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1,049 records · Page 59

WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6)

This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new and modified files); and September 2025 (v6; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocols; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) surface water major cations and anions and averages; (4) sediment grain size data; (5) sediment iron (II) data and averages; (6) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment specific surface area; (11) sediment percent carbon and nitrogen; (12) sediment gravimetric moisture and averages; (15) sediment X-ray diffraction (XRD) data; (16) sediment adenosine triphosphate (ATP) and averages; (17) a subfolder with sediment incubation respiration data, scripts, and plots; (18) surface water and sediment FTICR methods; and (19) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS).The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗

A review of NEST models for liquid xenon and an exhaustive comparison with other approaches

This paper discusses the microphysical simulation of interactions in liquid xenon, the active detector medium in many leading rare-event searches for new physics, and describes experimental observables useful for understanding detector performance. The scintillation and ionization yield distributions for signal and background are presented using the Noble Element Simulation Technique (NEST), a toolkit based on experimental data and simple empirical formulas, which mimic previous microphysics modeling but are guided by data. The NEST models for light and charge production as a function of the particle type, energy, and electric field are reviewed, along with models for energy resolution and final pulse areas. NEST is compared with other models or sets of models and validated against real data, with several specific examples drawn from XENON, ZEPLIN, LUX, LZ, PandaX, and table-top experiments used for calibrations.

WIMPs↗

Cross-correlation of Dark Energy Survey Year 3 lensing data with ACT and Planck thermal Sunyaev-Zel’dovich effect observations. I. Measurements, systematics tests, and feedback model constraints

Here we present a tomographic measurement of the cross-correlation between thermal Sunyaev-Zel’dovich (TSZ) maps from Planck and the Atacama Cosmology Telescope and weak galaxy lensing shears measured during the first three years of observations of the Dark Energy Survey. This correlation is sensitive to the thermal energy in baryons over a wide redshift range and is therefore a powerful probe of astrophysical feedback. We detect the correlation at a statistical significance of 21σ, the highest significance to date. We examine the TSZ maps for potential contaminants, including cosmic infrared background and radio sources, finding that cosmic infrared background has a substantial impact on our measurements and must be taken into account in our analysis. We use the cross-correlation measurements to test different feedback models. In particular, we model the TSZ using several different pressure profile models calibrated against hydrodynamical simulations. Our analysis marginalizes over redshift uncertainties, shear calibration biases, and intrinsic alignment effects. We also marginalize over Ω m and σ 8 using Planck or DES priors. We find that the data prefer the model with a low amplitude of the pressure profile at small scales, compatible with a scenario with strong active galactic nuclei feedback and ejection of gas from the inner part of the halos. When using a more flexible model for the shear profile, constraints are weaker, and the data cannot discriminate between different baryonic prescriptions.

79 ASTRONOMY AND ASTROPHYSICS↗

A real-time search for Type Ia Supernovae with late-time interactions with circumstellar material in ZTF data

While it is generally accepted that Type Ia supernovae (SNe Ia) are the terminal explosions of white dwarfs (WDs), the nature of their progenitor systems and the mechanisms that lead up to these explosions remain widely debated. In rare cases, the SN ejecta interact with circumstellar material (CSM) that had previously been ejected from the progenitor system. The longer the delay between the creation of the CSM and the SN explosion, the greater the distance between the SN explosion site and the CSM and the later the onset of the interaction. The unknown distance between the CSM and SN explosion site makes it impossible to predict when the interaction will start. If the time between the SN explosion and the onset of the CSM interaction is of the order of several months to years, the SN has generally faded and it is no longer actively followed up on. This makes it even more difficult to detect the interaction while it is happening. In this work, we report on a real-time monitoring programme running between 13 November 2023 and 9 July 2024. It monitored 6914 SNe Ia for signs of late-time rebrightening using the Zwicky Transient Facility (ZTF). Flagged candidates were rapidly followed up on with photometry and spectroscopy to confirm the late-time excess and its position. We report the discovery of a ∼50 day rebrightening event in SN 2020qxz around 1200 rest-frame days after the peak of its light curve. SN 2020qxz exhibited signs of an early CSM interaction, but had faded from view over two years before its reappearance. Initial follow-up spectroscopy revealed the presence of four emission lines, while later follow-up spectroscopy showed that these had faded shortly after the end of the ZTF-detected rebrightening event. Our best match for these emission lines are H β (blueshifted by ∼5900 km s −1 ) and Ca II λ8542 , N I λ8567 , and K I λλ8763, 8767 (all blueshifted by 5100 km s −1 ; although we note that the line identifications are uncertain). This shows that catching and following up on late-time interactions as they occur can offer new clues on the nature of the progenitor systems that produce these SNe by putting constraints on the possible type of donor star. The only way to do this systematically is to use large sky surveys such as ZTF and the upcoming Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) to monitor a large sample of objects for the rare events that reappear long after the object has faded from view.

circumstellar matter↗

Technology Strategy Assessment: Findings from Storage Innovations 2030 Bidirectional Hydrogen Storage

Hydrogen is the most common element in the universe, comprising nearly 75% of all normal matter, and it has been used by scientists for centuries, but it was not fully recognized as an element until 1766, when it was isolated by Henry Cavendish. Early work focused on the generation of hydrogen through the oxidation of metals in water, which released hydrogen gas. Hydrogen’s lighter-than-air and flammable properties were immediately used in engines, zeppelins, and as feedstock for a wide variety of chemical reactions. Several approaches were developed for the production of hydrogen with the most common being associated with the production and conversion of hydrocarbon-based fuels. Coal gasification, steam methane reforming, and other reformation processes provide the majority of current hydrogen production due to the relatively low cost of hydrogen produced through these processes. More than 95% of hydrogen production is used for industrial processes rather than energy storage. To facilitate affordable decarbonization of these industrial processes and to advance the use of hydrogen as a fuel in transportation, DOE launched the Hydrogen Shot as part of the Energy Earthshots Initiative. The goal of the Hydrogen Shot is to reduce the cost of clean hydrogen by 80% to $1/kg of clean hydrogen production within one decade (known as the “1 1 1” goal). This is distinct from the Long-Duration Storage Shot, which is the primary focus of this report; however, it is intrinsically linked to bidirectional hydrogen storage. Several important chemical synthesis processes are dependent upon hydrogen, and the production and use of hydrogen is generally driven by its connection to one of these markets. For example, ammonia is one of the most highly produced chemicals in the world and it depends chiefly on hydrogen. Ammonia is primarily used for agricultural fertilizer and is considered to be largely responsible for a doubling of agricultural production per unit of land over the last century. Another one of hydrogen’s primary uses is as a catalyst in petroleum refining during the desulfurization process. Beyond chemical production, hydrogen is used as a reductant in the production of steel and has been demonstrated as a substitute for metallurgical coal in the production of raw iron. It is even used in the hydrogenation reaction for food products to create more shelf-stable semi-solid fats. However, while hydrogen is produced on the order of 100 million metric tons/year globally to feed these industries, more than 95% of hydrogen is produced from hydrocarbons that emit CO2 during the process. Conversely, electrolysis is a process by which electricity is used to separate hydrogen and oxygen in water molecules, usually across a membrane. Hydrogen production via electrolysis lowers the carbon intensity of produced hydrogen when coupled with low-carbon electricity. Currently, global electrolysis capacity is on the order of 1 GW, which equates to about 500 metric tons/day of hydrogen production. To support large-scale industrial decarbonization, capacity will likely need to increase by two to three orders of magnitude. Electrolysis technology is broadly separated into groups that are defined by the electrolyte used, with further subdivision based on the operating characteristics. The majority of commercial electrolyzer systems are based around three main technology groups: liquid alkaline, proton exchange membrane, and solid oxide. Liquid Alkaline (LA) electrolysis is the oldest, most mature, least expensive, and most common commercial technology, with 400 plants in operation by 1902. Its hydrogen output is low relative to the size of the system due to a low current density. LA electrolysis utilizes a liquid potassium hydroxide solution as the electrolyte. Proton exchange membrane (PEM) electrolysis (also known as polymer electrolyte membrane electrolysis), described in 1960, relies on an acid-impregnated polymer membrane as the electrolyte and typically offers three to six times higher hydrogen production per unit cell area than LA electrolysis. Solid oxide electrolysis, or high-temperature electrolysis, utilizes a ceramic cell as the electrolyte and operates on steam rather than liquid water, enabling electrical efficiencies of more than 90%, which is up from 60% with PEM. Two pre-commercial electrolyzer technologies to note are alkaline exchange membrane (AEM) and proton-conducting solid oxide electrolysis cell (SOEC). AEM potentially has the advantages of both LA and PEM technologies in that it is able to use low-cost materials like LA but with the ability to operate at higher output pressures with a smaller footprint like PEM. Proton-conducting SOEC is similar to commercial SOEC, which uses an oxide-conducting ceramic; however, it uses a proton-conducting ceramic that has the potential to operate at lower temperatures and has lower capital costs. Each of these technologies is experiencing a rapid improvement in performance and a reduction in installed cost, and each appears to be well suited to specific applications. Besides differences in the type of electrolyzer used, the main difference in the architecture of bidirectional hydrogen systems is how the hydrogen is stored. Currently, the most cost-effective way to store large amounts of hydrogen gas is underground, such as in large salt caverns that have been hollowed out. These salt caverns are geographically concentrated in small portions of the United States and are not generally near large metropolitan areas; however, other subsurface architectures are being investigated to expand this reach. A more widely deployable option is aboveground pressurized tanks. These systems are about 10 times as expensive because of the materials and safety margins required to hold hydrogen at high pressures. A third option is using materials-based storage, such as liquid organic hydrogen carriers. By reversibly attaching the produced hydrogen to other molecules, it can be stored at near atmospheric pressure and room temperature. This has the potential to reduce the material cost of storage but may result in a reduction in the efficiency of the process because there are both hydrogen uptake and release processes. While materials-based storage has not been used extensively for large-scale hydrogen storage in the past, there is currently significant activity regarding developing materials and processes for use in large-scale hydrogen storage applications. Electrolysis-produced hydrogen offers an unusual opportunity for energy storage applications. Unlike more conventional energy storage approaches, such as batteries, which operate entirely within electrical markets, hydrogen is a valuable product beyond the electric market and can be directed to the most lucrative use. Hydrogen also can be directly converted back to electricity using either a fuel cell or turbine, or it can be sold to other markets, such as chemical synthesis, steel production, or even export. In this way, excess electricity can be upgraded to the most valuable product. Finally, its use can be actively managed between multiple off-takers; for example, local hydrogen storage can provide a specific amount of stored electricity and any excess can be exported to ammonia production. This flexibility is amplified by the fact that hydrogen storage has fully decoupled power and energy components, which allows for affordable scaling options. Together, this allows a substantial amount of creativity to enable the economic utilization of variable power resources while supporting decarbonization of the industry.

08 HYDROGEN↗