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Python-EPICS RF Conditioning Automatic Control System at the Spallation Neutron Source
The RF Test Facility (RFTF) at the Spallation Neutron Source (SNS) is used for the conditioning of RF compo-nents such as ceramic vacuum windows and power cou-plers prior to their installation in the H- ion linear accel-erator. This process exposes components to high-power RF fields and thermal cycling to improve performance and remove surface impurities. To automate and optimize this process, a Python-based EPICS control system was developed alongside targeted hardware upgrades. The system enables real-time monitoring and control of RF power levels, temperature, and vacuum pressure. A user-friendly graphical interface was implemented using CS-Studio (Phoebus), allowing operators to adjust parameters and collect data efficiently. The system integrates a High-Power Protection Module (HPM) for interlocks based on vacuum and arc detection, ensuring safe operation. These upgrades have significantly improved the efficiency, accuracy, and safety of RF conditioning at the SNS RFTF. This paper describes the updated RF conditioning sys-tem, highlighting the software and hardware develop-ments and their application in support of the Proton Pow-er Upgrade (PPU) project.
Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy
Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM’s high affinity with various analytical options into account. As is well known, STEM’s image acquisition time needs to be further shortened to capture a targeted phenomenon in real-time as STEM’s current temporal resolution is far below the conventional TEM’s. However, rapid image acquisition in the millisecond per frame or faster generally causes image distortion, poor electron signals, and unidirectional blurring, which are obstacles for realizing video-rate STEM observation. Here we show an image correction framework integrating deep learning (DL)-based denoising and image distortion correction schemes optimized for STEM rapid image acquisition. By comparing a series of distortion corrected rapid scan images with corresponding regular scan speed images, the trained DL network is shown to remove not only the statistical noise but also the unidirectional blurring. This result demonstrates that rapid as well as high-quality image acquisition by STEM without hardware modification can be established by the DL. The DL-based noise filter could be applied to in-situ observation, such as dislocation activities under external stimuli, with high spatio-temporal resolution.
Sub-Ångström-resolution MicroED Using a Direct Detection Camera
The recent development of microcrystal electron diffraction (MicroED) in electron cryo-microscopy (cryo-EM) has addressed many of the challenges of X-ray crystallography [1], filling a long-standing need in organic chemistry for rapid and accurate high-resolution structural characterization [2]. Like X-ray crystallography, this is also a diffraction technique requiring crystalline specimens, but because high energy electrons have a more favorable ration of elastic to inelastic scattering [3], MicroED can acquire data using crystals that are orders of magnitude smaller than those needed for X-ray crystallography—even as small as 50 nm in length [4]. Additionally, the instrumentation required for MicroED is readily available, as many chemistry and biology labs already have access to cryo-capable transmission electron microscopes (TEM).
Automated phase segmentation and quantification of high-resolution TEM image for alloy design
In the alloy design and development process, a wealth of atomically resolved structural high-resolution transmission electron microscopy (HRTEM) images are produced. Identifying the different nano-precipitate phases and tracking their evolution under various compositions and during manufacturing or post-processing requires hundreds of HRTEM images and thousands of precipitates. The nanoscopic phase information labeling and analysis purely relies on humans are prohibitively costly and time-consuming, sometimes not reliable because of the lack of authoritative knowledge. Here, in this work, we develop a novel unsupervised machine learning approach coupled with adaptive computer vision techniques with features in the Fourier space to automatically determine the number of phases and segment/quantify the phases with nanoscale resolution, allowing for quantitative correlation between nanostructure formation, processing and functional properties. To automate the phase extraction/quantification and ascertain its applicability, we have applied the developed framework to the HRTEM images from several alloy systems, processing conditions, image magnifications, and phase types and morphologies (precipitates, nano-twins, stacking faults, crystalline matrix, and amorphous structures) for verification. This study paves the road for compression, visualization, and translation of raw image structural data into physically relevant information in real-time with minimal human supervision. It shows the promise of enabling high-throughput materials characterization for the acceleration of alloy manufacturing and design.
Ultrafast photoinduced charge carrier dynamics of L-cysteine and oleylamine stabilized CsPbBr3 perovskite quantum dots coupled with gold nanoparticles
We have synthesized L-cysteine and oleylamine stabilized CsPbBr3 perovskite quantum dots (PQDs) and coupled them with gold nanoparticles (AuNPs). The PQDs and AuNPs, as well as their hybrid nanostructures (HNS), were characterized using UV–visible (UV–vis) and photoluminescence (PL) spectroscopy. The UV–vis spectra show absorption bands of the HNS at 503 and 520 nm, attributed mainly to PQDs and AuNPs, respectively. The PQDs show a strong excitonic PL band peaked at 513 nm from PQDs. The HR-TEM results show the formation of hybrid structures between PQDs and AuNPs, which is also supported by the PL quenching of the PQDs by the coupled AuNPs. Ultrafast dynamics of the exciton and charge carriers in the HNS and pristine PQD were studied using femtosecond transient absorption. Multiexponential fitting of the dynamic data revealed the existence of shallow and deep trap states in pristine PQDs and ultrafast electron transfer from PQDs to AuNPs in the HNS. A kinetic model was proposed to account for the key dynamic processes involved and to extract the time for electron transfer from PQDs to AuNPs in the HNS, found to be ∼2 ps. Dynamic processes in pristine PQDs are largely unchanged by HNS formation with AuNPs.
Irradiation-assisted Stress Corrosion Cracking of PWRirradiated Type 347 Stainless Steel (NSUF CINR 19-16567)
This document serves as the final report for NSUF Project 19-16567 (Reference 1) and its supporting contracts (References 2 and 3). It documents completion of testing of specimens fabricated as previously described in Reference 4. At a high level, the purpose of the testing is to generate data to support continued operation of light water reactors (LWR) and provide further understanding of irradiation-assisted stress corrosion crack (IASCC) initiation in baffle-former bolt materials. Baffle-former bolts that had been extracted from commercial pressurized water reactors (PWRs) were machined into miniature 4-point bend specimens and TEM samples at the Westinghouse Churchill Site hot cell facility. These samples were then shipped to the University of Michigan to undergo IASCC initiation testing in simulated PWR primary coolant containing either LiOH or KOH additions. Overall, 15 samples were tested, 8 in water with KOH additions and 7 in water with LiOH additions, until indications of cracking were observed. Crack initiation stress was similar in both water environments at 50-60% of the irradiated yield stress. Post-test examination of the cracking sites, including scanning electron microscopy and transmission electron microscopy, demonstrated that the crack initiation mechanism and crack initiation rates were similar between the two different water chemistries. A grain boundary oxidation and oxide fracture mechanism was observed to be responsible for crack initiation.
Highly Conductive Multifunctional Graphene Polycarbonate Nanocomposites
Graphene nanosheet bisphenol A polycarbonate nanocomposites (0.027 2.2 vol %) prepared by both emulsion mixing and solution blending methods, followed by compression molding at 287 C, exhibited dc electrical percolation threshold of approx.0.14 and approx.0.38 vol %, respectively. The conductivities of 2.2 vol % graphene nanocomposites were 0.512 and 0.226 S/cm for emulsion and solution mixing. The 1.1 and 2.2 vol % graphene nanocomposites exhibited frequency-independent behavior. Inherent conductivity, extremely high aspect ratio, and nanostructure directed assembly of the graphene using PC nanospheres are the main factors for excellent electrical properties of the nanocomposites. Dynamic tensile moduli of nanocomposites increased with increasing graphene in the nanocomposite. The glass transition temperatures were decreased with increasing graphene for the emulsion series. High-resolution electron microscopy (HR-TEM) and small-angle neutron scattering (SANS) showed isolated graphene with no connectivity path for insulating nanocomposites and connected nanoparticles for the conductive nanocomposites. A stacked disk model was used to obtain the average particle radius, average number of graphene layers per stack, and stack spacing by simulation of the experimental SANS data. Morphology studies indicated the presence of well-dispersed graphene and small graphene stacking with infusion of polycarbonate within the stacks.
Dynamic compressed sensing for real-time tomographic reconstruction
Electron tomography has achieved higher resolution and quality at reduced doses with recent advances in compressed sensing. Compressed sensing (CS) exploits the inherent sparse signal structure to efficiently reconstruct three-dimensional (3D) volumes at the nanoscale from undersampled measurements. However, the process bottlenecks 3D reconstruction with computation times that run from hours to days. Here we demonstrate a framework for dynamic compressed sensing that produces a 3D specimen structure that updates in real-time as new specimen projections are collected. Researchers can begin interpreting 3D specimens as data is collected to facilitate high-throughput and interactive analysis. Using scanning transmission electron microscopy (STEM), we show that dynamic compressed sensing accelerates the convergence speed by ~3-fold while also reducing its error by 27% for a Au/SrTiO 3 nanoparticle specimen. Before a tomography experiment is completed, the 3D tomogram has interpretable structure within ~33% of completion and fine details are visible as early as ~66%. Upon completion of an experiment, a high-fidelity 3D visualization is produced without further delay. Additionally, we report reconstruction parameters that tune data fidelity can be manipulated throughout the computation without re-running the entire process.
On diamond, graphitic and amorphous carbons in primitive extraterrestrial solar system materials
Carbon is among the most abundant elements in the universe and carbon chemistry in meteorites and comets is an important key to understanding many Solar System and interstellar processes. Yet, the mineralogical properties and interrelations between various structural forms of elemental carbon remain ambiguous. Crystalline elemental carbons include rhombohedral graphite, hexagonal graphite, cubic diamond, hexagonal diamond (i.e., lonsdaleite or carbon-2H) and chaoite. Elemental carbon also occurs as amorphous carbon and poorly graphitized (or turbostratic) carbon but of all the forms of elemental carbon only graphite is stable under physical conditions that prevail in small Solar System bodies and in the interstellar medium. The recent discovery of cubic diamond in carbonaceous chondrites and hexagonal diamond in chondritic interplanetary dust particles (IDPs) have created a renewed interest in the crystalline elemental carbons that were not formed by shock processes on a parent body. Another technique, Raman spectroscopy, confirms a widespread occurrence of disordered graphite in the Allende carbonaceous chondrite and in chondritic IDPs. Elemental carbons have also been identified by their characteristic K-edge features in electron energy loss spectra (EELS). However, the spectroscopic data do not necessarily coincide with those obtained by selected area electron diffraction (SAED). In order to interpret these data in terms of rational crystalline structures, it may be useful to consider the principles underlying electron diffraction and spectroscopic analyses. Electron diffraction depends on electron scattering, on the type of atom and the distance between atoms in a crystal lattice. Spectroscopic data are a function of the type of atom and the energy of bonds between atoms. Also, SAED is a bulk sampling technique when compared to techniques such as Raman spectroscopy or EELS. Thus, it appears that combined analyses provide contradictory results and that amorphous, or short-range ordered, carbon identified by conventional TEM imaging and SAED may show evidence for sp(3) bonds in EELS spectra. It is suggested that complex, nanometer-scale, mineralogical interrelations are common to all elemental carbons irrespective of their origin. The subsequent thermal history, or energy balance, will determine the ultimate microstructure.
Randomly Layered Superstructure of In 2 O 3 Truncated Nano-Octahedra and Its High-Pressure Behavior
This study outlines the synthesis and characterization of a unique superlattice composed of vertex-truncated indium oxide (In 2 O 3 ) nano-octahedra, along with an exploration of its response to high-pressure conditions. Here, using a bright-field transmission electron microscope (BF-TEM), we determined an average circumradius of 15.2 nm for these octahedral building blocks. The resilience and response of the superlattice to pressure variations, peaking at 18.01 GPa, were examined by employing synchrotron-based Wide-Angle X-ray Scattering (WAXS) and Small-Angle X-ray Scattering (SAXS) techniques. The WAXS data revealed no phase transitions, reinforcing the stability of the 2D superlattice comprised of random layers in alignment with a 2D p31m symmetry. Notably, the SAXS data unveiled a pressure-induced, irreversible octahedron translation and ligand interaction occurring within the random layer. Through our examination of these pressure-sensitive behaviors, we identified a distinctive translation model inherent to octahedra and observed modulation in the superlattice cell parameter induced by pressure. This research signifies a noteworthy progression in deciphering the intricate behaviors of 2D superlattices under high-pressure conditions.
Nature and origin of matrix in the unique type 3 chondrite, Kakangari
The characteristics of the matrix of the Kakangari type-3 chondrite were investigated using high-resolution TEM and AEM. It was found that the textural and mineralogical characteristics of the fine-grained matrix of Kakangari are different from those of the other chondrite groups. Unlike the matrix phases present in the CV3 and CO3 chondrites, the matrix mineralogy of Kakangari contains clear information regarding its possible origin and thermal history. Data obtained are most consistent with an origin of Kakangari by annealing, without melting at high temperature (possibly during a chondrule-forming thermal event), of clusters or aggregates of ultrafine-grained amorphous or crystalline phases. The precursor materials were most likely to have been a mixture of nebula and presolar dust. Chondrules in Kakangari were probably formed from identical precursors to the matrix, but reached temperatures sufficiently high to produce melting.
TEM Cell Testing of Cable Noise Reduction Techniques from 2 MHz to 200 MHz -- Part 2
This paper presents empirical results of cable noise reduction techniques as demonstrated in a TEM cell operating with radiated fields from 2 - 200 MHz. It is the second part of a two-paper series. The first paper discussed cable types and shield connections. In this second paper, the effects of load and source resistances and chassis connections are examined. For each topic, well established theories are compared to data from a real-world physical system. Finally, recommendations for minimizing cable susceptibility (and thus cable emissions) are presented. There are numerous papers and textbooks that present theoretical analyses of cable noise reduction techniques. However, empirical data is often targeted to low frequencies (e.g. <50 KHz) or high frequencies (>100 MHz). Additionally, a comprehensive study showing the relative effects of various noise reduction techniques is needed. These include the use of dedicated return wires, twisted wiring, cable shielding, shield connections, changing load or source impedances, and implementing load- or source-to-chassis isolation. We have created an experimental setup that emulates a real-world electrical system, while still allowing us to independently vary a host of parameters. The goal of the experiment was to determine the relative effectiveness of various noise reduction techniques when the cable is in the presence of radiated emissions from 2 MHz to 200 MHz.
Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders *
Abstract Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization, and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we explore the shift-invariant variational autoencoders (shift-VAEs) that allow disentangling characteristic repeating features in the images, their variations, and shifts that inevitably occur when randomly sampling the image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data. We further introduce an approach for training shift-VAEs that allows finding the latent variables that comport to known physical behavior. In this specific case, the condition is that the latent variable maps should be smooth on the length scale of the atomic lattice (as expected for physical order parameters), but other conditions can be imposed. The opportunities and limitations of the shift VAE analysis for pattern discovery are elucidated.
Bridging the length scales on mechanical property evaluation (Final Report)
The development of small-scale mechanical testing in combination with microstructural investigation is of great interest to the nuclear materials community for both materials development and monitoring applications. Dramatically reducing the sample sizes to reduce radioactivity and obtaining mechanical properties of irradiated samples is truly intriguing. Moreover, such studies promise a range of benefits including cost reduction, fundamental insight in structure-property relationships, increased statistics on less sample material, and reinvestigation of prior irradiated and tested reactor samples while simultaneously enabling the generation of mechanical test data on ion beam irradiated materials with limited penetration depths. Small scale materials testing on sub-sized samples has been studied for several decades, though it has only been after the development of micro-testing based on Focused Ion Beam (FIB) sample manufacturing in that orders of magnitude smaller samples could really be investigated in a quantitative manner. In recent years, small scale mechanical testing techniques at a number of length scales has been developed for both unirradiated and irradiated (ion and neutron) materials. Technological advances made in this field have enabled ex-situ and in-situ transmission electron microscopy (TEM) and scanning electron microscopy (SEM) examination, thus leading to more accurate measurements as well as additional mechanistic information. A recent review of the benefits of these techniques show that these techniques are at a stage to tackle multi-scale ranges of materials investigations and can be utilized to obtain fundamental science-based understanding of nuclear materials. Considering the tremendous advances made, one can see how small-scale mechanical testing techniques combined with modeling can enable true small scale to bulk scale mechanical property correlations. However, for the engineering community to adapt this approach fully, one needs to demonstrate that a) that these techniques can produce results with high fidelity and reproducibility, b) generate engineering stress-strain data that one can utilize to understand bulk behavior, and c) generate new insight into relevant phenomena fostering the true understanding of radiation damage and microstructure in materials for nuclear applications. It is the objective of this proposal to bridge the length scale between macro- and micro- scale mechanical testing of unirradiated and irradiated materials. This involves the development and demonstration of procedures for multi-scale mechanical testing that enable high fidelity reproducibility of data and the generation bulk property data from small-scale mechanical tests. Through this, the proposal aims to enhance the confidence in the obtained data at the smaller length scales and enhance the insight provided from these techniques for bulk scale applications on both unirradiated and irradiated materials.
Feasibility study of a high spatial and time resolution beam emission spectroscopy diagnostic for localized density fluctuation measurements in Lithium Tokamak eXperiment-β (LTX-β)
Trapped electron mode (TEM) is the main source of turbulence predicted for the unique operation regime of a flat temperature profile under low-recycling conditions in the LTX-β tokamak, while ion temperature gradient driven turbulence may also occur with gas fueling from the edge. To investigate mainly TEM scale density fluctuations, a high spatial and time resolution 2D beam emission spectroscopy (BES) diagnostic is being developed. Apart from spatially localized density turbulence measurement, BES can provide turbulence flow and flow shear dynamics. This BES system will be realized using an avalanche photodiode-based camera and narrow band interference filter. The system can acquire data at 2 MHz. Simulations with the Simulation of Spectra (SOS) code indicate that a high signal to noise ratio can be achieved with the proposed system. This will enable sampling the density fluctuations at this high time resolution. Finally, the design considerations and system optimization using the SOS code are presented.
Diffusion processes in Al2O3 scales - Void growth, grain growth, and scale growth
The internal microstructure and growth kinetics of Al2O3 scales on Ni-15Cr-13Al (wt percent) are investigated by TEM and analyzed in relation to models of diffusivity. Polished arc-melted specimens were oxidized in 1-atm air at 1100 C for 0.1, 1.0, and 20 hours and ion-thinned for TEM at 100 kV. The frequency distribution of void size and grain size is determined for different oxidation times and scale depths. The kinetics of microvoid growth and of grain and scale growth are plotted and related via simplified models to lattice and grain-boundary oxygen diffusivity, respectively. Good agreement is found between model predictions and data obtained by Oishi and Kingery (1960) on oxygen diffusion in bulk Al2O3. The further implications and limitations of these findings are discssed.
NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images
Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.