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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Macroscopic Monochalcogenide van der Waals Ferroics: Growth, Domain Structures, and Curie Temperature

Two-dimensional and layered van der Waals materials promise to overcome the limitations of conventional ferroelectrics in terms of miniaturization and material integration, but synthesis has produced only small (up to few micrometer-sized) ferroic crystals. Here, we report the realization of in-plane ferroelectric few-layer crystals of the monochalcogenides tin(II) sulfide and selenide (SnS, SnSe) whose linear dimensions exceed the current state of the art by up to 1 order of magnitude. Such large crystals allow the investigation of ferroic domain patterns that are unaffected by edges and finite-size effects. Analysis of the abundant stripe domains by electron microscopy and nanobeam electron diffraction shows two distinct domain types: twin domains separated by positively charged walls with alternating head-to-head and tail-to-tail polarization as well as not previously observed purely rotational domains connected by neutral domain walls with head-to-tail dipoles. Access to large crystals allowed the determination of the Curie temperature of few-layer SnSe van der Waals ferroelectrics, and it enables the investigation of this class of ferroelectrics by widely available methods such as polarized optical microscopy. Furthermore, the combination with layer transfer protocols promises uniform materials for exploring fundamentals and for implementing devices for information processing and energy conversion.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics‐Informed Machine Learning Method for Large‐Scale Data Assimilation Problems

Abstract We develop a physics‐informed machine learning approach for large‐scale data assimilation and parameter estimation and apply it for estimating transmissivity and hydraulic head in the two‐dimensional steady‐state subsurface flow model of the Hanford Site given synthetic measurements of said variables. In our approach, we extend the physics‐informed conditional Karhunen‐Loéve expansion (PICKLE) method to modeling subsurface flow with unknown flux (Neumann) and varying head (time‐dependent Dirichlet) boundary conditions. We demonstrate that the PICKLE method is comparable in accuracy with the standard maximum a posteriori (MAP) method, but is significantly faster than MAP for large‐scale problems. Both methods use a mesh to discretize the computational domain. In MAP, the parameters and states are discretized on the mesh; therefore, the size of the MAP parameter estimation problem directly depends on the mesh size. In PICKLE, the mesh is used to evaluate the residuals of the governing equation, while the parameters and states are approximated by the truncated conditional Karhunen‐Loéve expansions with the number of parameters controlled by the smoothness of the parameter and state fields, and not by the mesh size. For a considered example, we demonstrate that the computational cost of PICKLE increases near linearly (as N 1.15 ) with the number of grid nodes N , while that of MAP increases much faster (as N 3.28 ). We also show that once trained for one set of Dirichlet boundary conditions (i.e., one river stage), the PICKLE method provides accurate estimates of the hydraulic head for any value of the Dirichlet boundary conditions (i.e., for any river stage).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Fast myosin binding protein C knockout in skeletal muscle alters length-dependent activation and myofilament structure

In striated muscle, the sarcomeric protein myosin-binding protein-C (MyBP-C) is bound to the myosin thick filament and is predicted to stabilize myosin heads in a docked position against the thick filament, which limits crossbridge formation. Here, we use the homozygous Mybpc2 knockout (C2 -/- ) mouse line to remove the fast-isoform MyBP-C from fast skeletal muscle and then conduct mechanical functional studies in parallel with small-angle X-ray diffraction to evaluate the myofilament structure. We report that C2 -/- fibers present deficits in force production and calcium sensitivity. Structurally, passive C2 -/- fibers present altered sarcomere length-independent and -dependent regulation of myosin head conformations, with a shift of myosin heads towards actin. At shorter sarcomere lengths, the thin filament is axially extended in C2 -/- , which we hypothesize is due to increased numbers of low-level crossbridges. These findings provide testable mechanisms to explain the etiology of debilitating diseases associated with MyBP-C.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Myosin in autoinhibited off state(s), stabilized by mavacamten, can be recruited in response to inotropic interventions

Mavacamten is a FDA-approved small-molecule therapeutic designed to regulate cardiac function at the sarcomere level by selectively but reversibly inhibiting the enzymatic activity of myosin. It shifts myosin toward ordered off states close to the thick filament backbone. It remains elusive whether these myosin heads in the off state(s) can be recruited in response to physiological stimuli when required to boost cardiac output. We show that cardiac myosins stabilized in these off state(s) by mavacamten are recruitable by 1) Ca 2+ , 2) increased chronotropy [heart rate (HR)], 3) stretch, and 4) β-adrenergic (β-AR) stimulation, all known physiological inotropic interventions. At the molecular level, we show that Ca 2+ increases myosin ATPase activity by shifting mavacamten-stabilized myosin heads from the inactive super-relaxed state to the active disordered relaxed state. At the myofilament level, both Ca 2+ and passive lengthening can shift mavacamten-ordered off myosin heads from positions close to the thick filament backbone to disordered on states closer to the thin filaments. In isolated rat cardiomyocytes, increased stimulation rates enhanced shortening fraction in mavacamten-treated cells. This observation was confirmed in vivo in telemetered rats, where left-ventricular dP/dt max , an index of inotropy, increased with HR in mavacamten-treated animals. Finally, we show that β-AR stimulation in vivo increases left-ventricular function and stroke volume in the setting of mavacamten. Our data demonstrate that the mavacamten-promoted off states of myosin in the thick filament are at least partially activable, thus preserving cardiac reserve mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Assessing the impacts of wakes on floating wind farms with shared anchors

This paper examines wake effects for floating wind farms with shared anchors. Three 20-turbine farms are examined: a grid-formation baseline with no shared anchors, a farm based on 3-line anchors, and a farm based on 6-line anchors, governed by a wind turbine spacing of 8 rotor diameters. The IEA 15 MW reference turbine on the UMaine semisubmersible platform was used with a taut mooring system in deep- water depths representative of U.S. west coast lease areas. A steady-state wake model showed that, when evaluating a sweep of wind headings, the baseline design had the lowest wake losses, with a value of 11.7 %, followed by the 6-line at 12.9% and the 3-line at 13.8%. Dynamic simulations were run in FAST.Farm to analyse the effects of wakes on mean anchor loads for wind headings that showed significant wake losses. The baseline farm showed the largest anchor load reductions due to wake effects (up to 16%), followed by the 3-line farm (up to 8%), and the 6-line farm, which showed relatively consistent load magnitudes on the 6-line anchors across all headings.

17 WIND ENERGY↗
DOE OSTI2025

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Evaluating Transfer and Pumping of Slurries from Pulsed Jet Mixed Vessels

Due to gravity, solids in slurries will settle if density differences between the solids and liquid are positive (i.e., particle has a negative buoyant force) unless rheological properties and flow conditions are adequate to overcome the gravitational effects. The rate of settling depends on the force balance of the particle, which includes the surface forces associated with fluid rheology. Given the same fluid and solid properties, particles that are larger and denser tend to settle faster. When pumping slurry into a vessel at concentrations precluding hindered settling with insufficient mixing, particle and density distributions can result in preferential settling, creating stratification in the solids concentration within the vessel. For vessels with transfer line inlets located in the lower portion of the tank, the stratified solids concentration may be detrimental to the transfer system performance. Elevated concentrations of solids in the slurry entrained at the inlet to the transfer line can result in the effective viscosity or slurry bulk density exceeding the design limits of the pump. These conditions could result in plugging of the transfer line or onset of cavitation of the pumps because of excessive pressure drop. These conditions can be exacerbated with periodic inlet conditions existing at the transfer line inlet. Periodic conditions can result when vessel mixing is intermittent such as with pulsed jet mixers (PJM). The transfer line inlet conditions are impacted by the periodic nature of the PJM operations with respect to suspension of solids and their transport to the inlet of the transfer line. A scaling approach is presented, and corresponding test requirements are developed for assessing the prevention of plugging the pipeline. Line plugging mechanisms are addressed that do not include plugging due to steady-state high-density slurry entering the transfer line and reducing the net positive suction head available (NPSHA) at the pump inlet to below that required for pump operation. Items considered include the transition to reduced relative flow velocities, such that the critical pipe velocity for solids deposition, Ucd, is not maintained, and segregation of heavy solids during the transport. The recommended requirements to prevent plugging include: • Limits for viscosity and density for entrained slurry to prevent the pressure drop in the pipeline from exceeding pump capacity. • Limits for viscosity and density for entrained slurry to prevent the net positive suction head available (NPSHA) from falling below the net positive suction head required (NPSHR) for operating the pump. • Transfer line velocity and flow rate requirements to maintain solids in suspension, while avoiding line plugging that results from deposition of solids within the transfer line. This paper describes the development of the scaling and testing requirements to verify that proposed approaches for transfer and pump out are appropriately developed

periodic flow, mixing, pipeline plugging, scaling,↗
DOE OSTI2020

m2p (Monomers to Polymers)

The software is Python-based code that takes monomers, represented as SMILES, and "reacts" them together to form long polymer chains, represented as SMILES, based on known polymer chemistries. The software is used in a machine-learning pipeline where a database and machine-learning approaches are used to predict polymer properties base on molecular structure. This software is necessary to represent the polymer chain, instead of simple repeat units, as the software statistically represents different molecular architectures that occur during reactions (e.g. head-to-head, head-to-tail, and tail-to-tail). It is capable of building polymer chains with five different chemistries: vinyl/olefin, polyamide, polycarbonate, polyester, and polyimides. The user is able to specify different monomer pairings and degree of polymerization for different polymer chains.

Wilson, Nolan↗
DOE OSTI2025

pixelvar79/ESGAN-Flowering-Detection-paper

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

Varela, Sebastian↗
DOE OSTI2024

Calibration of V-Notch and Compound Weirs for Subsurface Drainage Water Level Control Structures

Highlights Accurate discharge estimation is important when evaluating edge-of-field conservation practices. V-notch weir equations were developed for three sizes of subsurface drainage water level control structures. Compound weir equations were developed for subsurface drainage water level control structures. The compound weir equation accurately estimates discharge for flows within and overtopping the V-notch. Abstract.Numerous edge-of-field conservation practices use subsurface drainage water level control structures to monitor water levels and estimate discharge. In a control structure, procedures for calculating discharge when flow depth (head) exceeds the V-notch depth and overflows in the rectangular portion of the compound weir (CW) are ambiguous. In this study, we developed calibration equations for V-notch weirs in Agri Drain inline water level control structures of different sizes for flows within the V-notch and overtopping flow events. The discharge equation for overtopping events (Q CW , L s -1 ) was determined as: Q CW = a 1 (h b1 -h 1 b1 )+a 2 (W e -W v )h 1 b2 , where h and h 1 are heads above vertex/bottom and top of V-notch (cm), respectively, W is the effective crest width of rectangular weir (cm), W v is the top width of V-notch (cm), a 1 and b 1 are parameters for V-notch weir obtained by calibration, and a 2 and b 2 are calibration parameters for rectangular weir obtained from literature. Results were compared with a weir equation available in the literature (Q V+R ), which combines a V-notch equation with a head equal to V-depth and a rectangular weir equation for flow above V-depth. Discharge at overflow was estimated with high accuracy with Q CW, whereas Q V+R underestimated discharge (e.g., PBIAS of 0.67% vs. 17.82% for a 15.2 cm structure). An example using Q V+R resulted in a 14% lower annual estimation of nitrate-N load diverted to a saturated buffer than Q CW due to underestimation of drainage discharge during overflow events. Results suggest that the developed equation (Q CW ) accurately estimates discharge and will thus improve the estimated N load compared to Q V+R . Keywords: Compound weir, Flow monitoring, Subsurface drainage, V-notch weir, Water level control structure, Weir calibration.

Agriculture↗
DOE OSTI2026

Raw_data_Batch_I: Lisle to Waterfall Glen

Date of collection: May 11, 2023 Location: DuPage County, IL This dataset contains lidar and vision data collected between Lisle, IL, and the Waterfall Glen parking lot. The vehicle started near Cass School District 63, headed east along IL 34. The vehicle then turned south along IL 83 until Interstate 55. Finally, the vehicle turned southwest along I 55 until Exit 273A and headed toward the Waterfall Glen parking lot. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![lisle waterfall image](lisle-waterfall.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Raw_data_Batch_I: Randall Road

Date of collection: June 3, 2022 Location: Randall Road, DuPage County, IL Content: “North to South” “South to North” This data set contains lidar and vision data collected along Randall Road in DuPage County, Illinois. The “South to North” folder starts at 1480 N. Orchard Road, Aurora, IL 60506, headed north along Randall Road until 238 N. Randall Road, St. Charles, IL 60174. The “North to South” folder starts from 238 N. Randall Road, St. Charles, IL 60174, headed south along Randall Road until 1480 N. Orchard Road, Aurora, IL. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![randall road image](randall-road.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

In-situ measurements of chemically and radiatively important atmospheric gases from the Advanced Global Atmospheric Gas Experiment (AGAGE) and affiliated stations.

In the ALE/GAGE/AGAGE global network program, continuous high frequency gas chromatographic measurements of four biogenic/anthropogenic gases (methane, CH4; nitrous oxide, N2O; hydrogen, H2; and carbon monoxide, CO) and several anthropogenic gases that contribute to stratospheric ozone destruction and/or to the greenhouse effect have been carried out at five globally distributed sites for several years. The program, which began in 1978, is divided into three parts associated with three changes in instrumentation: the Atmospheric Lifetime Experiment (ALE), which used Hewlett Packard HP5840 gas chromatographs; the Global Atmospheric Gases Experiment (GAGE), which used HP5880 gas chromatographs; and the present Advanced GAGE (AGAGE). AGAGE uses two types of instruments: a gas chromatograph with multiple detectors (GC-MD), and a gas chromatograph with mass spectrometric analysis (GC-MS). Beginning in January 2004, an improved cryogenic preconcentration system (Medusa) replaced the absorption-desorption module in the GC-MS systems at Mace Head and Cape Grim; this provided improved capability to measure a broader range of volatile perfluorocarbons with high global warming potentials. The Medusa GC-MS systems were subsequently used at other AGAGE stations (Trinidad Head, Barbados, American Samoa, Zeppelin, Jungfraujoch, and Goan) after the initial setup at Mace Head and Cape Grim. More information may be found at the AGAGE home page: https://agage.mit.edu/instruments.Compared to the previous version (https://data.ess-dive.lbl.gov/view/doi:10.3334/CDIAC/ATG.DB1001), the latest update (Agage_gcmd_gcms_data_2021_04_07_tar.gz) includes 6 months of new data through the end of March 2020. Two new compounds, HCFC-132b, and HCFC-133a, are also included in this version. For a complete list of released compounds and used standard scales, please see “AGAGE_scale_2021_v1.pdf” file. Additional information can also be found on the AGAGE website (https://agage.mit.edu).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Advanced Global Atmospheric Gases Experiment (AGAGE) Data

In the ALE/GAGE/AGAGE global network program, continuous high frequency gas chromatographic measurements of four biogenic/anthropogenic gases (methane, CH4; nitrous oxide, N2O; hydrogen, H2; and carbon monoxide, CO) and several anthropogenic gases that contribute to stratospheric ozone destruction and/or to the greenhouse effect have been carried out at five globally distributed sites for several years. The program, which began in 1978, is divided into three parts associated with three changes in instrumentation: the Atmospheric Lifetime Experiment (ALE), which used Hewlett Packard HP5840 gas chromatographs; the Global Atmospheric Gases Experiment (GAGE), which used HP5880 gas chromatographs; and the present Advanced GAGE (AGAGE). AGAGE uses two types of instruments: a gas chromatograph with multiple detectors (GC-MD), and a gas chromatograph with mass spectrometric analysis (GC-MS). Beginning in January 2004, an improved cryogenic preconcentration system (Medusa) replaced the absorption-desorption module in the GC-MS systems at Mace Head and Cape Grim; this provided improved capability to measure a broader range of volatile perfluorocarbons with high global warming potentials. The Medusa GC-MS systems were subsequently used at other AGAGE stations (Trinidad Head, Barbados, American Samoa, Zeppelin, Jungfraujoch, and Goan) after the initial setup at Mace Head and Cape Grim. More information may be found at the AGAGE home page: https://agage.mit.edu/instruments.Compared to the previous version (https://data.ess-dive.lbl.gov/datasets/doi: doi:10.15485/1781803), the latest update (Agage_gcmd_gcms.data.2022_01_03.tar.gz) includes 6 months of new data through the end of September 2020. For a complete list of released compounds and used standard scales, please see “AGAGE_scale_2021_v1.pdf” file. Additional information can also be found on the AGAGE website (https://agage.mit.edu).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The dataset of in-situ measurements of chemically and radiatively important atmospheric gases from the AGAGE and affiliated stations (2022R1)

In the ALE/GAGE/AGAGE global network program, continuous high frequency gas chromatographic measurements of four biogenic/anthropogenic gases (methane, CH4; nitrous oxide, N2O; hydrogen, H2; and carbon monoxide, CO) and several anthropogenic gases that contribute to stratospheric ozone destruction and/or to the greenhouse effect have been carried out at five globally distributed sites for several years. The program, which began in 1978, is divided into three parts associated with three changes in instrumentation: the Atmospheric Lifetime Experiment (ALE), which used Hewlett Packard HP5840 gas chromatographs; the Global Atmospheric Gases Experiment (GAGE), which used HP5880 gas chromatographs; and the present Advanced GAGE (AGAGE). AGAGE uses two types of instruments: a gas chromatograph with multiple detectors (GC-MD), and a gas chromatograph with mass spectrometric analysis (GC-MS). Beginning in January 2004, an improved cryogenic preconcentration system (Medusa) replaced the absorption-desorption module in the GC-MS systems at Mace Head and Cape Grim; this provided improved capability to measure a broader range of volatile perfluorocarbons with high global warming potentials. The Medusa GC-MS systems were subsequently used at other AGAGE stations (Trinidad Head, Barbados, American Samoa, Zeppelin, Jungfraujoch, and Goan) after the initial setup at Mace Head and Cape Grim. More information may be found at the AGAGE home page: https://agage.mit.edu/instruments. Data from the AGAGE and affiliated stations (total of 9 sites) between August 1993 and March 2021 are provided in “Agage_gcmd_gcms_data_2022_05_26_tar.gz” (compressed tar file). The metadata file has information on each station and currently released species. The standard scales used in archived species are listed in "AGAGE_scale_2022_v1.pdf". Additional information can be found on the AGAGE website (https://agage.mit.edu).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The dataset of in-situ measurements of chemically and radiatively important atmospheric gases from the AGAGE and affiliated stations (2022R2)

In the ALE/GAGE/AGAGE global network program, continuous high frequency gas chromatographic measurements of four biogenic/anthropogenic gases (methane, CH4; nitrous oxide, N2O; hydrogen, H2; and carbon monoxide, CO) and several anthropogenic gases that contribute to stratospheric ozone destruction and/or to the greenhouse effect have been carried out at five globally distributed sites for several years. The program, which began in 1978, is divided into three parts associated with three changes in instrumentation: the Atmospheric Lifetime Experiment (ALE), which used Hewlett Packard HP5840 gas chromatographs; the Global Atmospheric Gases Experiment (GAGE), which used HP5880 gas chromatographs; and the present Advanced GAGE (AGAGE). AGAGE uses two types of instruments: a gas chromatograph with multiple detectors (GC-MD), and a gas chromatograph with mass spectrometric analysis (GC-MS). Beginning in January 2004, an improved cryogenic preconcentration system (Medusa) replaced the absorption-desorption module in the GC-MS systems at Mace Head and Cape Grim; this provided improved capability to measure a broader range of volatile perfluorocarbons with high global warming potentials. The Medusa GC-MS systems were subsequently used at other AGAGE stations (Trinidad Head, Barbados, American Samoa, Zeppelin, Jungfraujoch, and Goan) after the initial setup at Mace Head and Cape Grim. More information may be found on the AGAGE home page: https://agage.mit.edu/instruments.Data from the AGAGE and affiliated stations (total of 9 sites) between August 1993 and December 2021 are provided in “Agage_gcmd_gcms_data_2022_11_15.tar.gz” (compressed tar file). The metadata file has information on each station and currently released species. The standard scales used in archived species are listed in "AGAGE_scale_2022_v2.pdf". Additional information such as the mission and scientific objectives of the ALE/GAGE/AGAGE program can be found on the AGAGE website (https://agage.mit.edu/about/our-mission).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The dataset of in-situ measurements of chemically and radiatively important atmospheric gases from the Advanced Global Atmospheric Gas Experiment (AGAGE) and affiliated stations (2023R1)

In the ALE/GAGE/AGAGE global network program, continuous high frequency gas chromatographic measurements of four biogenic/anthropogenic gases (methane, CH4; nitrous oxide, N2O; hydrogen, H2; and carbon monoxide, CO) and several anthropogenic gases that contribute to stratospheric ozone destruction and/or to the greenhouse effect have been carried out at five globally distributed sites for several years. The program, which began in 1978, is divided into three parts associated with three changes in instrumentation: the Atmospheric Lifetime Experiment (ALE), which used Hewlett Packard HP5840 gas chromatographs; the Global Atmospheric Gases Experiment (GAGE), which used HP5880 gas chromatographs; and the present Advanced GAGE (AGAGE). AGAGE uses two types of instruments: a gas chromatograph with multiple detectors (GC-MD), and a gas chromatograph with mass spectrometric analysis (GC-MS). Beginning in January 2004, an improved cryogenic preconcentration system (Medusa) replaced the absorption-desorption module in the GC-MS systems at Mace Head and Cape Grim; this provided improved capability to measure a broader range of volatile perfluorocarbons with high global warming potentials. The Medusa GC-MS systems were subsequently used at other AGAGE stations (Trinidad Head, Barbados, American Samoa, Zeppelin, Jungfraujoch, and Goan) after the initial setup at Mace Head and Cape Grim. More information may be found on the AGAGE home page: https://agage.mit.edu/instruments.Data from the AGAGE and affiliated stations (total of 10 sites) between August 1993 and June 2022 are provided in “Agage_gcmd_gcms_data_2023_07_24.tar.gz” (compressed tar file). The metadata file has information on each station and currently released species. The standard scales used in archived species are listed in "AGAGE_scale_2023_v1.pdf". Additional information can be found on the AGAGE website (https://agage.mit.edu).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The dataset of in-situ measurements of chemically and radiatively important atmospheric gases from the Advanced Global Atmospheric Gas Experiment (AGAGE) and affiliated stations (2023R2)

In the ALE/GAGE/AGAGE global network program, continuous high frequency gas chromatographic measurements of four biogenic/anthropogenic gases (methane, CH4; nitrous oxide, N2O; hydrogen, H2; and carbon monoxide, CO) and several anthropogenic gases that contribute to stratospheric ozone destruction and/or to the greenhouse effect have been carried out at five globally distributed sites for several years. The program, which began in 1978, is divided into three parts associated with three changes in instrumentation: the Atmospheric Lifetime Experiment (ALE), which used Hewlett Packard HP5840 gas chromatographs; the Global Atmospheric Gases Experiment (GAGE), which used HP5880 gas chromatographs; and the present Advanced GAGE (AGAGE). AGAGE uses two types of instruments: a gas chromatograph with multiple detectors (GC-MD), and a gas chromatograph with mass spectrometric analysis (GC-MS). Beginning in January 2004, an improved cryogenic preconcentration system (Medusa) replaced the absorption-desorption module in the GC-MS systems at Mace Head and Cape Grim; this provided improved capability to measure a broader range of volatile perfluorocarbons with high global warming potentials. The Medusa GC-MS systems were subsequently used at other AGAGE stations (Trinidad Head, Barbados, American Samoa, Zeppelin, Jungfraujoch, and Goan) after the initial setup at Mace Head and Cape Grim. More information may be found on the AGAGE home page: https://agage.mit.edu/instruments.Data from the AGAGE and affiliated stations (total of 10 sites) between August 1993 and December 2022 are provided in “Agage_gcmd_gcms_data_2023_11_15.tar.gz” (compressed tar file). The metadata file has information on each station and currently released species. The standard scales used in archived species are listed in "AGAGE_scale_2023_v2.pdf". Additional information can be found on the AGAGE website (https://agage.mit.edu).

54 ENVIRONMENTAL SCIENCES↗