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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sources NASA NTRS DOE OSTI

DOE OSTI2022

A Robust Neural Network for Extracting Dynamics from Electrostatic Force Microscopy Data

Advances in scanning probe microscopy (SPM) methods such as time-resolved electrostatic force microscopy (trEFM) now permit the mapping of fast local dynamic processes with high resolution in both space and time, but such methods can be time-consuming to analyze and calibrate. Here, we design and train a regression neural network (NN) that accelerates and simplifies the extraction of local dynamics from SPM data directly in a cantilever-independent manner, allowing the network to process data taken with different cantilevers. We validate the NN’s ability to recover local dynamics with a fidelity equal to or surpassing conventional, more time-consuming, calibrations using both simulated and real microscopy data. We apply this method to extract accurate photoinduced carrier dynamics on n = 1 butylammonium lead iodide, a halide perovskite semiconductor film that is of interest for applications in both solar photovoltaics and quantum light sources. Lastly, we use SHapley Additive exPlanations to evaluate the robustness of the trained model, confirm its cantilever-independence, and explore which parts of the trEFM signal are important to the network.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Enabling reactive microscopy with MicroMator

Microscopy image analysis has recently made enormous progress both in terms of accuracy and speed thanks to machine learning methods and improved computational resources. This greatly facilitates the online adaptation of microscopy experimental plans using real-time information of the observed systems and their environments. Applications in which reactiveness is needed are multifarious. Here we report MicroMator, an open and flexible software for defining and driving reactive microscopy experiments. It provides a Python software environment and an extensible set of modules that greatly facilitate the definition of events with triggers and effects interacting with the experiment. We provide a pedagogic example performing dynamic adaptation of fluorescence illumination on bacteria, and demonstrate MicroMator’s potential via two challenging case studies in yeast to single-cell control and single-cell recombination, both requiring real-time tracking and light targeting at the single-cell level.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Observation of formation and local structures of metal-organic layers via complementary electron microscopy techniques

Abstract Metal-organic layers (MOLs) are highly attractive for application in catalysis, separation, sensing and biomedicine, owing to their tunable framework structure. However, it is challenging to obtain comprehensive information about the formation and local structures of MOLs using standard electron microscopy methods due to serious damage under electron beam irradiation. Here, we investigate the growth processes and local structures of MOLs utilizing a combination of liquid-phase transmission electron microscopy, cryogenic electron microscopy and electron ptychography. Our results show a multistep formation process, where precursor clusters first form in solution, then they are complexed with ligands to form non-crystalline solids, followed by the arrangement of the cluster-ligand complex into crystalline sheets, with additional possible growth by the addition of clusters to surface edges. Moreover, high-resolution imaging allows us to identify missing clusters, dislocations, loop and flat surface terminations and ligand connectors in the MOLs. Our observations provide insights into controllable MOL crystal morphology, defect engineering, and surface modification, thus assisting novel MOL design and synthesis.

36 MATERIALS SCIENCE↗

DOE OSTI2023

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↗

DOE OSTI2023

Simultaneous bright- and dark-field X-ray microscopy at X-ray free electron lasers

Abstract The structures, strain fields, and defect distributions in solid materials underlie the mechanical and physical properties across numerous applications. Many modern microstructural microscopy tools characterize crystal grains, domains and defects required to map lattice distortions or deformation, but are limited to studies of the (near) surface. Generally speaking, such tools cannot probe the structural dynamics in a way that is representative of bulk behavior. Synchrotron X-ray diffraction based imaging has long mapped the deeply embedded structural elements, and with enhanced resolution, dark field X-ray microscopy (DFXM) can now map those features with the requisite nm-resolution. However, these techniques still suffer from the required integration times due to limitations from the source and optics. This work extends DFXM to X-ray free electron lasers, showing how the $$10^{12}$$ 10 12 photons per pulse available at these sources offer structural characterization down to 100 fs resolution (orders of magnitude faster than current synchrotron images). We introduce the XFEL DFXM setup with simultaneous bright field microscopy to probe density changes within the same volume. This work presents a comprehensive guide to the multi-modal ultrafast high-resolution X-ray microscope that we constructed and tested at two XFELs, and shows initial data demonstrating two timing strategies to study associated reversible or irreversible lattice dynamics.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Unraveling the impact of initial choices and in-loop interventions on learning dynamics in autonomous scanning probe microscopy

The current focus in Autonomous Experimentation (AE) is on developing robust workflows to conduct the AE effectively. This entails the need for well-defined approaches to guide the AE process, including strategies for hyperparameter tuning and high-level human interventions within the workflow loop. This paper presents a comprehensive analysis of the influence of initial experimental conditions and in-loop interventions on the learning dynamics of Deep Kernel Learning (DKL) within the realm of AE in scanning probe microscopy. We explore the concept of the “seed effect,” where the initial experiment setup has a substantial impact on the subsequent learning trajectory. Additionally, we introduce an approach of the seed point interventions in AE allowing the operator to influence the exploration process. Using a dataset from Piezoresponse Force Microscopy on PbTiO 3 thin films, we illustrate the impact of the “seed effect” and in-loop seed interventions on the effectiveness of DKL in predicting material properties. The study highlights the importance of initial choices and adaptive interventions in optimizing learning rates and enhancing the efficiency of automated material characterization. This work offers valuable insights into designing more robust and effective AE workflows in microscopy with potential applications across various characterization techniques.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy

Enhancing the reliability and reproducibility of optical microscopy by reducing specimen irradiance continues to be an important biotechnology target. As irradiance levels are reduced, however, the particle nature of light is heightened, giving rise to Poisson noise, or photon sparsity that restricts only a few (0.5%) image pixels to comprise a photon. Photon sparsity can be addressed by collecting approximately 200 photons per pixel; this, however, requires long acquisitions and, as such, suboptimal imaging rates. Here, we introduce near-zero photon bioimaging, a method that operates at kHz rates and 10,000-fold lower irradiance than standard microscopy. To achieve this level of performance, we uniquely combined a judiciously designed epifluorescence microscope enabling ultralow background levels and AI that learns to reconstruct biological images from as low as 0.01 photons per pixel. We demonstrate that near-zero photon bioimaging captures the structure of multicellular and subcellular features with high fidelity, including features represented by nearly zero photons. Beyond optical microscopy, the near-zero photon bioimaging paradigm can be applied in remote sensing, covert applications, and biomedical imaging that utilize damaging or quantum light.

AI↗

DOE OSTI2023

Probe microscopy is all you need *

We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods. Indeed, the tremendous progress achieved by machine learning (ML) and artificial intelligence over the last decade has been largely achieved via the utilization of static data sets, from the paradigmatic MNIST to the bespoke corpora of text and image data used to train large models such as GPT3, DALL·E and others. However, it is now recognized that continuous, minute improvements to state-of-the-art do not necessarily translate to advances in real-world applications. We argue that a promising pathway for the development of ML methods is via the route of domain-specific deployable algorithms in areas such as electron and scanning probe microscopy and chemical imaging. This will benefit both fundamental physical studies and serve as a test bed for more complex autonomous systems such as robotics and manufacturing. Favorable environment characteristics of scanning and electron microscopy include low risk, extensive availability of domain-specific priors and rewards, relatively small effects of exogenous variables, and often the presence of both upstream first principles as well as downstream learnable physical models for both statics and dynamics. Recent developments in programmable interfaces, edge computing, and access to application programming interfaces (APIs) facilitating microscope control, all render the deployment of ML codes on operational microscopes straightforward. We discuss these considerations and hope that these arguments will lead to create novel set of development targets for the ML community by accelerating both real world ML applications and scientific progress.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Squeezed Light Stimulated Raman Scattering (SRS) Microscopy for Bioenergy Applications

Stimulated Raman scattering (SRS) microscopy using picosecond near-IR pulses have provided a great penetration depth with reduced fluorescence interference when imaging biological samples for bioenergy applications. These tools have provided insight into 1) tracking the degradation of chemical composites in biomass feedstocks to investigate the recalcitrant factors during the deconstruction processes, 2) monitoring the production of chemicals in photosynthetic plants and wood-digesting microorganisms, and 3) probing plant-bacteria interactions. However, the above processes are usually slow and require continuous imaging for an extended period. This is challenging for classic SRS because the laser power needed to achieve enough sensitivity causes photodamage in the samples during such long experiments. Quantum-squeezed light with reduced noise in the intensity quadrature can improve the sensitivity of classic SRS microscopy beyond the shot noise limit. The successful squeezing of one of the picosecond pulses in the above SRS will improve sensitivity and reduce photodamage, greatly expanding the range of studies available to SRS microscopy.

BIOMASS FUELS↗

DOE OSTI2026

Enabling Bipolar Membrane Manufacturing Using Microscopy

Bipolar membranes (BPMs) used for water treatment suffer from interface instabilities, causing premature failure and decreased performance. Recent research has highlighted the impacts of membrane morphology on mechanical behavior and performance. Improved material performance of thin film membranes requires enhanced transport properties coupled with better control of the mechanical characteristics to avoid premature failure of the film. Thus, morphology is a key parameter when developing and evaluating novel membrane materials. Microscopy offers unique advantages for nano-scale characterization of thin film membrane interfaces and morphologies. In this study, the interface junction of BPMs was evaluated using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX). BPMs were fabricated using (1) hot pressing lamination, (2) bench-scale solution casting, and (3) roll-to-roll sequential casting (R2R). Both commercial and in-house fabricated ion exchange membranes were used in the BPMs. The BPMs were comprised of a cation exchange membrane and an anion exchange membrane with a catalyst layer sandwiched in between. Cross sectional SEM and EDX images of the membrane interface junction were compared among the different fabrication techniques and materials. The bench cast catalyst layers were nonuniform compared to the R2R and hot pressed membranes, demonstrating the importance of precise catalyst application conditions. Different thicknesses of the BPMs and individual membrane layers revealed that polymer solution concentration needs to be optimized during manufacturing. This work shows that microscopy can identify key processing parameters that affect BPM interface junction quality at the microscale to enable manufacturing of high performance BPMs.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Enabling Roll-to-Roll Manufacturing of Bipolar Membranes Using Microscopy

36 MATERIALS SCIENCE↗

DOE OSTI2022

A versatile enhanced freeze-substitution protocol for volume electron microscopy

Volume electron microscopy, a powerful approach to generate large three-dimensional cell and tissue volumes at electron microscopy resolutions, is rapidly becoming a routine tool for understanding fundamental and applied biological questions. One of the enabling factors for its adoption has been the development of conventional fixation protocols with improved heavy metal staining. However, freeze-substitution with organic solvent-based fixation and staining has not realized the same level of benefit. Here, we report a straightforward approach including osmium tetroxide, acetone and up to 3% water substitution fluid (compatible with traditional or fast freeze-substitution protocols), warm-up and transition from organic solvent to aqueous 2% osmium tetroxide. Once fully hydrated, samples were processed in aqueous based potassium ferrocyanide, thiocarbohydrazide, osmium tetroxide, uranyl acetate and lead acetate before resin infiltration and polymerization. We observed a consistent and substantial increase in heavy metal staining across diverse and difficult-to-fix test organisms and tissue types, including plant tissues ( Hordeum vulgare ), nematode ( Caenorhabditis elegans ) and yeast ( Saccharomyces cerevisiae ). Our approach opens new possibilities to combine the benefits of cryo-preservation with enhanced contrast for volume electron microscopy in diverse organisms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

How Precisely Can Individual Molecules Be Analyzed? A Case Study on Locally Quantifying Forces and Energies Using Scanning Probe Microscopy

Recent advances in scanning probe microscopy methodology have enabled the measurement of tip-sample interactions with picometer accuracy in all three spatial dimensions, thereby providing a detailed site-specific and distance-dependent picture of the related properties. This paper explores the degree of detail and accuracy that can be achieved in locally quantifying probe-molecule interaction forces and energies for adsorbed molecules. Toward this end, cobalt phthalocyanine (CoPc), a promising CO 2 reduction catalyst, was studied on Ag(111) as a model system using low-temperature, ultrahigh vacuum noncontact atomic force microscopy. Data were recorded as a function of distance from the surface, from which detailed three-dimensional maps of the molecule's interaction with the tip for normal and lateral forces as well as the tip-molecule interaction potential were constructed. The data were collected with a CO molecule at the tip apex, which enabled a detailed visualization of the atomic structure. Determination of the tip-substrate interaction as a function of distance allowed isolation of the molecule-tip interactions; when analyzing these in terms of a Lennard-Jones-type potential, the atomically resolved equilibrium interaction energies between the CO tethered to the tip and the CoPc molecule could be recovered. Interaction energies peaked at less than 160 meV, indicating a physisorption interaction. As expected, the interaction was weakest at the aromatic hydrogens around the periphery of the molecule and strongest surrounding the metal center. In conclusion, the interaction, however, did not peak directly above the Co atom but rather in pockets surrounding it.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Measuring the Burgers vector of dislocations with dark-field X-ray microscopy

The subsurface dynamics of dislocations are essential to many properties of bulk crystalline materials. However, it is challenging to characterize a bulk crystal by conventional transmission electron microscopy (TEM) due to the limited penetration depth of electrons. A novel X-ray imaging technique – dark-field X-ray microscopy (DFXM) – was developed to image hierarchical dislocation structures in bulk crystals. While today's DFXM can effectively map the line structures of dislocations, it is still challenging to quantify the Burgers vectors, the key characterization governing the dislocation behaviors. Here, we extend the 'invisibility criterion' formalism from the TEM theory to the geometrical-optics model of DFXM and demonstrate the consistency between DFXM and dark-field TEM using multi-diffraction-peak imaging for a single edge dislocation. Due to the practical difficulty of multi-peak DFXM experiments, we further study how the Burgers vector effect is encoded for a single-peak DFXM experiment. Using the geometrical-optics DFXM simulation, we explore the asymmetry of rocking tilt scans at different rolling tilts and develop a new method to characterize the Burgers vector. The conclusions of this study advance our understanding of the use of DFXM in characterizing individual dislocations, enabling the connection from bulk DFXM imaging to dislocation mechanics.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Three-Dimensional Single-Molecule Localization Microscopy in Whole-Cell and Tissue Specimens

Super-resolution microscopy techniques are versatile and powerful tools for visualizing organelle structures, interactions, and protein functions in biomedical research. However, whole-cell and tissue specimens challenge the achievable resolution and depth of nanoscopy methods. We focus on three-dimensional single-molecule localization microscopy and review some of the major roadblocks and developing solutions to resolving thick volumes of cells and tissues at the nanoscale in three dimensions. These challenges include background fluorescence, system- and sample-induced aberrations, and information carried by photons, as well as drift correction, volume reconstruction, and photobleaching mitigation. We also highlight examples of innovations that have demonstrated significant breakthroughs in addressing the abovementioned challenges together with their core concepts as well as their trade-offs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

3D Visualization of Proteins within Metal–Organic Frameworks via Ferritin‐Enabled Electron Microscopy

Abstract Electron tomography holds great promise as a tool for investigating the 3D morphologies and internal structures of metal‐organic framework‐based protein biocomposites (protein@MOFs). Understanding the 3D spatial arrangement of proteins within protein@MOFs is paramount for developing synthetic methods to control their spatial localization and distribution patterns within the biocomposite crystals. In this study, the naturally occurring iron oxide mineral core of the protein horse spleen ferritin (Fn) is leveraged as a contrast agent to directly observe individual proteins once encapsulated into MOFs by electron microscopy techniques. This methodology couples scanning electron microscopy, transmission electron microscopy, and electron tomography to garner detailed 2D and 3D structural interpretations of where proteins spatially lie in Fn@MOF crystals, addressing the significant gaps in understanding how synthetic conditions relate to overall protein spatial localization and aggregation. These findings collectively reveal that adjusting the ligand‐to‐metal ratios, protein concentration, and the use of denaturing agents alters how proteins are arranged, localized, and aggregated within MOF crystals.

Chemistry↗

DOE OSTI2025

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

In materials science, plant biology, agriculture, and environmental research, the automated analysis of high-magnification, complex microscopy images, such as those generated by freeze-fracture electron microscopy (FF-TEM), remains a critical challenge that limits the scalability of data interpretation. We present a deep learning computer vision pipeline for high-throughput detection and morphological characterization analysis of cellulose synthase complexes (CSCs, or rosettes) in FF-TEM images. The pipeline integrates preprocessing, detection, human-in-the-loop verification, and semantic segmentation to quantify features such as rosette diameter and inter-lobe spacing. The approach was trained and tested on a curated dataset of high-resolution FF-TEM micrographs of Physcomitrium patens, expanded via strategic tiling and augmentation to over 650 images. We compare YOLOv8 and YOLOv9 architectures and demonstrate that YOLOv9 achieves superior performance in both localization accuracy (mAP50-95 = 0.854) and inference speed. The resulting distributions revealed biological variability consistent with prior manual studies, validating the approach for high-throughput applications. Our results show that the pipeline achieves human-expert level accuracy while dramatically reducing analysis time, enabling scalable, reproducible structural characterization of intramembrane protein complexes. The pipeline is broadly applicable to other domains requiring precise interpretation of complex microscopy data and establishes a foundation for future artificial intelligence (AI)-assisted workflows in biological imaging.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Correlative nano-imaging of metals and proteins in primary neurons by synchrotron X-ray fluorescence and STED super resolution microscopy: Experimental validation

It is becoming increasingly clear that biological metals such as iron, copper or zinc are involved in synaptic functions, and in particular in the mechanisms of synaptogenesis and subsequent plasticity. Understanding the role of metals on synaptic functions is a difficult challenge due to the very low concentration of these elements in neurons and to the submicrometer size of synaptic compartments. To address this challenge we have developed a correlative nano-imaging approach combining metal and protein detection. First, stimulated emission depletion (STED) microscopy, a super resolution optical microscopy technique, is applied to locate fluorescently labeled proteins. Then, synchrotron radiation induced X-ray fluorescence (SXRF) is performed on the same regions of interest, e.g. synaptic compartments. Here, we present here the principle scheme that allows this correlative nano-imaging and its experimental validation. We applied this correlative nano-imaging to the study of the physiological distribution of metals in synaptic compartments of primary rat hippocampal neurons. We thus compared the nanometric distribution of metals with that of synaptic proteins, such as PSD95 or cytoskeleton proteins. Compared to correlative imaging approaches currently used to characterize synaptic structures, such as electron microscopy correlated with optical fluorescence, our approach allows for ultra-sensitive detection of trace metals using highly focused synchrotron radiation beams. We provide proof-of-principle for correlative imaging of metals and proteins at the synaptic scale and discuss the present limitations and future developments in this area.

59 BASIC BIOLOGICAL SCIENCES↗