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Results for “QUANTITATIVE 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

A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images

Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime of wear and cyclic contact. To meet this demand, enamel possesses a combination of high hardness and resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely to decussation of the enamel rods, the principal unit of its microstructure. As such, enamel is inspiring the design of next‐generation structural materials. However, quantitative descriptions of the decussated enamel rod microstructure remain limited due to challenges encountered in applying computed tomography and in acquiring quality images appropriate for traditional digital processing methods. Here, a machine learning segmentation method is applied to images of the enamel obtained using scanning electron microscopy to support quantitative analysis of the microstructure. A pretrained convolutional neural network is used to expand the input training image dataset to allow the training of a random forest classifier, which ultimately segments the image with a very small training set ( n = 3 images). A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. The methodology applied here is equally applicable to other hard tissues.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Quantitative Analysis of the Semiconductor–Electrolyte Interface Using Cyclic Voltammetry Measurements

Small changes in the chemical potential at a semiconductor interface can result in dramatic changes to the space-charge layer that underpins applications in the electronic and photovoltaic industries as well as in photoelectrochemical cells for fuel production. There has hence been great interest in techniques that directly probe the space-charge layer, yet many fail at the semiconductor–electrolyte interface due to the potential drop in the electric double-layer region of the electrolyte. This article demonstrates that photovoltages, obtained from straightforward cyclic voltammetry measurements, provide an experimental and quantitative approach for characterizing the semiconductor–electrolyte interface. Key parameters accessible through this approach include the flat-band potential ( E fb ), the fraction of the total potential that drops across the space-charge layer (γ sc ) and the electric double layer, as well as the surface recombination lifetime (τ s ). Here, we report photovoltage measurements for p -type Si(111) photoelectrodes in contact with electrolytes containing redox-active species with a range of known reduction potentials that exceed the 1.1 eV bandgap. In tetrabutylammonium [NBu 4 ] + electrolyte, the flat-band potential determined for hydrogen-terminated ( p -Si–H), methyl-terminated ( p -Si–CH 3 ), and chemically oxidized ( p -Si–cSiO x ) surfaces were −0.02, −0.31, and 0.30 V vs Fc +/0 , respectively, agreeing well with expected shifts arising from surface dipole modifications. The quantitative analysis also reveals that 67% of the applied bias drops across the space-charge layer for p -Si–H, 73% for p -Si–CH 3 , and only 44% for p -Si–cSiO x . The remaining potential drop is attributed to the interfacial surface layer, which consists of a molecular dipole or oxide overlayer, and the Helmholtz layer within the electrolyte. When the larger [NBu 4 ] + electrolyte was replaced with Li + , the flat-band position showed minimal changes, but the fraction of the potential drop across the space-charge layer increased significantly, consistent with the small cation altering the structure of the electric double layer.

electrolytes↗
DOE OSTI2025

Front-end engineering design (FEED) studies: a quantitative analysis

NETL has devised a methodology for normalizing FEED study metrics of interest and allowing cautious quantitative comparison across FEED studies. This presentation introduces the novel quantitative comparison methodology and presents results from utilizing this methodology to examine data presented in recent FEED study reports. The quantitative methodology developed for the examination of FEED study performance and cost also allows comparison of real-world performance and costs against NETL TEA model predicted performance and cost. Learnings from examining NETL model predicted performance and cost versus real world reported values are highlighted. These learnings provide insight into NETL TEA model uncertainty and highlight opportunities for further model development.

FEED Studies↗
DOE OSTI2026

A Multiplexed Quantitative Analysis of Germline Single Amino Acid Variants by Targeted Proteomics in Nondepleted Human Plasma

Single amino acid variants (SAAVs) in protein sequences are often a direct result of single-nucleotide polymorphisms (SNPs). Certain germline SAAVs have shown biological relevance in different disease conditions but lack precise quantification in circulation, which could hinder functional investigations and progress in biomarker development. Here, we have developed a multiplexed liquid chromatography-selected reaction monitoring (LC-SRM) assay that monitors 5 wild-type and variant peptide pairs (Complement Factor B: CFB-R32Q/R32W, Clusterin: CLU-N317H, Fetuin B: FETUB-K360R, and Kininogen: KNG1-L212P) in nondepleted human plasma. The assay was optimized for imprecision, linearity, stability, and calibration assessments with CVs of under 20%. The wild-type and variant peptide pairs were characterized in a set of healthy individual plasma samples. These target identifications were also validated by SNP genotyping with more than 99% accuracy. For all protein targets, we observed significantly lower concentrations of WT species in the presence variant peptides. In CFB, the concentration of R32Q was significantly lower than its counterpart R32W variant and WT species. Furthermore, our results distinguished phenotypes of homozygosity and heterozygosity of the SAAV presence through direct concentration level characterization. These findings provide some insights into how SAAVs affect quantitative assessments of target peptides. The assay demonstrates a platform for proteogenomic analyses with potential applications in both research and clinical settings.

genetics↗
DOE OSTI2025

Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data

Machine-learned interatomic models represent a significant advancement in simulation methods, extending the predictive ability of first-principles methods to previously inaccessible length and time scales. However, the data-driven nature of these models can lead to difficult-to-detect errors that can compromise prediction accuracy. To address this challenge, we introduce a novel fingerprinting approach based on the Chebyshev Interaction Model for Efficient Simulation (ChIMES) ML-IAM graph-based descriptor. Our strategy enables efficient and statistically rigorous analysis of system configurations used in ML-IAM training and those generated by their application, e.g., in molecular dynamics simulations. We demonstrate that these fingerprints can effectively assess novelty of a configuration relative to an existing data set and determine dissimilarity among individual configurations, which are two key tasks in workflows for active learning-based ML-IAM training, data set curation, and on-the-fly uncertainty quantification.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Quantitative Analysis and Prediction of Thermal Runaway Metrics of High-Nickel Oxide Cathodes by Machine Learning Models

The pursuit of higher energy density in lithium-ion batteries has made high-nickel (Ni) layered oxides leading cathode candidates for next-generation electric vehicles. However, their poor thermal stability, particularly at Ni contents ≥ 90%, increases the risk of cathode-initiated thermal runaway. Furthermore, we present a data-driven framework combining linear and nonlinear machine learning models to predict key thermal runaway descriptors from a high-throughput differential scanning calorimetry database. With cathode composition and state of charge (SOC) as input features, the ensemble model accurately predicts peak temperature, heat release, and peak heat flow. SHAP analysis identifies Ni content and SOC as the dominant factors controlling thermal runaway temperature, while SOC primarily governs heat release and peak heat flow. Al, Mg, and Mn improve thermal stability by strengthening metal–oxygen bonding and delaying structural transformation, whereas B mainly reduces heat release through surface passivation. Validation with a new cathode composition confirms accurate prediction of SOC-dependent thermal runaway behavior and critical SOC.

25 ENERGY STORAGE↗
DOE OSTI2025

Quantitative analysis of leakage current in III-nitride micro-light-emitting diodes

In this study, the electrical characteristics under forward- and reverse-bias conditions of III-nitride blue and green micro-light-emitting diodes (μLEDs) are analyzed. A fitting model is proposed to determine the contributions of reverse leakage current and the effectiveness of sidewall treatments. Moreover, the forward-bias currents of the μLEDs are examined using the extracted ideality factor to examine the impacts of sidewall defects. The results show that sidewall treatments are highly effective for suppression of leakage currents. From the efficiency perspective, higher wall-plug efficiency (WPE) than external quantum efficiency (EQE) is observed when the operating voltage is lower than the photon voltage in both blue and green 20 × 20 μm 2 devices. This enhancement of the WPE over the EQE is due to the suppression of Shockley–Read–Hall (SRH) nonradiative recombination. These observations indicate that μLEDs with sidewall treatments not only improve optical performance but also further enhance the electrical performance of devices by suppressing the leakage current paths due to SRH nonradiative recombination processes.

42 ENGINEERING↗
DOE OSTI2026

Quantitative analysis of zonal flow influence on turbulent plasmas driven by trapped electrons

The role of zonal flow (ZF) in the turbulence saturation of trapped electron modes (TEM) in magnetically confined plasmas is revisited. Here, in this study, we examine ZF excitation and saturation mechanisms in TEM turbulence using detailed free-energy transfer diagnostics from nonlinear gyrokinetic simulations. When the ion channel becomes subdominant, electrons shift to transferring energy to the zonal component, which results in zonal flows always playing a significant though subdominant role, in the case of the temperature gradient driven TEM. The velocity-space structure of energy transfer reveals that trapped electrons directly couple to ZF. Regarding the saturation physics, the zonal flow advection, drift-wave–drift-wave interactions, and stable modes at the pump waves are compared. When ZF saturation is weak, drift-wave–drift-wave interactions play a larger role in maintaining turbulence saturation. The findings elucidate the roles of nonzonal to zonal flow coupling, stable modes, and nonzonal wave-wave interactions, and provide a basis for improving reduced models of turbulent transport.

Song, Jiheon [Hanyang University, Seoul (Korea, Re↗
DOE OSTI2026

Dataset for Top Model Decision Tree: Selecting Segmentation Models for Reliable Quantitative Analysis in Low- and Ultralow-Dose CryoEM

Motivation Multiple deep learning model architectures can be used to segment bacterial membranes in cryoEM images. However, an AI-based tool advancement is often presented with only a single segmentation model for broad use, and this single model may show inconsistent results across datasets from different users. Here, we present the Top Model Decision Tree, a model screening framework to screen for the best model to generate bacterial inner and outer membrane masks based on user priorities. We use pre-trained segmentation models from YOLOv11, YOLO26, U-Net, Detectron2 and SAM3 fine-tuned on bacterial inner and outer membranes imaged with cryoEM. Run the Framework This notebook must be opened in Google Colab. Mount Google Drive and run with a GPU-based runtime. Open the notebook and follow steps to git clone in folders and files within this repository. There will be a repeating top_model_decision_tree.ipynb (notebook clone) that will not be used. Save your .png binary mask files and .csv table outputs within your Google Drive or download before closing the notebook. The models and all analysis/training scripts are available at [GitHub: https://github.com/Lynnicia/CryoEM_membranes_top_model_decision_tree and https://github.com/Sireesiru/Semantic-Segmentation-of-bacterial-cell-envelope-using-U-Nets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Quantitative Analysis of Rhodobacter sphaeroides Storage Organelles via Cryo-Electron Tomography and Light Microscopy

Bacterial cytoplasmic organelles are diverse and serve many varied purposes. Here, we employed Rhodobacter sphaeroides to investigate the accumulation of carbon and inorganic phosphate in the storage organelles, polyhydroxybutyrate (PHB) and polyphosphate (PP), respectively. Using cryo-electron tomography (cryo-ET), these organelles were observed to increase in size and abundance when growth was arrested by chloramphenicol treatment. The accumulation of PHB and PP was quantified from three-dimensional (3D) segmentations in cryo-tomograms and the analysis of these 3D models. The quantification of PHB using both segmentation analysis and liquid chromatography and mass spectrometry (LCMS) each demonstrated an over 10- to 20-fold accumulation of PHB. The cytoplasmic location of PHB in cells was assessed with fluorescence light microscopy using a PhaP-mNeonGreen fusion-protein construct. The subcellular location and enumeration of these organelles were correlated by comparing the cryo-ET and fluorescence microscopy data. A potential link between PHB and PP localization and possible explanations for co-localization are discussed. Finally, the study of PHB and PP granules, and their accumulation, is discussed in the context of advancing fundamental knowledge about bacterial stress response, the study of renewable sources of bioplastics, and highly energetic compounds.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Quantitative Analysis of Fission-Product Surrogates in Molten Salt Chloride Aerosols

This work demonstrates laser-induced breakdown spectroscopy (LIBS) applied to a stream of aerosolized salt from molten eutectic LiCl-KCl. We demonstrate analytical capabilities to track fission-product surrogates of Cs, Sr, Pr, and Nd simultaneously, with application to monitor salts in pyroprocessing schemes and molten salt reactors. This work demonstrates limits of detection using LIBS on the order of 100 μg/g, which proves potentially applicable to monitoring fission-product concentrations in pyroprocessing applications. Additionally, this work explores fundamental aspects of plasma temperature and plasma electron density of the aerosolized species during LIBS with a specific focus on potential non-uniform plasma conditions in the aerosol.

74 - ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Solar cities: A case study analysis of city-level enablers of expanded solar energy access

Rooftop solar photovoltaic (PV) adoption can benefit households by reducing electricity bills and enhancing energy resiliency. Low and moderate-income (LMI) households have been less likely to adopt PV and experience these benefits in the United States than higher-income households. Adopter income trends are often explored through quantitative analysis with limited explanatory power. Our quantitative analysis only explains around one-third of city-level variation in LMI adoption trends through socioeconomic factors such as median home values and income inequality and PV market factors such as cumulative adoption and incentives. We implement semi-structured interviews in three case studies of cities with relatively high rates of LMI PV adoption to better understand the factors that explain PV adopter income trends. The case studies partly reiterate findings from quantitative analysis, such as the role of PV incentives. The case studies reveal a broader set of LMI adoption drivers that are missed in quantitative analyses. The case studies show how city contexts can affect LMI adoption, such as the role of supportive city governments. The case studies also reveal the importance of partnerships, such as partnerships between city governments and state LMI PV program implementers. Finally, interviewees emphasized the importance of building trust among prospective LMI PV adopters. Interviewees suggested that partnerships, outreach, and consumer protection measures were crucial to building trust in PV installers among LMI households.

Adoption↗
DOE OSTI2026

Affine Transformations to Enable Machine Learning for Semi-Quantitative EDS Analysis

Energy Dispersive X-ray Spectroscopy (EDS) is an essential technique for determining elemental concentrations and distributions within microstructures, critical for materials discovery, optimization, and qualification. However, most published EDS data is qualitative because current quantitative EDS analysis methods require extensive calibration and post-processing, limiting their practicality and widespread adoption. This work seeks to establish a framework for accelerated EDS characterization and spectrum analysis that can leverage ML to analyze correlations between various elemental compositions and resulting EDS spectra. The complex physics and data result in a high-dimensional problem that grows exponentially with the number of elements in the system and the complexity of the spectrum analysis. ML provides a way to compute and optimize the results of this highly dimensional problem in a flexible way to tailor it to the user’s specific needs and material system. However, the framework emphasizes transparency through a strictly mathematical affine transformation, so the analysis remains understandable and reviewable to facilitate adoption by the scientific community. While currently implemented methods are simplistic and unvalidated, further development and demonstration of this framework could enable high-throughput, accurate, and accessible EDS characterization.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Quantitative Particle Analysis of Neptunium-237 Oxides: Optimization of MAMA Analysis for Modified Direct Denitration Products

The production of plutonium-238 through irradiation of neptunium-237 ( 237 Np) target materials for the use in radioisotope thermoelectric generators is paramount for continued deep space exploration. This work employs scanning electron microscopy to analyze 237 Np materials coupled with a well-developed image analysis framework (Morphological Analysis for Material Attribution, or MAMA) to determine the degree of micron-scale homogeneity in the materials. This work demonstrated how the quantification of particle characteristics can validate production materials and affirm the qualitative similarities observed in micrographs. The 237 Np oxide particle analysis determined that the materials from five production runs were quantitatively homogenous (significant at α = 0.05) in particle area, circularity, equivalent circular diameter, and ellipse aspect ratio, with two of the sampling dates having statistically significant different means for one of the four characteristics. Furthermore, these metrics not only confirm general homogeneity of the material but also expand the application of MAMA workflows to 237 Np materials, demonstrating the utility of MAMA analysis for a wider breadth of nuclear materials than previously reported. In the open literature, this study is the first time that these microanalytical techniques were applied to 237 Np materials to this degree.

MAMA↗
DOE OSTI2024

Uncertainty Quantification and Sensitivity Analysis for Quantitative Risk Assessments of Hydrogen Infrastructure

Typical QRAs provide deterministic estimates and understanding of risks posed but are constructed using significant assumptions and uncertainties due to limited data availability and historical momentum of using nominal estimates. This report presents a hydrogen QRA analysis using HyRAM+ that incorporates uncertainty with Latin hypercube sampling and sensitivity analysis using linear regression.

08 HYDROGEN↗