures with repeating subunits of nucleotides, which are composed of nucleobases (nitrogen heterocycles) connected to sugar-phosphate. Due to the instability of some nucleobases (e.g. cytosine), difficulty of synthesis and instability of D-ribose, and the likely scarcity of polyphosphates necessary for the modern nucleotides, alternative nucleotides have been proposed for constructing the first genetic material. Thus, we have begun to investigate the chemistry of nitrogen heterocycles in plausible, complex prebiotic mixtures in an effort to identify robust reactions and potential alternative nucleotides. We have taken a complex prebiotic mixture produced by a spark discharge acting on a gas mixture of N2, CO2, CH4, and H2, and reacted it with four nitrogen heterocycles: uracil, 5-hydroxymethyluracil, guanine, and isoxanthopterin (2-amino-4,7-dihydroxypteridine). The products of the reaction between the spark mixture and each nitrogen heterocycle were characterized by liquid chromatography coupled to UV spectroscopy and Orbitrap mass spectrometry. We found that the reaction between the spark mixtUl'e and isoxanthopterin formed one major product, which was a cyanide adduct. 5-hydroxymethyluracil also reacted with the spark mixture to form a cyanide adduct, uracil-5-acetonitrile, which has been synthesized previously by reacting HCN with S-hydroxymethyluracil. Unlike isoxanthopterin, the chromatogram of the 5-hydroxymethyluracil reaction was much more complex with multiple products including spark-modified dimers. Additionally, we observed that HMU readily self-polymerizes in solution to a variety of oligomers consistent with those suggested by Cleaves. Guanine and uracil, the biological nucleobases, did not react with the spark mixture, even at high temperature (100 C). This suggests that there are alternative nucleobases which are more reactive under prebiotic conditions and may have been involved in producing precursor nucleotides.

Smith, Karen E.↗

NASA NTRS1993

Nonlinear random response prediction using MSC/NASTRAN

An equivalent linearization technique was incorporated into MSC/NASTRAN to predict the nonlinear random response of structures by means of Direct Matrix Abstract Programming (DMAP) modifications and inclusion of the nonlinear differential stiffness module inside the iteration loop. An iterative process was used to determine the rms displacements. Numerical results obtained for validation on simple plates and beams are in good agreement with existing solutions in both the linear and linearized regions. The versatility of the implementation will enable the analyst to determine the nonlinear random responses for complex structures under combined loads. The thermo-acoustic response of a hexagonal thermal protection system panel is used to highlight some of the features of the program.

Robinson, J. H.↗

NASA NTRS1999

Automatic Inspection of Aircraft Using Open-Architecture Robotic Platforms

Aging aircraft structures and composite materials are requiring rapid inspection of large areas. Removal from the aircraft for NDE at an inspection facility is not economical as compared to field inspection. Detection and characterization of defects are labor intensive, time consuming and when the process is manual the results are subjected to human error. These limitations of NDE created a need for portable, user friendly inspection systems that can rapidly scan large areas of complex structures and locate all the detrimental material conditions. Addressing this need has been an evolutionary process that followed the technology trend, and unique devices were developed to allow rapid field inspection. This development involves multi-disciplinary approaches to integrate NDE, telerobotics, neural networks, advanced materials science, imbedded computing and automated control. These efforts have led to the development of various portable inspection systems and the current trend is towards fully automatic systems that will operate autonomously.

Bar-Cohen, Yoseph↗

NASA NTRS2022

A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing

Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning methods, especially deep learning (DL), across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in Earth systems. A difficult test for DL-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner. A DL emulation that passes this test may be expected to perform even better than simple models with respect to capturing complex processes and spatiotemporal dependencies. Here, we examine, with a case study in satellite-based remote sensing, the hypothesis that DL approaches can credibly represent the simulations from a surrogate model with comparable computational efficiency. Our results are encouraging in that the DL emulation reproduces the results with acceptable accuracy and often even faster performance. We discuss the broader implications of our results in light of the pace of improvements in high-performance implementations of DL and the growing desire for higher resolution simulations in the Earth sciences.

Bayesian Deep Learning↗

DOE OSTI2025

Radiation‐Resistant Aluminum Alloy for Space Missions in the Extreme Environment of the Solar System

Future human exploration of the solar system demands advanced materials capable of withstanding extreme environments, particularly exposure to solar energetic particle radiation. Current material selection criteria for space applications prioritize a high strength-to-weight ratio, high corrosion resistance and manufacturability, favoring age-hardenable Al-based alloys. However, conventional precipitation-hardened Al alloys suffer from irradiation-assisted dissolution of strengthening phases at doses as low as 0.2 displacements-per-atom (dpa), undermining their performance. Furthermore, these alloys develop radiation-induced defects, such as dislocation loops and voids, even at low doses. This study presents a novel ultrafine-grained (UFG) Al-based alloy, designed using the crossover alloying concept and strengthened by T-phase precipitates, featuring a chemically-complex structure with 162 atoms in its unit cell composed of Mg 32 (Zn,Al) 49 . It is showed that T-phase precipitates have exceptional radiation tolerance up to 24 dpa. Owing to the nanoscale UFG structure, dislocation loops are suppressed, and voids are only observed beyond 75 dpa. Microtensile tests up to 20 dpa confirm the preservation of mechanical performance under irradiation. The results underline the potential of this alloy as a radiation-resistant, lightweight material for future space applications. Three key strategies enable this performance: (i) stabilization of a UFG microstructure, (ii) T-phase precipitation featuring a highly negative Gibbs free energy and chemically-complex giant unit cell, and (iii) precise process control to prevent grain growth during heat treatment and irradiation.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Striped Data Analysis Framework

A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. We have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.

Gutsche, Oliver↗

DOE OSTI2018

Striped Data Server for Scalable Parallel Data Analysis

A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. As a result, we have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2026

Poisson-response Tensor-on-Tensor Regression and Applications

We introduce Poisson-response tensor-on-tensor regression (PToTR), a novel regression framework designed to handle tensor responses composed element-wise of random Poisson-distributed counts. Tensors, or multi-dimensional arrays, composed of counts are common data in fields such as inter national relations, social networks, epidemiology, and medical imaging, where events occur across multiple dimensions like time, location, and dyads. PToTR accommodates such tensor responses alongside tensor covariates, providing a versatile tool for multi dimensional data analysis. We propose algorithms for maximum likelihood estimation under a canonical polyadic (CP) structure on the regression coefficient tensor that satisfy the positivity of Poisson parameters and then provide an initial theoretical error analysis for PToTR estimators. We also demonstrate the utility of PToTR through three concrete applications: longitudinal data analysis of the Integrated Crisis Early Warning System database, positron emission tomography (PET) image reconstruction, and change-point detection of communication patterns in longitudinal dyadic data. These applications highlight the versatility of PToTR in addressing complex, structured count data across various domains.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Active Learning for Metamaterial Optimization on HPC and QC Integrated Systems

Active learning algorithms, integrating machine learning, quantum computing and optics simulation in an iterative loop, offer a promising approach to optimizing metamaterials. However, these algorithms can face difficulties in optimizing highly complex structures due to computational limitations. High-performance computing (HPC) and quantum computing (QC) integrated systems can address these issues by enabling parallel computing. In this study, we develop an active learning algorithm working on HPC-QC integrated systems. We evaluate the performance of optimization processes within active learning (i.e., training a machine learning model, problem-solving with quantum computing, and evaluating optical properties through wave-optics simulation) for highly complex metamaterial cases. Our results showcase that utilizing multiple cores on the integrated system can significantly reduce computational time, thereby enhancing the efficiency of optimization processes. Therefore, we expect that leveraging HPC-QC integrated systems helps effectively tackle large-scale optimization challenges in general.

Kim, Seongmin↗

DOE OSTI2024

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network

Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024

Zhang, Hengrui (ORCID:0000000231831654)↗

DOE OSTI2020

A review of the application of 2D isotropic-anisotropic correlation NMR spectroscopy in structural studies of chalcogenide glasses

The application of solid-state high-resolution NMR spectroscopy in the structural investigation of chalcogenide glasses in Ge/As/P/Si-X (X = S, Se,Te) systems has remained challenging even for the spin-1/2 nuclides ( 29 Si, 31 P, 77 Se, 125 Te), owing to their low natural abundance (except for 31 P), slow spin-lattice relaxation rate and large CSA and chemical shift distribution induced line broadening effects. However, most of these deleterious effects can be successfully overcome in two-dimensional (2D) isotropic-anisotropic correlation NMR experiments, especially when performed at high magnetic field and in conjunction with the Car-Purcell-Meiboom-Gill (CPMG) echo train acquisition. Here, we present a short introduction to the basic principles of such experiments and review their applications over the last decade in deciphering various short- and intermediate- range structural characteristics of chalcogenide glasses in S-Se, Se-Te, Ge-Se, As-Se and Si-Se systems as well as in investigating the molecular dynamics in a P-Se supercooled liquid. We anticipate possible future applications of these 2D isotropic-anisotropic correlation NMR experiments, particularly in conjunction with density functional theory-based calculations of NMR chemical shift tensor parameters and additional signal enhancement schemes, in addressing complex structural correlations and distributions in chalcogenide glasses.

2D NMR↗