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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Discovery of a Natural Product That Binds to the Mycobacterium tuberculosis Protein Rv1466 Using Native Mass Spectrometry

Elucidation of the mechanism of action of compounds with cellular bioactivity is important for progressing compounds into future drug development. In recent years, phenotype-based drug discovery has become the dominant approach to drug discovery over target-based drug discovery, which relies on the knowledge of a specific drug target of a disease. Still, when targeting an infectious disease via a high throughput phenotypic assay it is highly advantageous to identifying the compound’s cellular activity. A fraction derived from the plant Polyalthia sp. showed activity against Mycobacterium tuberculosis at 62.5 μge/μL. A known compound, altholactone, was identified from this fraction that showed activity towards M. tuberculosis at an minimum inhibitory concentration (MIC) of 64 μM. Retrospective analysis of a target-based screen against a TB proteome panel using native mass spectrometry established that the active fraction was bound to the mycobacterial protein Rv1466 with an estimated pseudo-Kd of 42.0 ± 6.1 µM. Our findings established Rv1466 as the potential molecular target of altholactone, which is responsible for the observed in vivo toxicity towards M. tuberculosis .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Supported-Single Nickel Atom Catalysts for the Methanation of Carbon Dioxide

Synthesis of twenty-seven bimetallic catalysts consisting of nickel and one of nine different dopants (B, Co, Cu, Fe, Mg, Mn, Sn, V, and Zn) supported on three different metal oxides (Al 2 O 3 , CeO 2 , and SiO 2 ) is carried out via organometallic grafting. The catalysts are evaluated for their activity and selectivity for the CO 2 methanation reaction at a feed ratio of H 2 /CO 2 of 4 at 300 °C in a high-throughput flow reactor system. After in situ pre-activation (500 °C in H 2 ), Ni/Co/CeO 2 exhibited high conversion (84.3%) and selectivity for methane (99.6%). Ni/Co/CeO 2 was characterized by high-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction, H 2 -temperature-programmed reduction (H 2 -TPR), and CO 2 -temperature-programmed desorption (CO 2 -TPD). HRTEM showed the presence of single Ni and Co atoms on ceria after pre-reduction at 500 °C and after the methanation reaction at 300 °C for 15 h. XPS determined that the strong interaction between Ni, Co, and ceria increased after the reduction, leading to a charge transfer between Ni and Ce that created oxygen vacancies in ceria. Nickel was found to be Ni 2+ in the as-prepared material and was partially reduced in the presence of cobalt and after the activation in H 2 at 500 °C. The DFT results show that both nickel and cerium exhibit lower Bader charges in the Ni/Co/CeO 2 system, confirming that the presence of cobalt enhances the reduction of both Ni and Ce through electronic interactions. This indicates that single cationic Ni atoms are highly effective for the methanation reaction. In conclusion, the organometallic grafting technique is found to be efficient for synthesizing catalysts with highly homogeneous dispersed species at low metal loadings (0.16 wt % Ni–0.15 wt % Co), which leads to high turnover frequency (up to 248.7 h –1 ) and durability for methanation.

CO2 conversion↗
DOE OSTI2025

Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing

We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Integrated Micro‐Scale Concentrating Photovoltaics: A Scalable Path Toward High‐Efficiency, Low‐Cost Solar Power

The global energy market is seeing increases in the electricity demand of a couple of percentage points annually. The photovoltaic (PV) industry is also growing rapidly every year. One of the PV technologies is concentrator photovoltaics (CPV). CPV uses high‐efficiency multijunction solar cells and optics to concentrate sunlight, thereby significantly reducing the amount of semiconductor material needed. Yet, due to the high upfont manufacturing cost of CPV, it currently does not offer a competitive price against silicon PV. With this a new branch is introduced to the industry, micro‐CPV, which can be broadly explained as the miniaturization of the solar cells and optical components. The motivation for micro‐CPV is lowering the cost by decreasing the material volume and enabling new system architectures and high‐throughput manufacturing methods, while still maintaining high electrical efficiencies, taking advantage of a lower thermal load, shorter optical paths, lower resistive losses, and lower material volumes. Herein, a comprehensive review of the technological advances is presented, key synergies between micro‐CPV and other industries sharing similar challenges are identified, exemplified by micro‐light emitting diodes display manufacturing. New assembly process development in these industries will facilitate commercial adoption of micro‐CPV with continued miniaturization while driving down the cost.

Jost, Norman↗
DOE OSTI2019

Manufacturing Cost Analysis for Proton Exchange Membrane Water Electrolyzers

Hydrogen is produced by several means including water electrolysis where water molecules are split into hydrogen and oxygen. While there are several commercial electrolysis systems, proton exchange membrane (PEM) technology has emerged as a technology of choice in the last decade or so because of the versatile properties it possesses. High current and power densities and high efficiency values are among the advantages of the PEM electrolyzer over other commercial or near-commercial electrolysis systems such as alkaline or solid oxide. This study attempts to study the impact of manufacturing economies of scale on the cost of the PEM electrolyzer and how this can be linked to reducing the cost of hydrogen production. We found that adoption of the high throughput, automated manufacturing processes that have high process yields such as roll-to-roll manufacturing of catalyst coated membrane and advanced coating processes for metal plates, could push the cost for the PEM electrolyzer stack to the $\$323$/kW level for an overall production volume of 10MW (e.g. 1 MW systems x 10 units/year). The stack cost can be pushed as low as $\$165$/kW at 10,000 systems/year. We do not expect to see same rate of the cost reduction in the balance of plant because most parts are outsourced from part suppliers who offer limited discounts on their products. System cost including stack and balance of plant, could also be pushed down with economies of scale. The uninstalled system cost can be reduced from $\$515$/kW at 10 units/year to $\$230$/kW at 10,000 units/year.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus

The multicomponent Ti alloys, specifically the -phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young’s modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nb- and Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young’s moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young’s modulus. Furthermore, we implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predicting oxidation damage in ultra high-temperature borides: A machine learning approach

Ultra-high temperature (UHT) borides are ceramics materials with melting points above 3000 °C for structural applications in extreme environments. However, at temperatures exceeding 1600 °C and under oxidizing conditions, the material suffers from detrimental degradation. Optimized design and performance of diboride materials under such extreme conditions requires filling the missing composition-microstructure-oxidation gap. This study proposes a computational data-driven framework to connect the processing and microstructure of Ultra-high temperature borides with the oxidation damage. Random Forest Regressor (RFR) model is adopted to forecast the oxide scale thickness developed after oxidation testing based on processing variables and microstructural features. The model trained on a dataset consisting of 107 samples of experimental data extracted from the literature aims to predict oxidation damage. With proper data manipulation and fine model tuning, the predictor could forecast the oxide scale thickness of UHT diborides with a Mean Absolute Error of 37.45 μm and an R-square of 0.83. This model could be used as a high-throughput scheme to design and test new UHT diborides materials computationally. Furthermore, a model with larger composition capabilities could also be developed in the future as more experimental data become available.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A rapid thioacidolysis method for biomass lignin composition and tricin analysis

Abstract Background Biomass composition varies from plant to plant and greatly affects biomass utilization. Lignin is a heterogeneous phenolic polymer derived mainly from p -coumaryl, coniferyl, and sinapyl alcohols and makes up to 10–25% of lignocellulosic biomass. Recently, tricin, an O -methylated flavone, was identified as a lignin monomer in many grass species. Tricin may function as a nucleation site for lignification and is advocated as a novel target for lignin engineering to reduce lignin content and improve biomass digestibility in grasses. Thioacidolysis is an analytical method that can be adapted to analyze both lignin monomeric composition and tricin content in the lignin polymer. However, the original thioacidolysis procedure is complex, laborious, and time consuming, making it difficult to be adopted for large-scale screening in biomass research. In this study, a modified, rapid higher throughput thioacidolysis method was developed. Results In combination with gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS), the modified thioacidolysis method can be used to simultaneously characterize the lignin composition and tricin content using 2–5 mg of dry samples. The modified method eliminates the solvent extraction and drastically improves the throughput; 80 samples can be processed in one day per person. Our results indicate that there is no significant difference in the determination of lignin S/G ratio and tricin content between the original and modified methods. Conclusions A modified thioacidolysis protocol was established. The results demonstrate that the modified method can be used for rapid, high-throughput, and reliable lignin composition and tricin content analyses for screening transgenic plants for cell wall modifications or in large-scale genome-wide association studies (GWAS).

09 BIOMASS FUELS↗
DOE OSTI2025

End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment

X-ray crystallography reconstruction, which transforms discrete X-ray diffraction patterns into three-dimensional molecular structures, relies critically on accurate Bragg peak finding for structure determination. As X-ray free electron laser (XFEL) facilities advance toward MHz data rates (1 million images per second), traditional peak finding algorithms that require manual parameter tuning or exhaustive grid searches across multiple experiments become increasingly impractical. While deep learning approaches offer promising solutions, their deployment in high-throughput environments presents significant challenges in automated dataset labeling, model scalability, edge deployment efficiency, and distributed inference capabilities. We present an end-to-end deep learning pipeline with three key components: (1) a data engine that combines traditional algorithms with our peak matching algorithm to generate high-quality training data at scale, (2) a modular architecture that scales from a few million to hundreds of million parameters, enabling us to train large expert-level models offline while deploying smaller, distilled models at the edge, and (3) a decoupled producer-consumer architecture that separates specialized data source layer from model inference, enabling flexible deployment across diverse computing environments. Using this integrated approach, our pipeline achieves accuracy comparable to traditional methods tuned by human experts while eliminating the need for experiment-specific parameter tuning. Although current throughput requires optimization for MHz facilities, our system's scalable architecture and demonstrated model compression capabilities provide a foundation for future high-throughput XFEL deployments.

Wang, Cong↗
DOE OSTI2022

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Abstract Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Sensitivity analysis of thermal contact conductance modeling to inform MiniFuel irradiation capsule designs

The MiniFuel irradiation platform has been developed by Oak Ridge National Laboratory as a flexible, high-throughput separate effects testing capability within the High Flux Isotope Reactor (HFIR). Finite element thermal models are relied upon to design MiniFuel experiments to achieve a specific time-averaged irradiation temperature for experimental objectives. A previous study identified that uncertainty in the component heat generation rates and thermal contact conductance (TCC) model are the most significant contributors to predicted fuel temperature variance. To address both sources of uncertainty, this work performs sensitivity analysis on the TCC model to identify high-impact, high-uncertainty parameters that contribute to fuel temperature variance. The TCC model is analyzed in increasing detail, first using a standalone Python code, then again after coupling Python to the BISON fuel performance code. Furthermore, the parameters with the largest contributions to fuel temperature variance which can be reduced through design changes are identified as the initial subcapsule gas pressure, contact pressure between the fuel and dish, and the effective surface roughness of the interface. A set of design recommendations for future capsule designs has been established and applied to reduce the previously quantified average fuel temperature uncertainty ranges of ± 40 °C in the HFIR vertical experiment facilities (VXF) and ± 80 °C in the removable beryllium (RB) reflector to approximately ± 32 °C and ± 53 °C, respectively. This equates to a 21 % and 33 % reduction in the uncertainty range of the average fuel temperature for VXF and RB, respectively.

BISON↗
DOE OSTI2021

Scaling-Up and Roll-to-Roll Processing if Highly Conductive Sulfide Solid-State Electrolytes (CRADA 510)

This project aims to identify and address key technical barriers in scale-up synthesis and roll-to-roll processing of highly conductive solid-state electrolytes (SSEs) by leveraging PNNL’s patent pending SSEs, Ampcera Inc.’s environmentally friendly manufacturing capability, and UW’s rollto-roll processing. The ultimate goals are 1) development of novel sulfide SSEs with room temperature Li+ conductivity > 5 mS/cm and Li/SSE interfacial resistance <5 Ωcm2, 2) scalable production of sulfide SSEs at 1 metric ton/year, and 3) a high throughput roll-to-roll processing of highly conductive (>1 mS/cm) and ultra-thin solid film (10-100 μm). The materials and the solid film achieved in the project will be tested at practical conditions and validated in a realistic pouch cell format with achievable energy > 300 Wh/Kg.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A novel statistical methodology for quantifying the spatial arrangements of axons in peripheral nerves

A thorough understanding of the neuroanatomy of peripheral nerves is required for a better insight into their function and the development of neuromodulation tools and strategies. In biophysical modeling, it is commonly assumed that the complex spatial arrangement of myelinated and unmyelinated axons in peripheral nerves is random, however, in reality the axonal organization is inhomogeneous and anisotropic. Present quantitative neuroanatomy methods analyze peripheral nerves in terms of the number of axons and the morphometric characteristics of the axons, such as area and diameter. In this study, we employed spatial statistics and point process models to describe the spatial arrangement of axons and Sinkhorn distances to compute the similarities between these arrangements (in terms of first- and second-order statistics) in various vagus and pelvic nerve cross-sections. We utilized high-resolution transmission electron microscopy (TEM) images that have been segmented using a custom-built high-throughput deep learning system based on a highly modified U-Net architecture. Our findings show a novel and innovative approach to quantifying similarities between spatial point patterns using metrics derived from the solution to the optimal transport problem. We also present a generalizable pipeline for quantitative analysis of peripheral nerve architecture. Our data demonstrate differences between male- and female-originating samples and similarities between the pelvic and abdominal vagus nerves.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

French Innovative Thermal Treatment Processes for The Management Radioactive Organic Liquid Waste - 19017

The nuclear industry as well as hospitals or some research laboratories produce Radioactive Organic Liquid Waste (ROLW) such as scintillation cocktails for medical imaging, labeled molecules for medical research or liquids from former activities. In order to be compatible with disposal facilities requirements, this waste must be inerted and stabilized in solid matrix. The route generally used is incineration in the Centraco facilities at the Marcoule site (Gard, France), ashes produced being cemented downstream. However, liquids whose chemical or radiological characteristics are not compatible with the incinerator operating rules (high level of {sup 14}C or halogen content, corrosive gas production etc.) cannot follow this path. The purpose of the MILOR project is then to propose alternative paths to treat all kinds of ROLW. Initiated in September 2017, this project has the technical objective of developing, over a four-year period, two pilot-scale mineralization processes. Two variants of complementary plasma technologies are evaluated: aerial plasma with the 'IDOHL' process and submerged plasma with the 'ELIPSE' process. Compared with other methods, thermal plasmas have unique characteristics such as rapid decomposition with high throughput, fast startup and shutdown, and high-energy delivery. The process called IDOHL consists of a process involving an induction plasma in which the liquids are directly introduced. It has been designed for organic liquids without mineral load and for treatment rates less than 0.5 l/h. This type of plasma allows the destruction of liquid organohalogens without any prior evaporation or treatment. Specific gas treatment systems have been developed to direct the gas produced to a dry or wet gas treatment system. One aim of the project is to subsequently install the process in a radioactive zone at Saclay DRF. The second called ELIPSE consists of a breakthrough process involving a submerged plasma at the heart of which organic liquids are also introduced. Current research has demonstrated that such a technique may enable the instantaneous and complete destruction of liquids with a wide variety of constituents, such as chlorine, fluorine, or phosphorus. The submersion solution offers many advantages: quenching and cleaning of combustion gases; filtering of the particles they contain; and cooling maintained for the entire process, which guarantees excellent corrosion control. An advantage of this type of design is that the gas treatment system can be reduced to a demister-condenser followed by a simple safety filter, thereby offering the additional advantage of an extremely compact treatment system. This design also allows the ELIPSE process to become by this way an embeddable process if required. The MILOR project, financed by the French State through investment program is a tripartite project bringing together Andra, in charge of radioactive waste management, the CEA in charge of leading scientific and technological researches to propose effective ways of treatment and the company A3i whose role is to ensure the subsequent industrial development. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Fluorescent amplification for next generation sequencing (FA-NGS) library preparation

BACKGROUND: Next generation sequencing (NGS) has become a universal practice in modern molecular biology. As the throughput of sequencing experiments increases, the preparation of conventional multiplexed libraries becomes more labor intensive. Conventional library preparation typically requires quality control (QC) testing for individual libraries such as amplification success evaluation and quantification, none of which occur until the end of the library preparation process. RESULTS: In this study, we address the need for a more streamlined high-throughput NGS workflow by tethering real-time quantitative PCR (qPCR) to conventional workflows to save time and implement single tube and single reagent QC. We modified two distinct library preparation workflows by replacing PCR and quantification with qPCR using SYBR Green I. qPCR enabled individual library quantification for pooling in a single tube without the need for additional reagents. Additionally, a melting curve analysis was implemented as an intermediate QC test to confirm successful amplification. Sequencing analysis showed comparable percent reads for each indexed library, demonstrating that pooling calculations based on qPCR allow for an even representation of sequencing reads. To aid the modified workflow, a software toolkit was developed and used to generate pooling instructions and analyze qPCR and melting curve data. CONCLUSIONS: We successfully applied fluorescent amplification for next generation sequencing (FA-NGS) library preparation to both plasmids and bacterial genomes. As a result of using qPCR for quantification and proceeding directly to library pooling, the modified library preparation workflow has fewer overall steps. Therefore, we speculate that the FA-NGS workflow has less risk of user error. The melting curve analysis provides the necessary QC test to identify and troubleshoot library failures prior to sequencing. While this study demonstrates the value of FA-NGS for plasmid or gDNA libraries, we speculate that its versatility could lead to successful application across other library types.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A Simple and Flexible Infusion Platform for Automated Native Mass Spectrometry Analysis

High throughput native mass spectrometry analysis of proteins and protein complexes has been enabled by recent development of infusion and liquid chromatography (LC) systems, which often include complete LC pumps without fully utilizing their gradient flows. We demonstrated a lower-cost infusion cart for native mass spectrometry applications using a single isocratic solvent pump that can operate at both nano- and high-flow configurations (0.05-150 $μ$L/min) for both infusion and online buffer exchange experiments. In conclusion, the platform is controlled via open-source software and can potentially be expanded for customized experimental designs, offering a lower cost alternative to laboratories with limited budgets and/or needs in student training.

47 OTHER INSTRUMENTATION↗