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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Autogenous acid production using a regulated bio-oxidation method for economical recovery of REEs and critical metals from coal-based resources

Acid is a necessary component for the successful leaching of minerals containing REEs (rare earth elements) and other sources. Pyrite (FeS2), which is present in REE containing minerals, can be used to produce acid. Pyrite is utilized as a source to create ferric sulfate and sulfuric acid in the presence of A. Ferroxidans. Such microorganism-based bio-oxidation necessitates the establishment of optimal conditions for continual acid production. For efficient acid generation, additional optimization was done by varying residence time, solid percentage, and airflow rate. A step-by-step analysis and associated details are offered in this work, which used pyrite to create acid for leaching. For the bio-oxidation of pyrite with varying purity, an automated bioreactor was used. Scanning electron microscopy with energy-dispersive X-ray spectroscopy was used to examine the morphology and elemental content of bioleaching sediments. Eh and pH measurements were continuously done to ensure healthy operation of the bioreactors. The analysis suggested that, the Eh value in the range of ∼ 640–680 mV, pH range of ∼ 1.3–1.5 and residence time of 4–6 days with 6% solid feed are favorable conditions of operation.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Multi-contrast machine learning improves schistosomiasis diagnostic performance

Schistosomiasis currently affects over 250 million people and remains a public health burden despite ongoing global control efforts. Conventional microscopy is a practical tool for diagnosis and screening ofSchistosoma haematobium, but identification of eggs requires a skilled microscopist. Here we present a machine learning (ML)-based strategy for automated detection ofS. haematobiumthat combines two imaging contrasts, brightfield (BF) and darkfield (DF), to improve diagnostic performance. We collected BF and DF images of urine samples, many of them containingS. haematobiumeggs, during two different field studies in Côte d’Ivoire using a mobile phone-based microscope, the SchistoScope. We then trained separate egg-detection ML models and compared the patient-level performance of BF and DF models alone to combinations of BF and DF models, using annotations from trained microscopists as the gold standard. We found that models trained on DF images, and almost all BF and DF combinations, performed significantly better than models trained on BF images only. When models were trained on images from the first field study (n = 349 patients, 748 images of each contrast), patient-level classification performance on patient images from the second study (n = 375 patients, 752 images of each contrast) met the WHO Diagnostic Target Product Profile (TPP) sensitivity and specificity for the monitoring and evaluation use case (sensitivity for all models and combinations was >75% when evaluated at a confidence score threshold that resulted in specificity >96.5%). When we used images from both field studies for the training set, performance of the models was improved. Overall, this work shows that the use of DF and BF increases the performance of ML models on images from devices with low-cost optics, while retaining the portability, power, and time-to-results of the WHO’s diagnostic TPP. DF requires no additional sample preparation and does not increase the complexity of the imaging system. It thus offers a practical means to improve performance of automated diagnostics forS. haematobiumas well as other microscopy-based diagnostics.

Infectious Diseases↗

DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO 3 , with results showing that, with just 20% of the area sampled, most high-response clusters were captured. Furthermore, this approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2004

Space Station Biological Research Project (SSBRP) Cell Culture Unit (CCU) and incubator for International Space Station (ISS) cell culture experiments

The CCU and Incubator are habitats under development by SSBRP for gravitational biology research on ISS. They will accommodate multiple specimen types and reside in either Habitat Holding Racks, or the Centrifuge Rotor, which provides selectable gravity levels of up to 2 g. The CCU can support multiple Cell Specimen Chambers, CSCs (18, 9 or 6 CSCs; 3, 10 or 30 mL in volume, respectively). CSCs are temperature controlled from 4-39 degrees C, with heat shock to 45 degrees C. CCU provides automated nutrient supply, magnetic stirring, pH/O2 monitoring, gas supply, specimen lighting, and video microscopy. Sixty sample containers holding up to 2 mL each, stored at 4-39 degrees C, are available for automated cell sampling, subculture, and injection of additives and fixatives. CSCs, sample containers, and fresh/spent media bags are crew-replaceable for long-term experiments. The Incubator provides a 4-45 degrees C controlled environment for life science experiments or storage of experimental reagents. Specimen containers and experiment unique equipment are experimenter-provided. The Specimen Chamber exchanges air with ISS cabin and has 18.8 liters of usable volume that can accommodate six trays and the following instrumentation: five relocatable thermometers, two 60 W power outlets, four analog ports, and one each relative humidity sensor, video port, ethernet port and digital input/output port.

NASA Center ARC↗

DOE OSTI2020

Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images

Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Alignment of Au nanorods along de novo designed protein nanofibers studied with automated image analysis

In this paper, we focus on exploring the directional assembly of anisotropic Au nanorods along de novo designed 1D protein nanofiber templates. Using machine learning and automated image processing, we analyze scanning electron microscopy (SEM) images to study how the attachment density and alignment fidelity are influenced by variables such as the aspect ratio of the Au nanorods, and the salt concentration of the solution. We find that the Au nanorods prefer to align parallel to the protein nanofibers. This preference decreases with increasing salt concentration, but is only weakly sensitive to the nanorod aspect ratio. While the overall specific Au nanorod attachment density to the protein fibers increases with increasing solution ionic strength, this increase is dominated primarily by non-specific binding to the substrate background, and we find that greater specific attachment (nanorods attached to the nanofiber template as compared to the substrates) occurs at the lower studied salt concentrations, with the maximum ratio of specific to non-specific binding occurring when the protein fiber solutions are prepared in 75 mM NaCl concentration.

36 MATERIALS SCIENCE↗

NASA NTRS1989

Ecology of micro-organisms in a small closed system - Potential benefits and problems for Space Station

The probable sources and implications of microbial contamination on the proposed Space Station are discussed. Because of the limited availability of material, facilities and time on the Space Station, we are exploring the feasibility of replacing traditional incubation methods for assessing microbial contamination with rapid, automated methods. Some possibilities include: ATP measurement, microscopy and telecommunications, and molecular techniques such as DNA probes or monoclonal antibodies. Some of the important ecological factors that could alter microbes in space include microgravity, exposure to radiation, and antibiotic resistance.

Rodgers, E. B.↗

DOE OSTI2023

Cyber Framework for Steering and Measurements Collection Over Instrument-Computing Ecosystems

We propose a framework to develop cyber solutions to support the remote steering of science instruments and measurements collection over instrument-computing ecosystems. It is based on provisioning separate data and control connections at the network level, and developing software modules consisting of Python wrappers for instrument commands and Pyro server-client codes that make them available across the ecosystem network. We demonstrate automated measurement transfers and remote steering operations in a microscopy use case for materials research over an ecosystem of Nion microscopes and computing platforms connected over site networks. The proposed framework is currently under further refinement and being adopted to science workflows with automated remote experiments steering for autonomous chemistry laboratories and smart energy grid simulations.

Al Najjar, Anees↗

DOE OSTI2020

Crowd-Sourced Data and Analysis Tools for Advancing the Chemical Vapor Deposition of Graphene: Implications for Manufacturing

Industrial production of graphene by chemical vapor deposition (CVD) requires more than the ability to synthesize large domain, high-quality graphene in a lab reactor. The integration of graphene in the fabrication process of electronic devices requires the cost-effective and environmentally friendly production of graphene on dielectric substrates, but current approaches can only produce graphene on metal catalysts. Sustainable manufacturing of graphene should also conserve the catalyst and reaction gases, but today the metal catalysts are typically dissolved after synthesis. Progress toward these objectives is hindered by the hundreds of coupled synthesis parameters that can strongly affect CVD of low-dimensional materials and poor communication in the published literature of the rich experimental data that exists in individual laboratories. We report here on a platform, "graphene recipes for synthesis of high quality material" (Gr-ResQ: pronounced graphene rescue), which includes powerful new tools for data-driven graphene synthesis. At the core of Gr-ResQ is a crowd-sourced database of CVD synthesis recipes and associated experimental results. The database captures similar to 300 parameters ranging from synthesis conditions such as a catalyst material and preparation steps, to ambient lab temperature and reactor details, as well as resulting Raman spectra and microscopy images. These parameters are carefully selected to unlock the potential of machine-learning models to advance synthesis. A suite of associated tools enable fast, automated, and standardized processing of Raman spectra and scanning electron microscopy images. To facilitate community-based efforts, Gr-ResQ provides tools for cyber-physical collaborations among research groups, allowing experiments to be designed, executed, and analyzed by different teams. Gr-ResQ also allows publication and discovery of recipes via the Materials Data Facility, which assigns each recipe a unique identifier when published and collects parameters in a search index. We envision that this holistic approach to data-driven synthesis can accelerate CVD recipe discovery and production control and open opportunities for advancing not only graphene but also many other 1D and 2D materials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science [Slides]

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

AutoEMX v1.

The invention consists in the full automation of compositional analysis of inorganic powder samples by scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS). The measurements and analysis are controlled via python-based software, Auto-SEMEDS. Auto-SEMEDS fully automates the SEM-EDS measurements, and analyses the collected data via the use of machine-learning (ML) algorithms, which have never been used before for such scope. Auto-SEMEDS enables the identification in fully-automated fashion of the individual material phases present in a powder sample. Similar technologies, such as commercial SEM-EDS software, can automatically classify particles based on their composition, but they have significant limitations. These solutions typically provide inaccurate composition measurements and struggle to identify single phases in lab samples, where phases are often closely intermixed. In contrast, Auto-SEMEDS achieves unprecedented accuracy in composition measurements of powder samples, and furthermore leverages machine learning algorithms to effectively discern intermixed phases. Notably, while previous studies have demonstrated accurate measurements on individual particles, Auto-SEMEDS stands out by successfully analyzing mixture of different phases, a capability that has not been reported in the literature until now.

Giunto, Andrea [Lawrence Berkeley National Laborat↗

NASA NTRS2003

The Fluids Integrated Rack and Light Microscopy Module Integrated Capabilities

The Fluids Integrated Rack (FIR), a facility class payload, and the Light Microscopy Module (LMM), a subrack payload, are scheduled to be launched in 2005. The LMM integrated into the FIR will provide a unique platform for conducting fluids and biological experiments on ISS. The FIR is a modular, multi-user scientific research facility that will fly in the U.S. laboratory module, Destiny, of the International Space Station (ISS). The first payload in the FIR will be the Light Microscopy Module (LMM). The LMM is planned as a remotely controllable, automated, on-orbit microscope subrack facility, allowing flexible scheduling and control of fluids and biology experiments within the FIR. Key diagnostic capabilities for meeting science requirements include video microscopy to observe microscopic phenomena and dynamic interactions, interferometry to make thin film measurements with nanometer resolution, laser tweezers for particle manipulation, confocal microscopy to provide enhanced three-dimensional visualization of structures, and spectrophotometry to measure photonic properties of materials. The LMM also provides experiment sample containment for frangibles and fluids. This paper will provide a description of the current FIR and LMM designs, planned capabilities and key features. In addition a brief description of the initial five experiments planned for LMM/FIR will be provided.

Motil, Susan M.↗

DOE OSTI2023

Automated High‐Throughput Fatigue Testing of Freestanding Thin Films

Abstract Mechanical testing at small length scales has traditionally been resource‐intensive due to difficulties with meticulous sample preparation, exacting load alignments, and precision measurements. Microscale fatigue testing can be particularly challenging due to the time‐intensive, tedious repetition of single fatigue experiments. To mitigate these challenges, this work presents a new methodology for the high‐throughput fatigue testing of thin films at the microscale. This methodology features a microelectromechanical systems‐based Si carrier that can support the simultaneous and independent fatigue testing of an array of samples. To demonstrate this new technique, the microscale fatigue behavior of nanocrystalline Al is efficiently characterized via this Si carrier and automated fatigue testing with in situ scanning electron microscopy. This methodology reduces the total testing time by an order of magnitude, and the high‐throughput fatigue results highlight the stochastic nature of the microscale fatigue response. This manuscript also discusses how this initial capability can be adapted to accommodate more samples, different materials, new geometries, and other loading modes.

Barrios, Alejandro↗

DOE OSTI2024

The radiation instability of thermally stable nanocrystalline platinum gold

Here, recent experimentally validated alloy design theories have demonstrated nanocrystalline binary alloys that are stable against thermally induced grain growth. An open question is whether such thermal stability also translates to stability under irradiation. In this study, we investigate the response to heavy ion irradiation of a nanocrystalline platinum gold alloy that is known to be thermally stable from previous studies. Heavy ion irradiation was conducted at both room temperature and elevated temperatures on films of nanocrystalline platinum and platinum gold. Using scanning/transmission electron microscopy equipped with energy-dispersive spectroscopy and automated crystallographic orientation mapping, we observe substantial grain growth in the irradiated area compared to the controlled area beyond the range of heavy ions, as well as compositional redistribution under these conditions, and discuss mechanisms underpinning this instability. These findings highlight that grain boundary stability against one external stimulus, such as heat, does not always translate into grain boundary stability under other stimuli, such as displacement damage.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Automated SEM analysis of particles in Hanford tank waste

Multiple bench-scale filtration campaigns of Hanford tank waste supernatant on a backpulseable dead-end filtration skid have provided greater insight into the solids that cause fouling and reduce filter performance. The solids collected during each campaign were concentrated from the backpulse solutions and examined using automated particle analysis (APA) methods with scanning electron microscopy (SEM) and x-ray energy dispersive spectroscopy (EDS), to categorize particle types and their morphological characteristics. Finally, we show that with APA, thousands of particles can be analyzed that can provide accurate insight into the phases that may be impacting filter performance.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Autonomous fabrication of tailored defect structures in 2D materials using machine learning-enabled scanning transmission electron microscopy

Materials with tailored quantum properties can be engineered from atomic-scale assembly techniques, but existing methods often lack the agility and accuracy to precisely and intelligently control the manufacturing process. Here, we demonstrate a fully autonomous approach for fabricating atomic-level defects using electron beams in scanning transmission electron microscopy (STEM) that combines advanced machine learning and automated beam control. As a proof of concept, we achieved controlled fabrication of MoS-nanowire (MoS-NW) edge structures by iterative and targeted exposure of MoS 2 monolayer to a focused electron beam to selectively eject sulfur atoms, utilizing high-angle annular dark-field (HAADF) imaging for feedback-controlled monitoring of structural evolution of defects. A machine learning framework combining a random forest model and a convolutional neural network (CNN) was developed to decode the HAADF image and accurately identify atomic positions and species. This atomic-level information was then integrated into an autonomous decision-making platform, which applied predefined fabrication strategies to instruct beam control about atomic sites to be ejected. The selected sites were subsequently exposed to a localized electron beam using an FPGA-controlled scan routine with precise control over beam positioning and duration. While the MoS-NW edge structures produced exhibit promising mechanical and electronic properties, the proposed methods to build the autonomous fabrication framework is material-agnostic and can be extended to other 2D materials for the creation of diverse defect structures and heterostructures beyond Mo S2 .

Engineering↗