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Results for “high-throughput statistical analysis”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization

Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure–property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co 3 O 4 nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the sizedependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an “onset radius,” which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Statistical White-Line Analysis in High-Throughput TXM-XANES for Chemical State Quantification

The transmission X-ray microscopy (TXM) based X-ray absorption near-edge structure (XANES) technique provides three-dimensional mapping of element-specific chemical states at nanometer-scale spatial resolution and micrometer-scale fields of view. However, compared to conventional volume-averaged XANES (VA-XANES) measurements, the inherently small voxel size in TXM-XANES leads to a lower signal-to-noise ratio, making full-spectrum analysis computationally demanding and less robust. Here, we present the structural and compositional conditions for a statistical white-line analysis framework under which chemical state information can be directly extracted from the white-line peak position in voxel spectra without the need for voxel-wise background subtraction or normalization, under well-defined structural and compositional conditions. The method is validated on layered oxide cathode materials, where low-order polynomial fitting accurately reproduces white-line features, and the extracted energy distributions correlate strongly with VA-XANES results. This statistical approach enables high-throughput, dose-efficient, and noise-robust chemical state quantification in TXM-XANES, offering broad applicability to functional materials requiring nanoscale oxidation-state mapping.

TXM↗
DOE OSTI2025

High-throughput micro-scale bandgap mapping for perovskite-inspired materials with complex composition space

Abstract To realize the full promise of high-throughput experimental workflows, the rate of sample synthesis must be matched by that of characterization. Of growing interest are contactless optical techniques that can rapidly measure material homogeneity and properties. Here, we present a hyperspectral imaging method to measure local optical bandgap distributions within samples, utilizing spatially-resolved reflectance spectra coupled with automated data analysis. We collect approximately one million optical bandgap data across the compositional space of Cs 3 (Bi x Sb 1-x ) 2 (Br y I 1-y ) 9 perovskite-inspired materials. Our results show non-monotonic bandgap variations (i.e., bandgap bowing) along six composition gradient sequences, in addition to identifying samples with multiple bandgaps in statistics. High-throughput transient absorption spectroscopy reveals that within these compositions, the depletion of the ground state carriers to excited states occurred at discrete energy levels with independent carrier dynamics, consistent with the bandgap observation and indicative of phase separation. This work demonstrates the potential for rapid optical measurements to assess material quality and homogeneity in a high-throughput experimental setting, supporting screening and recipe optimization of optoelectronic material candidates with desired carrier dynamics and optical properties.

Science & Technology - Other Topics↗
DOE OSTI2024

Self-Assembly of a Triblock Copolymer in the Presence of a Rigid Conjugated Polyelectrolyte

The properties of conducting polymers are strongly influenced by structural changes induced by long-range order, which can be achieved by using block copolymers that self-assemble into crystalline structures. These blends result in unique mesophases distinct from the pure components with self-assembly behavior modulated by solution conditions and polymer architectures. High-throughput small-angle X-ray scattering data of aqueous Pluronic P123 (PEO 20 –PPO 70 –PEO 20 ) and conjugated polyelectrolyte poly[3-(potassium-4-butanoate) thiophene-2,5-diyl] (PPBT) blends at various concentrations and temperatures were automatically classified into phase maps by autophasemap, an unsupervised statistical analysis algorithm. The outlined phase boundaries revealed that adding PPBT to high P123 concentrations induced a transition from cubicly ordered spherical micelles to hexagonally packed cylindrical micelles. Shear alignment via rheological small-angle neutron scattering of the blends produced monolithic oriented cubic and hexagonal crystal gels. Furthermore, these insights into the self-assembly of conductive polymer blends will aid in the design of soft materials with tunable structural and electronic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Exploring Domain-Wall Pinning in Ferroelectrics via Automated High-Throughput Atomic Force Microscopy

Domain-wall dynamics in ferroelectric materials are strongly position-dependent, since each polar interface is locked into a unique local microstructure. This necessitates spatially resolved studies of wall pinning using scanning-probe microscopy techniques. The pinning centers and pre-existing domain walls are usually sparse within the image plane, precluding the use of dense hyperspectral imaging modes and requiring time-consuming human experimentation. Here, a large-area epitaxial PbTiO 3 film on cubic KTaO 3 was investigated to quantify the electric-field-driven dynamics of the polar–strain domain structures using ML-controlled automated piezoresponse force microscopy. Analysis of 1500 switching events reveals that domain-wall displacement depends not only on field parameters but also on the local ferroelectric–ferroelastic configuration. For example, twin boundaries in polydomains regions, like a 1 – /c+ ∥ a 2 – /c – , stay pinned up to a certain level of bias magnitude and change only marginally as the bias increases from 20 to 30 V, whereas single-variant boundaries, like the a 2 + /c + ∥ a 2 – /c – stack, are already activated at 20 V. These statistics on the possible ferroelectric and ferroelastic wall orientations, together with the automated high-throughput AFM workflow, can be distilled into a predictive map that links domain configurations to pulse parameters. Here, this microstructure-specific rule set forms the foundation for the design of ferroelectric memories.

automated scanning probe microscopy↗
DOE OSTI2025

TransPlatformer

We propose TransPlatformer for translating toxicogenomics from one platform to another. Transcriptomic profiling has evolved through multiple generations of technology, from microarrays (e.g., Affymetrix, CodeLink) to more recent high-throughput sequencing and targeted panels such as S1500+. Microarrays, which dominated gene expression studies in the early 2000s, provided affordable and high-throughput transcript quantification but suffered from cross-hybridization issues and limited dynamic range . RNA-Seq, introduced in the late 2000s, revolutionized transcriptomics by enabling unbiased and comprehensive gene expression analysis, albeit at higher costs and computational demands . Despite advances, many studies rely on historical microarray data, necessitating the translation of legacy data into modern platforms to ensure continuity and comparability. This translation is complicated by factors such as platform-specific probe design, differences in transcript coverage, and batch effects . Existing methods for cross-platform mapping include statistical normalization, machine learning models, and biological anchoring approaches. The ability to translate transcriptomic data between platforms has broad implications, including enhanced meta-analyses, improved toxicological modeling, and better integration of historical datasets with contemporary research. TransPlatformer seeks to contribute to this effort by evaluating translation methodologies and proposing novel strategies to improve cross-platform gene expression harmonization. In this repository there are code examples for TransPlatformer implementation

Cong, Guojing↗
DOE OSTI2025

Evaluation of a high-throughput method for processing sponge-stick samples to detect viable, non-spore-forming biothreat agents

After a bioterrorism incident, surface sampling is often used to determine the extent of contamination and exposure, guiding decontamination efforts and decisions for re-occupancy of affected sites. The sponge-stick (SS) is a preferred and commonly used device for sample collection to detect both spore-forming and non-spore-forming biothreat agents from non-porous surfaces. Here, in this study, a recently developed high-throughput method (HTM) for processing SS samples to detect viable Bacillus anthracis spores was adapted for detection of non-spore-forming biothreat agents, Yersinia pestis and Francisella tularensis. The scalable HTM was used to process up to 20 SS samples simultaneously, compared to the current stomacher-based method which processes one SS at a time. Comparisons of the HTM and the stomacher-based method were statistically indistinguishable for most experiments (P > 0.05) with HTM recoveries of 37–60 % for Y. pestis inoculated at 102–103 cells/SS and held 48 h at 4 °C to mimic sample transport/storage. The HTM was integrated with Rapid Viability-Polymerase Chain Reaction (RV-PCR) analysis to detect viable Y. pestis in the presence of particulate contamination (Arizona Test Dust, ATD). This approach detected Y. pestis inoculated at 20 cells/SS and ATD did not impact detection (P > 0.05). F. tularensis showed significantly lower recoveries between no-hold time and 48-h hold time (4 °C, P < 0.05) using the HTM, which further testing showed could be due to toxicity of the neutralizing buffer used for SS pre-wetting. With modifications, this method could enhance throughput capacity while maintaining similar recovery efficiencies to current methods for other non-spore-forming bacterial pathogens.

Biological and medical sciences↗
DOE OSTI2026

High-throughput Single-Cell Proteomics and Transcriptomics from the Same Cells with a Nanoliter-Scale Spin-Transfer Approach

Single-cell multiomic platforms provide a comprehensive snapshot of cellular states and cell types by offering critical insights into the spatiotemporal regulation of biomolecular networks at a systems level, thereby defining the basis of multicellularity. Here, we introduce nanoSPINS, an advanced platform that enables high-throughput profiling and integrative analysis of the transcriptome and proteome from the same single cells using RNA sequencing and isobaric labeling LC-MS-based proteomics, respectively. NanoSPINS can efficiently transfer mRNA-containing droplets across two microarrays via a centrifugation-based approach, while proteins are retained on the initial platform. Benchmarking of nanoSPINS on two cell lines demonstrates its ability to generate global proteomic and transcriptomic profiles that align well with previously established methodologies/platforms. The incorporation of isobaric TMTpro labeling into this single-cell multiomics platform significantly enhances the throughput of single-cell proteomic analyses. Through the high-throughput quantification of the proteome and transcriptome, nanoSPINS not only facilitates the identification of molecular features at both mRNA and protein level but also provides larger sample sizes for improved statistical power in clustering and differential abundance. Given the broad applicability of single-cell multiomics in biological research and clinical settings, we believe nanoSPINS represents a powerful platform for the characterization of heterogeneous cell populations.

multi 'omics↗
DOE OSTI2025

The Profiled Feldman-Cousins Method for Confidence Interval Construction for the Nova 3-Flavor Oscillation Analysis

The small interaction cross-section of neutrinos makes experimental neutrino physics particularly responsive to technological advancements. A significant development leveraged by the NOvA experiment is large-scale parallel processing, enabling novel computational approaches to longstanding experimental challenges. Central to managing the resulting high-throughput data is NOvA’s implementation of the Freight Train model, designed for efficient data production and handling.This dissertation details the methodology and execution of the NOvA 2024 3-Flavor Oscillation Analysis, supported by a comprehensive dataset spanning ten years. It emphasizes frequentist results refined through the Feldman-Cousins (FC) technique, specifically addressing confidence interval corrections in parameter estimation. The computational intensity associated with Feldman-Cousins arises from extensive Monte Carlo simulations, which were substantially mitigated through parallel computing on the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC), employing the MPI framework.To further enhance computational efficiency, an Importance Sampling method is introduced and evaluated, demonstrating significant potential to reduce complexity, particularly in exploring extreme parameter space regions. This thesis presents both the successful application of advanced computational resources and the development of sophisticated statistical techniques, aiming to enhance the precision and scope of neutrino oscillation analyses.

Dye ajdye11190@gmail.com, Andrew Joseph [Mississip↗
DOE OSTI2025

Unlocking hidden information in sparse small-angle neutron scattering measurements

Hypothesis Small-Angle Neutron Scattering (SANS) is a powerful technique for studying soft matter systems such as colloids, polymers, and lyotropic phases, providing nanoscale structural insights. However, its effectiveness is limited by low neutron flux, leading to long acquisition times and noisy data. Here, we hypothesize that Bayesian statistical inference using Gaussian Process Regression (GPR) can reconstruct high-fidelity scattering data from sparse measurements by leveraging intensity smoothness and continuity. Experiments and Simulations The method was benchmarked computationally and validated through SANS experiments on various soft matter systems, including wormlike micelles, colloidal suspensions, polymeric structures, and lyotropic phases. GPR-based inference was applied to both experimental and synthetic data to evaluate its effectiveness in noise reduction and intensity reconstruction. Findings GPR significantly enhances SANS data quality and therefore reducing measurement times by up to two orders of magnitude. This cost-effective approach maximizes experimental efficiency, enabling high-throughput studies and real-time monitoring of dynamic systems. It is particularly beneficial for weakly scattering and time-sensitive studies. Beyond SANS, this framework applies to other low-SNR techniques, including laboratory-based small-angle X-ray scattering and various dynamical scattering methods. Furthermore, it offers transformative potential for compact neutron sources, enhancing their viability for structural analysis in resource-limited settings.

Small angle neutron scattering↗
DOE OSTI2024

Performance Prediction of High‐Entropy Perovskites La 0.8 Sr 0.2 Mn x Co y Fe z O 3 with Automated High‐Throughput Characterization of Combinatorial Libraries and Machine Learning

Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition–structure–performance relationships of high-entropy La 0.8 Sr 0.2 Mn x Co y Fe z O 3±𝞭 perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells. Following the deposition of a continuous compositional map using thin-film combinatorial pulsed laser deposition, compositional, structural, and performance properties are characterized using six different techniques with mapping capabilities. Random forests effectively model electrochemical performance, consistently identifying Fe-rich oxides as optimal compounds with the lowest area-specific resistance values for oxygen electrodes at 700 °C. Additionally, the models identify a statistical correlation between oxygen sublattice distortion—derived from spectral analysis of Raman-active modes—and enhanced performance.

high entropy oxides↗
DOE OSTI2025

Stochastic room temperature creep of 316 L stainless steel

The creep behavior of 316 L stainless steel at room temperature was evaluated as a function of time and applied stress using a new high-throughput approach. Several common creep models were evaluated against the observations, leading to deeper analysis of a stress-dependent modified logarithmic creep model. Within this model, multiple sources of uncertainty were compared. Aleatoric stochastic variation between samples under nominally identical conditions was identified as the primary contributor to uncertainty in creep response. Under any particular set of conditions, the sample-to-sample variability in creep strain was as high as a factor of two, highlighting the engineering importance of characterizing large statistical datasets. The model's extrapolation capabilities were assessed by comparing predictions derived from calibration on partial, shorter-duration subsets of the data. In conclusion, these findings underscore the importance of accounting for stochastic effects in predictive modeling of aging phenomena.

High-throughput↗
DOE OSTI2026

Mass Spectrometer Transient Analysis

This software implements a complete preprocessing pipeline for transient mass spectrometry (MS) data collected during TAP (Temporal Analysis of Products) experiments. It is designed to extract chemically meaningful fluxes from overlapping ion signals by applying a calibrated defragmentation matrix and solving the resulting linear system using non-negative least squares (NNLS) regression. The core script, preprocess_mass_spec.py, performs the following operations: Gain correction: Applies amplifier gain scalars derived from inert-packed calibration pulses to normalize signal intensities across AMUs and acquisition settings. Background subtraction: Removes experiment baselines using user-defined time windows, ensuring compatibility with slow-diffusing species and preventing negative values that would interfere with NNLS. Options to subtract before and after defragmentation. Defragmentation: Constructs a fragmentation matrix A from zeroth moments of calibration pulses (equal molar gas:inert mixtures) and solves Ax=b at each time point, where b is the raw MS signal and x is the estimated species flux. The matrix is normalized to inert signals and accounts for instrument-specific fragmentation behavior. Pulse-mode handling: Supports both averaged and individual pulse modes, enabling statistical treatment of fluxes and calculation of standard deviations. Integration and output: Computes zeroth moments (integrated fluxes) and exports time-resolved and integrated data in CSV format, suitable for downstream kinetic modeling. The software is validated using both virtual TAP simulations (VTAP) and experimental data from propane dehydrogenation (PDH) on CrOx/Al2O3 catalysts. It preserves temporal resolution by applying NNLS point-by-point across the pulse duration (typically 6,000+ time slices per pulse), leveraging the linear superposition principle to reconstruct full flux profiles. The defragmented outputs are compatible with kinetic extraction methods such as the G and Y procedures, which are used to derive rate–concentration relationships from TAP data. The details of these validations are discussed in detail in the supporting manuscript and supporting information. Example data and output files are also included. The methodology is robust to experimental noise and drift, with calibration protocols that account for pulse size effects, MS aging, and inert gas normalization. The software is modular, reproducible, and tailored for high-throughput TAP-MS workflows in catalysis research.

Kristy, Stephen [Idaho National Laboratory (INL), ↗
DOE OSTI2026

Synthetic data-driven deep learning for label-free autonomous atomic force microscopy

Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator input and by the scarcity of annotated experimental AFM datasets needed to enable data-driven automation. Here, we introduce SimuScan, a synthetic-data–driven framework that enables reliable AFM feature identification, segmentation, and targeted imaging without requiring large manually labeled experimental datasets. SimuScan generates tunable, high-fidelity synthetic AFM images of defined morphologies while incorporating realistic experimental artifacts, including tip–sample convolution, noise, flattening distortions, and surface debris. These datasets are shown to support scalable, label-free training of modern deep learning models for AFM analysis. When integrated into data-driven AFM workflows, SimuScan-trained models can locate and analyze nanoscale structures across large datasets and guide targeted follow-up imaging. We validate this approach on nanostructured surfaces, DNA assemblies, and bacterial cells, demonstrating robust generalization across diverse sample types with minimal operator intervention. More broadly, this work establishes a general strategy for generating explicitly conditioned, task-relevant synthetic data to improve the reliability of downstream models in autonomous microscopy.

Millan-Solsona, Ruben [Oak Ridge National Laborato↗
DOE OSTI2025

A hybrid neural architecture: Online attosecond x-ray characterization

The emergence of high-repetition-rate x-ray free-electron lasers (XFELs), such as SLAC’s LCLS-II, serves as our canonical example for autonomous controls that necessitate high-throughput diagnostics paired with streaming computational pipelines capable of single-shot analysis with extremely low latency. We present the deterministic characterization with an integrated parallelizable hybrid resolver architecture, a hybrid machine learning framework designed for fast, accurate analysis of XFEL diagnostics using angular streaking-based sinogram images. This architecture integrates convolutional neural networks and bidirectional long short-term memory models to denoise input, identify x-ray sub-spike features, and extract sub-spike relative delays with sub-30 attosecond temporal resolution. Deployed on low-latency hardware, it achieves over 10 kHz throughput with 168.3 μs inference latency, indicating scalability to 14 kHz with field-programmable gate array integration. By transforming regression tasks into classification problems and leveraging optimized error encoding, we achieve high precision with low-latency performance that is critical for real-time streaming event selection and experimental control feedback signals. This represents a key development in real-time control pipelines for next-generation autonomous science, generally, and high repetition-rate x-ray experiments in particular.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2025

Automated segmentation of soft X-ray tomography: Native cellular structure with submicron resolution at high-throughput for whole-cell quantitative imaging in yeast

Soft X-ray tomography (SXT) is an invaluable tool for quantitatively analyzing cellular structures at suboptical isotropic resolution. However, it has traditionally depended on manual segmentation, limiting its scalability for large datasets. Here, we leverage a deep learning-based autosegmentation pipeline to segment and label cellular structures in hundreds of cells across three Saccharomyces cerevisiae strains. This task-based pipeline uses manual iterative refinement to improve segmentation accuracy for key structures, including the cell body, nucleus, vacuole, and lipid droplets, enabling high-throughput and precise phenotypic analysis. Using this approach, we quantitatively compared the three-dimensional (3D) whole-cell morphometric characteristics of wild-type, VPH1-GFP, and vac14 strains, uncovering detailed strain-specific cell and organelle size and shape variations. We show the utility of SXT data for precise 3D curvature analysis of entire organelles and cells and detection of fine morphological features using surface meshes. Our approach facilitates comparative analyses with high spatial precision and statistical throughput, uncovering subtle morphological features at the single-cell and population level. This workflow significantly enhances our ability to characterize cell anatomy and supports scalable studies on the mesoscale, with applications in investigating cellular architecture, organelle biology, and genetic research across diverse biological contexts.

Chen, Jianhua [Lawrence Berkeley National Laborato↗
DOE OSTI2025

RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

Abstract We introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command-line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelization on high-performance cluster systems, which enables high-throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.

Kainer, David (ORCID:0000000172714676)↗
DOE OSTI2026

High-Temperature SiC Cladding End Plug Irradiation Design and HFIR Readiness

This report documents the design of a High Flux Isotope Reactor (HFIR) irradiation experiment intended to evaluate irradiation effects on the hermeticity of silicon carbide (SiC) end plug specimens under a radial fast neutron flux gradient at representative light-water reactor (LWR) temperatures of approximately 300 °C. The overarching goal of this work is to statistically evaluate SiC end plug hermeticity and mechanical properties following irradiation using the high-throughput irradiation capability discussed here. Each specimen consists of a short section of SiC fiber–reinforced SiC (SiC/SiC) tube with a single monolithic SiC end plug joined to one end. The experiment allows up to 66 specimens to be irradiated in six different stacks within a dry, sealed irradiation capsule derived from the previously developed high-temperature SiC/SiC cladding bowing experiment. The neutronics basis, thermal analysis, and HFIR readiness of the experiment are discussed in this report for two possible design cases. The first design case is based on existing approval documentation and components that are on hand and approved for use, so the experiment insertion would require only specimen receipt, specimen pre-irradiation characterization, experiment assembly, and final fabrication package approval. The second design case provides improved thermal robustness and the preferred end plug geometry but requires fabrication of a modified holder and revisions to the HFIR approval documentation, in addition to the other activities required for the first design case, before insertion.

Hott, Daniel [Oak Ridge National Laboratory (ORNL)↗