Rewards-based image analysis in microscopy
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Barakati, Kamyar↗
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Microscopy modality transfer of steel microstructures: Inferring scanning electron micrographs from optical microscopy using generative AI
Scanning electron microscopy (SEM) is resource intensive, which limits its throughput in some applications. As an alternative, we propose applying computer vision and machine learning to generate high-quality synthetic SEM micrographs from micrographs obtained using light optical microscopy (LOM). Working with a correlated LOM/SEM dataset of dual-phase steel images, we test generative models of various architectures, including encoder-decoder networks, generative adversarial networks (GANs), and diffusion-based models. We find that the diffusion models significantly outperform other methods on both qualitative and quantitative assessments, while preserving key metallurgical meaning. This work establishes diffusion as the state-of-the-art for microscopy modality transfer and demonstrates the potential of AI-powered microscopy to enhance LOM with micron scale structural recreation.
Computer vision↗
Methodology of Atomic Force Microscopy Visualization of Electrode–Electrolyte Interfaces
Electrochemical atomic force microscopy (EC-AFM) provides unprecedented insights into the microstructure of electrode–electrolyte interfaces during electrochemical reactions. However, performing EC-AFM measurements has many challenges, for example, drift, contamination, and probe degradation. We present solutions to these experimental issues through electrochemical cell design and carefully chosen experimental parameters. The possibility that the probes can react with the interface during scanning, generating false-positive electrochemical dynamics, is discussed as an example of the challenge of high-fidelity EC-AFM measurement. Here, we demonstrate this effect in highly ordered pyrolytic graphite and show that we could use electrochemical control of the AFM probe to enable high-fidelity in situ AFM visualization of solid–liquid interfaces during electrochemical reactions.
Electrochemical cells↗
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↗
Surface deposition of nanometer scale carbon structures on Bi 2 Sr 2 CaCu 2 O 8+δ using a low-cost scanning electron microscope
In this work, we investigate optically active nanostructures on Bi 2 Sr 2 CaCu 2 O 8+δ (BSCCO). The nanostructures are constructed from carbon nanocrystals formed using the electron beam of a scanning electron microscope to locally decompose residual hydrocarbons at the BSCCO surface. Atomic force microscopy is used to characterize the dimensions of these structures as a function of beam exposure time. This technique has been used to induce localized intercalation to form carbon nanoparticles up to tens of nanometers into a variety of 2D materials. Cross-sectional scanning transmission electron microscopy in BSCCO shows the presence of these carbon deposits on the surface, but there is no evidence they penetrate into the BSCCO substrate. Therefore, this technique is not suitable for localized intercalation in BSCCO, and it does have promise as a cost-effective approach to pattern nanometer scale etch stops.
2D materials↗
The ABCs of phase retrieval: Connecting the acronyms of scanning transmission electron microscopy
High-resolution scanning transmission electron microscopy (S/TEM) is an indispensable tool for characterizing the structure and properties of materials down to the atomic scale. Conventional S/TEM imaging, however, is limited by the phase problem, whereby the phase of the electron exit wave is lost upon detection. Recent advances in diffractive imaging and 4D-STEM have enabled a range of phase-retrieval techniques that computationally reconstruct the missing information encoded in the phase of the transmission function. These approaches offer improved dose efficiency and enhanced sensitivity to weakly scattering signals, extending quantitative imaging to beam-sensitive materials composed of light elements. In this work, we introduce the phase problem in electron microscopy and survey the diverse landscape of phase-retrieval techniques used in the field. Despite their many acronyms and algorithmic variations, these techniques share a common physical and mathematical foundation. We present a unified framework that connects these seemingly distinct methods, from parallax imaging and tilt-corrected bright-field (tcBF-STEM), to aberration-corrected bright-field (acBF-STEM), optimum bright-field (OBF-STEM) and single-sideband (SSB) ptychography, as well as first-moment integrated center of mass techniques (iCOM) and iterative ptychographic algorithms. Based on these insights, we discuss the opportunities and practical limitations of applying these methods across different materials systems, detector designs, and microscope configurations.Graphical abstractRepresentative electron microscopy configurations used for phase retrieval and diffractive imaging in S/TEM: (a) Zernike phase-contrast transmission electron microscopy (TEM), (b) small-convergence-angle four-dimensional scanning transmission electron microscopy (4D-STEM) for nanobeam-based phase reconstruction methods, and (c) large-convergence-angle 4D-STEM for ptychographic and related diffractive imaging techniques reviewed in this work.
36 MATERIALS SCIENCE↗
Operando microscopy for neuromorphic hardware
Microscopy techniques can uncover the physical properties and dynamic behaviours of materials, driving the discovery of emergent phenomena and guiding the design of next-generation computing hardware. As artificial intelligence becomes pervasive, the demand for high-performance materials to support sustainable information technologies is growing. Here, this Review highlights state-of-the-art imaging from electron and X-ray to optical techniques to probe the dynamics of neuromorphic materials, including operando characterization of devices. We examine design principles for neuromorphic materials, along with obstacles that hinder their development. Emphasis is placed on spatially and temporally resolved approaches that capture state changes including phase transitions, ferroic switching and spin-wave propagation that emulate biological components such as neurons, synapses and their connectivity. We discuss challenges in operando characterization and the integration of artificial intelligence-driven analysis for feedback-guided material discovery. Finally, we outline opportunities for real-time imaging of neuromorphic systems, paving the way towards adaptive, brain-inspired hardware.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Automated scanning probe microscopy of combinatorial ferroelectric libraries: Gaussian-process-guided exploration and noise-aware experiment planning
Combinatorial materials libraries provide an efficient route for mapping composition–property relationships, but their broader impact depends on rapid, quantitative, and functionally relevant characterization. Scanning Probe Microscopy (SPM), including piezoresponse force microscopy (PFM), offers significant potential for quantitative, functionally relevant combi-library readouts. Here, we implement a fully automated SPM workflow for ferroelectric combinatorial libraries and benchmark Gaussian-process-based Bayesian optimization strategies for autonomous experiment planning. The workflow integrates automated probe motion, contact optimization, imaging, and dual amplitude resonance tracking-PFM spectroscopy, and uses scalarized spectroscopic observables to guide subsequent measurements. Stage motion, probe engagement, in-contact tuning, imaging, spectroscopy, and the choice of the next measurement location all proceed without human input. We demonstrate the approach on Sm-doped BiFeO 3 and Zn x Mg 1−x O libraries. By comparing vanilla Bayesian optimization with a measured-noise variant, we show that explicit treatment of local reproducibility can improve modeling of composition-dependent response when the measured variance is physically meaningful, but can also reduce robustness when variability is dominated by outliers or topographic artifacts. Furthermore, these results establish automated SPM as a bridge between combinatorial synthesis and quantitative functional characterization.
Liu, Yu [University of Tennessee, Knoxville, TN (U↗
Impact of the Annealing Temperature on the Local Se Distribution and Optical Properties in CdSexTe1-x Solar Cells by Multimodal X-ray Microscopy
The introduction of Se in CdTe solar-cell absorbers has led to multiple beneficial effects in the devices, including an increased short-circuit current and an extended charge-carrier lifetime. A critical manufacturing step of CdSexTe1-x solar cells is CdCl2 annealing at high temperatures, during which Se diffuses through the absorber layer. Understanding the local Se distribution in the absorber and its impact on optical properties of the material is key for the further development of this thin-film solar cell technology. In a multimodal x-ray scanning microscopy approach, we investigated the impact of the annealing temperature of CdSexTe1-x solar cells on the Se distribution and on optical characteristics of the material. For this purpose, we exploited an upgraded x-ray excited optical luminescence (XEOL) detection unit that enables simultaneous spectrally and temporally resolved XEOL mapping at synchrotron facilities that are ideal for studying thin-film solar-cell materials with hard x-rays featuring high penetration depth and lateral resolution.
14 SOLAR ENERGY↗
Atomic Evolution of Hydrogen Intercalation Wave Dynamics in Palladium Nanocrystals Revealed by Liquid-Phase Transmission Electron Microscopy
Solute-intercalation-induced phase separation creates spatial heterogeneities in host materials, a phenomenon ubiquitous in batteries, hydrogen storage, and other energy devices. Despite many efforts, probing intercalation processes at the atomic scale has been a significant challenge. By utilizing liquid-phase transmission electron microscopy (TEM), we study hydrogen (de)intercalation in palladium nanocrystals as a model system and have achieved unprecedented atomic-resolution imaging of hydrogen intercalation wave dynamics. Our observations reveal that intercalation wave mechanisms, instead of shrinking-core mechanisms, prevail at ambient temperature for palladium nanocubes ranging from ∼60 nm down to ∼10 nm. Systematic image analysis uncovers the atomic evolution of the hydrogen intercalation wave, transitioning from nonplanar and inclined boundaries to those closely aligned with {100} planes. Our kinetic Monte Carlo simulations demonstrate that the observed intercalation wave dynamics correspond to sorption pathways minimizing the lattice mismatch strain at the phase boundary. In conclusion, unveiling the atomic intercalation pathways holds profound implications for engineering intercalation-mediated devices and advancements in energy sciences.
Lee, Daewon [Lawrence Berkeley National Laboratory↗
Comprehensive structural characterization of charged polymers involved in moisture-driven direct air capture
This study provides a comprehensive structural characterization of commercially available alkaline anion-exchange polymers (Fumasep FAA-3 and IRA 900) used in moisture-driven direct air capture (DAC) of carbon dioxide. Using X-ray diffraction, SAXS/WAXS, atomic force microscopy, FIB-SEM, and transmission electron microscopy, the authors identify nanoscale clustering, porosity, swelling behavior, and humidity-dependent structural changes that influence CO₂ adsorption and release. These findings establish structure–function relationships critical for designing more durable and energy-efficient DAC polymer materials.
36 MATERIALS SCIENCE↗
Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes
Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engineering. Here, we overcome this limitation using an artificial intelligence-guided electron microscopy workflow to map the 3D topology and clustering of atomic vacancies in Ti3C2TX MXene. Our approach reconstructs the 3D coordinates of vacancies across hundreds of thousands of lattice sites, generating robust statistical insight into their distribution that can be correlated with specific synthesis pathways. This large-scale data enables us to classify a hierarchy of defect structures-from isolated vacancies to nanopores-revealing their preferred formation and interaction mechanisms, as corroborated by molecular dynamics simulations. This work provides a generalizable framework for understanding and ultimately controlling point defects across large volumes, paving the way for the rational design of defect-engineered functional 2D materials.
2D materials↗
Evolution of magnetic bubble domains in the uniaxial ferromaget CeRu 2 Ga 2 B inferred from the Hall effect and ac magnetic susceptibility
We study the Hall effect, AC magnetic susceptibility (χ ac ), and magnetic force microscopy of the uniaxial ferromagnet CeRu 2 Ga 2 B with a centrosymmetric crystal structure. We observe a finite topological Hall effect (THE) within the ordered phase, before the magnetization is polarized by applied field. By comparing the field dependences of the area fraction of the magnetic bubbles, the derivative of χ ac , and the THE signal, we deduce that the magnetic bubbles in CeRu 2 Ga 2 B evolve from the trivial to topological spin texture with field. Our findings enable the expansion of the search for magnetic materials hosting topological spin textures to include uniaxial ferromagnets and open a new possibility to tailor the topological spin texture.
AC magnetic susceptibility↗
Real-space visualization of a defect-mediated charge density wave transition
Here, we study the coupled charge density wave (CDW) and insulator-to-metal transitions in the 2D quantum material 1T-TaS 2 . By applying in situ cryogenic 4D scanning transmission electron microscopy with in situ electrical resistance measurements, we directly visualize the CDW transition and establish that the transition is mediated by basal dislocations (stacking solitons). We find that dislocations can both nucleate and pin the transition and locally alter the transition temperature T c by nearly ~75 K. This finding was enabled by the application of unsupervised machine learning to cluster five-dimensional, terabyte scale datasets, which demonstrate a one-to-one correlation between resistance—a global property—and local CDW domain-dislocation dynamics, thereby linking the material microstructure to device properties. This work represents a major step toward defect-engineering of quantum materials, which will become increasingly important as we aim to utilize such materials in real devices.
4D-STEM↗
Microscopic Scattering Approach to In-Gap States
We develop a microscopic scattering formalism to describe Yu-Shiba-Rusinov (YSR) states due to a single Cr adatom on the Bi-terminated surface of beta Bi2Pd, by combining ab initio Wannier functions with a real-space Green's function approach in the Bogoliubov-de Gennes formalism[1]. Our framework reproduces key scanning tunneling spectroscopy features, including a single particle-hole asymmetric YSR peak and isotropic dIdV maps around the impurity. Decomposing the YSR states reveals contributions from four nearly degenerate C4v representations, with energy broadening masking their individual signatures. Spin-orbit coupling induces partial spin polarization, while the spatial asymmetry between particle and hole components arises from Cr d-Bi p hybridization. These results highlight the importance of realistic band structures and microscopic modeling for interpreting STM data for magnetic in-gap states on superconductors. Further advances examining layered 2D material surfaces, such as NbSe2, will be described[2]. For this system the superconducting properties are obtained from a full anisotropic Eliashberg calculation of the superconducting order parameter along with the charge density wave gap. Additional features associated with proposals to measure the dynamics of these individual YSR states will be presented. [1] arXiv:2507.08740 [2] arXiv:2507.11856
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Electrode Erosion and Prefire Studies Towards Fusion Scale Pulsed Power
This study presents a comprehensive investigation of electrode erosion and discharge behavior in spark gap switches over long switching cycle lifetimes. Brass, copper–tungsten (CuW), and stainless steel electrodes are tested under controlled conditions to quantify material degradation, debris accumulation, and changes in breakdown voltage. High-resolution imaging and statistical analysis of spark channel locations and gap breakdown voltages reveal how surface evolution influences long-term performance and reliability. These results provide essential data for lifetime modeling and inform design strategies for pulsed power systems in emerging applications such as private sector fusion energy and large-scale facilities like Sandia’s Z Machine and proposed ZX upgrades, where high repetition reliability and predictable behavior are critical.
electrical breakdown↗
Sintering effects on NpO 2 grain size and morphology: The role of precursor Np phase
Neptunium dioxide (NpO 2 ) is a key phase in nuclear material processing as a target material for the production of plutonium-238 ( 238 Pu) and has been historically synthesized via the calcination of a Np oxalate precursor. Alternative synthesis methods for NpO 2 are now more prevalent, necessitating their study and comparison with the more common oxalate route. The purpose of this work was to investigate the microstructural properties of NpO 2 synthesized from a nitrate-based Np precursor phase via the assessment of NpO 2 particle size and morphology as a function of calcination temperature and time. Scanning electron microscopy (SEM) was used to probe the primary grain size and morphology of NpO 2 after calcination at temperatures ranging from 700 to 1100 °C and hold times ranging from 1 to 10 h. Post-image analysis using ImageJ software enabled the quantification of mean particle diameter. This analysis indicated that particle diameter increases with both increasing calcination temperature and hold time. Primary particles were shown to be clumped in irregular patterns into the overall rough, blocky aggregates, but this macroscopic morphology was not affected by calcination time or temperature. Although trends in primary grain size of NpO 2 were consistent with available literature from other Np precursor phases, the macroscopic morphology of the NpO 2 aggregates was quite different than reported for other precursors. Through comparison with historical literature on Np oxalate, this work emphasizes the importance of Np precursor on the physical properties of NpO 2 .
38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
Polarized target nuclear magnetic resonance measurements with deep neural networks
Continuous-wave Nuclear Magnetic Resonance (CW-NMR) operated in constant-current mode has served as a foundational technique for polarization measurement in solid-state dynamically polarized targets within nuclear and high-energy physics experiments for several decades, and it remains an essential tool. Conventional Q-meter-based phase-sensitive detection is critical for precise real-time determination of target polarization during scattering runs. However, the accuracy and reliability of these measurements are frequently compromised by elevated noise levels, baseline drift, and systematic uncertainties arising from signal isolation and fitting, ultimately degrading the overall experimental figure of merit. In this work, we report the first successful application of neural network architectures to continuous-wave NMR polarization metrology. By leveraging advanced machine learning techniques for signal extraction and denoising, we achieve a substantial reduction of fitting uncertainties under a variety of realistic simulated and experimental conditions. These improvements translate directly into more robust real-time (online) polarization monitoring and higher precision in subsequent offline analysis. By reducing analysis-induced uncertainty, the resulting methodology can improve the effective figure of merit for scattering experiments employing dynamically polarized targets and provides a new toolset for NMR-based polarimetry in high-energy and nuclear physics.
Metrology↗