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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Variable stars in Local Group galaxies – V. The fast and early evolution of the low-mass Eridanus II dSph galaxy

ABSTRACT We present a detailed study of the variable star population of Eridanus II (Eri II), an ultra-faint dwarf galaxy that lies close to the Milky Way virial radius. We analyse multi-epoch g, r, i ground-based data from Goodman and the Dark Energy Camera, plus F475W, F606W, F814W space data from the Advanced Camera for Surveys. We report the detection of 67 RR Lyrae (RRL) stars and 2 Anomalous Cepheids, most of them new discoveries. With the RRL stars, we measure the distance modulus of Eri II, μ0 = 22.84 ± 0.05 mag (D⊙ = 370 ± 9 kpc), and derive a metallicity spread of 0.3 dex (0.2 dex intrinsic). The colour distribution of the horizontal branch (HB) and the period distribution of the RRL stars can be nicely reproduced by a combination of two stellar models of [Fe/H] = (−2.62, −2.14). The overall low metallicity is consistent with the red giant branch bump location, 0.65 mag brighter than the HB. These results are in agreement with previous spectroscopic studies. The more metal-rich RRL and the RRab stars have greater central concentration than the more metal-poor RRL and the RRc stars that are mainly located outside ∼1 rh. This is similar to what is found in larger dwarf galaxies such as Sculptor, and in agreement with an outside-in galaxy formation scenario. This is remarkable in such a faint dwarf galaxy with an apparently single and extremely short (<1 Gyr) star formation burst. Finally, we have derived new and independent structural parameters for Eri II and its star cluster using our new data that are in very good agreement with previous estimates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Enabling modern data discovery for atmospheric measurements

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

On the pressure effect in energetic deposition of Cu thin films by modulated pulsed power magnetron sputtering: A global plasma model and experiments

The modulated pulsed power magnetron sputtering (MPPMS) discharge processes are numerically modeled and experimentally investigated, in order to explore the effect of the pressure on MPPMS discharges as well as on the microstructure of the deposited thin films. A global plasma model has been developed based on a volume-averaged global description of the ionization region, considering the loss of electrons by cross-B diffusion. The temporal variations of internal plasma parameters at different pressures from 0.1 to 0.7 Pa are obtained by fitting the model to duplicate the experimental discharge data, and Cu thin films are deposited by MPPMS at the corresponding pressures. The surface morphology, grain size and orientation, and microstructure of the deposited thin films are investigated by scanning electron microscopy, transmission electron microscopy, and x-ray diffraction. By increasing the pressure from 0.1 to 0.7 Pa, both the ion bombardment energy and substrate temperature which are estimated by the modeled plasma parameters decrease, corresponding to the observed transition of the deposited thin films from a void free structure with a wide distribution of grain size (zone T) into an underdense structure with a fine fiber texture (zone 1) in the extended structure zone diagram (SZD). The microstructure and texture transition of Cu thin films are well-explained by the extended SZD, suggesting that the primary plasma processes are properly incorporated in the model. The results contribute to the understanding of the characteristics of MPPMS discharges, as well as its correlation with the microstructure and texture of deposited Cu thin films.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Temporal structure of blobs in NSTX

The time dependence of the blob pulse shape and the waiting time between blobs was found using data from the gas puff imaging diagnostic in NSTX. The database used was of 103 shots from 2010 as described in a previous paper (Zweben et al., Phys. Plasmas 29, 012505 (2022).]. The blob pulse shape was well fit by an exponential rise and fall where the average rise time was τ r = 9.0 ± 2.7 μs and the average fall time was τ f = 16.6 ± 5.8 μs. The waiting times between blob pulses above a threshold of three times the mean had a broad distribution with an average of τ w = 1.2 ± 0.85 ms over the database. The blob intermittency parameter γ b = τ d /τ w , where the blob pulse duration was τ d = τ r + τ f , ranged from γ b ~1% to 5% for shots in this database and increased almost linearly with the blob fraction.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space

The discovery of the molecular candidates for application in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries necessitates the development of machine learning algorithms capable of rapid exploration of chemical spaces targeting the desired functionalities. Here, we introduce a novel approach for active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data followed by introducing them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods, such as SISSO and active learning, into a single framework. The primary focus of constructing this framework is to approximate physical laws in an active learning regime toward a more robust predictive performance, as traditional evaluation on hold-out sets in machine learning does not account for out-of-distribution effects which may lead to a complete failure on unseen chemical space. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Effective Radii of Young, Massive Star Clusters in Two LEGUS Galaxies

We present a study of the effective (half-light) radii and other structural properties of a systematically selected sample of young, massive star clusters (≥5 × 10{sup 3} M{sub ⊙} and ≤200 Myr) in two nearby spiral galaxies, NGC 628 and NGC 1313. We use Hubble Space Telescope (HST) WFC3/UVIS and archival ACS/WFC data obtained by the Legacy Extragalactic UV Survey (LEGUS), an HST Treasury Program. We measure effective radii with GALFIT, a two-dimensional image-fitting package, and with a new technique to estimate effective radii from the concentration index of observed clusters. The distribution of effective radii from both techniques spans ∼0.5–10 pc and peaks at 2–3 pc for both galaxies. We find slight positive correlations between effective radius and cluster age in both galaxies, but no significant relationship between effective radius and galactocentric distance. Clusters in NGC 1313 display a mild increase in effective radius with cluster mass, but the trend disappears when the sample is divided into age bins. We show that the vast majority of the clusters in both galaxies are much older than their dynamical times, suggesting they are gravitationally bound objects. We find that about half of the clusters in NGC 628 are underfilling their Roche lobes, based on their Jacobi radii. Our results suggest that the young, massive clusters in NGC 628 and NGC 1313 are expanding, due to stellar mass loss or two-body relaxation, and are not significantly influenced by the tidal fields of their host galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Allosterically coupled conformational dynamics in solution prepare the sterol transfer protein StarD4 to release its cargo upon interaction with target membranes

Complex mechanisms regulate the cellular distribution of cholesterol, a critical component of eukaryote membranes involved in regulation of membrane protein functions directly and through the physiochemical properties of membranes. StarD4, a member of the steroidogenic acute regulator-related lipid-transfer (StART) domain (StARD)-containing protein family, is a highly efficient sterol-specific transfer protein involved in cholesterol homeostasis. Its mechanism of cargo loading and release remains unknown despite recent insights into the key role of phosphatidylinositol phosphates in modulating its interactions with target membranes. We have used large-scale atomistic Molecular dynamics (MD) simulations to study how the dynamics of cholesterol bound to the StarD4 protein can affect interaction with target membranes, and cargo delivery. We identify the two major cholesterol (CHL) binding modes in the hydrophobic pocket of StarD4, one near S136&S147 (the Ser-mode), and another closer to the putative release gate located near W171, R92&Y117 (the Trp-mode). We show that conformational changes of StarD4 associated directly with the transition between these binding modes facilitate the opening of the gate. To understand the dynamics of this connection we apply a machine-learning algorithm for the detection of rare events in MD trajectories (RED), which reveals the structural motifs involved in the opening of a front gate and a back corridor in the StarD4 structure occurring together with the spontaneous transition of CHL from the Ser-mode of binding to the Trp-mode. Further analysis of MD trajectory data with the information-theory based NbIT method reveals the allosteric network connecting the CHL binding site to the functionally important structural components of the gate and corridor. Mutations of residues in the allosteric network are shown to affect the performance of the allosteric connection. These findings outline an allosteric mechanism which prepares the CHL-bound StarD4 to release and deliver the cargo when it is bound to the target membrane.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations

TURBOMOLE is a collaborative, multi-national software development project aiming to provide highly efficient and stable computational tools for quantum chemical simulations of molecules, clusters, periodic systems, and solutions. The TURBOMOLE software suite is optimized for widely available, inexpensive, and resource-efficient hardware such as multi-core workstations and small computer clusters. TURBOMOLE specializes in electronic structure methods with outstanding accuracy–cost ratio, such as density functional theory including local hybrids and the random phase approximation (RPA), GW-Bethe–Salpeter methods, second-order Møller–Plesset theory, and explicitly correlated coupled-cluster methods. TURBOMOLE is based on Gaussian basis sets and has been pivotal for the development of many fast and low-scaling algorithms in the past three decades, such as integral-direct methods, fast multipole methods, the resolution-of-the-identity approximation, imaginary frequency integration, Laplace transform, and pair natural orbital methods. This review focuses on recent additions to TURBOMOLE’s functionality, including excited-state methods, RPA and Green’s function methods, relativistic approaches, high-order molecular properties, solvation effects, and periodic systems. A variety of illustrative applications along with accuracy and timing data are discussed. Moreover, available interfaces to users as well as other software are summarized. TURBOMOLE’s current licensing, distribution, and support model are discussed, and an overview of TURBOMOLE’s development workflow is provided. Challenges such as communication and outreach, software infrastructure, and funding are highlighted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

LiDAR-Aided Interior Orientation Parameters Refinement Strategy for Consumer-Grade Cameras Onboard UAV Remote Sensing Systems

Unmanned aerial vehicles (UAVs) are quickly emerging as a popular platform for 3D reconstruction/modeling in various applications such as precision agriculture, coastal monitoring, and emergency management. For such applications, LiDAR and frame cameras are the two most commonly used sensors for 3D mapping of the object space. For example, point clouds for the area of interest can be directly derived from LiDAR sensors onboard UAVs equipped with integrated global navigation satellite systems and inertial navigation systems (GNSS/INS). Imagery-based mapping, on the other hand, is considered to be a cost-effective and practical option and is often conducted by generating point clouds and orthophotos using structure from motion (SfM) techniques. Mapping with photogrammetric approaches requires accurate camera interior orientation parameters (IOPs), especially when direct georeferencing is utilized. Most state-of-the-art approaches for determining/refining camera IOPs depend on ground control points (GCPs). However, establishing GCPs is expensive and labor-intensive, and more importantly, the distribution and number of GCPs are usually less than optimal to provide adequate control for determining and/or refining camera IOPs. Moreover, consumer-grade cameras with unstable IOPs have been widely used for mapping applications. Therefore, in such scenarios, where frequent camera calibration or IOP refinement is required, GCP-based approaches are impractical. To eliminate the need for GCPs, this study uses LiDAR data as a reference surface to perform in situ refinement of camera IOPs. The proposed refinement strategy is conducted in three main steps. An image-based sparse point cloud is first generated via a GNSS/INS-assisted SfM strategy. Then, LiDAR points corresponding to the resultant image-based sparse point cloud are identified through an iterative plane fitting approach and are referred to as LiDAR control points (LCPs). Finally, IOPs of the utilized camera are refined through a GNSS/INS-assisted bundle adjustment procedure using LCPs. Seven datasets over two study sites with a variety of geomorphic features are used to evaluate the performance of the developed strategy. The results illustrate the ability of the proposed approach to achieve an object space absolute accuracy of 3–5 cm (i.e., 5–10 times the ground sampling distance) at a 41 m flying height.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Linear starch and hexanoic acid complexation evaluated by isothermal titration calorimetry

Linear starch form inclusion complexes with fatty acids through hydrophobic interaction. The complexation between starch and fatty acid is affected by many factors, such as starch degree of polymerization (DP), fatty acid structure, and complexation conditions. There is limited information on the use of isothermal titration calorimetry (ITC) in understanding starch‐fatty acid interactions. The effects of temperature and concentration of hexanoic acid on the complexation of debranched waxy maize starch and potato amylose were investigated using ITC. The starch samples were characterized for their molecular size distribution and iodine affinity. Potato amylose and debranched waxy maize starch displayed a major peak DP of 330 and 25, and iodine affinity of 2.9 and 13.9, respectively. The ITC titration data were fitted best to a one set of binding site model. Complexation was exothermic and spontaneous for all reactions based on changes in enthalpy of free energy (ΔG). The binding affinity ( K a ) of debranched waxy maize starch for hexanoic acid decreased with an increase in temperature from 25 to 45°C. The K a and number of binding sites ( n ) for debranched waxy maize exceeded those of potato amylose, indicating a higher complexation of debranched waxy maize for hexanoic acid. The number of binding sites ( n ) and enthalpy of binding (Δ H ) values for both starches remained independent of temperature change. The ITC results demonstrated that debranched waxy maize starch was favored over potato amylose to complex hexanoic acid.

Arijaje, Emily Oluwaseun↗
DOE OSTI2022

Designing Glass and Crystalline Phases of Metal–Bis(acetamide) Networks to Promote High Optical Contrast

Owing to their high tunability and predictable structures, metal–organic materials offer a powerful platform to study glass formation and crystallization processes and to design glasses with unique properties. In this work, we report a novel series of glass-forming metal–ethylenebis(acetamide) networks that undergo reversible glass and crystallization transitions below 200 °C. The glass-transition temperatures, crystallization kinetics, and glass stability of these materials are readily tunable, either by synthetic modification or by liquid-phase blending, to form binary glasses. Pair distribution function (PDF) analysis reveals extended structural correlations in both single and binary metal–bis(acetamide) glasses and highlights the important role of metal–metal correlations during structural evolution across glass–crystal transitions. Notably, the glass and crystalline phases of a Co–ethylenebis(acetamide) binary network feature a large reflectivity contrast ratio of 4.8 that results from changes in the local coordination environment around Co centers. These results provide new insights into glass–crystal transitions in metal–organic materials and have exciting implications for optical switching, rewritable data storage, and functional glass ceramics.

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

Systematic Bayesian evaluation of resonance parameters in 19 Ne for the 15 O ⁡(𝛼, 𝛾)⁢ 19 Ne and 18 F ⁡(𝑝, 𝛼)⁢ 15 O reactions

Here, we present a comprehensive evaluation of the nuclear structure properties of 19 Ne using a novel and rigorous Bayesian statistical framework. Precise characterization of 19 Ne resonance parameters is critical for accurately determining reaction rates of the astrophysically significant 15 O ⁡(𝛼, 𝛾)⁢ 19 Ne and 18 F ⁡(𝑝, 𝛼)⁢ 15 O reactions, which govern breakout from the hot CNO cycle in x-ray bursts and influence 𝛾-ray emission in novae, respectively. By reconstructing likelihood functions from published experimental data—including asymmetric uncertainties and upper or lower limits—we derive posterior distributions for resonance energies, decay widths, and branching ratios. Our Bayesian approach systematically incorporates previously reported discrepancies among measurements, providing a statistically robust and consistent treatment of these uncertainties. The evaluated resonance parameters and associated uncertainties provide crucial input for stellar nucleosynthesis modeling, contributing to a refined understanding of explosive astrophysical phenomena.

Kim, Sohyun H. [Sungkyunkwan Univ., Suwon (Republi↗
DOE OSTI2019

Effect of Heat Treatment on the Dispersion of the Magnetic Anisotropy of MnSb Nanoinclusions Embedded in Thin GaMnSb Films

We observed a temperature-controlled increase in the magnetic anisotropy and its dispersion in thin GaMnSb films with MnSb nanoinclusions obtained by pulsed laser deposition. The data of transmission electron microscopy indicate that in the samples, a transition of the crystalline structure of magnetic MnSb nanoinclusions from hexagonal (spatial group (sp. gr.) P6{sub 3}/mmc) to cubic (sp. gr. F-43m) takes place. Analysis of the temperature dependences of the magnetic moment m(T), measured using a SQUID magnetometer, obtained for both unannealed and annealed samples cooled in a zero magnetic field and a magnetic field of 10 kOe, indicates that this mechanism is not unique. In unannealed samples, the distribution of the magnetic anisotropy of MnSb nanoinclusions, determined from the dependences of m(T), is unimodal. In the annealed samples, the same dependence becomes multimodal. This means that several thermally activated processes occur in the samples during annealing, resulting in several “populations” of nanoinclusions present in the annealed thin films. The contribution to the increase in the magnetic anisotropy during annealing can result in the structural phase transition, the mismatch of the crystal lattices between MnSb and GaSb, an increase in the average volume of MnSb nanoinclusions, and a change in their stoichiometry.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Thermal pressure in the cold neutral medium of nearby galaxies

Dynamic and thermal processes regulate the structure of the multiphase interstellar medium (ISM), and ultimately establish how galaxies evolve through star formation. Thus, to constrain ISM models and better understand the interplay of these processes, it is of great interest to measure the thermal pressure (P{sub th}) of the diffuse, neutral gas. By combining [C ii] 158 μm, H i, and CO data from 31 galaxies selected from the Herschel KINGFISH sample, we have measured thermal pressures in 534 predominantly atomic regions with typical sizes of ∼1 kiloparsec. We find a distribution of thermal pressures in the P{sub th}/k∼10{sup 3}--10{sup 5} K cm{sup −3} range. For a sub-sample of regions with conditions similar to those of the diffuse, neutral gas in the Galactic plane, we find thermal pressures that follow a log-normal distribution with a median value of P{sub th}/k ≈ 3600 K cm{sup −3}. These results are consistent with thermal pressure measurements using other observational methods. We find that P{sub th} increases with radiation field strength and star formation activity, as expected from the close link between the heating of the gas and the star formation rate. Our thermal pressure measurements fall in the regime where a two-phase ISM with cold and warm neutral media could exist in pressure equilibrium. Finally, we find that the midplane thermal pressure of the diffuse gas is about ∼30% of the vertical weight of the overlying ISM, consistent with results from hydrodynamical simulations of self-regulated star formation in galactic disks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Semimicroscopic description of isoscalar giant multipole resonances in medium-mass closed-shell nuclei

Main properties of isoscalar giant multipole resonances (up to L=3), including L=0,and 2 overtones, in medium-mass closed-shell nuclei are described within the semimicroscopic particle-hole dispersive optical model. Further, main properties are characterized by the energy-averaged strength distribution, projected (one-body) transition density, and probabilities of direct one-nucleon decay. Calculation results obtained for characteristics of the mentioned resonances in the 48 Ca, 90 Zr, and 132 Sn nuclei are compared with available experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

TEDS Test Plan for Performance Characterization of Packed-bed Thermal Energy Storage Unit

This report proposes a set of test plans for performance measurement and characterization of the packed-bed thermal energy storage (TES) unit for TEDS. The main goals of this test plan are three-fold: (i) support the high-fidelity data measurement for TEDS, (ii) characterize thermal and structural performance of the TEDS TES unit, and (iii) produce high quality validation-oriented data. To achieve the goals, it is first suggested to revisit the instrument calibration and data sampling protocols applied during the startup and commissioning tests of TEDS, based on the observation from the recent CFD validation study using the TEDS TES operational data. The uncertainty quantification of the thermal and hydraulic measurements is also suggested. Lastly, test matrices are provided for charge, discharge, and standby modes of the packed-bed TES unit, respectively, to characterize the thermal and structural performances during the envisioned TEDS operations.

25 ENERGY STORAGE↗
DOE OSTI2022

Mapping structural heterogeneity at the nanoscale with scanning nano-structure electron microscopy (SNEM)

Here, in this work, we explore the use of scanning electron diffraction (also known as 4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order (structure and chemistry) as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr 65 Cu 17.5 Ni 10 Al 7.5 bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don’t require energy filters on the instrument. We spatially map different quantities of interest (QoI’s), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI’s. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.

36 MATERIALS SCIENCE↗