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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

Climate adaptation and sustainability in switchgrass: exploring plant-microbe-soil interactions across continental scale environmental gradients

Less carbon-intensive energy sources are needed to reduce greenhouse gas emissions and their predicted role in climate change. There is growing interest in the potential of biofuels for meeting this need. A critical question is whether large-scale biofuel production can be sustainable over the time scales needed to mitigate our carbon debt from fossil fuel consumption. The carbon balance and ultimately the sustainability of biofuel feedstock production is the result of complex climate-coupled interactions between carbon fixation, sequestration, and release through combustion. Similarly, the long-term productivity of biofuels depends on the environmental factors limiting plant growth. These factors are often related to soil resources which involve complex interactions at the plant-microbe-soil interface impacting their availability and cycling. Our collaborative project addressed sustainable switchgrass (Panicum virgatum) production by exploring Plant Systems, Plant-Microbiome Interactions, and Ecosystem Processes through the integrating lens of Multi-Scale Modeling. Our research was based on detailed characterization of genetically diverse switchgrass genotypes planted in common gardens across a continental latitudinal gradient. The underlying theme of our Plant Systems research was the use of locally adapted plant material to explore plant function, to understand the mechanistic basis of environmental interactions, and to discover the plant genes important for adaptation and sustainability in the face of climate change. Our Plant-Microbiome Interaction project characterized the microbial communities associated with switchgrass using genomic tools. Our Ecosystem Processes research focused on carbon cycle responses at the ecosystem level using stand level plantings. Finally, our Multi-Scale Modeling helped to define conditions of a sustainable biofuel system and identify key tradeoffs between genetic diversity, productivity, and ecosystem services. Genome-wide association analyses were used to identify alleles that contribute to successful establishment and biomass production across North America. Together, our work provided a baseline analyses of the potential of switchgrass as a biofuel feedstock. Our project resulted in a number of successful outcomes. First, we were successful in collecting switchgrass germplasm across the species range, propagating the material, and establishing common garden experiments across the species range. In collaboration with DOE JGI, we successfully assembled the first tetraploid switchgrass genome and published this resource with an analyses of the genetic basis local adaptation from our gardens (Lowry et al. 2019, Lovell et al. 2021). The gardens were used to characterize the genetic architecture for a number of important plant phenotypes. Our project also conducted extensive sampling and sequencing to characterize the bacterial and fungal associates of switchgrass roots and leaves. We showed that host genotype, location, and harvesting practices can play a role in microbiome assembly (Singer et al. 2019 & 2022, Van Wallendael et al. 2020 & 2022, Edwards et al. 2023). Our ecosystem processes work created baseline dataset of carbon and nutrient cycling in realistic stand plantings of switchgrass. Data from this experiment provided new insight into the role of plant traits, phenology, and local environments in ecosystem processes like soil respiration, net-ecosystem exchange, and dynamics of soil and plant nutrients (Ricketts et al. 2023). Finally, our crop modelling experiments help to characterize the sensitivity of common modeling frameworks to parameters, identify key limiters of productivity across large geographic scales, and leverage patterns of local adaptation in prediction. Ultimately, these studies help to identify critical plant-microbe-soil traits that may be manipulated, through breeding or agronomic management, to improve the sustainability of biofuel feedstocks.

09 BIOMASS FUELS↗

District heating utilizing waste heat of a data center: High-temperature heat pumps

Data centers are energy-intensive facilities with substantial low-grade waste heat. High-temperature heat pumps can be critical in boosting the data center’s waste heat for district heating, improving the system-level energy efficiency of data centers, and reducing CO 2 emissions in district heating. This study built thermodynamic models to assess high-temperature heat pumps with six configurations using low global warming potential refrigerants to supply heat up to 120 °C. The heat pump configurations include single-stage or two-stage cycles with advanced components, such as internal heat exchanger, economizer, flash tank, or parallel compressor. The refrigerants include R1234ze(Z), R1233ed(E), R1224yd(Z), R600, and R600a, and R245fa is used as a reference. A case study was carried out to recover the waste heat from the Frontier high-performance computing data center and provide hot water for district heating at the US Department of Energy’s Oak Ridge National Laboratory campus. The optimized performance of high-temperature heat pumps is characterized with various effectiveness of internal heat exchangers, and the operating parameters of economizer or flash tank, as well as their combination. The results show that the configurations of two-stage cycles with internal heat exchanger + flash tank and internal heat exchanger + economizer/parallel-compressor provide the highest coefficient of performance under scenarios of the maximum allowable value and a fixed value (0.3) of the internal heat exchangers’ effectiveness, respectively. R1234ze(Z) and R600a are the most promising refrigerants, considering trade-offs between the coefficient of performance and the volumetric heating capacity. The single-stage cycle with internal heat exchanger + economizer/parallel-compressor using R1234ze(Z) is recommended for utilizing Fronter’s waste heat in district heating. A one mega-watt high-temperature heat pump will reduce 33,100–33,200 metric tons of CO2 emission annually, corresponding to 85.4 %–85.6 % of equivalent CO2 emissions from natural gas boilers. Here, this study provides good guidelines for designing and deploying high-temperature heat pumps to support sustainable data centers and decarbonize district heating in the US.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NEPATEC2.0: NEPA Text Corpus v2.0

The National Environmental Policy Act of 1969, as amended (NEPA), is a major environmental law in the United States, requiring Federal agencies to consider and document potential environmental impacts before deciding on a proposed action. Modernization of NEPA and permitting processes faces significant challenges due to the lack of standardized formats and interoperable systems for organizing and sharing NEPA-related information across agencies. Much of the information gathered during NEPA reviews is written into documents such as categorical exclusions, environmental assessments, and environmental impact statements, then filed in predominately independent agency file stores that may or may not be publicly accessible. The application of metadata and data standards, such as those recommended by the Council on Environmental Quality (CEQ), to NEPA documents offers a shared vocabulary and structure for key entities like projects, processes, and documents that can streamline information exchange and enhance collaboration across systems. In this work, we publicly release NEPATEC2.0, an expanded corpus of NEPA documents with associated metadata. NEPATEC2.0 encompasses approximately 120,000 documents from 60,000 projects prepared by more than 60 different agencies. Modeled to align with CEQ metadata standards, NEPATEC2.0 promotes consistency in environmental reviews and supports the ongoing effort to modernize permitting technologies by facilitating more transparent, efficient, and data-driven decision-making. Importantly, NEPATEC2.0 demonstrates the possibilities and limitations of large language model-based prompting to extract information from NEPA documents at scale.

environmental review↗

NEPATEC v2.0: Standardized Metadata and Text Corpus of National Environmental Policy Act Documents

The National Environmental Policy Act of 1969, as amended (NEPA), is a major environmental law in the United States, requiring Federal agencies to consider and document potential environmental impacts before deciding on a proposed action. Modernization of NEPA and permitting processes faces significant challenges due to the lack of standardized formats and interoperable systems for organizing and sharing NEPA-related information across agencies. Much of the information gathered during NEPA reviews is written into documents such as categorical exclusions, environmental assessments, and environmental impact statements, then filed in predominately independent agency file stores that may or may not be publicly accessible. The application of metadata and data standards, such as those recommended by the Council on Environmental Quality (CEQ), to NEPA documents offers a shared vocabulary and structure for key entities like projects, processes, and documents that can streamline information exchange and enhance collaboration across systems. In this work, we publicly release NEPATEC2.0, an expanded corpus of NEPA documents with associated metadata. NEPATEC2.0 encompasses approximately 120,000 documents from 60,000 projects prepared by more than 60 different agencies. Modeled to align with CEQ metadata standards, NEPATEC2.0 promotes consistency in environmental reviews and supports the ongoing effort to modernize permitting technologies by facilitating more transparent, efficient, and data-driven decision-making. Importantly, NEPATEC2.0 demonstrates the possibilities and limitations of large language model-based prompting to extract information from NEPA documents at scale.

54 ENVIRONMENTAL SCIENCES↗

Data and code for Daily and Multi-Day Extreme Rainfall Analysis Under Future Climates Using Stochastic Storm Transposition and NEX-GDDP-CMIP6 Over CONUS

This data package provides inputs, codes, and outputs for a comprehensive analysis of projected changes in extreme precipitation across 10 regions of the continental United States, using 34 downscaled Earth System Models (ESMs) from the NASA Earth Exchange Global Daily Downscaled Projections, Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) dataset. These models are part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), a coordinated climate modeling framework widely used to assess climate change impacts. The analysis applies a stochastic storm transposition method to quantify changes in extreme rainfall under two Shared Socioeconomic Pathway (SSP) climate scenarios—SSP2-4.5 (moderate emissions) and SSP5-8.5 (high emissions)—compared to historical conditions (1995–2014 vs. 2081–2100). The dataset includes rainfall depth estimates for extreme events with return periods from 2 to 500 years across multiple storm durations (1, 3, and 5 days) for each of the 10 U.S. regions. Weighted ensemble statistics are derived from individual ESM performance against historical precipitation patterns, enabling robust uncertainty quantification through both sign-based and permutation-test-based model agreement assessments. Key analyses address: (1) relative changes in extreme precipitation for each climate scenario, (2) differences between SSP scenarios (SSP5-8.5 vs. SSP2-4.5), (3) contrasts between rare and frequent events, and (4) variations between multi-day and daily storm durations. The workflow produces ensemble statistics—median, 5th, 25th, 75th, and 95th percentiles—along with model agreement metrics that identify regions and event types with robust climate change signals. The dataset includes: processed rainfall depth outputs (netCDF format) from the RainyDay Python package, ESM weights from historical performance evaluation using DayMet observations, ensemble statistics across all storm dimensions, and figures summarizing key findings.

54 ENVIRONMENTAL SCIENCES↗

High-Resolution Regional Atmosphere–Ocean–Wave Coupled Simulations of Hurricane Henri (2021)

To explore the integrated effects of ocean and ocean surface wave related physical processes on TC simulations, a set of three model simulations is performed. * In experiment 'A', the event is modeled using the Weather Research Forecasting (WRF) model alone with prescribed Sea Surface Temperature (SST) at 6-hour intervals. * In experiment ‘AO,’ WRF is coupled with the Finite Volume Community Ocean Model (FVCOM), enabling variable exchange between atmosphere and ocean, but without considering ocean surface wave-related physical processes. * In experiment ‘AOW’, WRF, FVCOM, and Simulating WAves Nearshore (SWAN) exchange variables with each other every hour through the OASIS3-MCT Coupler to allow direct and indirect atmosphere-ocean-wave interactions. * Observational data are also included in this dataset (Dropsonde, HRD-Radar, NDBC_wave). All simulations are initialized at 18:00 UTC on August 19, 2021, within a domain encompassing the western North Atlantic Ocean. The atmospheric domain features a horizontal resolution of 3 km. The ocean domain, which covers a substantial portion of the WRF ocean domain, employs an unstructured triangular grid with resolutions ranging from 3 km near the coast to 9 km in the open ocean, effectively resolving the complex coastline of the U.S. Northeast Coast. Initial and boundary conditions for the atmosphere model are obtained from the 6-hourly 0.25° NCEP (National Centers for Environmental Prediction) Global Forecast System (GFS; NCEP, 2015) data.

17 WIND ENERGY↗

High-Resolution Regional Atmosphere–Ocean–Wave Coupled Simulations of Hurricane Henri (2021)

To explore the integrated effects of ocean and ocean surface wave related physical processes on TC simulations, a set of three model simulations is performed. * In experiment 'A', the event is modeled using the Weather Research Forecasting (WRF) model alone with prescribed Sea Surface Temperature (SST) at 6-hour intervals. * In experiment ‘AO,’ WRF is coupled with the Finite Volume Community Ocean Model (FVCOM), enabling variable exchange between atmosphere and ocean, but without considering ocean surface wave-related physical processes. * In experiment ‘AOW’, WRF, FVCOM, and Simulating WAves Nearshore (SWAN) exchange variables with each other every hour through the OASIS3-MCT Coupler to allow direct and indirect atmosphere-ocean-wave interactions. * Observational data are also included in this dataset (Dropsonde, HRD-Radar, NDBC_wave). All simulations are initialized at 18:00 UTC on August 19, 2021, within a domain encompassing the western North Atlantic Ocean. The atmospheric domain features a horizontal resolution of 3 km. The ocean domain, which covers a substantial portion of the WRF ocean domain, employs an unstructured triangular grid with resolutions ranging from 3 km near the coast to 9 km in the open ocean, effectively resolving the complex coastline of the U.S. Northeast Coast. Initial and boundary conditions for the atmosphere model are obtained from the 6-hourly 0.25° NCEP (National Centers for Environmental Prediction) Global Forecast System (GFS; NCEP, 2015) data.

17 WIND ENERGY↗

High-Resolution Regional Atmosphere–Ocean–Wave Coupled Simulations of Hurricane Henri (2021)

To explore the integrated effects of ocean and ocean surface wave related physical processes on tropical cyclone simulations, a set of three model simulations is performed. * In experiment 'A', the event is modeled using the Weather Research Forecasting (WRF) model alone with prescribed Sea Surface Temperature (SST) at 6-hour intervals. * In experiment ‘AO,’ WRF is coupled with the Finite Volume Community Ocean Model (FVCOM), enabling variable exchange between atmosphere and ocean, but without considering ocean surface wave-related physical processes. * In experiment ‘AOW’, WRF, FVCOM, and Simulating WAves Nearshore (SWAN) exchange variables with each other every hour through the OASIS3-MCT Coupler to allow direct and indirect atmosphere-ocean-wave interactions. * Observational data are also included in this dataset (Dropsonde, HRD-Radar, NDBC_wave). All simulations are initialized at 18:00 UTC on August 19, 2021, within a domain encompassing the western North Atlantic Ocean. The atmospheric domain features a horizontal resolution of 3 km. The ocean domain, which covers a substantial portion of the WRF ocean domain, employs an unstructured triangular grid with resolutions ranging from 3 km near the coast to 9 km in the open ocean, effectively resolving the complex coastline of the U.S. Northeast Coast. Initial and boundary conditions for the atmosphere model are obtained from the 6-hourly 0.25° NCEP (National Centers for Environmental Prediction) Global Forecast System (GFS; NCEP, 2015) data. These CSV files are derived from the NetCDF files in the c0 dataset. Unlike the original format, where geographic coordinates were stored in a separate file, each CSV now embeds the corresponding latitude and longitude values alongside the measured or simulated variables.

17 WIND ENERGY↗

Structural Distortions and Short‐Range Magnetism in a Honeycomb Iridate Cu 3 ZnIr 2 O 6

Layered honeycomb iridates receive significant attention in the materials chemistry and physics fields due to the relevance of their crystal structures to the Kitaev model of a quantum spin liquid (QSL). In quest of liquid‐like magnetic ground state signatures, first‐generation alkali metal iridates A 2 IrO 3 ≡ A 3 [AIr 2 ]O 6 (A = Li, Na) and second‐generation iridates T 3 [AIr 2 ]O 6 ( T = Cu, Ag, H) are developed. T 3 [AIr 2 ]O 6 is synthesized from A 3 [AIr 2 ]O 6 via metathesis reactions replacing alkali ions located between honeycomb layers. Herein, the next level of chemical and structural complexity is introduced by synthesizing the honeycomb iridate, Cu 3 ZnIr 2 O 6 , in which alkali ions between and within the honeycomb layers are both selectively exchanged with two different transition metals. Analysis of powder X‐Ray diffraction data reveals corrugation of the honeycomb layers in Cu 3 ZnIr 2 O 6 that hinders complete magnetic frustration and results in a spin glass behavior observed from magnetization and specific heat data. Thus, Cu 3 ZnIr 2 O 6 represents yet another model, which broadens understanding of intricate relationships between intralayer distortions and magnetism of prospective Kitaev QSL compounds.

36 MATERIALS SCIENCE↗

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Deep groundwater↗

Continuum of magnetic excitations in the Kitaev honeycomb iridate D 3 LiIr 2 O 6

Inelastic neutron scattering (INS) measurements of powder D 3 ( 7 Li)( 193 Ir) 2 O 6 reveal low energy magnetic excitations with a scattering cross-section that is broad in ∣Q∣ and energy transfer. The magnetic nature of the excitation spectrum is demonstrated by longitudinally polarized neutron scattering. The total magnetic moment of 1.8(4)μ B /Ir inferred from the observed magnetic scattering cross-section is consistent with the effective moment inferred from magnetic susceptibility data and expectations for the J eff = 1/2 single ion state. The rise in the dynamic correlation function $\mathcal{S}(Q,w)$ for ℏω < 5 meV can be described by a simple model assuming nearest-neighbor anisotropic spin exchange, such as that found in the Kitaev model. Exchange disorder associated with the D site likely plays an important role in stabilizing the low T quantum fluctuating state.

Halloran, Thomas [Johns Hopkins Univ., Baltimore, ↗

A Kinetic Model of the Long-Term Corrosion of Glass-Ceramic Materials

Multiphase waste forms show promise for increased waste loading and for the ability to dispose of contaminated solid and particulate waste through direct densification. However, achieving predictive capability for long-term durability of multiphase waste forms, and thus assessing their possible deployment, requires expanding the current, limited knowledge base. Here, we describe the development of a corrosion model of a two-phase waste form consisting of crystals of known volume fraction embedded in a glass matrix. This model accounts for the dissolution of both the crystalline and glass phases as well as the hydration of the glass phase through an ion exchange reaction. Because of the large difference in solubility between the two phases, the reactive surface of the crystalline phase is a function of the extent of dissolution of the glass phase in this model. Model parameterization was performed using corrosion data, such as from single-pass flow-through tests, for the individual phases. The parameterized corrosion model was evaluated against static dissolution test data for a glass-ceramic multiphase waste form. This evaluation demonstrated the model’s ability to reproduce the time-dependent release of key tracers of glass and crystalline phase dissolution. Hence, the development of a kinetic model provides a pathway for long-term durability predictions and thus the use of multiphase waste forms in nuclear cleanup missions.

Kerisit, Sebastien N.↗

Air temperature, Utqiagvik (Barrow), Alaska, July 15-17, 2022

Air temperature measured at 15 minute intervals using a HOBO 64K Pendant UA-001-64 housed in a solar radiation shield. Data were collected 15-27 July 2022, on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. The data package files are in .csv format, and include a data file and metadata file. These data were collected in support of stomatal response gas exchange measurements collected during this time period. See related data packages for leaf gas exchange data, leaf trait data (leaf mass per area, leaf nitrogen concentration), and phenocamera images collected at the same time and location. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - Simulated Marine Hydrokinetic Tidal Turbine

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset is part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with other energy technologies. This dataset contains inputs and outputs from simulations of a floating marine hydrokinetic turbine over approximately half a tidal cycle (~6.6 hours). Inflow conditions were derived from field measurements in Alaska’s Cook Inlet and represent a tidal environment in which the current speed ramps from near 0 m/s to a peak of 3 m/s and back. The original acoustic doppler current profiler dataset is publicly available on the Marine and Hydrokinetic Data Repository. In a full tidal cycle, the flow reverses and the rotor would reorient; this reversal was not modeled. In the Cook Inlet campaign , turbulence intensity was similar in both directions. Two inflow cases are included. In the first case, labeled “raw” in the files, the measured current time series was used directly in the InflowWind module of OpenFAST. Speed and direction were applied as a function of time and elevation, uniformly in the horizontal direction. With full spatial coherence, this approach captures high turbulent variability and results in pronounced power fluctuations, so it is considered a conservative, near-worst-case representation of loading. In the second case, labeled “average” in the files, a 30-minute moving average was applied to extract the slowly varying mean speed. The residual fluctuations about this mean were used to generate spatially varying, full-field turbulence inputs with TurbSim, giving a more physically realistic representation of the inflow across the rotor disk. Two random realizations were used to produce distinct inflow conditions for two OpenFAST simulations representing a two-turbine array. The same turbulence intensity is applied across the full time series, producing larger fluctuations at the start and end, where the mean speed is low. The second case is the more appropriate framework for performance and power assessment but overpredicts turbulence at lower flow speeds and underpredicts it at higher speeds. As the floating platform moves and the rotor changes its x-position, Taylor’s frozen turbulence hypothesis used by InflowWind assumes a constant rather than a time-varying mean velocity, introducing some inaccuracy in the velocity plane sampling. The turbine modeled is the 500-kW Reference Model 1, a horizontal-axis two-bladed hydrokinetic turbine on a four-column floating semisubmersible substructure . Simulations were performed using OpenFAST v4.1 with the Reference Open Source Controller (ROSCO) v2.10. All input files required to reproduce the simulations are included. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel . This unit supports up to 2.5 MW, but NLR has only a single 1.25-MW stack. The datasets report hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. The system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operating current of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The simulated tidal turbine time series data was translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz. Each zip file represents a single tidal electrolysis experiment and is named: {technology}_{inflow method}_{number of 500 kW tidal turbines connected} For instance, “tidal-500kW-RM1_average_2.zip” is a 6-hour experiment using the 500-kW tidal reference model, scaled by 2x (1-MW) to better match the electrolyzer maximum of 1.25MW, fed with the 30-minute moving average current case. Each zip folder contains the following files: A .csv file of raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wave power. A .csv file combines all tidal profiles as "combined_tidal_experiments.csv." A separate experiment, “characterization_200.zip,” shows the MC250 electrolyzer steady-state response with 30-minute load steps over 5 hours and is accessible with this entry.

08 HYDROGEN↗

CO 2 Sorption in Moisture Swing Anion Exchange Resins for Direct Air Capture: Experimental Isotherm Determination and Modeling

Moisture swing (MS) sorption is a promising direct air capture (DAC) technology to achieve negative CO 2 emissions and counter global warming. Here, in this study, MS CO 2 sorption in a model MS sorbent, IRA900, was investigated. IRA900 is a macroporous commercial strong-base anion exchange resin (AER) with quaternary ammonium functional groups. A rigorous CO 2 desorption process was developed to desorb all CO 2 from the sample and obtain CO 2 sorption isotherms as a function of relative humidity, CO 2 partial pressure, and temperature. CO 2 sorption increased with decreasing relative humidity, decreasing temperature, and increasing CO 2 partial pressure. Remarkably, the total CO 2 uptake from the sorption isotherms matched the ion exchange capacity (IEC) of the material, suggesting a stoichiometry of one CO 2 molecule reacting per active site. An isotherm model for CO 2 sorption starting with empty sorption sites (i.e., with the AER in the OH – form) described the experimental data well, supporting the hypothesis that the bicarbonate-loaded AER fully unloaded to the OH – state following rigorous CO 2 desorption. CO 2 sorption kinetics were studied, and carbon diffusion coefficients were estimated as a function of %RH and CO 2 partial pressure. Overall, this study provides a roadmap for the systematic evaluation of MS CO 2 DAC materials.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Vegetation Warming Experiment: Environmental Conditions, Utqiagvik (Barrow), Alaska, 2017

Environmental conditions measured in five warming chambers and paired ambient control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska from 22 June - 17 September, 2017. These data were recorded in support of the Zero Power Warming (ZPW) vegetation warming experiment, a series of single season vegetation warming treatments conducted over four years from 2017-2021 (no experiment in 2020). Air temperature and humidity, infrared surface (canopy) temperature, soil temperature, soil moisture, NDVI (normalized difference vegetation index), PRI (photochemical reflectance index), solar radiation and chamber venting were recorded in each chamber at 1 minute intervals. Ambient air temperature, humidity, solar radiation and uplooking PRI and NDVI were measured at a centrally located meteorology station. Vapor pressure deficit (VPD) was calculated and included in the final processed data products. Data has undergone full QA/QC and is presented as 1 minute data, and hourly and daily aggregate data products. This data package includes unprocessed raw data (*.dat files), processed data (*.csv) and metadata including a full description of sensors, calculations and processing (*.csv, *.pdf). See related NGEE-Arctic Vegetation Warming Experiment data packages for leaf-level gas exchange and other leaf trait data; chamber, plot and landscape phenocamera images; thaw depth, and GPS locations of chambers and ambient plots. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Vegetation Warming Experiment: Environmental conditions, Utqiagvik (Barrow), Alaska, 2021

Environmental conditions measured in five warming chambers and paired ambient control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska from 19 June - 17 September, 2021. These data were recorded in support of the Zero Power Warming (ZPW) vegetation warming experiment, a series of single season vegetation warming treatments conducted over four years from 2017-2021 (no experiment in 2020). Air temperature and humidity, infrared surface (canopy) temperature, soil temperature, soil moisture, NDVI (normalized difference vegetation index), PRI (photochemical reflectance index), solar radiation and chamber venting were recorded in each chamber at 1 minute intervals. Ambient air temperature, humidity, solar radiation and uplooking PRI and NDVI were measured at a centrally located meteorology station. Vapor pressure deficit (VPD) was calculated and included in the final processed data products. Data has undergone full QA/QC and is presented as 1 minute data, and hourly and daily aggregate data products. This data package includes unprocessed raw data (*.dat files), processed data (*.csv) and metadata including a full description of sensors, calculations and processing (*.csv, *.pdf). See related NGEE-Arctic "Vegetation Warming Experiment" data packages for leaf-level gas exchange and other leaf trait data; chamber, plot and landscape phenocamera images; thaw depth, and GPS locations of chambers and ambient plots. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Studies of Localization Effects on Transverse Kinematic Imbalance in the GENIE Generator

The forthcoming Deep Underground Neutrino Experiment (DUNE) requires precise modeling of neutrino--nucleus interactions to achieve its neutrino-oscillation measurement goals. GENIE, the Monte Carlo event generator used by DUNE and other Fermilab-based experiments as a central value model for neutrino-nucleus cross sections, exhibits known discrepancies in comparison to MicroBooNE cross section data, especially when considering neutrino scattering events' transverse kinematic imbalance (TKI). For quasielastic-like measurements, the data show a larger transverse missing momentum tail while maintaining a peak similar to that predicted by the baseline GENIE model. Previous attempts at a solution include variations of final-state interaction (FSI) strengths, incorrectly decreasing both the peak and tail, thus leaving the discrepancy unresolved. This work investigates whether inconsistencies between local and global treatments of intranuclear physics contribute to the observed mismodeling. In this work, GENIE FSI routines have been modified to directly utilize intranuclear particle positions, thereby introducing a newly localized momentum treatment in correlation with the nominal density in both the hA and hN intranuclear cascade models for their 2018 and 2025 variants. Meson-exchange-current (MEC) localization was also tested for the hN 2018 model. Comparisons of the intranuclear nucleons' ("scattering center") momentum and its radial dependence validate the localization implementation. However, while such consistent localization produces modest changes in the proton momentum spectrum at low-to-medium momenta, only small changes are observed in the TKI distributions. These changes are insufficient to account for the discrepancy with MicroBooNE data. Although a localized treatment improves the internal consistency of the GENIE model, the origin of the TKI discrepancy remains unresolved by such a solution.

Bulla, Braden [Ctr. Coll., Danville; Fermilab]↗