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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Preserving isohydricity: vertical environmental variability explains Amazon forest water-use strategies

Abstract Increases in hydrological extremes, including drought, are expected for Amazon forests. A fundamental challenge for predicting forest responses lies in identifying ecological strategies which underlie such responses. Characterization of species-specific hydraulic strategies for regulating water-use, thought to be arrayed along an ‘isohydric–anisohydric’ spectrum, is a widely used approach. However, recent studies have questioned the usefulness of this classification scheme, because its metrics are strongly influenced by environments, and hence can lead to divergent classifications even within the same species. Here, we propose an alternative approach positing that individual hydraulic regulation strategies emerge from the interaction of environments with traits. Specifically, we hypothesize that the vertical forest profile represents a key gradient in drought-related environments (atmospheric vapor pressure deficit, soil water availability) that drives divergent tree water-use strategies for coordinated regulation of stomatal conductance (gs) and leaf water potentials (ΨL) with tree rooting depth, a proxy for water availability. Testing this hypothesis in a seasonal eastern Amazon forest in Brazil, we found that hydraulic strategies indeed depend on height-associated environments. Upper canopy trees, experiencing high vapor pressure deficit (VPD), but stable soil water access through deep rooting, exhibited isohydric strategies, defined by little seasonal change in the diurnal pattern of gs and steady seasonal minimum ΨL. In contrast, understory trees, exposed to less variable VPD but highly variable soil water availability, exhibited anisohydric strategies, with fluctuations in diurnal gs that increased in the dry season along with increasing variation in ΨL. Our finding that canopy height structures the coordination between drought-related environmental stressors and hydraulic traits provides a basis for preserving the applicability of the isohydric-to-anisohydric spectrum, which we show here may consistently emerge from environmental context. Our work highlights the importance of understanding how environmental heterogeneity structures forest responses to climate change, providing a mechanistic basis for improving models of tropical ecosystems.

Forestry↗
DOE OSTI2021

Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity

Abstract Earth is currently undergoing a global increase in atmospheric vapor pressure deficit (VPD), a trend which is expected to continue as climate warms. This phenomenon has been associated with productivity decreases in ecosystems and yield penalties in crops, with these losses attributed to photosynthetic limitations arising from decreased stomatal conductance. Such VPD increases, however, have occurred over decades, which raises the possibility that stomatal acclimation to VPD plays an important role in determining plant productivity under high VPD. Furthermore, evidence points to more far‐ranging and complex effects of elevated VPD on plant physiology, extending to the anatomical, biochemical, and developmental levels, which could vary substantially across species. Because these complex effects are typically not considered in modeling frameworks, we conducted a quantitative literature review documenting temperature‐independent VPD effects on 112 species and 59 traits and physiological variables, in order to develop an integrated and mechanistic physiological framework. We found that VPD increase reduced yield and primary productivity, an effect that was partially mediated by stomatal acclimation, and also linked with changes in leaf anatomy, nutrient, and hormonal status. The productivity decrease was also associated with negative effects on reproductive development, and changes in architecture and growth rates that could decrease the evaporative surface or minimize embolism risk. Cross‐species quantitative relationships were found between levels of VPD increase and trait responses, and we found differences across plant groups, indicating that future VPD impacts will depend on community assembly and crop functional diversity. Our analysis confirms predictions arising from the hydraulic corollary to Darcy's law, outlines a systemic physiological framework of plant responses to rising VPD, and provides recommendations for future research to better understand and mitigate VPD‐mediated climate change effects on ecosystems and agro‐systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Separation of overlapping sources in bioacoustic mixtures

Source separation is an important step to study signals that are not easy or possible to record individually. Common methods such as deep clustering, however, cannot be applied to signals of an unknown number of sources and/or signals that overlap in time and/or frequency—a common problem in bioacoustic recordings. This work presents an approach, using a supervised learning framework, to parse individual sources from a spectrogram of a mixture that contains a variable number of overlapping sources. This method isolates individual sources in the time-frequency domain using only one function but in two separate steps, one for the detection of the number of sources and corresponding bounding boxes, and a second step for the segmentation in which masks of individual sounds are extracted. This approach handles the full separation of overlapping sources in both time and frequency using deep neural networks in an applicable manner to other tasks such as bird audio detection. This paper presents method and reports on its performance to parse individual bat signals from recordings containing hundreds of overlapping bat echolocation signals. This method can be extended to other bioacoustic recordings with a variable number of sources and signals that overlap in time and/or frequency.

Izadi, Mohammad Rasool↗
DOE OSTI2025

CareWELL: Multimodal Region Representation Learning with Spatial Contexts for Urban Health

Rapid urbanization affects living environments by intensifying exposure to air pollution, heat, noise, and urban dynamics, which together contribute to uneven health outcomes across neighborhoods. For instance, cardiovascular, respiratory, and mental health conditions are each influenced by distinct exposures such as air pollution, extreme temperatures, or limited access to green space. These heterogeneous patterns require understanding the characteristics of geographic regions in order to explain why urban health risks vary across urban areas. Recent work in self-supervised region representation learning provides a promising way to model such characteristics from multimodal geospatial data. However, existing methods face two major limitations: (i) they often depend on non-public datasets, limiting reproducibility and applicability, and (ii) their generic pretraining objectives overlook health-relevant determinants, including temporal variability in environmental exposures and inequalities in social conditions. To address these gaps, we propose Context-Aware Region rEpresentation with Weather, Environment, and Location Learning (CareWELL). CareWELL leverages large language models to encode seasonal variability in weather, employs contrastive learning to align geo-coordinate and weather representations, and introduces a context-aware objective that integrates socio-demographic factors while preserving spatial correlations. We evaluate CareWELL by predicting six urban health outcomes in Manhattan, New York City, and demonstrate that CareWELL consistently outperforms state-of-the-art baselines as well as a traditional spatial computing method. These results suggest the importance of context-aware pretraining objectives for learning health-relevant region representations.

Namgung, Min [ORNL]↗
DOE OSTI2024

III-nitride m-plane violet narrow ridge edge-emitting laser diodes with sidewall passivation using atomic layer deposition

A novel deep-ridge laser structure with atomic-layer deposition (ALD) sidewall passivation was proposed that enhances the optical characteristics of 8-µm ridge width III-nitride violet lasers on freestanding m -plane GaN substrates. The internal loss was determined using the variable stripe length method, where the laser structure with ALD sidewall passivation showed lower internal loss compared to the conventional shallow-ridge laser design. ALD sidewall passivation plays a critical role in device improvements; compared to the lasers without ALD sidewall passivation, the lasers with ALD sidewall passivation yield improved optoelectrical performance and longer lifetime under continuous-wave operation at high current density. This work demonstrates the importance of ALD sidewall passivation to laser performance, which enables high energy efficiency.

Wong, Matthew S.↗
DOE OSTI2023

Exploring unsupervised top tagging using Bayesian inference

Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Beyond Standard Model physics as well. Although there exists outstanding top-tagger algorithms, their construction and their expected performance rely on Montecarlo simulations, which may induce potential biases. For these reasons we develop two simple unsupervised top-tagger algorithms based on performing Bayesian inference on a mixture model. In one of them we use as the observed variable a new geometrically-based observable \tilde{A}_{3} A ̃ 3 , and in the other we consider the more traditional \tau_{3}/\tau_{2} τ 3 / τ 2 N N -subjettiness ratio, which yields a better performance. As expected, we find that the unsupervised tagger performance is below existing supervised taggers, reaching expected Area Under Curve AUC \sim 0.80-0.81 ∼ 0.80 − 0.81 and accuracies of about 69% - − 75% in a full range of sample purity. However, these performances are more robust to possible biases in the Montecarlo that their supervised counterparts. Our findings are a step towards exploring and considering simpler and unbiased taggers.

Alvarez, Ezequiel↗
DOE OSTI2020

Quasi-stationary Planetary Scale Waves in Modern Climate Models

We evaluate climate model simulations of geopotential height at the largest space-time scales, considering the first Fourier components (in longitude) of long-term climatological means. Quasi-stationary planetary scale waves emerge from this analysis. Variations of these waves are important components of climate change, for example associated with regional drought. Systematic examination of their long-term climatology provides context for understanding their evolution in time. We compare four reanalysis datasets with “historical runs” from the latest version of the Coupled Model Intercomparison Project (CMIP) and find general agreement, but we also identify model errors outside the range of both observational uncertainty and the uncertainty implied by interannual variability. Errors in wave phase have possible implications for climate model projections of regional climate and of shifting weather patterns. One model in the set we examine, originally created nearly 20 years before the others, exhibits noticeably greater errors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Science Plan for the Deployment of the Third ARM Mobile Facility to the Southeastern United States at the Bankhead National Forest, Alabama (AMF3 BNF)

In 2018, the U.S. Department of Energy (DOE) held a workshop for the Atmospheric Radiation Measurement (ARM) (Mather and Voyles 2013) user facility to discuss critical climate challenges and locations where key ARM Mobile Facility (AMF) observational assets could impact Earth system modeling (ESM). As an outcome, the southeast United States (SE U.S.) was identified as a high-priority region to target climate-process studies that promote a deeper understanding of the climate system and bolster ARM interactions with the community to drive ESM advancement. The DOE ARM user facility is a globally recognized leader in deploying and operating strategically located observation sites around the world for studying the properties of aerosols and clouds and their interaction with radiation, precipitation, and the Earth’s surface. In partnering with the DOE Atmospheric System Research (ASR) program, ARM solicited a multi-agency Site Science Team approach to provide input and close interaction with ARM management towards a successful SE U.S. deployment of the ARM third Mobile Facility (AMF3) (Miller et al. 2016). These efforts included identifying key locations, science drivers and instruments, and measurement strategies to address the wider climate-process needs and ESM improvement. Community input served a vital role in establishing, refining, and informing the relevant drivers and decisions regarding this AMF3 deployment. The team has identified Northern Alabama (N. AL) as regionally representative to unlock the key opportunities that will improve our understanding and model representation of aerosol, cloud, and land-surface processes and their couplings in the SE U.S. A defining aspect of the AMF3 deployment is its commitment to long-term (anticipated five-year) observations to mitigate potential seasonal-to-annual variability that often limits appropriate attribution of phenomena to local or larger-scale processes. The proposed location may leverage nearby surface networks and multi-agency and partner assets to enrich this multi-year deployment. One motivation is to understand the role of spatiotemporal variability (thermodynamic, land-surface) across aspects of the climate system, with our AMF3 team anticipating future demands on characterizing the relationships between local-to-regional cloud development and surface processes across a diverse patchwork of natural, managed, and urban landscapes as found throughout the N. AL regions. The main site targets an intact, representative, forested region – the Bankhead National Forest (BNF) – underscoring further team commitment to regionally important land-atmosphere two-way interactive studies “from the canopy to the clouds”, with enhanced tower instrumentation augmenting traditional ARM capabilities adjacent to this site. Multiple supplemental sites will also be distributed across this region, prioritizing added needs for biodiversity. Anticipated high-priority cloud science themes will target N. AL as a regional SE U.S. hotbed for high-impact weather, convective cloud onset, and shallow-to-deep cloud transitioning. Anticipated aerosol drivers will focus on chemical processes that control the evolution of organic aerosol, the seasonality and spatial distribution of water vapor and particle-phase water, and its role on aerosol optical properties. Anticipated land-atmosphere drivers consider the two-way feedbacks between surface influence on aerosols, clouds, and precipitation properties and the associated radiative impacts on plant physiology and canopy-scale fluxes. Emphasis will include the study of the impact of surface processes on aerosols via precursor emission, and on clouds via moisture flux and thermal development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements

Cloud condensation nuclei (CCN) number concentrations are an important aspect of aerosol–cloud interactions and the subsequent climate effects; however, their measurements are very limited. We use a machine learning tool, random decision forests, to develop a random forest regression model (RFRM) to derive CCN at 0.4 % supersaturation ([CCN0.4]) from commonly available measurements. The RFRM is trained on the long-term simulations in a global size-resolved particle microphysics model. Using atmospheric state and composition variables as predictors, through associations of their variabilities, the RFRM is able to learn the underlying dependence of [CCN0.4] on these predictors, which are as follows: eight fractions of PM 2.5 (NH 4 , SO 4 , NO 3, secondary organic aerosol (SOA), black carbon (BC), primary organic carbon (POC), dust, and salt), seven gaseous species (NO x , NH 3 , O 3 , SO 2 , OH, isoprene, and monoterpene), and four meteorological variables (temperature (T), relative humidity (RH), precipitation, and solar radiation). The RFRM is highly robust: it has a median mean fractional bias (MFB) of 4.4 % with ≈96.33 % of the derived [CCN0.4] within a good agreement range of -60% 2.5 speciation (NH 4 , SO 4 , NO 3 , and organic carbon (OC)), NO x , O 3 , SO 2 , T, and RH, as well as [CCN0.4] are available. We modify, optimize, and retrain the developed RFRM to make predictions from 19 to 9 of these available predictors. This retrained RFRM (RFRM-ShortVars) shows a reduction in performance due to the unavailability and sparsity of measurements (predictors); it captures the [CCN0.4] variability and magnitude at SGP with ≈67.02 % of the derived values in the good agreement range. This work shows the potential of using the more commonly available measurements of PM 2.5 speciation to alleviate the sparsity of CCN number concentrations' measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Automated identification of local contamination in remote atmospheric composition time series

Abstract. Atmospheric observations in remote locations offer a possibility of exploring trace gas and particle concentrations in pristine environments. However, data from remote areas are often contaminated by pollution from local sources. Detecting this contamination is thus a central and frequently encountered issue. Consequently, many different methods exist today to identify local contamination in atmospheric composition measurement time series, but no single method has been widely accepted. In this study, we present a new method to identify primary pollution in remote atmospheric datasets, e.g., from ship campaigns or stations with a low background signal compared to the contaminated signal. The pollution detection algorithm (PDA) identifies and flags periods of polluted data in five steps. The first and most important step identifies polluted periods based on the derivative (time derivative) of a concentration over time. If this derivative exceeds a given threshold, data are flagged as polluted. Further pollution identification steps are a simple concentration threshold filter, a neighboring points filter (optional), a median, and a sparse data filter (optional). The PDA only relies on the target dataset itself and is independent of ancillary datasets such as meteorological variables. All parameters of each step are adjustable so that the PDA can be “tuned” to be more or less stringent (e.g., flag more or fewer data points as contaminated). The PDA was developed and tested with a particle number concentration dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic. Using strict settings, we identified 62 % of the data as influenced by local contamination. Using a second independent particle number concentration dataset also collected during MOSAiC, we evaluated the performance of the PDA against the same dataset cleaned by visual inspection. The two methods agreed in 94 % of the cases. Additionally, the PDA was successfully applied to a trace gas dataset (CO2), also collected during MOSAiC, and to another particle number concentration dataset, collected at the high-altitude background station Jungfraujoch, Switzerland. Thus, the PDA proves to be a useful and flexible tool to identify periods affected by local contamination in atmospheric composition datasets without the need for ancillary measurements. It is best applied to data representing primary pollution. The user-friendly and open-access code enables reproducible application to a wide suite of different datasets. It is available at https://doi.org/10.5281/zenodo.5761101 (Beck et al., 2021).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations

Photosynthesis plays an important role in carbon, nitrogen, and water cycles. Ecosystem models for photosynthesis are characterized by many parameters that are obtained from limited in situ measurements and applied to the same plant types. Previous site-by-site calibration approaches could not leverage big data and faced issues like overfitting or parameter non-uniqueness. Here we developed an end-to-end programmatically differentiable (meaning gradients of outputs to variables used in the model can be obtained efficiently and accurately) version of the photosynthesis process representation within the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) model. As a genre of physics-informed machine learning (ML), differentiable models couple physics-based formulations to neural networks (NNs) that learn parameterizations (and potentially processes) from observations, here photosynthesis rates. We first demonstrated that the framework was able to correctly recover multiple assumed parameter values concurrently using synthetic training data. Then, using a real-world dataset consisting of many different plant functional types (PFTs), we learned parameters that performed substantially better and greatly reduced biases compared to literature values. Further, the framework allowed us to gain insights at a large scale. Our results showed that the carboxylation rate at 25 °C (V c,max25 ) was more impactful than a factor representing water limitation, although tuning both was helpful in addressing biases with the default values. This framework could potentially enable substantial improvement in our capability to learn parameters and reduce biases for ecosystem modeling at large scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Science Plan for the Deployment of the Third ARM Mobile Facility to the Southeastern United States at the Bankhead National Forest, Alabama (AMF3 BNF)

In 2018, the U.S. Department of Energy (DOE) held a workshop for the Atmospheric Radiation Measurement (ARM) (Mather and Voyles 2013) user facility to discuss critical climate challenges and locations where key ARM Mobile Facility (AMF) observational assets could impact Earth system modeling (ESM). As an outcome, the southeast United States (SE U.S.) was identified as a high-priority region to target climate-process studies that promote a deeper understanding of the climate system and bolster ARM interactions with the community to drive ESM advancement. The DOE ARM user facility is a globally recognized leader in deploying and operating strategically located observation sites around the world for studying the properties of aerosols and clouds and their interaction with radiation, precipitation, and the Earth’s surface. In partnering with the DOE Atmospheric System Research (ASR) program, ARM solicited a multi-agency Site Science Team approach to provide input and close interaction with ARM management towards a successful SE U.S. deployment of the ARM third Mobile Facility (AMF3) (Miller et al. 2016). These efforts included identifying key locations, science drivers and instruments, and measurement strategies to address the wider climate-process needs and ESM improvement. Community input served a vital role in establishing, refining, and informing the relevant drivers and decisions regarding this AMF3 deployment. The team has identified Northern Alabama (N. AL) as regionally representative to unlock the key opportunities that will improve our understanding and model representation of aerosol, cloud, and land surface processes and their couplings in the SE U.S. A defining aspect of the AMF3 deployment is its commitment to long-term (anticipated five-year) observations to mitigate potential seasonal-to-annual variability that often limits appropriate attribution of phenomena to local or larger-scale processes. The proposed location may leverage nearby surface networks and multi-agency and partner assets to enrich this multi-year deployment. One motivation is to understand the role of spatiotemporal variability (thermodynamic, land-surface) across aspects of the climate system, with our AMF3 team anticipating future demands on characterizing the relationships between local-to-regional cloud development and surface processes across a diverse patchwork of natural, managed, and urban landscapes as found throughout the N. AL regions. The main site targets an intact, representative, forested region – the Bankhead National Forest (BNF) – underscoring further team commitment to regionally important land atmosphere two-way interactive studies “from the canopy to the clouds”, with enhanced tower instrumentation augmenting traditional ARM capabilities adjacent to this site. Multiple supplemental sites will also be distributed across this region, prioritizing added needs for biodiversity. Anticipated high-priority cloud science themes will target N. AL as a regional SE U.S. hotbed for high-impact weather, convective cloud onset, and shallow to-deep cloud transitioning. Anticipated aerosol drivers will focus on chemical processes that control the evolution of organic aerosol, the seasonality and spatial distribution of water vapor and particle-phase water, and its role on aerosol optical properties. Anticipated land atmosphere drivers consider the two-way feedbacks between surface influence on aerosols, clouds, and precipitation properties and the associated radiative impacts on plant physiology and canopy-scale fluxes. Emphasis will include the study of the impact of surface processes on aerosols via precursor emission, and on clouds via moisture flux and thermal development.

Doppler lidar, aerosols, convection↗
DOE OSTI2020

An Overview of the State-of-the-Art Reactor Consequence Uncertainty Assessment Accident Progression Insights

The U.S. Nuclear Regulatory Commission (NRC) with Sandia National Laboratories (Sandia) have completed three uncertainty analyses (UAs) as part of the State-of-the-Art Reactor Consequence Analyses (SOARCA) program. The SOARCA UAs included an integrated evaluation of uncertainty in accident progression, radiological release, and offsite health consequence projections. The UA for Peach Bottom, a boiling-water reactor (BWR) with a Mark I containment located in the State of Pennsylvania, analyzed the unmitigated long-term station blackout SOARCA scenario. The UA for Sequoyah, a 4-loop Westinghouse pressurized-water reactor (PWR) located in the State of Tennessee, analyzed the unmitigated short-term station blackout SOARCA scenario, with a focus on issues unique to the ice condenser containment and the potential for early containment failure due to hydrogen deflagration. The UA for Surry, a 3-loop Westinghouse PWR with a sub-atmospheric large dry containment located in the State of Virginia, analyzed the unmitigated short-term station blackout SOARCA scenario including the potential for thermally-induced steam-generator tube rupture. These three UAs are currently documented in three NUREG/CR reports. This report provides input to planned NRC documentation on the insights and findings from the SOARCA UA program. The purpose of the summary report is to provide a useful reference for regulatory applications that require the evaluation of offsite consequence risk from beyond design basis event severe accidents. This report focuses on the accident progression and source term insights developed from the MELCOR analyses. MELCOR is the NRC's best-estimate, severe accident computer code used in the SOARCA UAs. In anticipation of the SOARCA UA insights work, NRC and Sandia benchmarked the response of the Peach Bottom model to selected reference calculations from the Peach Bottom SOARCA UA. Peach Bottom was the first SOARCA UA performed and was completed in 2015 using the MELCOR 1.8.6 code. The PWR SOARCA UAs evolved the original methodology and utilized the updated MELCOR 2.2 computer code. The Peach Bottom model has been systematically updated for other NRC research efforts and has been updated to MELCOR 2.2. computer code. The findings from the new reference calculations using the updated model with the MELCOR 2.2 code are also integrated into the report. A second objective is an assessment of the applicability of the results to the other nuclear reactors in the U.S. As the key findings are reviewed, judgments are presented on the applicability of the results to other U.S. nuclear power plants. An important objective of the SOARCA program relied on high- fidelity plant-specific modeling. However, the nature of the insights and conclusions allowed judgements to be made on the applicability of the various insights to the same general classification of plant (i.e., BWR or PWR) or the entire fleet of plants. Finally, the results from the SOARCA UA accident progression calculations contain a wealth of information not previously documented in the NUREG/CRs. This report includes new but related information that can be used to benchmark past or support future regulatory decisions related to severe accidents. The new work includes a benchmark of the NUREG-1465 licensing source term definitions, the variability of key accident progression events and timing to radionuclide release, and an improved understanding of the timing and source terms from consequential steam generator tube ruptures. iii ACKNOWLEDGEMENTS The Sandia authors gratefully acknowledge the significant technical and programmatic contributions from the NRC SOARCA team which are reflected throughout the report. Dr. Tina Ghosh has been involved throughout the SOARCA UAs, providing the primary managerial and technical oversight. The long lists of NRC and Sandia contributors from the SOARCA UAs are cited in the three NUREG/CRs and are also gratefully acknowledged by the small team of authors compiling the results of their efforts. Significant technical contributions, advice, and reviews were provided by Dr. Hossein Esmaili, Dr. Alfred Hathaway, and Dr. Edward Fuller (retired) of the NRC. Dr. Randal Gauntt (retired), Mr. Patrick Mattie, Mr. Joseph Jones (retired), and Dr. Doug Osborn from Sandia are recognized as the SOARCA UA managers guiding the past efforts. There is a comparable list of project managers at the NRC including Ms. Patricia Santiago, Dr. Salman Haq, and Mr. Jon Barr. Sadly, we have lost Mr. Charlie Tinkler and Mr. Robert Prato, who were important contributors to the original SOARCA project. Finally, Mr. Kyle Ross and Mr. Mark Leonard have also retired but were significant technical contributors. Mr. Kyle Ross was the technical lead on all three SOARCA UAs and the original pressurized water reactor SOARCA study. Mr. Leonard was the technical lead on the original boiling water reactor SOARCA study and a key contributor to the first Peach Bottom SOARCA UA. iv

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

The Role of Electrolyte Composition in Enabling Li Metal‐Iron Fluoride Full‐Cell Batteries

Abstract FeF 3 conversion cathodes, paired with Li metal, are promising for use in next‐generation secondary batteries and offer a remarkable theoretical energy density of 1947 Wh kg −1 compared to 690 Wh kg −1 for LiNi 0.5 Mn 1.5 O 4 ; however, many successful studies on FeF 3 cathodes are performed in cells with a large (>90‐fold) excess of Li that disguises the effects of tested variables on the anode and decreases the practical energy density of the battery. Herein, it is demonstrated that for full‐cell compatibility, the electrolyte must produce both a protective solid‐electrolyte interphase and cathode‐electrolyte interphase and that an electrolyte composed of 1:1.3:3 (m/m) LiFSI, 1,2‐dimethoxyethane, and 1,1,2,2‐tetrafluoroethyl‐2,2,3,3‐tetrafluoropropyl ether fulfills both these requirements. This work demonstrates the importance of verifying electrode level solutions on the full‐cell level when developing new battery chemistries and represents the first full cell demonstration of a Li/FeF 3 cell, with both limited Li and high capacity FeF 3 utilization.

25 ENERGY STORAGE↗
DOE OSTI2022

Mouse models of Ebola virus tolerance and lethality: characterization of CD-1 mice infected with wild-type, guinea pig-adapted, or mouse-adapted virus

Development of lethal models of Ebola virus disease has been achieved by the serial passage of virus isolates from human cases in mice and guinea pigs. Use of mice infected with non-adapted virus has been limited due to the absence of overt clinical disease. In recent years, newly recognized sequelae identified in human cases has highlighted the importance of continued investigations of non-lethal infection both in humans and animal models. Here, we revisit the use of rodent-adapted and non-adapted Ebola virus (EBOV) in mice to investigate infection tolerance and future utility of these models in pathogenesis and therapeutic intervention studies. We found that like non-adapted wild-type EBOV, guinea pig-adapted EBOV resulted in widespread tissue infection, variably associated with tissue pathology, and alterations in clinical and immunological analytes in the absence of overt disease. Notably, infection with either non-lethal variant did not greatly differ from lethal mouse-adapted EBOV until near the time end-point criteria are reached in these mice. Furthermore, these data support future investigations of pathogenesis, convalescence, and sequelae in mouse models of virus tolerance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Characterizing model uncertainties in simulated coast-to-offshore wind over the northeast U.S. using multi-platform measurements from the TCAP field campaign

Numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, are widely used to provide estimates of the offshore wind energy resource owing to their large spatial coverage compared to available observations. Nevertheless, spatiotemporal distribution of model biases is highly dependent on factors including model configuration, location, and the interplay of multi-scale physical processes. Here, in this study, we focus on the characterization of model uncertainties in simulated coast-to-offshore winds over the northeast U.S., by varying sea surface temperature (SST) forcings, surface layer (SL) and planetary boundary layer (PBL) parameterizations, as well as identifying biases that may be directly passed from initial and boundary conditions. Multiple measurements, including aircraft data collected during the U.S. Department of Energy's Two-Column Aerosol Project (TCAP) experiment, are used to constrain the model results and facilitate quantitative comparisons. Our analysis indicates while SST forcing has notable impacts on simulated air temperature and moisture within PBL, the modeled winds are in general more sensitive to the choices of SL and PBL physics than to SST. The model’s forcing data not only controls the vertical dependence of wind speed errors, but also alters regional variability in wind speed’s spatial correlation. Bias comparisons between ERA5 reanalysis and ensemble simulations revealed significant similarity, particularly in wind speed biases during winter, underscoring their dependency on initial and boundary conditions. Coastal and offshore near-surface wind speed biases tend to exhibit much higher similarity in winter than in summer due to the presence of much stronger and more persistent synoptic wind conditions. This study highlights the importance of accurate atmospheric forcing and parameterization choices in improving wind forecasts and suggests the potential for extrapolating coastal wind biases to offshore locations, aiding wind energy forecasting and informing the Wind Forecast Improvement Project-3 (WFIP3).

17 WIND ENERGY↗
DOE OSTI2024

Heterogeneity in Permeability and Particulate Organic Carbon Content Controls the Redox Condition of Riverbed Sediments at Different Timescales

Abstract The hydrological and biogeochemical properties of the hyporheic zone in stream and riverine ecosystems have been extensively studied over the past two decades. Although it is widely acknowledged that sediment heterogeneity can influence biogeochemical reactions, little effort has been made to understand the role of heterogeneity on the spatiotemporal variability of riverbed redox conditions under changing flow dynamics at different timescales. Here we integrate a mechanistic model and field data to demonstrate that heterogeneity in permeability plays a vital role in modulating sediment redox conditions at both seasonal (annual) and event (daily‐to‐weekly) timescales, whereas heterogeneity in particulate organic carbon (POC) content only has a comparable influence on redox conditions at the seasonal timescale. These findings underscore the importance of accurately characterizing sediment heterogeneity, in terms of permeability and POC content, in quantifying biogeochemical dynamics in the riverbed and hyporheic zones of riverine ecosystems.

Geology↗
DOE OSTI2020

Human-induced changes to the global ocean water masses and their time of emergence

The World Ocean is rapidly changing, with global and regional modification of temperature and salinity, resulting in widespread and irreversible impacts. While the most pronounced observed temperature and salinity changes are located in the upper ocean, changes in water masses at depth have been identified and will probably strengthen in the future. In this study, using 11 climate models, we define when anthropogenic temperature and salinity changes are expected to emerge from natural variability in the ocean interior along density surfaces. The models predict that in 2020, 20–55% of the Atlantic, Pacific and Indian basins have an emergent anthropogenic signal; reaching 40–65% in 2050 and 55–80% in 2080. The well-ventilated Southern Ocean water masses emerge very rapidly, as early as the 1980–1990s, while the Northern Hemisphere water masses emerge in the 2010–2030s. Our results highlight the importance of maintaining and augmenting an ocean observing system capable of detecting and monitoring persistent anthropogenic changes.

54 ENVIRONMENTAL SCIENCES↗