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Results for “High throughput characterization”

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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Spectral interferometry-based microwave-frequency vibrometry for integrated acoustic wave devices

Microwave phononics is a promising platform for sensing, computing, and quantum information science; thus, sensitive and high-throughput characterization tools are needed not only for device verification and optimization but also for revealing transient and nonlinear dynamics. Existing interferometric optical vibrometers for 2D mapping are challenged by operating point stabilization, surface reflectivity contrast, and long acquisition time. Here, we use spectral interferometry, which is insensitive to these factors and utilizes a continuous raster scanning scheme for vibration mapping with high throughput. We intensity-modulate our broadband light source with an electro-optic modulator to resolve vibrations at microwave frequencies. Our system requires no fast photodetector or digitizer operating in the microwave frequency range. We image the 1 GHz vibration field of a 300 × 150 µm 2 area of an entire surface acoustic wave device in 10 min with simultaneous surface profilometry. Our system has a vibration sensitivity of 120 fm/sqrt(Hz) and a linear throughput of 0.77 mm/s on the chip surface. The technique offers capabilities for characterizing a wide range of acoustic wave and micromechanical devices to better understand their behavior and performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Segmentation method comparison for residual fiber length measurement across tiled microscopy images

Fiber length distribution (FLD), in part, governs mechanical properties in discontinuous fiber composites, yet manual measurement methods limit the high-throughput characterization needed for materials design optimization. This study compares deep learning segmentation approaches for automated FLD measurement in large-field microscopy, evaluating how method choice affects the microstructural descriptors used in structure-property-processing relationships. A critical challenge is that high-resolution microscopy images (10,000×10,000 pixels) must be tiled for deep learning analysis, fragmenting fibers at boundaries. We demonstrate that segmentation method proves crucial for measurement accuracy. For example, instance segmentation with Slicing Aided Hyper Inference (SAHI) preserves individual fiber integrity across tiles while semantic segmentation prioritizes speed. Comparing against manual measurement of extracted carbon fibers, YOLOv11-SAHI matched manual ground truth (238 μm weighted mean) with 40x speedup (4.5 vs 167 minutes per image). U-Net provides rapid quantification although it is at the cost of reduced accuracy due only reliably measuring stand-alone fibers. Our comparative analysis reveals that instance segmentation with SAHI better preserves length measurements while semantic segmentation prioritizes speed, providing empirical guidance for method selection. The characterization provides essential inputs for mechanical property prediction models and inverse design workflows, accelerating composite materials development cycles.

Additive manufacturing↗
DOE OSTI2023

A Novel Platform for Evaluating Dose Rate Effects on Oxidative Damage to Peptides: Toward a High-Throughput Method to Characterize the Mechanisms Underlying the FLASH Effect

High dose rate radiation has gained considerable interest recently as a possible avenue for increasing the therapeutic window in cancer radiation treatment. The sparing of healthy tissue at high dose rates relative to conventional dose rates, while maintaining tumor control, has been termed the FLASH effect. Although the effect has been validated in animal models using multiple radiation sources, it is not yet well understood. Here, in this work, we demonstrate a new experimental platform for quantifying oxidative damage to protein sidechains in solution as a function of radiation dose rate and oxygen availability using liquid chromatography mass spectrometry. Using this reductionist approach, we show that for both X-ray and electron sources, isolated peptides in solution are oxidatively modified to different extents as a function of both dose rate and oxygen availability. Our method provides an experimental platform for exploring the parameter space of the dose rate effect on oxidative changes to proteins in solution.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Near-infrared spectroscopy as a green analytical tool for sustainable biomass characterization for biofuels and bioproducts: An overview

Biomass, a widely used renewable energy source, requires characterization to optimize biofuel and bioproduct processes, customize feedstocks, and ensure economic and environmental sustainability. Conventional wet-chemistry methods for biomass analysis are slow, expensive, and require significant reagents and skilled personnel. In contrast, near-infrared (NIR) spectroscopy, a faster, cost-effective, and reagent-free green technology, enables non-destructive biomass analysis with minimal sample preparation. This study provides an overview of the fundamentals of NIR spectroscopy and explores its recent applications for analyzing various biomass properties important to the biofuel and bioproduct industry. The study also critically evaluates the challenges and opportunities of using NIR spectroscopy for biomass analysis. This review aims to guide future research for rapid and high throughput characterization of biomass in the biomass industry, supporting the United Nations’ sustainable development goal (SDG) 7: producing affordable and sustainable energy.

Biofuels↗
DOE OSTI2022

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

Abstract Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗
DOE OSTI2022

Revealing room temperature ferromagnetism in exfoliated Fe 5 GeTe 2 flakes with quantum magnetic imaging

Van der Waals (vdW) material Fe 5 GeTe 2 , with its long-range ferromagnetic ordering near room temperature, has significant potential to become an enabling platform for implementing novel spintronic and quantum devices. To pave the way for applications, it is crucial to determine the magnetic properties when the thickness of Fe 5 GeTe 2 reaches the few-layers regime. However, this is highly challenging due to the need for a characterization technique that is local, highly sensitive, artifact-free, and operational with minimal fabrication. Prior studies have indicated that Curie temperature T C can reach up to close to room temperature for exfoliated Fe 5 GeTe 2 flakes, as measured via electrical transport; there is a need to validate these results with a measurement that reveals magnetism more directly. Here, we investigate the magnetic properties of exfoliated thin flakes of vdW magnet Fe 5 GeTe 2 via quantum magnetic imaging technique based on nitrogen vacancy centers in diamond. Through imaging the stray fields, we confirm room-temperature magnetic order in Fe 5 GeTe 2 thin flakes with thickness down to 7 units cell. The stray field patterns and their response to magnetizing fields with different polarities is consistent with previously reported perpendicular easy-axis anisotropy. Furthermore, we perform imaging at different temperatures and determine the Curie temperature of the flakes at ≈300 K. These results provide the basis for realizing a room-temperature monolayer ferromagnet with Fe 5 GeTe 2 . This work also demonstrates that the imaging technique enables rapid screening of multiple flakes simultaneously as well as time-resolved imaging for monitoring time-dependent magnetic behaviors, thereby paving the way towards high throughput characterization of potential two-dimensional (2D) magnets near room temperature and providing critical insights into the evolution of domain behaviors in 2D magnets due to degradation.

2D magnet↗
DOE OSTI2025

High-throughput multimodal exploration of a nanocrystalline Cu-Ag library

Sputter-deposited, nanocrystalline Cu-Ag thin films produced across a broad compositional and deposition-parameter space were evaluated to unravel the process-structure-property relationships important for creating hard, conductive electrical contacts and coatings. Combinatorial deposition involving pulsed direct current magnetron sputtering of elemental targets enabled swift examination of nearly the full range of alloy compositions and a relevant portion of deposition atomistics. Several high-throughput characterization modalities were employed to evaluate the chemistry, structure, and properties of the films. The resultant hardness, modulus, film density, crystal texture, and resistivity were analyzed in terms of key deposition characteristics (incident atom kinetic energy and incidence angle) predicted by binary-collision, kinematic Monte Carlo simulations. The study revealed improved hardness, parabolic resistivity dependence on composition, and compositional and process dependencies of film tarnishing. The results are discussed in the context of variations in microstructure and film density. Transmission electron microscopy and X-ray diffraction demonstrate several forms of compositional variation including solute segregation to grain boundaries as well as periodic, intragranular compositional modulations. Annealing of a Cu-rich alloy film exhibiting grain boundary segregation showed that this as-deposited, compositional variation is not stable above 100 °C. Finally, the Cu-Ag system is shown to have potential for hard, conductive, tarnish-resistant and room temperature-stable nanocrystalline thin films across the composition space.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Three-dimensional morphology of an ultrafine Al-Si eutectic produced via laser rapid solidification

Al-Si alloys processed by laser rapid solidification yield eutectic microstructures with ultrafine and interconnected fibers. Such fibrous structures have long been thought to bear resemblance to those formed in impurity-doped alloys upon conventional casting. Here, we show that any similarity is purely superficial. By harnessing high-throughput characterization and computer vision techniques, we perform a three-dimensional analysis of the branching behavior of the ultrafine eutectic and compare it against an impurity-modified eutectic as well as a random fractal (as a benchmark). Differences in the branching statistics point to different microstructural origins of the impurity- and quench-modified eutectic. Finally, our quantitative approach is not limited to the data presented here but can be used to extract abstract information from other volumetric datasets, without customization.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Rapid and flexible segmentation of electron microscopy data using few-shot machine learning

Abstract Automatic segmentation of key microstructural features in atomic-scale electron microscope images is critical to improved understanding of structure–property relationships in many important materials and chemical systems. However, the present paradigm involves time-intensive manual analysis that is inherently biased, error-prone, and unable to accommodate the large volumes of data produced by modern instrumentation. While more automated approaches have been proposed, many are not robust to a high variety of data, and do not generalize well to diverse microstructural features and material systems. Here, we present a flexible, semi-supervised few-shot machine learning approach for segmentation of scanning transmission electron microscopy images of three oxide material systems: (1) epitaxial heterostructures of SrTiO 3 /Ge, (2) La 0.8 Sr 0.2 FeO 3 thin films, and (3) MoO 3 nanoparticles. We demonstrate that the few-shot learning method is more robust against noise, more reconfigurable, and requires less data than conventional image analysis methods. This approach can enable rapid image classification and microstructural feature mapping needed for emerging high-throughput characterization and autonomous microscope platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structural and photoelectrochemical properties in the thin film system Cu–Fe–V–O and its ternary subsystems Fe–V–O and Cu–V–O

Thin-film material libraries in the ternary and quaternary metal oxide systems Fe–V–O, Cu–V–O, and Cu–Fe–V–O were synthesized using combinatorial reactive co-sputtering with subsequent annealing in air. Their compositional, structural, and functional properties were assessed using high-throughput characterization methods. Prior to the investigation of the quaternary system Cu–Fe–V–O, the compositions (Fe 61 V 39 )O x and (Cu 52 V 48 )O x with promising photoactivity were identified from their ternary subsystems Fe–V–O and Cu–V–O, respectively. Two Cu–Fe–V–O material libraries with (Cu 29-72 Fe 4-27 V 22-57 )O x and (Cu 11-55 Fe 27-73 V 12-34 )O x composition spread were investigated. Seven mixed ternary and quaternary phase regions were identified: I (α-Cu 3 FeV 6 O 26 /FeVO 4 ), II (Cu 5 V 2 O 10 /FeVO 4 /α-Cu 3 Fe 4 V 6 O 26 ), III (Cu 5 V 2 O 10 ), IV (Cu 5 V 2 O 10 /FeVO 4 , V (FeVO 4 /γ-Cu 2 V 2 O 7 /α-Cu 3 Fe 4 V 6 O 26 ), VI (β-Cu 2 V 2 O 7 /α-Cu 3 Fe 4 V 6 O 26 /FeVO 4 ), and VII (β-Cu 3 Fe 4 V 6 O 26 /FeVO 4 ). Furthermore, in the investigated composition range, two photoactive regions, (Cu 53 Fe 7 V 40 )O x and (Cu 45 Fe 21 V 34 )O x , were identified, exhibiting 103 μ A/cm 2 and 108 μ A/cm 2 photocurrent density for the oxygen evolution reaction at 1.63 V vs reversible hydrogen electrode, respectively. The highest photoactive region (Cu 45 Fe 21 V 34 )O x comprises the dominant α-Cu 3 Fe 4 V 6 O 24 phase and minor FeVO 4 phase. This photoactive region corresponds to having an indirect bandgap of 1.87 eV and a direct bandgap of 2.58 eV with an incident photon-to-current efficiency of 30% at a wavelength of 310 nm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Protorheology

We all instinctively poke, bounce, scoop, and observe materials to understand rheological properties quickly. Yet, these observations are rarely analyzed quantitatively. To address this, here we introduce the paradigm of protorheology: approximate quantitative inference from simple observations. Several case studies demonstrate how protorheology is an inclusive entry to rheology for a broad range of practitioners and strengthens the confidence and interpretation of accurate laboratory measurements. We survey a range of creative tests according to which rheological phenomenon is revealed. Some new working equations are derived, and all working equations are summarized for convenient reference and comparison across different methods. Finally, this establishes a framework to enable increased use of photos, videos, and quantitative inference and to support the increasing interest in digital image analysis, inverse methods, and high-throughput characterization being applied to rheological properties.

42 ENGINEERING↗
DOE OSTI2023

Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats

The LDRD ER “Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats” was conceived to address a need for rapid, scalable, evaluation of computationally designed therapeutic or prophylactic antibodies and vaccine antigens, two important classes of protein medical countermeasure (MCM). This was done in complement to a computationally driven LDRD 20ERD032 “Active Learning for Rapid Design of Vaccines and Antibodies.” Natural antibodies and antigens are often insufficiently broad or robust across different pathogens and their variants. Leveraging a collaboration of simulation driven machine learning, structural expertise, and high-throughput characterization of candidate antibodies, we successfully re-targeted three different anti-SARS-CoV-1 antibodies to neutralize SARS-CoV-2 in vitro. Our antibody design work reached its most important stage in rapid response to the emergence of the Omicron variant of concern (VOC) in late 2021. In a matter of weeks, we computationally designed derivative antibodies of COV2-2130, one of two antibodies from Vanderbilt that form the basis of the AstraZeneca Evusheld prophylactic drug product. This drug product suffers a serious loss of efficacy against Omicron BA.1 and BA.1.1, the first Omicron strains. Our designs were successful, including a pair of designs which provide potent neutralization of not only Omicron BA.1 and BA.1.1, but also the earlier Delta variant, and subsequent Omicron strains including BA.2, BA.4, BA.5, and BA.2.75, demonstrating that our multi-target design process can, by its nature, produce robust antibody designs that strictly improve over the parental antibody. These results, recognized by a 2022 Director’s Science and Technology award, have enabled the follow-on GUIDE program, to commence in FY23.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

High-throughput spin-bath characterization of spin defects in semiconductors

Detailed knowledge of the local environments of spin defects in semiconductors, such as nitrogenvacancy (NV) centers in diamond or divacancies in silicon carbide, is crucial for optimizing control and entanglement protocols in quantum sensing and information applications. However, at present a direct experimental characterization of individual defect environments is not scalable, as conventional spin-bath measurements are time consuming and difficult to automate. Achieving high-throughput characterization requires short experiments to probe the spin bath. However, with fewer and noisier measurements, the inverse problem of recovering spin-bath properties from measured data becomes ill posed, with multiple spin baths having a high likelihood of yielding the same data. In this work, we present a set of computational tools to resolve the ill-posed inverse problem of recovering the atomic positions and hyperfine couplings of random nuclei surrounding spin defects from sparse, noisy experimental coherence data, which can be obtained in hours. Here, we use a trans-dimensional Bayesian approach that incorporates ab initio data to yield full posterior distributions over nuclear spin environments, enabling robust recovery from limited data. We also provide practical tools and guidelines to determine the limits of detectability for hyperfine couplings under specific dynamical decoupling sequences and sampling conditions. In addition, we demonstrate how the tools developed here, in combination with ab initio simulations of spin baths, can guide the design of efficient experimental protocols for application-specific high-throughput screening. To showcase the utility of our approach, we apply it to design fast dynamical decoupling experiments to characterize the spin baths often individual NV centers in diamond. While the primary focus is on accelerating spin-bath characterization of spin defects, this Bayesian approach also lays the foundation for digital-twin studies of spin defects, where a virtual model of the spin-defect system evolves in real time with ongoing experimental measurements. Together, the set of tools we designed and applied paves the way for scalable deployment of spin defects in semiconductors for quantum sensing and information applications.

Bayesian methods↗
DOE OSTI2026

Robust and High Throughput Tools for Characterization of Wet Cellulosic Biogas Feedstocks and Co-Products: Cooperative Research and Development Final Report, CRADA Number CRD-21-21472

This project will develop standardized analytical procedures to support understanding of the chemical composition of wet manure feedstocks, and the digestate formed following anaerobic digestion of organic waste to produce renewable natural gas (RNG). The developed methods will be validated to improve component mass closure, eliminate ambiguities around key constituents, and document the precision and accuracy of the methods. Following validation of the analytical laboratory methods for characterization of wet organic waste feedstocks and digestate, NLR will use the same samples to build and validate near-infrared (NIR) models for high throughput analysis, potentially deployable at commercial facilities, enabling increased production of low carbon intensity RNG from wet organic wastes.

09 BIOMASS FUELS↗
DOE OSTI2023

Deciphering phase evolution in complex metal oxide thin films via high-throughput materials synthesis and characterization

Discovery of structure-property relationships in thin film alloys of complex metal oxides enabled by high-throughput materials synthesis and characterization facilities is demonstrated here with a case-study. In this study, thin films of binary transition metal oxides (Ti–Zn) are prepared by pulsed laser deposition with continuously varying Ti:Zn ratio, creating combinatorial samples for exploration of the properties of this material family. The atomic structure and electronic properties are probed by spatially resolved techniques including x-ray absorption near edge structures (XANES) and x-ray fluorescence (XRF) at the Ti and Zn K-edge, x-ray diffraction, and spectroscopic ellipsometry. The observed properties as a function of Ti:Zn ratio are resolved into mixtures of five distinguishable phases by deploying multivariate curve resolution analysis on the XANES spectral series, under constraints set by results from the other characterization techniques. First-principles computations based on density function theory connect the observed properties of each distinct phase with structural and spectral characteristics of crystalline polymorphs of Ti–Zn oxide. Continuous tuning of the optical absorption edge as a function of Ti:Zn ratio, including the unusual observation of negative optical bowing, exemplifies a functional property of the film correlated to the phase evolution.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High Throughput expression and characterization of laccases in Saccharomyces cerevisiae

Laccases are oxidative enzymes containing 4 conserved copper heteroatoms. Laccases catalyze cleavage of bonds in lignin using radical chemistry, yet their exact specificity for bonds (such as the β-O-4 or C-C) in lignin remains unknown and may vary with the diversity of laccases across fungi, plants and bacteria. Bond specificity may perhaps even vary for the same enzyme across different reaction conditions. Determining these differences has been difficult due to the fact that heterologous expression of soluble, active laccases has proven difficult. Here we describe the successful heterologous expression of functional laccases in two strains of Saccharomyces cerevisiae, including one we genetically modified with CRISPR. We phylogenically map the enzymes that we successfully expressed, compared to those that did not express. We also describe differences protein sequence differences and pH and temperature profiles and their ability to functionally express, leading to a potential future screening platform for directed evolution of laccases and other ligninolytic enzymes such as peroxidases.

59 BASIC BIOLOGICAL SCIENCES↗