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DOE OSTI2026

Ultrasound Characterization of Plastic Bonded Explosives With Varied Binder Content and Density

Advanced explosive formulations and methods of production require rapid characterization for both processes and materials. Here, we present results for ultrasound characterization of a plastic bonded explosive (PBX) formulation as a function of binder weight percent and pressing density using through-transmission sound speed measurements. The explosive in this study was composed of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) combined with the fluoropolymer binder Kynar Ultraflex B. Binder content ranged from 2.6 wt.% to 11.5 wt.% with associated explosive formulation densities from 1.69 g/cc to 1.89 g/cc. We find linear and nonlinear changes in longitudinal and shear sound speeds, peak frequencies, and measured elastic properties for the different PBX formulations. These measured changes can differentiate the tested formulations, highlighting the potential for ultrasound as a nondestructive characterization method for newly-formulated PBXs. We also demonstrate through-transmission measurements using different coupling media and present results from an ultrasound technique called resonant ultrasound spectroscopy (RUS). Both transmission and RUS approaches may allow for rapid and even automated characterization of PBX components suitable for high-throughput manufacturing operations.

Engineering↗

DOE OSTI2024

Experimental and numerical investigation of fracture conductivity between non-smooth rock surfaces with and without proppant

The enhancement of fracture conductivity is vital for the efficient recovery of subsurface resources, such as geothermal energy and petroleum hydrocarbons. Proppants, granular materials injected into hydraulic fractures to maintain their conductivity, have been studied primarily in the context of smooth fractures (i.e., fractures between smooth rock surfaces). However, non-smooth fractures (i.e., fractures between rough rock surfaces) are common in geoenergy reservoirs and thus require further investigations. In this study, we conducted laboratory measurements of fracture conductivity on shale slabs with non-smooth surfaces and carried out numerical simulation using the lattice Boltzmann (LB) method, which aimed to investigate the conductivity of non-smooth fractures with and without proppants placement. When ceramic proppant with an areal concentration of 2 lb/ft 2 was placed in the fracture, the conductivity was enhanced by roughly 3-8 times compared to fractures without proppant. In fractures with proppant, gas-measured conductivity was higher than that measured with water due to proppant embedment caused by water. The experiments demonstrate the advantages of using proppant in fractures, even if the rock surface roughness can provide certain fracture conductivity via the self-propping mechanism. For fractures without proppants, high rock surface roughness is not necessarily favorable for enhancing fracture conductivity because the self-propping mechanism requires shear slip along the fracture surface. If there is no shear slip, high rock surface roughness can cause a detrimental effect on the fracture conductivity due to the interlocking effect. Utilizing advanced experimental equipment and LB modeling, this research explores the interplays between proppant placement, fracture geometry, and stress conditions to develop a comprehensive understanding of the productivity in non-smooth fractures. Further, the outcomes of this investigation indicate the importance of creating fractures with surface roughness during hydraulic fracturing and will contribute to the development of more efficient stimulation techniques for subsurface energy extraction.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Semi-supervised graph neural networks for pileup noise removal

Abstract The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton–proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup. The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing. This semi-supervised approach does not depend on the neutral particle pileup label information from simulation, and thus allows us to perform training directly on experimental data. The performance of this approach is found to be consistently better than widely-used domain algorithms and comparable to the fully-supervised training using simulation truth information. The study serves as the first attempt at applying semi-supervised learning techniques to pileup mitigation, and opens up a new direction of fully data-driven machine learning pileup mitigation studies.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Project 18-15263 X-ray Studies of Interfacial Molecular Complexes in ALSEP Back-Extraction (Research Performance Final Report)

The objective of the project was to use synchrotron X-rays to measure the molecular-scale organization of extractants, complexants, buffers, and metal ions at the organic-aqueous (liquid-liquid) interface during solvent back-extraction under conditions relevant to ALSEP (Actinide-Lanthanide Separation Process). The capability of X-ray fluorescence near total reflection (XFNTR) was extended to explore the distribution of Eu(III) between the liquid-liquid interface and bulk organic and aqueous phases which contained extractants HDEHP (bis(2-ethylhexyl) phosphoric acid) and HEHEHP (2-ethylhexyl phosphonic acid mono-2-ethylhexyl ester), the complexant DTPA (diethylene triamine pentaacetic acid), and citric and nitric acids. Although an Am-compatible sample cell was developed, extending x-ray measurements of flat liquid-liquid interfaces to the actinide Am(III) did not prove to be possible within constraints imposed by the Advanced Photon Source. However, preliminary measurements with a drop cell may eventually provide a path forward to investigate Am(III) at a liquid interface. It was also demonstrated that competitive binding that occurs during back-extraction of lanthanide ions to either a phosphoric acid extractant (DHDP, di-hexadecyl phosphate) at the liquid interface or to complexant DTPA in the bulk aqueous phase could be reversibly tuned by X-ray exposure. Separately, X-ray measurements from the liquid-vapor interface explored the role of the aqueous solubility of HDEHP and HEHEHP and their interactions with a range of lanthanides and transition metal ions. Finally, the capability of X-ray absorption spectroscopy (EXAFS) was extended to probe metal ion coordination in lanthanide ion-extractant complexes at the liquid-vapor interface. Complementary to the specific interfacial systems studied during this project, the development of x-ray techniques expands our ability to understand ion ordering at liquid interfaces.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey

The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Integrating Predictions for Improving Defect Classification Accuracy in NDT-based Assessment of Concrete - 20229

There is an increasing need to create predictive models for defect classification in concrete using the output of non-destructive testing (NDT) techniques. Recent advancement of machine-learning algorithms has offered several techniques for developing classification models for different types of data sets. However, the performance of these algorithms is very uncertain, mainly when applied to small and noisy datasets. For example, when human access is limited (e.g., nuclear facility), robot-based NDT is preferred. But compared to manual tests with humans present on site, the data sets are small, and more noise can exist. Therefore, it is imperative to develop new approaches to ensure a consistently high classification accuracy for inadequate data sets. This study explores the classification performance on NDT dataset using classifiers from different machine-learning algorithms, namely k-Nearest Neighbor (kNN), Decision Tree, Naive Bayes, Logistic Regression, and Support Vector Machine (SVM). The authors further integrated the predictions from these classifiers using proposed methods. The integration strategy combines the output of the classifiers based on two different measures, accuracy, and performance (ACC and PERF), using equations such as sum, average, and square-root-of-sums-of-squares (SRSS). Our results reveal varying classification accuracies across individual classifiers with different misclassifications across the test data set. The integration strategy provided significant improvement in the classification accuracy compared to the individual classifiers. Furthermore, the results indicate minimal variation across the integration methods as compared to the variation across the individual classifiers. To conclude, prediction integration offers a unique approach for combining the output of multiple classifiers to create redundancies with the potential of achieving high classification performance and improved reliability in predictive models for defect detection in concrete. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

An extensible vector toolkit and parts library for advanced engineering of plant genomes

Abstract Plant biotechnology is rife with new advances in transformation and genome engineering techniques. A common requirement for delivery and coordinated expression in plant cells, however, places the design and assembly of transformation constructs at a crucial juncture as desired reagent suites grow more complex. Modular cloning principles have simplified some aspects of vector design, yet many important components remain unavailable or poorly adapted for rapid implementation in biotechnology research. Here, we describe a universal Golden Gate cloning toolkit for vector construction. The toolkit chassis is compatible with the widely accepted Phytobrick standard for genetic parts, and supports assembly of arbitrarily complex T‐DNAs through improved capacity, positional flexibility, and extensibility in comparison to extant kits. We also provision a substantial library of newly adapted Phytobricks, including regulatory elements for monocot and dicot gene expression, and coding sequences for genes of interest such as reporters, developmental regulators, and site‐specific recombinases. Finally, we use a series of dual‐luciferase assays to measure contributions to expression from promoters, terminators, and from cross‐cassette interactions attributable to enhancer elements in certain promoters. Taken together, these publicly available cloning resources can greatly accelerate the testing and deployment of new tools for plant engineering.

42 ENGINEERING↗

DOE OSTI2024

Probing elemental diffusion and radiation tolerance of perovskite solar cells via non-destructive Rutherford backscattering spectrometry

Mixed organic–inorganic halide perovskite-based solar cells have attracted interest in recent years due to their potential for both terrestrial and space applications. Analysis of interfaces is critical to predicting device behavior and optimizing device architectures. Most advanced tools to study buried interfaces are destructive in nature and can induce further degradation. Ion beam techniques, such as Rutherford backscattering spectrometry (RBS), is a useful non-destructive method to probe an elemental depth profile of multilayered perovskite solar cells (PSCs) as well as to study the inter-diffusion of various elemental species across interfaces. Additionally, PSCs are becoming viable candidates for space photovoltaic applications, and it is critical to investigate their radiation-induced degradation. RBS can be simultaneously utilized to analyze the radiation effects induced by He+ beam on the device, given their presence in space orbits. In the present work, a 2 MeV He+ beam was used to probe the evidence of elemental diffusion across PSC interfaces with architecture glass/ITO/SnO2/Cs0.05(MA0.17FA0.83)0.95Pb(I0.83Br0.17)3/spiro-OMeTAD/MoO3/Au. During the analysis, the device active area was exposed to an irradiation equivalent of up to 1.62 × 1015 He+/cm2, and yet, no measurable evidence (with a depth resolution ∼1 nm) of beam-induced ion migration was observed, implying high radiation tolerance of PSCs. On the other hand, aged PSCs exhibited indications of the movement of diverse elemental species, such as Au, Pb, In, Sn, Br, and I, in the active area of the device, which was quantified with the help of RBS.

14 SOLAR ENERGY↗

DOE OSTI2024

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Challenges in the study of chemistry and photochemistry at air–water interfaces: Toward in situ monitoring of reaction kinetics with spectroscopic techniques

Air–water interfaces, including those on droplet surfaces, have been the subject of many recent experiments due to their propensity for unique chemistry. Here, in this study, an overview of some recent advancements in understanding interfacial reaction kinetics is provided, highlighting non-surface-specific methods—such as mass spectrometry—compared with the advantages of surface-specific techniques—such as reflection–absorption spectroscopy, sum frequency generation, and photoelectron spectroscopy. This Perspective discusses the information depth and time constraints of common surface-specific spectroscopic methods that need to be addressed to monitor interfacial chemistry in situ and uses a few key examples from the literature as case studies. It concludes by advocating for the continued development of advanced spectroscopic methods to further investigate interfacial chemistry, underscoring the need for interdisciplinary collaboration to bridge the gap between molecular-level insights and macroscopic observations in future research.

Air-water interface↗

DOE OSTI2021

Advances in secondary ion mass spectrometry for N-doped niobium

Accurate secondary ion mass spectroscopy measurement of nitrogen in niobium relies on the use of closely equivalent standards, made by ion implantation, to convert nitrogen signal intensity to nitrogen content by determination of relative sensitivity factors (RSFs). Accurate RSF values for ppm-range nitrogen contents are increasingly critical, as more precision is sought in processes for next-generation superconducting radiofrequency (SRF) accelerator cavities. Furthermore, factors influencing RSF value measurements were investigated with the aim of reliably attaining better than 10% accuracy in nitrogen concentrations at various depths into the bulk. This has been accomplished for materials typical of SRF cavities at the cost of increased attention to all aspects.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Structural basis of differential gene expression at eQTLs loci from high-resolution ensemble models of 3D single-cell chromatin conformations

Abstract Motivation Techniques such as high-throughput chromosome conformation capture (Hi-C) have provided a wealth of information on nucleus organization and genome important for understanding gene expression regulation. Genome-Wide Association Studies have identified numerous loci associated with complex traits. Expression quantitative trait loci (eQTL) studies have further linked the genetic variants to alteration in expression levels of associated target genes across individuals. However, the functional roles of many eQTLs in noncoding regions remain unclear. Current joint analyses of Hi-C and eQTLs data lack advanced computational tools, limiting what can be learned from these data. Results We developed a computational method for simultaneous analysis of Hi-C and eQTL data, capable of identifying a small set of nonrandom interactions from all Hi-C interactions. Using these nonrandom interactions, we reconstructed large ensembles (×105) of high-resolution single-cell 3D chromatin conformations with thorough sampling, accurately replicating Hi-C measurements. Our results revealed many-body interactions in chromatin conformation at the single-cell level within eQTL loci, providing a detailed view of how 3D chromatin structures form the physical foundation for gene regulation, including how genetic variants of eQTLs affect the expression of associated eGenes. Furthermore, our method can deconvolve chromatin heterogeneity and investigate the spatial associations of eQTLs and eGenes at subpopulation level, revealing their regulatory impacts on gene expression. Together, ensemble modeling of thoroughly sampled single-cell chromatin conformations combined with eQTL data, helps decipher how 3D chromatin structures provide the physical basis for gene regulation, expression control, and aid in understanding the overall structure-function relationships of genome organization. Availability and implementation It is available at https://github.com/uic-liang-lab/3DChromFolding-eQTL-Loci.

Du, Lin (ORCID:0009000289869812)↗

DOE OSTI2025

Ultralow-temperature cryogenic transmission electron microscopy using a new helium flow cryostat stage

Advances in cryogenic electron microscopy have opened new avenues for probing quantum phenomena in correlated materials. This study reports the installation and performance of a new side-entry condenZero cryogenic cooling system for JEOL (Scanning) Transmission Electron Microscopes (S/TEM), utilizing compressed liquid helium (LHe) and designed for imaging and spectroscopy at ultra-low temperatures. The system includes an external dewar mounted on a vibration-damping stage and a pressurized, low-noise helium transfer line with a remotely controllable needle valve, ensuring stable and efficient LHe flow with minimal thermal and mechanical noise. Performance evaluation demonstrates a stable base temperature of 4.37 K measured using a Cernox bare chip sensor on the holder with temperature fluctuations within ±0.004 K. Complementary in-situ electron energy-loss spectroscopy (EELS) via aluminum bulk plasmon analysis was used to measure the local specimen temperature and validate cryogenic operation during experiments. The integration of cryogenic cooling with other microscopy techniques, including electron diffraction and Lorentz TEM, was demonstrated by resolving charge density wave (CDW) transitions in NbSe2 using electron diffraction, and imaging nanometric magnetic skyrmions in MnSi via Lorentz TEM. In conclusion, this platform provides reliable cryogenic operation below 7 K, establishing a low-drift route for direct visualization of electronic and magnetic phase transformations in quantum materials.

Charge density wave↗

DOE OSTI2024

When van der Waals Met Kagome: A 2D Antimonide with a Vanadium-Kagome Network

2D materials showcase unconventional properties emerging from quantum confinement effects. In this work, a “soft chemical” route allows for the deintercalation of K + from the layered antimonide KV 6 Sb 6 , resulting in the discovery of a new metastable 2D-Kagome antimonide K 0.1(1) V 6 Sb 6 with a van der Waals gap of 3.2 Å. The structure of K 0.1(1) V 6 Sb 6 was determined via the synergistic techniques, including X-ray pair distribution function analysis, advanced transmission electron microscopy, and density functional theory calculations. The K 0.1(1) V 6 Sb 6 compound crystallizes in the monoclinic space group C2/m (a = 9.57(2) Å, b = 5.502(8) Å, c = 10.23(2) Å, β = 97.6(2)°, Z = 2). The [V 6 Sb 6 ] layers in K 0.1(1) V 6 Sb 6 are retained upon deintercalation and closely resemble the layers in the parent compound, yet deintercalation results in a relative shift of the adjacent [V 6 Sb 6 ] layers. The magnetic properties of the K 0.1(1) V 6 Sb 6 phase in the 2–300 K range are comparable to those of KV 6 Sb 6 and another Kagome antimonide KV 3 Sb 5 , consistent with nearly temperature-independent paramagnetism. Electronic band structure calculation suggests a nontrivial band topology with flat bands and opening of band crossing afforded by deintercalation. Transport property measurements reveal a metallic nature for K 0.1(1) V 6 Sb 6 and a low thermal conductivity of 0.6 W K –1 m –1 at 300 K. Additionally, ion exchange in KV 6 Sb 6 via a solvothermal route leads to a successful partial exchange of K + with A + (A = Na, Rb, and Cs). Here, this study highlights the tunability of the layered structure of the KV 6 Sb 6 compound, providing a rich playground for the realization of new 2D materials.

2D↗

DOE OSTI2020

Resonant Soft X-Ray Scattering from Stripe-Ordered La 2 - x Ba x Cu O 4 Detected by a Transition-Edge Sensor Array Detector

Resonant soft x-ray scattering (RSXS) is a leading probe of valence-band order in materials best known for detecting charge-density-wave order in copper-oxide superconductors. One of the biggest limitations on the RSXS technique is the presence of a severe fluorescence background, which, like the RSXS cross section itself, is enhanced under resonant conditions. This background prevents the study of weak signals such as diffuse scattering from glassy or fluctuating order that is spread widely over momentum space. Recent advances in superconducting transition-edge-sensor (TES) detectors have led to major improvements in energy resolution and detection efficiency in the soft x-ray range. Here, we perform a RSXS study of stripe-ordered La 2 - x Ba x Cu O 4 at the Cu L 3 / 2 edge (932.2 eV) using a TES detector with 1.5-eV resolution, to evaluate its utility for mitigating the fluorescence-background problem. We find that, for suitable degree of detuning from the resonance, the TES rejects the fluorescence background, leading to a five to ten times improvement in the statistical quality of the data compared to an equivalent, energy-integrated measurement. We conclude that a TES introduces a promising method to reducing background in RSXS studies and may lead to discoveries in materials exhibiting valence-band order that is fluctuating or glassy.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Using High-Resolution NSRDB Data to Evaluate Cloud Mask Forecast from WRF-Solar EPS

Validating spatiotemporal distributions of cloud forecasts using numerical weather prediction (NWP) models is difficult as this requires high-quality cloud-property at significantly high spatial and temporal resolution over extended periods of time. Observations of cloud properties, such as cloud mask, cloud optical thickness, and cloud type, are vital for assessing the capability of NWP models to forecast various types of clouds. Using the National Solar Radiation Database (NSRDB), this research evaluates ensemble cloud-mask predictions from the WRF-Solar ensemble prediction system (WRF-Solar EPS). From the WRF-Solar EPS, day-ahead solar forecasts for the contiguous United States (CONUS) for 2018 are simulated. Given the NSRDB data is accessible at a resolution of 2 km, we can calculate the cloud fraction across the 9-km grid of WRF-Solar EPS. This allows us to spatially assess the cloud-mask forecasts using two methods against the high-resolution NSRDB: (a) considering all 2-km NSRDB clouds in the forecast domain (EMAll), and (b) using a minimum cloud fraction threshold of 50% to designate a pixel as cloudy (EMP50). The low-resolution cloud masks from WRF-Solar EPS are evaluated directly against the cloud-resolving scale gridded observations from NSRDB using EMAll. With EMP50, we presume that scenes with less than 50% cloud cover from the 2-km NSRDB are clear. Thus, this assessment approach allows for a fair comparison with WRF-Solar EPS resolved to a 9-km grid. A method of point-by-point verification is used to evaluate dichotomous (yes/no) cloud mask predictions against the NSRDB. For each pixel of model extent, cloud frequency and traditional metrics (e.g., probability of detection, false alarm rate, and hit rate, etc.) are computed and compared with satellite-derived data sets. Mismatched cloud frequency (MCF) is computed to measure the present capability of WRF-Solar EPS in representing various types of clouds, which are categorized using three levels of cloud top height (CTH) and cloud optical depth (COD) across entire CONUS. Preliminary results show that the WRF-Solar EPS provides MCF values ranging from 9% to 46%, 16% to 33%, and 8% to 27% for low-level, middle-level, and high-level clouds, respectively, for three CTHs. The model produces MCFs ranging from 27% to 46%, 13% to 34%, and 8% to 19% for thin, medium-thickness, and thick clouds, respectively, for three CODs. The presentation will include a detailed description of the current outcomes as well as potential future extensions. The evaluation approach established in this study is readily extensible to the evaluation of cloud predictions from different ensemble NWP models. In addition, the findings of the suggested evaluation technique aid in identifying model weaknesses and will ultimately lead to advances in WRF-Solar EPS's skill in predicting clouds and solar irradiance.

cloud mask forecast↗

DOE OSTI2023

Epitaxial growth and characterization of magnesium gallate (MgGa 2 O 4 ) thin films by pulsed laser deposition

To advance high power device application of MgGa 2 O 4 , it is imperative to achieve crystalline epitaxial thin films and to observe its materials’ characteristics within a wide range of growth conditions. However, there is no published work on the growth of this promising ultrawide bandgap material till this day. In this study, we report the crystalline growth of MgGa 2 O 4 thin films on c-plane (006) sapphire substrates using the pulsed laser deposition (PLD) technique in a broad range of temperature and oxygen pressure. The temperature range for this crystalline growth was within the temperature range of 300°C to 700°C and a pressure range of 1x10 -1 to 1x10 -3 Torr. Within this parameter range, the structural, chemical, optical, and surface property of MgGa 2 O 4 thin films have been investigated. Specifically, the acquired XRD patterns confirmed the film growth along the [111] preferential crystal orientation in the lattice. Here, the rocking curve measurement of the prominent (222) plane showed an increasing trend of the crystallinity with growth temperature and pressure. Furthermore, the XRD phi (φ) scan demonstrated the six-fold rotational symmetry of the MgGa 2 O 4 films and the epitaxial relationship of 30° between the film and the sapphire substrate. The XPS spectra confirmed the presence of +2 and +3 oxidation state for Mg and Ga respectively, in the films. The direct bandgap of MgGa 2 O 4 films was obtained ~5.27±0.03 eV by analyzing the UV-Vis absorbance spectra using the Tauc equation. The SEM images exhibited a granular surface morphology of the MgGa 2 O 4 films with an increasing trend in the grain size from low to high temperature and pressure. The refractive index and thickness of these films were in the range of ~1.90±0.02 and ~70±2.0 nm, respectively determined by fitting the spectroscopic ellipsometry data.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Multidimensional images and aberrations in STEM

Recent advances in scanning transmission electron microscopy (STEM) have led to increased development of multi-dimensional STEM imaging modalities and novel image reconstruction methods. This interest arises because the main electron lens in a modern transmission electron microscope usually has a diffraction-space information limit that is significantly better than the real-space resolution of the same lens. This state-of-affairs is sometimes shared by other scattering methods in modern physics and contributes to a broader excitement surrounding multidimensional techniques that scan a probe while recording diffraction-space images, such as ptychography and scanning nano-beam diffraction. However, the contrasting resolution in the two spaces raises the question as to what is limiting their effective performance. Here, we examine this paradox by considering the effects of aberrations in both image and diffraction planes, and likewise separate the contributions of pre- and post-sample aberrations. In conclusion, this consideration provides insight into aberration-measurement techniques and might also indicate improvements for super-resolution techniques.

Hoglund, Eric R.↗