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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Summer Aerosol and Trace Gas Observations in Houston, Texas Using an Adaptable Mobile Facility

An aerosol container featuring a shared inlet system was deployed to Houston, Texas in July 2022, enabling direct, high-time-resolution in situ measurements of aerosols and trace gases. The internal rack system and floorplan was designed for adaptable modularity to elucidate aerosol physicochemical processes at fine scales. The design allowed for the deployment of a core instrument suite and additional customized research grade instruments. A heterogeneous mixture of aerosols was observed during three regimes: (1) intermittent black carbon (BC) and diurnal variations in aerosol chemical composition, (2) observed particle growth associated with SO 2 , (3) transported supermicron dust. The high variability of observed particles and gases in high time resolution indicated a complex urban area with multiple local and regional sources and processes. Particle growth rates of 7–16 nm/hr were observed for submicron particles during periods when SO 2 was >0.5 ppbv. Two periods of multi-day long-range transport events of dust from the African Sahara were observed in the supermicron and submicron particle modes with total mass concentrations up to 30 μg m −3 . Aerosol scattering angstrom exponents and extinction coefficients (B ext ) increased with humidity as a function of particle composition. The measurements demonstrate collaborative capabilities that can be used to increase observations of aerosol processing, microphysical and optical properties, internal mixing state, and supermicron aerosol that are not parameterized or missing in global Earth energy system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Novel Hollow Fiber Membrane Reactor for High Purity H 2 Generation from Thermal Catalytic NH 3 Decomposition (Final Report)

Ammonia (NH 3 ), as a promising carbon neutral liquid fuel (CNLF) and an effective H 2 source, can be synthesized from air and water (N 2 extracted from air and H 2 from water) using renewable energy sources. To produce H 2 as an intermediate, it is essential to develop effective and economic NH 3 decomposition technologies. We have been developing an innovative hollow fiber membrane reactor for high purity H 2 generation from thermal catalytic NH 3 decomposition. The objective of the proposed research is to fabricate a compact and robust prototype with high energy efficiency (>80%) to deliver high purity (>99%) H2 at high rate per volume (>0.15 g H 2 /h/cm 3 ) from NH3 decomposition at low temperature (<450 °C) and high conversion (>99%). Our overall technical approach is to effectively combine an active catalyst for NH 3 decomposition with a selective H 2 membrane separation process in a novel, compact modular system to generate high purity H 2 and simultaneously achieve high NH 3 conversion and optimize the entire system energy efficiency. This novel integrated membrane reactor design, together with low-cost, highly active Ru-based catalyst, makes it most appropriate for the project objective. The proposed applied research and development work, therefore, represents an innovative and transformational solution to H 2 generation from thermal catalytic NH 3 decomposition. During the course of our research we i) synthesized and characterized ruthenium(Ru)-based catalysts, evaluated their catalytic performance for NH 3 decomposition and optimized the catalyst composition and performance based on machine learning, ii) prepared SAPO-34, MFI membranes on α-alumina hollow fibers and measured their performance for H 2 /N 2 , H 2 /N 2 /NH 3 separation and evaluated carbon molecular sieve (CMS) and Pd/Ag membranes for H 2 /N 2 , H 2 /N 2 /NH 3 separation at different conditions, iii) designed and evaluated several types of membrane reactors for H 2 production from NH 3 decomposition, and iv) evaluated the H 2 delivery cost base on catalysts development, membrane development and membrane reactor. For 3,1,12 RuYK catalyst, H 2 productivity of 6.35 mmol/min/gcat with NH 3 conversion of 95~98% was achieved at 5 bar and 400 °C; for SAPO-34 membrane, the best membrane showed H 2 permeance of 7.56×10 -7 mol/(m 2 ∙s∙Pa) and H 2 /N 2 selectivity of 23.1 at 20 °C; For CMS and Pd/Ag membranes, both showed superior stability for 71%H 2 /24%N 2 /5%NH 3 mixture separation at 450 °C and 7 bar for over 120 h; for the membrane reactor, high H 2 purity of >99.99% with NH 3 conversion of >99.4% were achieved in Pd/Ag membrane reactor at 450 °C, and the NH 3 concentration was always below 10 ppb in H 2 product. The H 2 generation rate was calculated to be as high as 0.47 g/h/cm 3 and the energy efficiency was calculated to be 88%. The membrane reactor showed superior stability for over 370 h and high H 2 purity, H 2 production rate and NH 3 conversion. Based on calculation, the H 2 delivery cost can be as low as $3.66/kg.

08 HYDROGEN↗
DOE OSTI2024

Abstract for CRADA between NETL and West Virginia University Research Corporation

The National Energy Technology Lab (NETL) will collaborate with West Virginia University to develop and demonstrate a dynamically operated modular, microwave-enhanced catalytic process for the co-production of ammonia and olefins. Ammonia and olefins (e.g., ethylene) are the most widely produced chemicals in the U.S. by volume, but their processes are also the most carbon and energy intensive as they rely on fossil-based feedstocks and combustion of fossil fuels for heating. The project will leverage the unique ability of microwaves to intensify process heating by providing pulsed, targeted energy to active sites of a mixed catalyst bed to convert a mixture of CH 4 /N 2 into ethylene and ammonia. The collaboration will facilitate the development of microwave technology that has the potential to utilize distributed waste resources like shale gas or biogas as well as renewable electricity to produce these valuable chemicals. It is expected that the results will decarbonize chemical production by using renewable energy and waste feedstocks, improve efficiency by reducing unit operations (H 2 production), and de-risk microwave technology for chemical production by demonstrating long-term and dynamic operability. This project was funded by an award from the Department of Energy’s EERE IEDO.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

FrESCO: Framework for Exploring Scalable Computational Oncology

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information from electronic health records. This data is then used to create summary statistics about cancer incidence and mortality to facilitate population health monitoring. Extracting phenotypic information from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating the information extraction process from cancer pathology reports has the potential to improve data quality by extracting information in a consistent manner across registries. It can also improve patient outcomes by reducing the time from diagnosis, enabling rapid case ascertainment for clinical trials. Here we present FrESCO, a modular deep-learning natural language processing (NLP) library initially designed for extracting pathology information from clinical text documents. This repository is not solely limited to clinical medical text, but may also be used by researchers just getting started with NLP methods and those looking for a robust solution for their classification problems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

FrESCO

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information and summary statistics about cancer prevalence to facilitate population level monitoring of cancer incidence. Extracting cancer phenotype from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating this information extraction process from cancer pathology reports has the potential to improve not only the quality of the data by extracting information in a consistent manner across registries, but to improve the quality of patient outcomes by reducing the time to assimilate new data and enabling time-sensitive applications such as precision medicine. Here we present FrESCO: Framework for Exploring Scalable Computational Oncology, a modular deep-learning natural language processing (NLP) library for extracting pathology information from clinical text documents.

Spannaus, Adam [Oak Ridge National Lab. (ORNL), Oa↗
DOE OSTI2025

MARIE: A Python-Based Framework for Comprehensive Fuel Recycling Modeling

One of the most pressing challenges to the continued deployment of nuclear energy systems is in the ultimate management and disposition of discharged fuel assemblies. While reprocessing and recovery of valuable materials from UNF assemblies has been considered as part of an overall strategy for minimization of the volume of reactor-based wastes to be managed, the deployment of commercial-scale reprocessing facilities presents an enormous economic challenge. The MARIE software package has been developed as a means of confronting this challenge. Representing components of a generic fuel reprocessing operation as individual physical processes, MARIE is designed as a modular framework intended to allow for analysis and cost-optimization for a hypothetical reprocessing facility while realistically accounting for the physical characteristics of the used fuel source term, such as decay heat, activity, and radiation dose (informing corresponding shielding requirements). Capabilities supported by MARIE include head-end operations such as fuel shearing, voloxidation, and dissolution; generic solvent extraction operations informed by available open-literature data; a suite of unit operations intended to represent electrochemical processing of used fuel assemblies (i.e., oxide reduction, electrorefining, and electrowinning); and finally, accounting for both costs and physical features of discharged waste streams, which can be used to inform follow-on analyses such as the feasibility of deep-borehole disposal of HLW. This paper presents an overview of the MARIE software capabilities, including how individual unit operations are implemented to enable a larger-scale optimization of a hypothetical reprocessing operation on aspects such as cost and recovery of valuable materials.

Skutnik, Steve [ORNL] (ORCID:000000016441135X)↗
DOE OSTI2020

Recent Progress in the Engineering of Polymeric Membranes for CO2 Capture from Flue Gas

CO2 capture from coal- or natural gas-derived flue gas has been widely considered as the next opportunity for the large-scale deployment of gas separation membranes. Despite the tremendous progress made in the synthesis of polymeric membranes with high CO2/N2 separation performance, only a few membrane technologies were advanced to the bench-scale study or above from a highly idealized laboratory setting. Therefore, the recent progress in polymeric membranes is reviewed in the perspectives of capture system energetics, process synthesis, membrane scale-up, modular fabrication, and field tests. These engineering considerations can provide a holistic approach to better guide membrane research and accelerate the commercialization of gas separation membranes for post-combustion carbon capture.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Disentangling direct from indirect relationships in association networks

Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal ( P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II

New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge machine learning (EdgeML) addresses this challenge by integrating machine learning (ML) algorithms with edge computing, enabling real-time data processing directly at the point of data generation. EdgeML is particularly beneficial for environments where immediate decisions are required, or where bandwidth and storage are limited. In this paper, we demonstrate a high-speed configurable ML model in a fully customizable EdgeML system using a field programmable gate array (FPGA). Our demonstration focuses on an angular array of electron spectrometers, referred to as the ‘CookieBox,’ developed for the Linac Coherent Light Source II project. The EdgeML system captures 51.2 Gbps from a 6.4 GS s −1 analog to digital converter and is designed to integrate data pre-processing and ML inside an FPGA. Our implementation achieves an inference latency of 0.2 µs for the ML model, and a total latency of 0.4 µs for the complete EdgeML system, which includes pre-processing, data transmission, digitization, and ML inference. The modular design of the system allows it to be adapted for other instrumentation applications requiring low-latency data processing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

X‐ray scattering reveals disordered linkers and dynamic interfaces in complexes and mechanisms for DNA double‐strand break repair impacting cell and cancer biology

Abstract Evolutionary selection ensures specificity and efficiency in dynamic metastable macromolecular machines that repair DNA damage without releasing toxic and mutagenic intermediates. Here we examine non‐homologous end joining (NHEJ) as the primary conserved DNA double‐strand break (DSB) repair process in human cells. NHEJ has exemplary key roles in networks determining the development, outcome of cancer treatments by DSB‐inducing agents, generation of antibody and T‐cell receptor diversity, and innate immune response for RNA viruses. We determine mechanistic insights into NHEJ structural biochemistry focusing upon advanced small angle X‐ray scattering (SAXS) results combined with X‐ray crystallography (MX) and cryo‐electron microscopy (cryo‐EM). SAXS coupled to atomic structures enables integrated structural biology for objective quantitative assessment of conformational ensembles and assemblies in solution, intra‐molecular distances, structural similarity, functional disorder, conformational switching, and flexibility. Importantly, NHEJ complexes in solution undergo larger allosteric transitions than seen in their cryo‐EM or MX structures. In the long‐range synaptic complex, X‐ray repair cross‐complementing 4 (XRCC4) plus XRCC4‐like‐factor (XLF) form a flexible bridge and linchpin for DNA ends bound to KU heterodimer (Ku70/80) and DNA‐PKcs (DNA‐dependent protein kinase catalytic subunit). Upon binding two DNA ends, auto‐phosphorylation opens DNA‐PKcs dimer licensing NHEJ via concerted conformational transformations of XLF‐XRCC4, XLF–Ku80, and LigIV BRCT –Ku70 interfaces. Integrated structures reveal multifunctional roles for disordered linkers and modular dynamic interfaces promoting DSB end processing and alignment into the short‐range complex for ligation by LigIV. Integrated findings define dynamic assemblies fundamental to designing separation‐of‐function mutants and allosteric inhibitors targeting conformational transitions in multifunctional complexes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Prefabricated Standard Modular Hydropower Installations for Low-cost Small Hydropower

The goal of the present project was to investigate the potential savings in greenfield hydropower project costs across sites having vastly different characteristics. Littoral Power Systems, Inc. (LPS) undertook the project described herein to explore the promise of Standard Modular Hydropower (SMH) to transform the processes of site identification, conceptual design, detailed design, and construction planning to support low- cost, environmentally attractive development of small hydropower facilities at new stream- reaches. This report includes descriptions of the work to develop these processes, the resulting facility designs achieved using these processes and the projected economics. Installation cost estimates for the resulting conceptual greenfield facility design at two different locations come in at $\$3,419$/kW for Balcony Falls and $\$2,124$/kW for LPS 566. The installation time from site preparation to commissioning-ready is estimated to be 58 weeks for the conceptual facility at Balcony Falls and 42 weeks for the conceptual facility at LPS 566. A real challenge to any hydropower facility – not only the two cases studied here – is that environmental and social conditions can change, and as such, the facility may be required to change. The h-Modulor modules connect uniquely and are structurally independent, enabling site configurations to be changed on the fly, as needed. These results show that LPS's h-Modulor SMH approach delivers an attractive balance between efficiency, economics, and environmental sustainability for small hydro greenfield sites.

13 HYDRO ENERGY↗
DOE OSTI2022

Data-driven simultaneous process optimization and adsorbent selection for vacuum pressure swing adsorption

Technologies for post-combustion carbon capture are essential for the reduction of greenhouse gas emissions to the atmosphere. However, they are still associated with high costs and energy consumption. Intensified processes for carbon capture have the potential to overcome these challenges due to their higher efficiency, lower capital cost, and increased operational flexibility. Here, this work investigates simultaneous optimization of process conditions and adsorbent selection for a modular Vacuum Pressure-Swing Adsorption system designed for CO 2 capture. Both surrogate-based Nonlinear Programming and Mixed-Integer Nonlinear Programming approaches are applied and compared in terms of computational efficiency and solution accuracy. Moreover, process performance results are examined by applying several data analytics techniques to gain insights into the material-process correlations. Data-driven classifiers and neural networks can accurately predict whether a material is likely to satisfy purity, recovery, and energy constraints when operated at optimal process conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Reproducibility of protein x-ray diffuse scattering and potential utility for modeling atomic displacement parameters

Protein structure and dynamics can be probed using x-ray crystallography. Whereas the Bragg peaks are only sensitive to the average unit-cell electron density, the signal between the Bragg peaks—diffuse scattering—is sensitive to spatial correlations in electron-density variations. Although diffuse scattering contains valuable information about protein dynamics, the diffuse signal is more difficult to isolate from the background compared to the Bragg signal, and the reproducibility of diffuse signal is not yet well understood. We present a systematic study of the reproducibility of diffuse scattering from isocyanide hydratase in three different protein forms. Both replicate diffuse datasets and datasets obtained from different mutants were similar in pairwise comparisons (Pearson correlation coefficient ≥0.8). The data were processed in a manner inspired by previously published methods using custom software with modular design, enabling us to perform an analysis of various data processing choices to determine how to obtain the highest quality data as assessed using unbiased measures of symmetry and reproducibility. The diffuse data were then used to characterize atomic mobility using a liquid-like motions (LLM) model. This characterization was able to discriminate between distinct anisotropic atomic displacement parameter (ADP) models arising from different anisotropic scaling choices that agreed comparably with the Bragg data. Our results emphasize the importance of data reproducibility as a model-free measure of diffuse data quality, illustrate the ability of LLM analysis of diffuse scattering to select among alternative ADP models, and offer insights into the design of successful diffuse scattering experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Cost estimation of balance of plant equipment scale up for proton exchange membrane water electrolyzer systems

Water electrolyzers that use electricity to split water into hydrogen and oxygen could be a key technology for increasing hydrogen supply to meet expanded and emerging market applications, although currently the capital costs of these electrolyzers are high. Here we examine cost reductions that might be achieved by scaling up proton exchange membrane (PEM) electrolyzer systems and leveraging economies of scale through balance of plant (BOP) components for system sizes between 1 MW and 1 GW. We estimate BOP equipment capital costs of about $\$$848/kW at 1 MW, potentially decreasing to $\$$87/kW at 1 GW (2022-dollar year basis) with most of the cost reduction happening as systems scale from 1 MW to 100 MW. We find that BOP subsystems hydrogen drying and water knockout benefited the most from economies-of-scale cost reductions, and piping, instrumentation, and housing and power electronics were less impacted. These cost reductions from economies of scale could be more significant than estimated cost reductions from manufacturing scale-up reported in literature. These results add to the knowledge base that could guide optimal system designs that balance process scale-up with plant modularization and numbering-up. We also estimate that scaling up BOP could potentially lower the levelized cost of hydrogen (LCOH) by $\$$1.7-$\$$4.6/kg, depending on the scale-up magnitude and the plant capacity factor.

08 HYDROGEN↗
DOE OSTI2023

Spin–vibronic coherence drives singlet–triplet conversion

Design-specific control over the transitions between excited electronic states with different spin multiplicities is of the utmost importance in molecular and materials chemistry. Previous studies have indicated that the combination of spin-orbit and vibronic effects, collectively termed the spin-vibronic effect, can accelerate quantum-mechanically forbidden transitions at non-adiabatic crossings. However, it has been difficult to identify precise experimental manifestations of the spin-vibronic mechanism. Here we present coherence spectroscopy experiments that reveal the interplay between the spin, electronic and vibrational degrees of freedom that drive efficient singlet-triplet conversion in four structurally related dinuclear Pt(II) metal-metal-to-ligand charge-transfer (MMLCT) complexes. Photoexcitation activates the formation of a Pt-Pt bond, launching a stretching vibrational wavepacket. The molecular-structure-dependent decoherence and recoherence dynamics of this wavepacket resolve the spin-vibronic mechanism. Further, we find that vectorial motion along the Pt-Pt stretching coordinates tunes the singlet and intermediate-state energy gap irreversibly towards the conical intersection and subsequently drives formation of the lowest stable triplet state in a ratcheting fashion. This work demonstrates the viability of using vibronic coherences as probes to clarify the interplay among spin, electronic and nuclear dynamics in spin-conversion processes, and this could inspire new modular designs to tailor the properties of excited states.

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

Design, tuning, and blackbox optimization of laser systems

Chirped pulse amplification (CPA) and subsequent nonlinear optical (NLO) systems constitute the backbone of myriad advancements in semiconductor manufacturing, communications, biology, defense, and beyond. Accurately and efficiently modeling CPA+NLO-based laser systems is challenging because of the complex coupled processes and diverse simulation frameworks. Our modular start-to-end model unlocks the potential for exciting new optimization and inverse design approaches reliant on data-driven machine learning methods, providing a means to create tailored CPA+NLO systems unattainable with current models. To demonstrate this new, to our knowledge, technical capability, we present a study on the LCLS-II photo-injector laser, representative of a high-power and spectro-temporally non-trivial CPA+NLO system.

47 OTHER INSTRUMENTATION↗