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DOE OSTI2026

4D-STEM Coupled with Unsupervised Machine Learning to Reveal at Large-Scale the Microstructural Evolution in Li- and Mn-Rich Cathodes

Li- and Mn-rich (LMR) layered oxides are known to exhibit a thin surface reconstruction layer, which grows during electrochemical cycling in a manner that depends on exposed crystallographic facets, cycling conditions, and electrolyte chemistry. Direct characterization of this layer has traditionally relied on high-resolution electron microscopy, which is inherently limited to small fields of view. Here, we employ four-dimensional scanning transmission electron microscopy (4D-STEM) combined with unsupervised machine-learning clustering to quantitatively map phase distributions over large areas and track their evolution in LMR cathodes during electrochemical aging. Our results show that the surface reconstruction layer consists predominantly of a rocksalt phase, whose thickness varies across different facets following activation cycling and becomes substantially thicker and more uniform during calendar aging. In contrast, a spinel-like phase is observed within the particle bulk. Large-area phase mapping and correlative high-resolution imaging reveal that this spinel-like phase preferentially nucleates at bulk crystallographic defects, including boundaries between 60°-rotated layered domains and associated mixed-phase regions, rather than exclusively at the particle surface. Our findings establish a mechanistic distinction between surface-driven rocksalt formation and bulk-defect-mediated spinel nucleation while demonstrating the unique capability of 4D-STEM to provide statistically robust, mesoscale insight into complex phase-evolution processes in LMR cathodes.

4D-STEM↗

DOE OSTI2026

Microfibril orientation and compositional heterogeneity in fiber and vessel cell walls of poplar xylem studied by AFM-IR and SFG spectroscopy

Understanding the structural organization of cellulose microfibrils (CMFs) within individual plant cell walls is essential for connecting cell wall architecture to its mechanical and physiological functions. However, due to the complex hierarchical structure and nanoscale heterogeneity of cell walls, it remains technically challenging to resolve detailed compositional and orientational information at subcellular levels of individual cell walls. This study investigates the internal 3D structure, chemical composition, and sublayer organization of fiber and vessel cell walls in the xylem tissue of a two-year-old field-grown hybrid poplar tree (Populus alba × P. glandulosa) using photothermal atomic force microscopy coupled with infrared spectroscopy (AFM-IR) and sum frequency generation (SFG) hyperspectral microscopy. AFM-IR provided nanoscale chemical imaging, revealing localized compositional heterogeneity, including variations between adjacent cell walls and transitional layers beyond the traditional S1, S2, and S3 sublayers. SFG microscopy revealed that CMFs in fiber walls are highly aligned along the stem axis, consistent with their role in mechanical support, while vessel cell walls exhibited slightly tilted CMFs, reflecting their function in hydraulic transport. Together, these results offer new insights into cell-type-specific CMF organization and compositional gradients in hybrid poplar xylem. These findings highlight the structural and chemical complexity of secondary cell walls in woody plants and demonstrate the value of AFM-IR and SFG spectroscopy in elucidating plant cell wall architecture.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

BioCARS: Synchrotron facility for probing structural dynamics of biological macromolecules

A major goal in biomedical science is to move beyond static images of proteins and other biological macromolecules to the internal dynamics underlying their function. This level of study is necessary to understand how these molecules work and to engineer new functions and modulators of function. Stemming from a visionary commitment to this problem by Keith Moffat decades ago, a community of structural biologists has now enabled a set of x-ray scattering technologies for observing intramolecular dynamics in biological macromolecules at atomic resolution and over the broad range of timescales over which motions are functionally relevant. Many of these techniques are provided by BioCARS, a cutting-edge synchrotron radiation facility built under Moffat leadership and located at the Advanced Photon Source at Argonne National Laboratory. BioCARS enables experimental studies of molecular dynamics with time resolutions spanning from 100 ps to seconds and provides both time-resolved x-ray crystallography and small- and wide-angle x-ray scattering. Structural changes can be initiated by several methods—UV/Vis pumping with tunable picosecond and nanosecond laser pulses, substrate diffusion, and global perturbations, such as electric field and temperature jumps. Studies of dynamics typically involve subtle perturbations to molecular structures, requiring specialized computational techniques for data processing and interpretation. In this review, we present the challenges in experimental macromolecular dynamics and describe the current state of experimental capabilities at this facility. As Moffat imagined years ago, BioCARS is now positioned to catalyze the scientific community to make fundamental advances in understanding proteins and other complex biological macromolecules.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

X-ray linear dichroic ptychography

Biominerals such as seashells, coral skeletons, bone, and tooth enamel are optically anisotropic crystalline materials with unique nanoscale and microscale organization that translates into exceptional macroscopic mechanical properties, providing inspiration for engineering new and superior biomimetic structures. Using Seriatopora aculeata coral skeleton as a model, here, we experimentally demonstrate X-ray linear dichroic ptychography and map the c-axis orientations of the aragonite (CaCO 3 ) crystals. Linear dichroic phase imaging at the oxygen K-edge energy shows strong polarization-dependent contrast and reveals the presence of both narrow (<35°) and wide (>35°) c-axis angular spread in the coral samples. These X-ray ptychography results are corroborated by four-dimensional (4D) scanning transmission electron microscopy (STEM) on the same samples. Evidence of co-oriented, but disconnected, corallite subdomains indicates jagged crystal boundaries consistent with formation by amorphous nanoparticle attachment. We expect that the combination of X-ray linear dichroic ptychography and 4D STEM could be an important multimodal tool to study nano-crystallites, interfaces, nucleation, and mineral growth of optically anisotropic materials at multiple length scales.

4D scanning transmission electron microscopy↗

DOE OSTI2026

Multi-modal characterization of nitrate reduction nano-catalysts with periodic strain distribution

Strain engineering serves as a pivotal strategy to optimize catalytic activity in electrocatalysis. However, the catalyst sizes under industrial conditions are usually large and even beyond nanometer regime. The critical methodological limitations on strain imaging of such catalysts with both large field of view and high spatial resolution obscure the mechanistic understanding of strain-performance correlations. Here, we present an optimized four-dimensional scanning transmission electron microscopy (4D-STEM) method to acquire strain mapping of both bulk and surface across particles up to 500 nm with 0.6 nm spatial resolution and 0.55% precision. We observe the ripple-like periodic strain coupled with elemental fluctuations inside a perovskite-type hydroxide CuCoSn(OH) 6 and find it correlated to electrocatalytic nitrate reduction (NO 3 – RR) absorption energy to achieve the 92.6% Faradaic efficiency and long-term test over 1000 h at membrane electrode assembly (MEA) for ammonia electrosynthesis. This universal framework design offers a practical method that not only develops an advanced measurement combining multi-modal characterization techniques but also reveals the intrinsic structure-property constitutive law of industry-level catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Imaging plant metabolism in situ

Abstract Mass spectrometry imaging (MSI) has emerged as an invaluable analytical technique for investigating the spatial distribution of molecules within biological systems. In the realm of plant science, MSI is increasingly employed to explore metabolic processes across a wide array of plant tissues, including those in leaves, fruits, stems, roots, and seeds, spanning various plant systems such as model species, staple and energy crops, and medicinal plants. By generating spatial maps of metabolites, MSI has elucidated the distribution patterns of diverse metabolites and phytochemicals, encompassing lipids, carbohydrates, amino acids, organic acids, phenolics, terpenes, alkaloids, vitamins, pigments, and others, thereby providing insights into their metabolic pathways and functional roles. In this review, we present recent MSI studies that demonstrate the advances made in visualizing the plant spatial metabolome. Moreover, we emphasize the technical progress that enhances the identification and interpretation of spatial metabolite maps. Within a mere decade since the inception of plant MSI studies, this robust technology is poised to continue as a vital tool for tackling complex challenges in plant metabolism.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

In-situ observations of cyclic deformation in an extruded Mg-2Nd-1Y-0.1Zr-0.1Ca alloy

In this study, the evolution of deformation mechanisms during cyclic loading in an extruded, solution-treated Mg–2Nd–1Y–0.1Zr–0.1Ca alloy was investigated using a combination of in-situ loading, scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and focused ion beam (FIB) nanofabrication. The initial microstructure exhibited a random crystallographic texture with no preferred grain orientation. Flat, rectangular dog-bone specimens were subjected to load-controlled, fully reversed fatigue for 50 cycles, during which the same region was sequentially mapped to track microstructural changes. After 10 cycles of loading deformation twins were observed. During tensile reloading detwinning or narrowing of those twinned regions occurred. After 20 cycles, detwinning ceased and residual twins remained in the material. SEM imaging revealed numerous surface slip traces after cyclic loading. EBSD-assisted slip trace analysis identified the activation of prismatic and pyramidal < c+a> slip systems during low-cycle fatigue. Site-specific scanning transmission electron microscopy (STEM) further revealed that deformation was also accommodated by basal < a> slip and the dissociation of < c+a> dislocations. Center-of-symmetry (COS) analysis confirmed that the dissociation of < c+a> dislocations resulted in the formation of I₁ intrinsic stacking faults after cyclic loading. These findings provide new insights into the complex interplay of dislocation mechanisms governing fatigue deformation in rare-earth-containing Mg alloys.

Cyclic deformation↗

DOE OSTI2026

Miscibility and Cocrystallization in Ethylene–Vinyl Alcohol Copolymer Blends

The compatibility among different ethylene−vinyl alcohol (EVOH) copolymer grades plays a crucial role in determining how easily they can be recycled when present together in a mixed waste stream. In this work, melt miscibility and cocrystallization in 50/50 wt/wt binary blends of EVOH copolymers with differing ethylene content were studied. Blends with up to a 21 mol % difference in composition were analyzed. Thin films of miscible blends appeared homogeneous in the melt as observed under an optical microscope, whereas AFM height images showed that immiscible blends underwent surface roughening due to phase separation. For copolymers with modest composition differences, the blend behaved as a single component, showing single, narrow melting and crystallization peaks in DSC thermograms, indicating cocrystallization at the level of crystal stems. Interestingly, even phase-separated blends demonstrated partial cocrystallization due to partial miscibility of the components in the molten phase-separated domains. The extent of cocrystallization is governed by the difference in freezing points of the components; rapid cooling increased cocrystallization in the blends. The maximum compositional difference allowed for miscibility is not constant but depends on the blend’s average comonomer content, in agreement with the “miscibility window” calculated from literature pressure−volume−temperature behavior for the individual EVOH copolymers and regular mixing theory. This study helps illuminate the effect of copolymer composition on miscibility, as well as cocrystallization behavior in blends of random copolymers with cocrystallizable units.

Cocrystallization↗

DOE OSTI2022

NucD-23 Nuclear Diagnostics Development: The future of neutron imaging and gamma reaction history

The Neutron Imaging team has deployed novel, active detection systems along two lines of sight at NIF. Studies aimed to characterize NIS sensitivity to implosion shape will be carried out to further extend reconstruction capabilities. Additionally, an improved camera timing system that will reduce uncertainties stemming from bang time jitter will also be fielded. The NIF GRH data recording system is more then 10 years old needs to be updated. Bandwidth and noise are a key limiting factor for improving data quality. New high bandwidth and low noise components will be tested at OMEGA in advance before fielding the components at NIF.

42 ENGINEERING↗

DOE OSTI2024

4D-STEM Mapping of Nanocrystal Reaction Dynamics and Heterogeneity in a Graphene Liquid Cell

Chemical reaction kinetics at the nanoscale are intertwined with heterogeneity in structure and composition. However, mapping such heterogeneity in a liquid environment is extremely challenging. Here, in this work, we integrate graphene liquid cell (GLC) transmission electron microscopy and four-dimensional scanning transmission electron microscopy to image the etching dynamics of gold nanorods in the reaction media. Critical to our experiment is the small liquid thickness in a GLC that allows the collection of high-quality electron diffraction patterns at low dose conditions. Machine learning-based data-mining of the diffraction patterns maps the three-dimensional nanocrystal orientation, groups spatial domains of various species in the GLC, and identifies newly generated nanocrystallites during reaction, offering a comprehensive understanding on the reaction mechanism inside a nanoenvironment. This work opens opportunities in probing the interplay of structural properties such as phase and strain with solution-phase reaction dynamics, which is important for applications in catalysis, energy storage, and self-assembly.

four-dimensional scanning transmission electron mi↗

DOE OSTI2026

Data for Yield from Iowa’s first commercial miscanthus fields: implications of spatial variability for productivity and sustainability beyond research plots

This dataset contains biomass yield measurements and associated vegetation index data collected from commercial Miscanthus × giganteus fields in eastern Iowa during the 2022–2023 growing seasons. The data support the analyses presented in the article: “Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots.” We collected 105 ground-truth biomass samples from four mature commercial fields (>4 years old) covering 92.81 ha. Samples were taken from 3 m² quadrats that were hand-harvested in alignment with commercial harvest timing. Stem biomass (excluding leaves) was weighed, moisture-corrected, and converted to dry-matter yield expressed in Mg DM ha⁻¹. Sampling locations were selected to capture spatial variability visible in aerial imagery and were recorded using RTK GPS. Each biomass observation was paired with vegetation indices derived from high-resolution PlanetScope satellite imagery (3 m resolution). Images were acquired throughout the growing season, and indices were calculated to evaluate their ability to predict end-of-season biomass yield. Statistical and machine learning approaches were used to identify key predictors, and a linear regression model based on end-of-July Green Normalized Difference Vegetation Index (GNDVI) was developed and evaluated. This repository includes the data used in that modeling workflow. Management practices, economic data, full imagery time series, and additional methodological details are described in the associated publication and are not included here. The dataset consists of three comma-separated value (CSV) files: 1. Combine_Groundtruth_Yield_VI_22_23.csv This file contains ground-truth biomass yield measurements and associated key vegetation index values collected during the 2022 and 2023 growing seasons. Rows: 105 observations Columns: Year — Year of observation (2022 or 2023) Field — Field location identifier Sample_number — Unique sample identifier GNDVI_End_Jul — Green Normalized Difference Vegetation Index calculated at end of July GNDVI_End_Aug — Green Normalized Difference Vegetation Index calculated at end of August NDRE_End_Aug — Normalized Difference Red Edge index calculated at end of August Biomass_Stem_Yield_MgDM/ha — Measured stem biomass yield (megagrams dry matter per hectare) 2. trainData_GNDVI.csv This file contains the subset of observations used to train the predictive relationship between July GNDVI and biomass yield. Rows: 76 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Stem_Yield_MgDM/ha — Observed stem biomass yield (Mg DM ha⁻¹) 3. testData_GNDVI.csv This file contains the test dataset used to evaluate model performance. Rows: 29 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Predicted_Yield_MgDM/ha — Model-predicted stem biomass yield (Mg DM ha⁻¹) Observed_Yield_MgDM/ha — Measured stem biomass yield (Mg DM ha⁻¹)

Potential yield, yield gap, in-field management, y↗

DOE OSTI2023

Real-space imaging of periodic nanotextures in thin films via phasing of diffraction data

New properties and exotic quantum phenomena can form due to periodic nanotextures, including Moire patterns, ferroic domains, and topologically protected magnetization and polarization textures. Despite the availability of powerful tools to characterize the atomic crystal structure, the visualization of nanoscale strain-modulated structural motifs remains challenging. Here, we develop nondestructive real-space imaging of periodic lattice distortions in thin epitaxial films and report an emergent periodic nanotexture in a Mott insulator. Specifically, we combine iterative phase retrieval with unsupervised machine learning to invert the diffuse scattering pattern from conventional X-ray reciprocal-space maps into real-space images of crystalline displacements. Our imaging in PbTiO 3 /SrTiO 3 superlattices exhibiting checkerboard strain modulation substantiates published phase-field model calculations. Furthermore, the imaging of biaxially strained Mott insulator Ca 2 RuO 4 reveals a strain-induced nanotexture comprised of nanometer-thin metallic-structure wires separated by nanometer-thin Mott-insulating-structure walls, as confirmed by cryogenic scanning transmission electron microscopy (cryo-STEM). The nanotexture in Ca 2 RuO 4 film is induced by the metal-to-insulator transition and has not been reported in bulk crystals. We expect the phasing of diffuse X-ray scattering from thin crystalline films in combination with cryo-STEM to open a powerful avenue for discovering, visualizing, and quantifying the periodic strain-modulated structures in quantum materials.

Science & Technology - Other Topics↗

DOE OSTI2024

DEM simulation of the compression of crushable sand: does the initial particle shape matter?

Advances in DEM modeling, combined with high-resolution X-ray tomography, opened the way for computer models based on virtual replicas of the particles which preserve nearly all facets of their geometry. This leads to simulation advantages, but also high computational costs. Here we tackle a question stemming from this trend: how accurate should particle models be to ensure accuracy? We address this question for the case of the compression of crushable sand. LS-DEM was used to generate three models of Ottawa sand (exact replicas, ellipsoids, and spheres) from digital images of its grains. Compression-induced crushing was simulated for all sets by tracking evolving size and shape distribution. The results confirm that exact replicas provide the closest match of the measurements. However, intermediate degrees of rendering (e.g. ellipsoids preserving volume and aspect ratio of the real grains) led to satisfactory results only marginally different from those of exact replicas. In conclusion, these findings provide an example of the protocols that may be followed to identify the optimal degree of particle approximation which should be regarded as mandatory to achieve a conscious, sustainable use of computational resources.

58 GEOSCIENCES↗

DOE OSTI2020

Bottom-Up Meets Top-Down: The Crossroads of Multiscale Chromatin Modeling

Chromatin can be viewed as a hierarchically structured fiber that regulates gene expression. It consists of a complex network of DNA and proteins whose characteristic dynamical modes facilitate compaction and rearrangement in the cell nucleus. These modes stem from chromatin's fundamental unit, the nucleosome, and their effects are propagated across length scales. Understanding the effects of nucleosome dynamics on the chromatin fiber, primarily through post-translational modifications that occur on the histones, is of central importance to epigenetics. Within the last decade, imaging and chromosome conformation capture techniques have revealed a number of structural and statistical features of the packaged chromatin fiber at a hitherto unavailable level of resolution. Such experiments have led to increased efforts to develop polymer models that aim to reproduce, explain, and predict the contact probability scaling and density heterogeneity. At nanometer scales, available models have focused on the role of the nucleosome and epigenetic marks on local chromatin structure. At micrometer scales, existing models have sought to explain scaling laws and density heterogeneity. Less work, however, has been done to reconcile these two approaches: bottom-up and top-down models of chromatin. Here, we highlight the multiscale simulation models that are driving toward an understanding of chromatin structure and function, from the nanometer to the micron scale, and we highlight areas of opportunity and some of the prospects for new frameworks that bridge these two scales. Taken together, experimental and modeling advances over the last few years have established a robust platform for the study of chromatin fiber structure and dynamics, which will be of considerable use to the chromatin community in developing an understanding of the interplay between epigenomic regulation and molecular structure.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images

Abstract The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that rapidly generalize to large data sets under varying experimental conditions. We address this by employing a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator, which augments simulated data with realistic spatial frequency information. This allows the CycleGAN to generate images nearly indistinguishable from real data and provide labels for ML applications. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set, collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our method produces adaptable FCNs that can adjust to dynamically changing experimental variables with minimal intervention, marking a crucial step towards fully autonomous harnessing of microscopy big data.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

ToF-SIMS imaging reveals that p -hydroxybenzoate groups specifically decorate the lignin of fibres in the xylem of poplar and willow

Esterified phenolics occur as γ-linked lignin acylations in many plant taxa but little is known about the distribution or function of such groups. In the family Salicaceae, p-hydroxybenzoate groups are present in the lignin of poplars (Populus spp.) and willows (Salix spp.). In this study, the distribution of cell wall-bound p-hydroxybenzoate was examined in different tissue and cell types of poplar and willow trees. This analysis showed that p-hydroxybenzoate groups were most prevalent in juvenile stems, and were elevated in tension wood and following nitrogen fertilisation. Closer examination of stem cross-sections using time-of-flight secondary-ion mass spectrometry (ToF-SIMS) directly showed that p-hydroxybenzoate groups occur predominantly in the cell walls of fibres and are largely absent from vessel elements. Furthermore, these results point to a role for ester-linked groups in modifying the syringyl-rich lignin of fibres, perhaps to strengthen the cell walls of these structural elements or to increase lignification rates.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Revealing Progressive Degradation of Cobalt Oxide Nanoparticles During Thermochemical Redox Cycling via Operando STEM-EELS

Metal oxides are promising materials for long-duration thermochemical energy storage. Efforts to characterize their reaction kinetics, conversion rate, and morphological evolution during thermochemical cycling have largely focused on bulk and microscale measurements. However, the design of nanostructured metal oxides could improve the reaction reversibility and kinetics, warranting the development of platforms to investigate how these materials behave at the nanoscale. Here, we demonstrate the use of correlative, time-resolved electron energy loss spectroscopy and imaging in an environmental transmission electron microscope for studying the thermochemical cyclability of cobalt oxide nanoparticles with high spatial and temporal resolution. The spectroscopic data reveal a striking decrease in reaction kinetics after the first cycle, resulting from sintering-driven nanostructural densification. Comparison between cycling in humid and dry air shows that atmospheric conditions can modulate reaction transition temperatures but have limited effects on sintering over multiple cycles, suggesting long-term durability will instead rely on synthetic and/or nanostructural modifications.

25 ENERGY STORAGE↗

DOE OSTI2023

A Processing and Analytics System for Microscopy Data Workflows: The Pycroscopy Ecosystem of Packages

Major advancements in fields as diverse as biology and quantum computing have relied on a multitude of microscopy techniques. Despite the considerable proliferation of these instruments, significant bottlenecks remain in terms of processing, analysis, storage, and retrieval of the acquired datasets. Aside from lack of file standards, individual domain-specific analysis packages are often disjoint from the underlying datasets, and thus keeping track of analysis and processing steps remains tedious for the end-user, hampering reproducibility. Here, in this study, the pycroscopy ecosystem of packages is introduced, an open-source python-based ecosystem underpinned by a common data model. The data model, termed the N-dimensional spectral imaging data format, is realized in pycroscopy's sidpy package. This package is built on top of dask arrays, thus leveraging dask array attributes, but expanding them to accelerate microscopy relevant analysis and visualization. Several examples of the use of the pycroscopy ecosystem to create workflows for data ingestion and analysis of scanning transmission electron microscopy (STEM) and scanning probe microscopy data are shown. Adoption of such standardized routines will be critical to usher in the next generation of autonomous instruments where processing, computation, and meta-data storage will be critical to overall experimental operations.

97 MATHEMATICS AND COMPUTING↗