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

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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

PNNL-Predictive-Phenomics/ProCaliper

ProCaliper is a Python library that curates, organizes, and computes protein structure features in a way that easily interfaces with user-provided experimental data. It extracts or computes protein binding site, active site, charge, pLDDT (order/disorder), acid dissociation, protonation, solvent accessible surface area, disulfide bond distance, and protein secondary structure data using precomputed protein structures and publicly available databases. It provides a unified API for integrating additional residue-level data and for visualizing residue features in 3D.

Rozum, Jordan [Pacific Northwest National Lab]↗
DOE OSTI2017

Early Assessment of Treatment Responses During Radiation Therapy for Lung Cancer Using Quantitative Analysis of Daily Computed Tomography

Purpose: To investigate early tumor and normal tissue responses during the course of radiation therapy (RT) for lung cancer using quantitative analysis of daily computed tomography (CT) scans. Methods and Materials: Daily diagnostic-quality CT scans acquired using CT-on-rails during CT-guided RT for 20 lung cancer patients were quantitatively analyzed. On each daily CT set, the contours of the gross tumor volume (GTV) and lungs were generated and the radiation dose delivered was reconstructed. The changes in CT image intensity (Hounsfield unit [HU]) features in the GTV and the multiple normal lung tissue shells around the GTV were extracted from the daily CT scans. The associations between the changes in the mean HUs, GTV, accumulated dose during RT delivery, and patient survival rate were analyzed. Results: During the RT course, radiation can induce substantial changes in the HU histogram features on the daily CT scans, with reductions in the GTV mean HUs (dH) observed in the range of 11 to 48 HU (median 30). The dH is statistically related to the accumulated GTV dose (R{sup 2} > 0.99) and correlates weakly with the change in GTV (R{sup 2} = 0.3481). Statistically significant increases in patient survival rates (P=.038) were observed for patients with a higher dH in the GTV. In the normal lung, the 4 regions proximal to the GTV showed statistically significant (P<.001) HU reductions from the first to last fraction. Conclusion: Quantitative analysis of the daily CT scans indicated that the mean HUs in lung tumor and surrounding normal tissue were reduced during RT delivery. This reduction was observed in the early phase of the treatment, is patient specific, and correlated with the delivered dose. A larger HU reduction in the GTV correlated significantly with greater patient survival. The changes in daily CT features, such as the mean HU, can be used for early assessment of the radiation response during RT delivery for lung cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Machine Learning Framework for Characterizing Processing–Structure Relationship in Block Copolymer Thin Films

The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by surface features, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were trained to predict domain orientation based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances (R 2 > 0.75), while AFM-based property predictions were less accurate (R 2 < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using SHapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Robust Biaxial Anisotropy and Switchable Néel Vectors in LaFeO 3 Epitaxial Films

Antiferromagnets with highly stable but switchable Néel vectors are desired for antiferromagnetic spintronics with ultrafast speed and terahertz frequencies. Electrical switching of antiferromagnetic insulators has been demonstrated using binary antiferromagnets, while large families of complex antiferromagnets such as perovskites are largely unexplored. Here, we show that epitaxial LaFeO 3 thin films on SrTiO 3 (001) exhibit clear, robust biaxial anisotropy with a spin-flop field of a few tesla. Angular-dependent spin-Hall magnetoresistance (SMR) characterizations of Pt/LaFeO 3 bilayers with the current channel along SrTiO 3 [100] and [110] reveal distinct, intriguing shapes and field dependence. Simulations using a macrospin model accurately describe the main behavior and fine features of the SMR data from which key antiferromagnetic parameters are extracted. Furthermore, remanent SMR measurement confirms the high fidelity of the Néel vector along either easy axis of the biaxial anisotropy, indicating that epitaxial films of LaFeO 3 and potentially other perovskite antiferromagnets offer an attractive platform for antiferromagnetic spintronics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Calorimeter with Bayesian unfolding of spectra of high-flux broadband x rays

We report the development of a multipurpose differential x-ray calorimeter with a broad energy bandwidth. The absorber architecture is combined with a Bayesian unfolding algorithm to unfold high energy x-ray spectra generated in high-intensity laser–matter interactions. Particularly, we show how to extract absolute energy spectra and how our unfolding algorithm can reconstruct features not included in the initial guess. The performance of the calorimeter is evaluated via Monte Carlo generated data. The method accuracy to reconstruct electron temperatures from bremsstrahlung is shown to be 5% for electron temperatures from 1 to 50 MeV. We study bremsstrahlung generated in solid target interaction showing an electron temperature of 0.56 ± 0.04 MeV for a 700 μm Ti titanium target and 0.53 ± 0.03 MeV for a 50 μm target. We investigate bremsstrahlung from a target irradiated by laser-wakefield accelerated electrons showing an endpoint energy of 551 ± 5 MeV, inverse Compton generated x rays with a peak energy of 1.1 MeV, and calibrated radioactive sources. The total energy range covered by all these sources ranges from 10 keV to 551 MeV.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

XRIPL

XRIPL is a library of tools for processing x-ray radiographs and extracting contours of interest within the image using computer vision techniques. Features include spatial calibration, denoising, background and attenuation correction through pseudo-flatfielding, watershed segmentation, contour processing, and visualization. The details of the XRIPL analysis pipeline are published in the Proceedings of the 23rd Topical Conference on High-Temperature Plasma Diagnostics Proceedings (LA-UR-20-30133) [1]. [1] P. M. Kozlowski, Y. Kim, B. M. Haines, H. F. Robey, T. J. Murphy, H. M. Johns, and T. S. Perry. Use of Computer Vision for analysis of image data sets from high temperature plasma experiments. Review of Scientific Instruments 92, 033532 (2021) https://doi.org/10.1063/5.0040285

Kozlowski, Pawel↗
DOE OSTI2024

AI Design Assistant

SAND2025-01930O The AI Design Assistant uses ChatGPT to provide a natural language interface to airfoil analysis tools (XFOIL). Most of the code base is glue code, connecting XFOIL (a tool for analyzing airfoils) to the OpenAI interface. Among the more novel features are an airfoil geometry class and methods on how to extract detailed data from XFOIL. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Karcher, Cody↗
DOE OSTI2019

5-Fluorouracil induced liver toxicity in patients with colorectal cancer: role of computed tomography texture analysis as a potential biomarker

Purpose: To assess if CT texture analysis (TA) can serve as a biomarker of liver toxicity in patients with colorectal cancer treated with 5-fluorouracil (5-FU)-based chemotherapy. Methods: In this IRB-approved, HIPAA-compliant retrospective study, patients with colorectal cancer treated with 5-FU-based regimens during 2008–2010 were identified from institutional electronic database. Total 43 patients (23 women; mean age 56 years) with normal baseline liver function tests (LFTs), availability of baseline (pre-chemotherapy) and first follow-up CT (median 1.7 months, interquartile range (IQR) 1.5–2.5) performed during chemotherapy were included. Two single-slice ROI of right and left liver lobe were obtained on baseline and first follow-up CT for TA. Texture features [mean, entropy, kurtosis, skewness, mean of positive pixel, standard deviation (SD)] were extracted using a commercially available software (TexRAD; Feedback Medical Ltd, Cambridge, UK). Changes in texture parameters between baseline and follow-up CT were evaluated with Wilcoxon signed-rank test for patients with and without LFT elevation during chemotherapy. Results: Patients with LFT elevation (n = 34; 79%) showed significantly different mean, entropy, skewness, and SD (p values range 0.007–0.047) between baseline and first follow-up CT. No significant changes in features were observed in patients without LFT elevation (n = 9; 21%). In 19 patients (56%), first follow-up CT was performed before elevation of LFTs was observed. Conclusions: This proof-of-concept study shows that there are early changes in liver texture on first follow-up CT in patients with LFT elevation during 5-FU-based chemotherapy for colorectal cancer. In more than 50% of cases, these changes occur before LFT elevation becomes evident on blood tests.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Tritium aspects of the fusion nuclear science facility

The Fusion Nuclear Science Facility (FNSF) is intended to bridge the technical gaps from ITER to DEMO, and provide a fully nuclear environment for the advancement of materials and blanket science, among other missions. Though smaller (~500 MW fusion power) than the envisioned DEMO, our FNSF design still seeks to test DEMO-relevant components and technologies, and to that end one of its goals is to achieve a tritium breeding ratio near one using a dual-cooled lead lithium (DCLL) blanket. We explore here the migration of bred tritium through in-vessel components and coolant loops, which is exacerbated by the relatively low solubility of tritium in PbLi and the high operating temperatures envisioned in later phases of operation. Some features inherent to the design of the DCLL help limit tritium losses, namely the relatively high PbLi flow rates; coupled with efficient tritium extraction, the DCLL potentially achieves low tritium permeation losses without the use of permeation barriers. We outline here the design of such an extraction system, and some of the materials challenges that must be met in order to realize it. The efficacy of these and other design features, such as coaxial piping, is demonstrated via a series of parameter studies. We also investigate the effect of parameter uncertainties, and find that the imprecise knowledge of the tritium solubility in PbLi in particular leads to very disparate estimates of tritium losses. Depending on the experimental value used, losses range from ~0.05 to over 6 grams of tritium per year. As a result, the former is acceptable based on estimated environmental releases and resultant dose to members of the public; the latter exceeds regulatory limits.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Controlling the intermediate structure of an ionic liquid for f-block element separations

Recent research has revealed molecular structure beyond the inner coordination sphere is essential in defining the performance of separations processes, but nevertheless remains largely unexplored. Here we apply small angle neutron scattering (SANS) and x-ray absorption fine structure (XAFS) spectroscopy to investigate the structure of an ionic liquid system studied for f-block element separations. SANS data reveal dramatic changes in the ionic liquid microstructure (~150 Å) which we demonstrate can be controlled by judicious selection of counter ion. Mesoscale structural features (> 500 Å) are also observed as a function of metal concentration. XAFS analysis supports formation of extended aggregate structures, similar to those observed in traditional solvent extraction processes, and suggest additional parallels may be drawn from further study. As a result, achieving precise tunability over the intermediate features is an important development in controlling mesoscale structure and realizing advanced new forms of soft matter.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data for FUN-PROSE: A Deep Learning Approach to Predict Condition-Specific Gene Expression in Fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Genomics↗
DOE OSTI2017

Extending a Tandem Mass Spectral Library to Include MS{sup 2} Spectra of Fragment Ions Produced In-Source and MS{sup n} Spectra

Tandem mass spectral library searching is finding increased use as an effective means of determining chemical identity in mass spectrometry-based omics studies. We previously reported on constructing a tandem mass spectral library that includes spectra for multiple precursor ions for each analyte. Here we report our method for expanding this library to include MS{sup 2} spectra of fragment ions generated during the ionization process (in-source fragment ions) as well as MS{sup 3} and MS{sup 4} spectra. These can assist the chemical identification process. A simple density-based clustering algorithm was used to cluster all significant precursor ions from MS{sup 1} scans for an analyte acquired during an infusion experiment. The MS{sup 2} spectra associated with these precursor ions were grouped into the same precursor clusters. Subsequently, a new top-down hierarchical divisive clustering algorithm was developed for clustering the spectra from fragmentation of ions in each precursor cluster, including the MS{sup 2} spectra of the original precursors and of the in-source fragments as well as the MS{sup n} spectra. This algorithm starts with all the spectra of one precursor in one cluster and then separates them into sub-clusters of similar spectra based on the fragment patterns. Herein, we describe the algorithms and spectral evaluation methods for extending the library. The new library features were demonstrated by searching the high resolution spectra of E. coli extracts against the extended library, allowing identification of compounds and their in-source fragment ions in a manner that was not possible before. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Transverse momentum structure of proton within the basis light-front quantization framework

We obtain the leading-twist valence quark transverse-momentum-dependent parton distribution functions (TMD PDFs) for the proton within the basis light-front quantization (BLFQ) framework. Our results are consistent with lattice QCD calculations and our previous results for the collinear limit. We also obtain consistency with the Soffer-type bounds. Within our approach, we find that six T-even TMDs in the leading twist are all independent of each other, and previously found model-dependent relations do not hold. This is a promising sign that our results are representative of future, more extensive treatments of QCD. Furthermore, we obtain a non-trivial x-dependence of the <(p ⊥ ) 2 > and some consistency with the Gaussian ansatz but only in the small <(p ⊥ ) 2 > region. Those features suggest our results may be a useful alternative in future experimental extractions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Ion Pairing and Molecular Orientation at Liquid/Liquid Interfaces: Self-Assembly and Function

We report that molecular orientation plays a pivotal role in defining the functionality and chemistry of interfaces, yet accurate measurements probing this important feature are few, due, in part, to technical and analytical limitations in extracting information from molecular monolayers. For example, buried liquid/liquid interfaces, where a complex and poorly understood balance of inter- and intramolecular interactions impart structural constraints that facilitate the formation of supramolecular assemblies capable of new functions, are difficult to probe experimentally. Here, we use vibrational sum-frequency generation spectroscopy, numerical polarization analysis, and atomistic molecular dynamics simulations to probe molecular orientations at buried oil/aqueous interfaces decorated with amphiphilic oligomers. We show that the orientation of self-assembled oligomers changes upon the addition of salts in the aqueous phase. The evolution of these structures can be described by competitive ion effects in the aqueous phase altering the orientations of the tails extending into the oil phase. These specific anionic effects occur via interfacial ion pairing and associated changes in interfacial solvation and hydrogen-bonding networks. These findings provide more quantitative insight into orientational changes encountered during self-assembly and pave the way for the design of functional interfaces for chemical separations, neuromorphic computing applications, and related biomimetic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Forecasting Battery Electrode Performance via Electrochemical Fluorescence Microscopy and Machine-Learning

Predicting lithium-ion battery performance is hindered by microscale electrode heterogeneities invisible to conventional diagnostics. Here, we combine electrochemical fluorescence microscopy (EFM), which maps electronic connectivity by visualizing an electrofluorophore reaction distribution, with a multitask ElasticNet regression to forecast discharge capacity from spatial heterogeneity. Analyzing 196 images from six pilot-scale LiNi 0.5 Mn 0.3 Co 0.2 O 2 cathodes with varying carbon loadings, we extract 62 descriptors that capture morphology and texture. A compact five-feature model predicts capacity across eight discharge rates, achieving a per-target R 2 of up to 0.63 and an overall R 2 of 0.92, with a mean absolute percentage error of less than 2%. This performance rivals impedance-based approaches while avoiding their reliance on postformation data and incomplete electronic network information. Our facile and rapid, image-driven method may enable electrode quality control upstream of costly cell assembly to offer a transformative tool for data-driven battery research and manufacturing.

battery electrodes↗
DOE OSTI2018

Investigation of Liquid Cloud Microphysical Properties of Deep Convective Systems: 2. Parameterization of Raindrop Size Distribution and its Application for Convective Rain Estimation

The liquid cloud microphysical properties for stratiform rain (SR) have been investigated in Part 1 of this series. Since convective rain (CR) has characteristics in raindrop size distribution (DSD) and precipitation that are distinct from SR, we investigate the CR properties in this study by using 20 hr of CR samples collected by 17 Automatic Parsivel Units disdrometers during the Midlatitude Continental Convective Clouds Experiment over the Atmospheric Radiation Measurement Southern Great Plains site. A full spectrum of DSD is constructed based on a total of 23 size channels (0.321 to 9.785 mm), and both Gamma and Exponential fitted functions are applied to extract the DSD shape parameters. Compared to SR properties, CR has distinct features including broader size range and narrower exponential slope parameter (λ E ). These results indicate that the assumption of constant exponential intercept parameter ( N 0E ) is inappropriate for CR rain rate estimates. Additionally, the subsetting scheme of the CR DSD spectra also has a strong impact on the Gamma/Exponential functions. Therefore, a new CR DSD parameterization scheme is developed by choosing the appropriate spectra subset with the constraint of rain rate and using the constant λ E instead of constant N 0E . With the input of the radar reflectivity at the lowest observed height, the newly calculated CR rain rates match well with the collocated surface rain gauge measurements (127 Mesonet stations and 17 Automatic Parsivel Units), while the rain rates calculated using traditional Z-R relationship are 3–4 times larger, indicating that constant λ E is a better assumption for CR DSD.

58 GEOSCIENCES↗
DOE OSTI2017

Synthesis and characterization of low viscosity hexafluoroacetylacetonate-based hydrophobic magnetic ionic liquids

Magnetic ionic liquids (MILs) are distinguished from traditional ionic liquids (ILs) by the incorporation of a paramagnetic component within their chemical structure. Hydrophobic MILs are novel solvents that can be used in many applications, including liquid–liquid extraction (LLE) and catalysis. Low viscosity and low water solubility are essential features that determine their feasibility in LLE. Here, we synthesized extremely hydrophobic MILs by using transition and rare earth metal hexafluoroacetylacetonate chelated anions paired with the trihexyl(tetradecyl)phosphonium ([P 66614 + ]) cation. Hydrophobic MILs exhibiting water solubilities less than 0.01% (v/v) were synthesized in a rapid two-step procedure. Furthermore, the viscosities of the MILs are among some of the lowest ever reported for hydrophobic MILs (276.5–927.9 centipoise (cP) at 23.7 °C) dramatically improving the ease of handling these liquids. For the first time, the magnetic properties of MILs possessing hexafluoroacetylacetonate chelated metal anions synthesized in this study are reported using a superconducting quantum interference device (SQUID) magnetometer. We also achieved an effective magnetic moments (μ eff ) as high as 9.7 and 7.7 Bohr magnetons (μ B ) by incorporating high spin dysprosium and gadolinium ions, respectively, into the anion component of the MIL. The low viscosity, high hydrophobicity, and large magnetic susceptibility of these MILs make them highly attractive and promising solvents for separations and purification, liquid electrochromic materials, catalytic studies, as well as microfluidic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗