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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Atmospheric Radiation Measurement (ARM) airborne field campaign data products between 2013 and 2018

Airborne measurements are pivotal for providing detailed, spatiotemporally resolved information about atmospheric parameters and aerosol and cloud properties, thereby enhancing our understanding of dynamic atmospheric processes. For 30 years, the US Department of Energy (DOE) Office of Science supported an instrumented Gulfstream 1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) Data Center and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated dataset was recently developed covering the final 6 years of G-1 operations (2013 to 2018, https://doi.org/10.5439/1999133; Mei and Gaustad, 2024). The integrated dataset includes data collected from 236 flights (766.4 h), which covered the Arctic, the US Southern Great Plains (SGP), the US West Coast, the eastern North Atlantic (ENA), the Amazon Basin in Brazil, and the Sierras de Córdoba range in Argentina. These comprehensive data streams provide much-needed insight into spatiotemporal variability in the thermodynamic quantities and aerosol and cloud properties for addressing essential science questions in Earth system process studies. This paper describes the DOE ARM merged G-1 datasets, including information on the acquisition, data collection challenges and future potentials, and quality control processes. It further illustrates the usage of this merged dataset to evaluate the Energy Exascale Earth System Model (E3SM) with the Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Role of Lignin Molecular Weight on Activated Carbon Pore Structure

Over the past decade, the production of biofuels from lignocellulosic biomass has steadily increased to offset the use of fuels from petroleum. To make biofuels cost-competitive, however, it is necessary to add value to the “ligno-” components (up to 30% by mass) of the biomass. The properties of lignin, in terms of molecular weight (MW), chemical functionality, and mineral impurities often vary from biomass source and biorefinery process, resulting in a challenging precursor for product development. Activated carbon (AC) is a feasible target for the lignin-rich byproduct streams because it can be made from nearly any biomass, and it has a market capacity large enough to use much of the lignin generated from the biorefineries. However, it is not known how the variability in the lignin affects the key properties of AC, because, until now, they could not be well controlled. In this work, various fractions of ultraclean (<0.6% ash) lignin are created with refined MW distributions using Aqueous Lignin Purification using Hot Agents (ALPHA) and used as precursors for AC. AC is synthesized via zinc chloride activation and characterized for pore structure and adsorption capacity. We show that AC surface area and the adsorption capacity increase when using lignin with increasing MW, and, furthermore, that reducing the mineral content of lignin can significantly enhance the AC properties. The surface area of the AC from the highest MW lignin can reach ~1830 m2/g (absorption capacity). Furthermore, single step activation carbonization using zinc chloride allows for minimal carbon burn off (<30%), capturing most of the lignin carbon compared to traditional burn off methods in biorefineries for heat generation.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Hydrologic Model Data for the East Fork Poplar Creek Watershed Simulated with the Advanced Terrestrial Simulator (ATS): Streamflow and Network Expansion–Contraction Dynamics

This dataset supports hydrologic modeling and stream network expansion–contraction analysis for the East Fork Poplar Creek (EFPC) Watershed in Tennessee. It includes a Jupyter notebook for model setup, model configuration files, simulation outputs, and derived products used to evaluate model performance and investigate stream dynamics under varying hydrologic conditions. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations using a stream-aligned mesh. Outputs include high-resolution time series of streamflow, active network length, water table depth, and related hydrologic variables. Also included are spatially explicit stream persistency indices and classifications of reaches as perennial or non-perennial. These data facilitate reproducibility and support further research on stream intermittency and variability in network extent.The model data archive is organized in following directories:1) model_setup_inputsContains the Watershed Workflow Jupyter notebooks (accessed through any open source code editor), selected input datasets, and resulting ATS input files, including XML files (access through any open source code editor), computational mesh (.exo files can be viewed using Paraview), and meteorological forcing files (.h5 files can be accessed through h5py python package and HDFView open source software). 2) model_outputsIncludes ATS simulation outputs relevant to this study. Time series of spatially integrated or averaged variables (e.g., streamflow, water table depth) are provided as CSV files. Select spatial fields (e.g., ponded depth and water table depth) are saved as pickled Python objects to reduce file size, and can be accessed through pickle package in Python. Key geometry objects from Watershed Workflow—such as the surface mesh and river tree—are also included to support analysis of streamflow persistency and expansion–contraction dynamics. These files can also be accessed through Watershed Workflow Python package.3) model_evaluationProvides observed streamflow time series and field survey-based flow regime classifications used to evaluate model performance. Jupyter notebooks for processing ATS outputs and comparing model predictions with observations to build confidence in the model prior to scientific analysis are also included.4) Q_L_relationshipsContains workflows for generating time series of discharge, active network length, and related hydrologic variables used in the stream network expansion–contraction analysis. Includes routines for delineating baseflow-dominated periods. For each catchment, notebooks and processed data (as pickled DataFrames accessed through Pandas Python package) are provided. 5) figure_scriptsProvides the Jupyter notebooks used to generate the figures presented in the paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Modular Biomass Gasification for Co-Production of Hydrogen and Power

The overall goal of this project was to demonstrate a novel process-intensified and modular Combined Hydrogen, Heat and Power (CH 2 P) production technology, targeting commercial scales of 5-to-10-megawatt electric (MWe) equivalent of hydrogen (H 2 ). The process technology integrates a novel adaptation of the steam-iron process to produce high purity H 2 from gasification of biomass and biomass blends with a compression-ready carbon dioxide (CO 2 ) stream, enabling significant progress towards achieving $1 per kilogram (kg) cost for clean H 2 .

08 HYDROGEN↗
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 OSTI2025

Coal-Waste-Enhanced Filaments for Additive Manufacturing of High-Temperature Plastics and Ceramic Composites

In the United States, coal waste from over a century of mining and burning coal for heat and electricity has accumulated as mountains of coal fly ash and bottom ash and acre-size ponds, coal fines and gob. These materials can be a problem for local communities and water systems. A cost-effective process to utilize high volumes of these coal wastes in a high-value product would be beneficial to those communities by reducing the amount of waste and providing jobs, manufacturing components, and materials from the waste. Many coal-to-products technologies (e.g., carbon fibers, graphene, carbon foam) rely on carefully choosing the starting material and then altering it chemically or thermally to make the products work. Due to the wide variability of composition and coal content in typical coal waste streams, many high-volume coal waste streams are likely to be unsuitable for use in those technologies. Semplastics’ technology has been shown to utilize most types of coal waste successfully without any pre-selection or pre-processing requirements other than a nominal particle-size reduction for wastes like bottom ash. This characteristic of Semplastics’ solution may enable the use of much larger volumes of a wider range of coal wastes than other coal-to-products technologies. In this project, Semplastics leveraged its unique experience with both coal waste (fly ash or coal combustion residuals), resin materials, and 3D printing to develop 3D printer filaments using common coal wastes – bituminous coal fines and fly ash – and researched the feasibility of using other forms of coal waste as fillers. Simple 3D-printed parts were successfully produced from the coal waste enhanced filaments, which were found to have improved strength and stiffness.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

RH-TRU Waste Streams: HFDA Crucibles Create New Complex Waste Stream

This project focuses on the removal of the Hot Fuel Dissolution Apparatus (HFDA) crucibles from the Hot Fuel Examination Facility’s (HFEF) hot cells to create space for upcoming projects. The project team has created a crucible and pyro-salt non-debris waste stream that can be disposed of at Waste Isolation Pilot Plant (WIPP). Chemical Compatibility Examinations (CCEs) must be performed on candidate waste by reviewing the types of chemicals used in the experiments, information found in INL Process Knowledge Summary Reports (PKSRs), and Environmental Protection Agency guides on chemical compatibility. The CCEs identify which chemicals can be safely combined to ensure adverse reactions do not occur in the waste packages. The CCEs are then used to update the PKSR, Basis of Knowledge, and Acceptable Knowledge documents and demonstrate compliance with the WIPP Waste Acceptance Criteria. Once documents are finalized, they are reviewed and approved by the WIPP Central Characterization Project (CCP) to ensure proper waste stream characterization. After the documents are approved, the crucibles can be packaged and shipped to the Idaho Nuclear Technical and Engineering Center for storage awaiting certification. CCP will certify the waste using the approved documents and a series of confirmatory testing before being shipped and disposed in WIPP’s deep geological repository. Currently, the CCE is in progress and the PKSRs are being updated.

Basis of Knowledge↗
DOE OSTI2026

Microbial valorization of lignin to malic acid by Aspergillus niger

Lignin is the largest renewable source of aromatic carbon, yet its heterogeneity and recalcitrance limit its use in higher-value bioconversion processes. In this study, Aspergillus niger was engineered to enable the bioconversion of lignin-derived aromatics and base-catalyzed depolymerized (BCD) lignin streams into malic acid, a value-added C4 dicarboxylic acid with broad industrial relevance. Overexpression of the C4 dicarboxylate transporter C4T318 from Aspergillus oryzae enhanced malic acid secretion, while medium optimization under buffered conditions further improved the production. The engineered strain efficiently assimilated representative lignin-derived aromatics, including 4-hydroxybenzoic acid and p-coumaric acid, producing up to 3.9 g/L malic acid. Conversion of BCD lignin liquors from poplar and sorghum demonstrated effective utilization of heterogeneous aromatic mixtures, generating up to 0.82 g/L malic acid. This work demonstrates direct fungal conversion of real lignin streams into malic acid and establishes A. niger as a promising platform for sustainable lignin valorization.

Aromatic bioconversion↗
DOE OSTI2024

Upcycling of Mixed Aluminum Alloy Shredder Scrap using Shear Processing

Conservation of critical materials is an increasing area of focus in the Unites States. In 2023, aluminum was added to the US Department of Energy Final Critical Materials List which has spurred public and private research into sustainable management of these resources. Additionally, efficiency in manufacturing and conservation of natural resources are growing concerns with targets to lower global carbon emissions, as primary aluminum alloy production is energy intensive, requiring 14 MWh of electricity plus 0.4 tonnes of CO2 per tonne of Al. Due to these factors, is essential that more sustainable manufacturing methods for aluminum alloys are developed going forward. To this end, much research is ongoing on the topic of more efficient utilization and recovery. However, most of this research still requires primary aluminum in the production process. Here, it will be attempted to bypass the use of primary aluminum and produce useful material recycled from 100% post-consumer scrap. Even considering recent developments in recycling of Al scrap, there is still a large amount of post-consumer scrap that is underutilized due to high impurity content, and that amount will increase significantly as more and more aluminum alloys are utilized in vehicles. This “scrap wave” is expected to cover 80% of the demand for automotive aluminum alloys by 2050 . A challenge to be addressed before the coming scrap wave can be fully utilized is that the tolerance of manufacturing techniques to impurities or off-spec alloy compositions must be increased. Particularly, in 5000-and 6000-series alloys (the most common wrought alloys in durable products), excess iron, copper, and silicon create brittle intermetallics during casting that remain in the extruded microstructure which limit the formability, ductility, and corrosion resistance of the alloy. Concerningly, many of the highest-volume post-consumer aluminum scrap streams such as automotive shredder scrap contain a mix of alloys including both wrought and cast alloys. Their compositions can vary widely depending on geography and the time of year. Because they are mixed, they often contain high content of multiple alloying elements such as Si and Cu in higher concentrations than are found in typical wrought alloys. They may also be contaminated with non-Al alloys from fasteners that get mixed in and often have high content of unwanted elements such as Fe. As a method for utilizing these underused scrap streams that are high in tramp elements, an emerging extrusion technology is being developed at the Pacific Northwest National Laboratory (PNNL) that aims to upcycle 100% post-consumer aluminum scrap directly into extruded components without the addition of primary aluminum . This new technology, called Shear Assisted Processing and Extrusion (ShAPE), is enabling a shift away from today’s recycling paradigm by reaching deeper into lower-value scrap streams, using shredder scrap as the extrusion billet material. Sometimes referred to as Twitch or Tweak, these scrap streams result from shredding and sorting of automobiles, building materials, appliances, and consumer goods. ShAPE combines the linear axis of conventional extrusion with a rotating extrusion die. This rotating die applies large strain to the material during extrusion, which breaks up large impurity-containing intermetallic particles, reducing their deleterious effects. This has been demonstrated for 6063 machining scrap spiked with excess Fe, and Twitch scrap high in Fe, Si and Cu where strength and ductility were retained for both feedstock compositions. Additionally, the extreme plastic deformation during ShAPE enables extrusion of billets with high Si that are too brittle for processing by conventional extrusion. By using 100% post-consumer shedder scrap as feedstock, ShAPE has the potential to slash embodied energy and carbon in extruded components by >80% compared to conventional extrusion of primary aluminum alloys.

Milligan, Brian K.↗
DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗
DOE OSTI2025

Trace elements and their isotopes in streams and rivers

The occurrence and speciation of trace elements in streams and rivers are controlled by interconnected hydrogeological, biogeochemical, and anthropogenic factors that include rock weathering, climate, vegetation, and land use within the watershed. This chapter provides a broad overview of trace element abundance, speciation, and mobility in streams and rivers, including an assessment of how trace elements are partitioned into dissolved, colloidal, and particulate phases. We also discuss how trace elements are mobilized into stream networks and transformed during transport. Select isotopic systems are reviewed to provide examples of how isotopic analyses can be used to understand geochemical and anthropogenic processes.

Herndon, Elizabeth [ORNL] (ORCID:0000000291945493)↗
DOE OSTI2025

Ubiquity and Causes of Soil Water Preferential Flow Across 17 Ecoregions

Abstract Preferential flow (PF) in soil causes the rapid transport of water, nutrients, and contaminants into the subsurface, influencing groundwater recharge and streamflow. Data scarcity has hindered the quantification of PF occurrence and the identification of its drivers across diverse ecoregions. We address this gap by analyzing high‐frequency, multi‐depth soil moisture data across 17 ecoregions in the USA, using ∼1,500 sensors at 40 sites. We discovered that PF is widespread, with sites experiencing PF in up to 60% of rainfall events ≥2 mm. Multiple approaches consistently show that PF is more likely to occur with increased peak rainfall intensity, finer textured material, low soil moisture variability, humid climate, and higher net primary productivity. This suggests that PF patterns could shift with projected climate changes, increasing uncertainty in predictions of groundwater recharge, water quality, and streamflow generation. Plain Language Summary Water can bypass part of the soil's matrix through a process called preferential flow (PF). This quick transport of water through the soil brings with it nutrients and contaminants and eventually makes it to groundwater and streams. To ensure ample amounts of good quality groundwater and surface water we need to understand when and where PF occurs. We inferred when PF occurred at 40 different sites across 17 ecoregions in the USA using soil moisture and rainfall data. We found that PF happened at all sites and in up to 60% of rainfall events ≥2 mm. Preferential flow was most likely at sites with high rainfall intensities, high clay content in soils, low variability in soil moisture, and high vegetation productivity. As rainfall intensities are predicted to increase due to climate change and vegetation becomes more productive, PF occurrence becomes more important for predicting groundwater recharge, water quality, and streamflow generation. Key Points Preferential flow (PF) is ubiquitous across the USA and occurs in up to 60% of all rainfall events ≥2 mm Rainfall intensity, soil texture, and antecedent soil moisture emerge as critical in generating PF across diverse ecoregions Two different PF detection approaches show similar relationships between key drivers and occurrence of PF

Li, Bonan↗
DOE OSTI2026

Artificial intelligence-driven municipal solid waste sortation and its significance on downstream waste valorization in the United States: Techno-economic and life cycle assessment

Municipal solid waste (MSW) generation is quickly increasing due to population growth, industrialization, and urbanization. Current sorting technologies, e.g., optical sorters have low sortation efficiency and produce contaminated sorted materials unsuitable for downstream valorization. This study evaluates a novel artificial neural network (ANN)-based MSW sortation system through sorting efficiencies, economic performance, and environmental impacts. ANN system can sort up to 8 plastic types and all organic waste (i.e., food, yard, and paper waste) with 99% and 93% recovery efficiency, respectively, compared to optical sortation which could separate only polyvinyl chloride (PVC) plastic ultimately producing mixed plastics (except PVC) and organics streams as products. Nonetheless, their sortation costs are similar, $28.7 for ANN-based sortation and $28 for optical sortation per metric ton (Mt) of MSW at processing capacity of 94,860 metric tons (Mt)/yr of MSW in the United States. Additionally, ANN system produced high purity feedstocks for downstream valorization. A biochar pyrolysis plant modeled to process 3.6 and 14 Mt/h of sorted yard waste outperformed biochar production from mixed organic fraction (MOF), achieving 8% lower production cost and 34% lower global warming impact (1.37 kg CO 2 eq. per kg) compared to biochar derived from mixed organic feedstock (2.08 kg CO 2 eq.).

09 BIOMASS FUELS↗
DOE OSTI2024

The Novel Charfuel® Coal Refining Process 18 TPD Pilot Plant Project for Co- Producing an Upgraded Coal Product, and Commercially Valuable Co- Products: Area of Interest #3 – Coal Beneficiation Pilot Plant Testing (Final Report)

Operation of Carbon Fuels, LLC’s (“CF”) existing, permitted 18 TPD pilot plant located in Golden, Colorado using two individually ranked (ASTM D 388) coal types (two campaigns), employing the novel Charfuel® coal refining process to produce an upgraded coal product and a number of high-valued organic and inorganic coproducts (for which there presently exists large commercial markets) in order to produce engineering and product data which will then be utilized toward the design of a commercial scale integrated facility (pre-feed document). Carbon Fuels, LLC has developed the Charfuel® Coal Refining Process which refines domestically abundant, raw coal (in the same manner as crude oil is refined) to produce the identical, high value co-products that are refined from crude oil. Thus, gasoline, jet fuel, “green diesel”, fuel oil, and marine fuels, as well as petrochemicals such as benzene, toluene, xylene, and methanol are refined from raw coal using this process. The Charfuel® Coal Refining Process is not a coal conversion process, like pyrolysis, or indirect liquefaction. Nor is it an alternative energy system. Rather it is a coal refining process that has the ability to economically produce products traditionally associated with the refining of crude oil but using only abundant, raw coal as the refinery feed stock. The Charfuel® Coal Refining Process is more economical than crude oil refining and is environmentally benign. Therefore, this value added process yields a return on investment well above 50% for a commercial facility. Furthermore, the Charfuel® process, unlike alternatives such as ethanol and hydrogen, can utilize the existing transportation, delivery, and other petroleum based systems. Hence, there is no need for new engines, pipelines, tankers, or product acceptance. As a result, the profitability of the process is increased. Objectives: (1) Operation of the integrated 18 tpd pilot plant, using two coal types (ranks); (2) Demonstration of process flexibility in being able to produce different products (gas, liquid, and char), as well as determination of operating parameters for identifying scale up criteria for two coal types (ranks); (3) Generation of engineering and design information (process specifications) for use in designing a commercial scale plant (scale-up); (4) Determination of important environmental issues surrounding the process and the products such as fate of trace elements (mercury and other heavy metals) and distributions of SO2, NOx, and CO 2 by analysis of effluent streams; (5) Production of sufficient product to allow reliable commercial economic evaluation of both the refined coal product and the coproducts; and, (6) Assessment of longer-term reliability of unit operations. Period 1: reconfiguration of the 18 TPD plant to meet specific FOA requirements and to qualify the facility for operation; and, Period 2: operation of the 18 TPD plant for two campaigns using two coals types (ranks) which are widely commercially used and abundant - the first being a subbituminous (Powder River Basin (“PRB”)) coal, and the second a bituminous (Illinois #6) coal.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Development of Crown Ether-Functionalized Polymeric Sorbents for Lithium-Ion Capture

The purpose of this Cooperative Research and Development Agreement (CRADA) was to develop and demonstrate new synthetic strategies for fabricating lithium-selective polymeric adsorbents by covalently incorporating crown ether functionalities into durable, processable support materials. These advanced sorbents are designed to selectively extract lithium ions (Li⁺) from dilute aqueous sources such as brine, seawater, industrial wastewater, and battery recycling streams.

36 MATERIALS SCIENCE↗
DOE OSTI2025

StreamGen: Connecting Populations of Streams and Shells to Their Host Galaxies

In this work, we study how the abundance and dynamics of populations of disrupting satellite galaxies change systematically as a function of host galaxy properties. We apply a theoretical model of the phase-mixing process to classify intact satellite galaxies and stellar streamlike and shell-like debris in ∼1500 Milky Way–mass systems generated by a semi-analytic galaxy formation code, SatGen. In particular, we test the effect of host galaxy halo mass, disk mass, ratio of disk scale height to length, and stellar feedback model on disrupting satellite populations. We find that the counts of tidal debris are consistent across all host galaxy models, within a given host mass range, and that all models can have streamlike debris on low-energy orbits, consistent with that observed around the Milky Way. However, we find a preference for streamlike debris on lower-energy orbits in models with a thicker (lower-density) host disk or on higher-energy orbits in models with a more massive host disk. Importantly, we observe significant halo-to-halo variance across all models. These results highlight the importance of simulating and observing large samples of Milky Way–mass galaxies and accounting for variations in host properties when using disrupting satellites in studies of near-field cosmology.

dark matter↗
DOE OSTI2026

A novel method for thermal noise reduction, enabling measurements of broadband, low-amplitude electron temperature fluctuations using individual radiometer channels

A new analysis method has been developed for measurements of broadband, low-amplitude turbulent electron temperature fluctuations in fusion plasmas using individual radiometer channels of a correlation electron cyclotron emission diagnostic. This method takes advantage of differences in the correlation time of thermal noise compared to the correlation time of plasma fluctuations in fusion reactors. The validation of this single-channel method is demonstrated using comparisons with the standard dual-channel radiometer spectral decorrelation method for measurements of turbulent electron temperature fluctuations in the core and edge of low confinement (L), improved confinement (I), and high confinement (H)-mode plasmas at the ASDEX Upgrade tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗