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At least 73 records · Page 4

Real-Time Interactive 4D-STEM Phase-Contrast Imaging From Electron Event Representation Data: Less computation with the right representation

The arrival of direct electron detectors (DED) with high frame-rates in the field of scanning transmission electron microscopy has enabled many experimental techniques that require collection of a full diffraction pattern at each scan position, a field which is subsumed under the name four dimensional-scanning transmission electron microscopy (4D-STEM). DED frame rates approaching 100 kHz require data transmission rates and data storage capabilities that exceed commonly available computing infrastructure. Current commercial DEDs allow the user to make compromises in pixel bit depth, detector binning or windowing to reduce the per-frame file size and allow higher frame rates. This change in detector specifications requires decisions to be made before data acquisition that may reduce or lose information that could have been advantageous during data analysis. The 4D Camera, a DED with 87 kHz frame-rate developed at Lawrence Berkeley National Laboratory, reduces the raw data to a linear-index encoded electron event representation (EER). Here we show with experimental data from the 4D Camera that linear-index encoded EER and its direct use in 4D-STEM phase contrast imaging methods enables real-time, interactive phase-contrast from large-area 4D-STEM datasets. Furthermore, we detail the computational complexity advantages of the EER and the necessary computational steps to achieve real-time interactive ptychography and center-of-mass differential phase contrast using commonly available hardware accelerators.

4D-STEM↗

Relaxation behavior in low-frequency complex conductivity of sands caused by bacterial growth and biofilm formation by Shewanella oneidensis under a high-salinity condition

Complex electrical conductivity is increasingly used to monitor subsurface processes associated with microbial activities because microbial cells mostly have surface charges and thus electrical double layers. Although highly saline environments are frequently encountered in coastal and marine sediments, there are limited data available on the complex conductivity associated with microbial activities under a high-salinity condition. Therefore, we have developed the spectral responses of complex conductivity of sand associated with bacterial growth and biofilm formation under a highly saline condition of approximately 1% salinity and approximately 2 S/m pore water conductivity with an emphasis on relaxation behavior. A column test is performed, in which the model bacteria Shewanella oneidensis MR-1 are stimulated for cell growth and biofilm formation in a sand pack, whereas the complex conductivity is monitored from 0.01 Hz to 10 kHz. The test results indicate that the real conductivity increases in the early stage due to the microbial metabolites and the increased surface conduction with cell growth but soon begin to decrease because of the reduction of charge passages due to bioclogging. However, the imaginary conductivity significantly increases with time, and clear bell-shaped relaxation behaviors are observed with the peak frequency of 0.1–1 Hz, associated with the double-layer polarization of cells and electrically conductive pili and biofilms. The Cole-Cole relaxation model appears to capture such relaxation behaviors well, and the modeling results indicate gradual increases in normalized chargeability and decreases in relaxation time during bacterial growth and biofilm formation in the highly saline condition. Comparison with previous literature confirms that the high-salinity condition further increases the normalized chargeability, whereas it suppresses the phase shift and thus the imaginary conductivity. Our results suggest that the complex conductivity can effectively capture microbial biomass formation in sands under a highly saline condition.

58 GEOSCIENCES↗

Conformational heterogeneity of the BTK PHTH domain drives multiple regulatory states

Full-length Bruton’s tyrosine kinase (BTK) has been refractory to structural analysis. The nearest full-length structure of BTK to date consists of the autoinhibited SH3–SH2–kinase core. Precisely how the BTK N-terminal domains (the Pleckstrin homology/Tec homology [PHTH] domain and proline-rich regions [PRR] contain linker) contribute to BTK regulation remains unclear. We have produced crystals of full-length BTK for the first time but despite efforts to stabilize the autoinhibited state, the diffraction data still reveal only the SH3–SH2–kinase core with no electron density visible for the PHTH–PRR segment. Cryo-electron microscopy (cryoEM) data of full-length BTK, on the other hand, provide the first view of the PHTH domain within full-length BTK. CryoEM reconstructions support conformational heterogeneity in the PHTH–PRR region wherein the globular PHTH domain adopts a range of states arrayed around the autoinhibited SH3–SH2–kinase core. On the way to activation, disassembly of the SH3–SH2–kinase core opens a new autoinhibitory site on the kinase domain for PHTH domain binding that is ultimately released upon interaction of PHTH with phosphatidylinositol (3,4,5)-trisphosphate. Membrane-induced dimerization activates BTK and we present here a crystal structure of an activation loop swapped BTK kinase domain dimer that likely represents the conformational state leading to trans-autophosphorylation. Together, these data provide the first structural elucidation of full-length BTK and allow a deeper understanding of allosteric control over the BTK kinase domain during distinct stages of activation.

59 BASIC BIOLOGICAL SCIENCES↗

Probing Electron Beam Induced Transformations on a Single-Defect Level via Automated Scanning Transmission Electron Microscopy

A robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, in this study, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects in graphene. The ELIT-based approach facilitates direct on-the-fly analysis of the STEM data and engenders real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

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.

36 MATERIALS SCIENCE↗

Atomically Dispersed Platinum in Surface and Subsurface Sites on MgO Have Contrasting Catalytic Properties for CO Oxidation

Atomically dispersed metals on metal oxide supports are a rapidly growing class of catalysts. Developing an understanding of where and how the metals are bonded to the supports is challenging because support surfaces are heterogeneous, and most reports lack a detailed consideration of these points. In this work, we report two atomically dispersed CO oxidation catalysts having markedly different metal–support interactions: platinum in the first layer of crystalline MgO powder and platinum in the second layer of this support. Structural models have been determined on the basis of data and computations, including those determined by extended X-ray absorption fine structure and X-ray absorption near edge structure spectroscopies, infrared spectroscopy of adsorbed CO, and scanning transmission electron microscopy. The data demonstrate the transformation of surface to subsurface platinum as the temperature of sample calcination increased. Catalyst performance data demonstrate the lower activity but greater stability of the subsurface platinum than of the surface platinum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Efficient analysis of small-angle scattering curves for large biomolecular assemblies using Monte Carlo methods

Structure elucidation from small-angle scattering curves of large biomolecular assemblies is notoriously challenging. This is because the simulation of high-resolution features in the structure of large macromolecular assemblies, such as de novo protein assemblies, is computationally demanding when it needs to cover a broad range of length scales. Conventional methods, such as the numerical approximation to the Debye equation or the use of spherical harmonics, do not scale well as the size of the assembly increases, which limits their application to small structures (e.g. individual proteins). This work explores the effectiveness of a Monte Carlo method to simulate and fit scattering curves for large biomolecular assemblies spanning over ranges covering atomic and molecular detail (e.g. spacing and orientation of proteins in an assembly) as well as large-scale (hundreds of nanometres) features. Owing to its speed and scalability, it can be combined with a fitting algorithm to extract structural features from experimental small-angle scattering curves in biomolecular assemblies that are otherwise intractable for interpretation. This work first demonstrates the effectiveness of the tool using experimental small-angle X-ray scattering (SAXS) data from tile-like proteins that assemble into 1D tube-like macromolecular structures. Here, the diameter distribution of tubes is extracted from SAXS fits, and this is quantitatively compared with distributions from electron microscopy. SAXS data are also obtained from 2D sheet-like protein assemblies, and the proposed method is used to quantify structural features such as the separation distance between protein building blocks and the flexing of the sheet. An open-source implementation of the methodology is provided for use in a broad range of biological systems involving multi-scale scattering analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A dictionary learning algorithm for compression and reconstruction of streaming data in preset order

There has been an emerging interest in developing and applying dictionary learning (DL) to process massive datasets in the last decade. Many of these efforts, however, focus on employing DL to compress and extract a set of important features from data, while considering restoring the original data from this set a secondary goal. On the other hand, although several methods are able to process streaming data by updating the dictionary incrementally as new snapshots pass by, most of those algorithms are designed for the setting where the snapshots are randomly drawn from a probability distribution. In this paper, we present a new DL approach to compress and denoise massive dataset in real time, in which the data are streamed through in a preset order (instances are videos and temporal experimental data), so at any time, we can only observe a biased sample set of the whole data. Here, our approach incrementally builds up the dictionary in a relatively simple manner: if the new snapshot is adequately explained by the current dictionary, we perform a sparse coding to find its sparse representation; otherwise, we add the new snapshot to the dictionary, with a Gram-Schmidt process to maintain the orthogonality. To compress and denoise noisy datasets, we apply the denoising to the snapshot directly before sparse coding, which deviates from traditional dictionary learning approach that achieves denoising via sparse coding. Compared to full-batch matrix decomposition methods, where the whole data is kept in memory, and other mini-batch approaches, where unbiased sampling is often assumed, our approach has minimal requirement in data sampling and storage: i) each snapshot is only seen once then discarded, and ii) the snapshots are drawn in a preset order, so can be highly biased. Through experiments on climate simulations and scanning transmission electron microscopy (STEM) data, we demonstrate that the proposed approach performs competitively to those methods in data reconstruction and denoising.

97 MATHEMATICS AND COMPUTING↗

AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy

Over the past several decades, electron and scanning probe microscopes have become critical components of condensed matter physics, materials science and chemistry research. At the same time, the infrastructure for establishing a connection between microscopy observations and materials behaviour over a broader parameter space is lacking. In this work, we introduce AtomAI, an open-source software package bridging instrument-specific Python libraries, deep learning and simulation tools into a single ecosystem. AtomAI allows direct applications of deep neural networks for atomic and mesoscopic image segmentation converting image and spectroscopy data into class-based local descriptors for downstream tasks such as statistical and graph analysis. For atomically resolved imaging data, the output is types and positions of atomic species, with an option for subsequent refinement. AtomAI further allows the implementation of a broad range of image and spectrum analysis functions, including invariant variational autoencoders for disentangling structural factors of variation and im2spec type of encoder–decoder models for mapping structure–property relationships. Finally, our framework allows seamless connection to the first principles modelling with a Python interface on the inferred atomic positions.

36 MATERIALS SCIENCE↗

Post Access Report: Initial testing of wave energy powered UV-C LED anti-biofouling system

In the experiment entitled “Initial Testing of Wave Energy Powered UV-C LED Anti-Biofouling System,” a UV-C LED was used to treat platinum-coated titanium coupons emulating CTD electrode material. An additional control set of coupons were exposed to seawater without the LED treatment. The experimental setup was deployed in seawater tanks at PNNL-Sequim’s Marine and Coastal Research Laboratory (MCRL) for the duration of six months. Tank seawater temperature data were collected for the duration of the experimental campaign. At two-month intervals triplicate sets of coupons were removed from both treatment and control test chambers and weighed to characterize biomass accumulation. After weighing, the coupons were photographed in initial condition, with a stain applied to highlight biofouling growth, and after rinsing off excess stain. These photographs were analyzed using PNNL’s Biofouling Growth Index (BGI) software and with a calculation of percentage dark pixels after conversion to grayscale. Additionally, at the final 6-month datapoint triplicate sets of control and test coupons were sent to PNNL-Richland’s Environmental Molecular Sciences Laboratory (EMSL) for scanning electron microscopy. All data organization and collection methods are described in further detail in the document “TEAMER_3newable_Data_Guide.pdf.” Biomass accumulation increased over time on both sets of coupons, and percent area biofouled demonstrated a similar trend. Analysis of the collected data resulted in the findings that treatment method has an impact on biofouling growth, though to a much less significant extent than time does. Additionally, qualitative findings included the presence of advanced life forms such as worms in the control test chamber, but not in the treated test chamber.

16 TIDAL AND WAVE POWER↗

High-Resolution Imaging of Unstained Polymer Materials

Electron microscopy has played an important role in polymer characterization. Traditionally, electron diffraction is used to study crystalline polymers while transmission electron microscopy is used to study microphase separation in stained block copolymers and other multiphase systems. We describe developments that eliminate the barrier between these two approaches - it is now possible to image polymer crystals with atomic resolution. The focus of this Review is on high-resolution imaging (30 Å and smaller) of unstained polymers. Recent advances in hardware allow for capturing numerous (as many as 105) low-dose images from an unperturbed specimen; beam damage is a significant barrier to high-resolution electron microscopy of polymers. Machine-learning-based software is then used to sort and average the images to retrieve pristine structural information from a collection of noisy images. Acknowledging the heterogeneity in polymer samples prior to averaging is essential. Molecular conformations in a wide range of amphiphilic block copolymers, polymerized ionic liquids, and conjugated polymers can be gleaned from two-dimensional projections (2D), three-dimensional (3D) tomograms, and four-dimensional (4D) scanning transmission electron microscopy (STEM) data sets where 2D diffraction patterns are taken as a function of position. Some methods such as phase contrast STEM have been used to image closely related materials such as metal-organic frameworks but not polymers. With improvements in hardware and software, such methods may soon be applied to polymers. Our goal is to provide a comprehensive understanding of the strategies toward the high-resolution imaging of radiation sensitive polymer materials at different length scales.

36 MATERIALS SCIENCE↗

Homogeneous condensation of gaseous mixtures of Si, Fe, O, N, and C in relative cosmic abundance and implications for astronomical condensation

The pressure versus temperature curves for homogeneous nucleation and condensation of two gaseous mixtures with nearly relative solar abundance of Si, Fe, O, N, and C in an excess of H were determined experimentally. Mixtures of CO, Fe(CO)5, H2, SiH4, and N2O in Ar were heated behind reflected shocks in a shock tube. The nucleation and condensation, which took place in the subsequent gas-dynamic expansion (cooling phase), was monitored by light scattering and turbidity. Grain morphologies and crystalline phases present in the condensates were determined by electron microscopy. These data cast doubt on the validity of both equilibrium and classical nucleation theoretical approaches to predict homogeneous condensation in a solar nebula or stellar atmosphere.

Stephens, J. R.↗

Transformation to Ni5Al3 in a 63.0 at. pct Ni-Al alloy

Microstructures of 63 at. pct P/M Ni-Al alloys with a composition close to the stoichiometry of the Ni5Al3 phase were investigated using homogenized and quenched specimens aged at low temperatures for various times. Results of analyses of XRD data and electron microscopy observations were used for quantitative phase analysis, performed to calculate the (NiAl + Ni5Al3)/Ni5Al3 phase boundary locations. The measured lattice parameters of Ni5Al3 phase formed at 823, 873, and 923 K indicated an increase in tetragonality of the phase with increasing nickel content.

Khadkikar, P. S.↗

Purification and immunolocalization of an annexin-like protein in pea seedlings

As part of a study to identify potential targets of calcium action in plant cells, a 35-kDa, annexin-like protein was purified from pea (Pisum sativum L.) plumules by a method used to purify animal annexins. This protein, called p35, binds to a phosphatidylserine affinity column in a calcium-dependent manner and binds 45Ca2+ in a dot-blot assay. Preliminary sequence data confirm a relationship for p35 with the annexin family of proteins. Polyclonal antibodies have been raised which recognize p35 in Western and dot blots. Immunofluorescence and immunogold techniques were used to study the distribution and subcellular localization of p35 in pea plumules and roots. The highest levels of immunostain were found in young developing vascular cells producing wall thickenings and in peripheral root-cap cells releasing slime. This localization in cells which are actively involved in secretion is of interest because one function suggested for the animal annexins is involvement in the mediation of exocytosis.

NASA Discipline Number 40-50↗

A Predictive Bubble Point Pressure Model for Porous Liquid Acquisition Device Screens

This article presents a simplified model for porous screen channel liquid acquisition devices based on a maximum bubble point pressure method from Adamson and Gast (1997). To validate the model, three 304 stainless steel (325 × 2300, 450 × 2750, and 510 × 3600) mesh samples were tested in methanol, acetone, isopropyl alcohol, and water. Screen pores are estimated based on analysis from scanning electron microscopy, historical data, and current test data. Results show that the bubble point pressure is proportional to the surface tension of the fluid only when accounting for nonzero contact angles. The previous assumption that bubble point pressure scales inversely with effective pore diameter is shown to be invalid, as the second finest 450 × 2750 produced the highest bubble point of the three screens. The simplified bubble point model can be used to make predictions for any pure fluid when pore diameters are based on bubble point tests and not SEM analysis.

Liquid Acquisition Device↗

Apollo Sample 64455: Petrologic and Geochemical Characterization of a Glass-Coated Impact Melt Rock

Introduction and Background: Impact melts are a key product of the impact cratering process. Impact melts can turbulently entrain pre-existing rock fragments, and melt part of those entrained materials until thermal equilibrium is achieved [1–3]. Lunar impact melt rocks are valuable for determining the precise ages of basin-forming and cratering events, which anchor the cratering chronology and dynamics of the entire solar system [4, and references therein]. Additionally, entrained clasts can inform on the petrology and age of crustal materials that predate the melt-forming impact. In lithologies where no pristine samples are available, entrained materials may hold the record of endogenous lunar volatiles, provided we can untangle their impact histories as high temperature impact melt can heat the clasts and may alter their native volatiles or isotopic signatures [5–6]. Here, we characterize an Apollo 16 sample, 64455,70B, in an effort to understand the physical, microstructural, and geochemical consequences of impact melt contact metamorphism. We acquired optical light and x-ray maps of the sample, as well as quantitative analyses via electron probe microanalysis (EPMA). These tasks allow us to better understand the interaction between impact melts and entrained clasts, ultimately closing knowledge gaps in high-temperature impact processing on planetary surfaces. Sample Description: Sample 64455 is an oblong crystalline rock encased in glass to form an egg-like shape [7–8]. The impact melt coating has altered the outer (1 to 2 mm) rind of the crystalline rock and has preserved schlieren. The interior crystalline rock is likely a crystalline impact melt rock based on its bulk Ir content of 2.25 parts per billion [9]. The composition of the impact melt glass rind cannot be solely derived from the crystalline interior, the glassy rind is more aluminous and less magnesian [10]. Neither the crystalline interior nor the glassy rind were radiometrically age dated, although several studies investigated its cosmogenic isotopes and exposure ages, which reveal the sample was exposed on the lunar surface for ~1–2 Ma [11–13]. Preliminary Results: The interior crystalline rock consists primarily of plagioclase with interstitial pyroxene and olivine. Plagioclase grains are zoned and compositions range from An97–89, while pyroxene compositions generally range from En68Fs19Wo13 to En82Fs15Wo3 and olivine are Fo77 to Fo85. Minor phases include Fe-Ni metal, Ca-phosphates, a K- and Si-rich phase, sulfides, and Fe-phosphides. The glass rind contains approximately 44 wt.% SiO2, 25 wt.% Al2O3, 6 wt.% FeO, 8 wt.% MgO, and 14 wt.% CaO, with other oxides at <1 wt.% each, although the composition of the glass becomes more variable closest to the crystalline rock. The rind consists of rounded plagioclase, pyroxene, and olivine crystals with interstitial melt. In some locations, the rounded crystals are surrounded by closely-packed, blade-like crystals, <5 µm in width. These results indicate that the crystalline interior has partially melted where in contact with the high-temperature impact melt and while further investigation is needed, pyroxene may have preferentially melted relative to olivine and plagioclase. Future Work: We plan to conduct electron backscatter diffraction (EBSD) mapping in order to determine crystallinity and crystal orientation. Next using transmission electron microscopy (TEM) data in conjunction with EBSD and EPMA information, we will model the diffusion of moderately volatile elements between the melt and remnant crystals.

A C Stadermann↗

Machine learning for automated experimentation in scanning transmission electron microscopy

Abstract Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows, as well as the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.

36 MATERIALS SCIENCE↗

Towards data-driven next-generation transmission electron microscopy

Electron microscopy touches on nearly every aspect of modern life, underpinning materials development for quantum computing, energy and medicine. We discuss the open, highly integrated and data-driven microscopy architecture needed to realize transformative discoveries in the coming decade.

36 MATERIALS SCIENCE↗