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Results for “spatiotemporal correlations”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Imaging Nanoscale Energy Transport and Conversion with Ultrafast Electron Microscopy (Final Technical Report)

Light-matter interactions are ubiquitous in nature and reside at the heart of innumerable technologies. The cascade of processes that occur when a material absorbs a photon of light are exceedingly complex and are interwoven in both space and time, rendering precise determination of the atomic-scale and ultrafast mechanisms immensely challenging. The advent of methods for generating short pulses of light several decades ago led to major advances in understanding the initial moments of light absorption and the resultant effects, though directly interrogating the response of the atoms within the material continued to prove challenging. More recently, methods for generating ultrashort pulses of X-rays and fast electrons have opened the way to probing photoinduced structural dynamics of a wide range of matter in multiple phases. An especially promising laboratory-scale method is ultrafast electron microscopy (UEM), wherein the modalities of conventional transmission electron microscopes (imaging, diffraction, spectroscopy) are extended into the femtosecond temporal regime. Here, the ultrafast imaging and diffraction modalities of UEM were used to study the transient structural aspects of photoexcitation of semiconducting materials (e.g., spatially-resolved electron-phonon coupling, excitation and emission of acoustic phonons, and discrete nanoscale scattering processes). The project had three main objectives: (1) determination of the excitation mechanisms of dense, hypersonic charge-carrier waves and the spatially mediated means by which they couple to the lattice via coherent phonon emission, especially with UEM imaging, wherein effects of nanoscale structural and morphological features on the coupling and relaxation dynamics are expected to dictate nucleation sites and preferred wave vectors, (2) elucidation of photoinduced acoustic-phonon seeding, emergence, propagation, and decay over nanoscale crystal regions and especially with respect to local strain fields and atomic-scale disruptions in lattice order, and (3) interwoven with the first two were efforts aimed at realizing combined angstrom-femtosecond spatiotemporal imaging with UEM. The outcomes and impacts of this project were the generation of new knowledge with respect to fundamental light-matter interactions and, especially, the spatially-mediated excitation and evolution of the structural response of materials following coherent photoexcitation. Importantly, the spatial and temporal resolutions of the UEM imaging modalities used are well-suited for such studies and enable spatially-resolved mechanisms to be determined as a function of atomic order, structural features, and morphology. In addition, ultrafast crystallographic measurements were used to correlate real- and reciprocal-space dynamics in order to determine atomic-scale preferential wave vectors and ultrafast scattering mechanisms, especially as dictated by specimen boundary conditions. The obtained results, as detailed in peer-reviewed publications and presentations, illustrate the importance of ultrafast, angstrom-scale real-space imaging for developing a comprehensive understanding of energy transport and conversion in materials.

47 OTHER INSTRUMENTATION↗
NASA NTRS1999

Nitrogen Species in the Post-Pinatubo Stratosphere: Model Analysis Utilizing UARS Measurements

We present an analysis of the impact of heterogeneous chemistry on the partitioning of nitrogen species measured by the Upper Atmosphere Research Satellite (UARS) instruments. The UARS measurements utilized include N2O, HNO3, and ClONO2 from the cryogenic limb array etalon spectrometer (CLAES), version 7 (v.7), and temperature, methane, ozone, H2O, HCl, NO and NO2 from the halogen occultation experiment (HALOE), version 18. The analysis is carried out for the UARS data obtained between January 1992 and September 1994 in the 100- to 1-mbar (approx. 17-47 km) altitude range and over 10 deg latitude bins from 70 deg S to 70 deg N. The spatiotemporal evolution of aerosol surface area density (SAD) is adopted from analysis of the Stratospheric Aerosol and Gas Experiment (SAGE) II data. A diurnal steady state photochemical box model, constrained by the temperature, ozone, H2O, CH4, aerosol SAD, and columns of O2 and O3 above the point of interest, has been used as the main tool to analyze these data. Total inorganic nitrogen (NOY) is obtained by three different methods: (1) as a sum of the UARS-measured NO, NO2, HNO3, and ClONO2; (2) from the N2O-NOY correlation; and (3) from the CH4-NOY correlation. To validate our current understanding of stratospheric heterogeneous chemistry for post-Pinatubo conditions, the model-calculated monthly averaged NO(x)/NO(y) ratios and the NO, NO2, and HNO3 profiles are compared with the UARS-derived data. In general, the UARS-constrained box model captures the main features of nitrogen species partitioning in the post-Pinatubo years, such as recovery of NO(x) after the eruption, their seasonal variability and vertical profiles. However, the model underestimates the NO2 content, particularly in the 30- to 7-mbar (approx. 23-32 km) range. Comparisons of the calculated temporal behavior of the partial columns of NO2 and HNO3 and ground-based measurements at 45 deg S and 45 deg N are also presented. Our analysis indicates that ground-based and HALOE v.18 measurements of the NO2 vertical columns are consistent within the range of their uncertainties and are systematically higher (up to 50%) than the model results at midlatitudes in both hemispheres. Reasonable agreement is obtained for HNO3 columns at 45 deg S, suggesting some problems with nitrogen species partitioning in the model. Outstanding uncertainties are discussed.

Danilin, Michael Y.↗
NASA NTRS1999

Nitrogen Species in the Post-Pinatubo Stratosphere: Model Analysis Utilizing UARS Measurements

We present an analysis of the impact of heterogeneous chemistry on the partitioning of nitrogen species measured by the Upper Atmosphere Research Satellite (UARS) instruments. The UARS measurements utilized include N2O, HNO3, and ClONO2 from the cryogenic limb array etalon spectrometer (CLAES), version 7 (v.7), and temperature, methane, ozone, H2O, HCl, NO and NO2 from the halogen occultation experiment (HALOE), version 18. The analysis is carried out for the UARS data obtained between January 1992 and September 1994 in the 100-to 1-mbar (approx. 17-47 km) altitude range and over 10 degrees latitude bins from 70 S to 70 N. The spatiotemporal evolution of aerosol surface area density (SAD) is adopted from analysis of the Stratospheric Aerosol and Gas Experiment (SAGE) II data. A diurnal steady state photochemical box model, constrained by the temperature, ozone, H2O, CH4, aerosol SAD, and columns of O2 and O3 above the point of interest, has been used as the main tool to analyze these data. Total inorganic nitrogen (NOy) is obtained by three different methods: (1) as a sum of the UARS-measured NO, NO2, HNO3, and ClONO2; (2) from the N2O-NOy correlation, and (3) from the CH4-NOy correlation. To validate our current understanding of stratospheric heterogeneous chemistry for post-Pinatubo conditions, the model-calculated monthly averaged NOx/NOy ratios and the NO, NO2, and HNO3 profiles are compared with the UARS-derived data. In general, the UARS-constrained box model captures the main features of nitrogen species partitioning in the post-Pinatubo years, such as recovery of NOx after the eruption, their seasonal variability and vertical profiles. However, the model underestimates the NO2 content, particularly in the 30- to 7-mbar (approx.23-32 km) range. Comparisons of the calculated temporal behavior of the partial columns of NO2 and HNO3 and ground-based measurements at 45 S and 45 N are also presented. Our analysis indicates that ground-based and HALOE v.18 measurements of the NO2 vertical columns are consistent within the range of their uncertainties and are systematically higher (up to 50%) than the model results at midlatitudes in both hemispheres. Reasonable agreement is obtained for HNO3 columns at 45 S, suggesting some problems with nitrogen species partitioning in the model. Outstanding uncertainties are discussed.

Danilin, Michael Y.↗
DOE OSTI2023

Historical spatiotemporal changes in fire danger potential across biomes

This study 1) identifies the seasons and biomes that exhibit significant (1980–2019) changes in fire danger potential, as quantified by the Canadian Fire Weather Index (FWI); 2) explores what types of fire behavior potentials may be contributing to changes in fire danger potential, as quantified by the United States Energy Release Component (ERC) and the Ignition Component (IC); 3) provides spatiotemporal insight on how fire danger potential and fire behavior potential are responding in relation to changes in seasonal precipitation totals and seasonal mean air temperature across biomes. Time series of these fire potentials, as well as seasonal mean temperature, and seasonal precipitation totals are generated using data from the 0.25° ECMWF spatial resolution Reanalysis 5th Generation (ERA5) and the Climatic Research Unit gridded Time Series (CRU TS). The Mann-Kendall test is then applied to identify significant spatiotemporal trends across each biome. Results indicate that the September–November season (SON) exhibits the greatest rate of increase in fire danger potential, followed by the June–August season (JJA), December, January–February season (DJF), and March–May season (MAM), and this is predominant over the Tropical and Subtropical Moist Broadleaf Forest Biome, as well as all vegetation types of the temperate biomes. Similarly, the temperate biomes experience the greatest rate of increase in fire intensity potential and ignition potential, but prevalent during the DJF and MAM seasons. Furthermore, there is a significant positive correlation between fire danger potential and seasonal mean air temperature during JJA in the Northern Hemisphere for the temperate biomes in North America and Europe, as well as the Tropical and Subtropical biomes in Africa. Our analysis provides quantitative insight as to how fire danger potential and fire behavior potential have been responding to changes in seasonal mean temperature and seasonal precipitation totals across different ecoregions around the world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Widespread underestimation of rain-induced soil carbon emissions from global drylands

Dryland carbon fluxes, particularly those driven by ecosystem respiration, are highly sensitive to water availability and rain pulses. However, the magnitude of rain-induced carbon emissions remains unclear globally. Here we quantify the impact of rain-pulse events on the carbon balance of global drylands and characterize their spatiotemporal controls. Using eddy-covariance observations of carbon, water and energy fluxes from 34 dryland sites worldwide, we produce an inventory of over 1,800 manually identified rain-induced CO2 pulse events. Based on this inventory, a machine learning algorithm is developed to automatically detect rain-induced CO2 pulse events. Our findings show that existing partitioning methods underestimate ecosystem respiration and photosynthesis by up to 30% during rain-pulse events, which annually contribute 16.9 ± 2.8% of ecosystem respiration and 9.6 ± 2.2% of net ecosystem productivity. We show that the carbon loss intensity correlates most strongly with annual productivity, aridity and soil pH. Finally, we identify a universal decay rate of rain-induced CO2 pulses and use it to bias-correct respiration estimates. Our research highlights the importance of rain-induced carbon emissions for the carbon balance of global drylands and suggests that ecosystem models may largely underrepresent the influence of rain pulses on the carbon cycle of drylands.

Nguyen, Ngoc B↗
DOE OSTI2026

Dependence of Deep Convective Cell Properties on Meteorological and Aerosol Conditions during TRACER

Deep convective cells significantly influence Earth’s energy balance and water cycle. However, their accurate representation in numerical models remains challenging due to their small spatiotemporal scales and limited observational constraints. This study examines over ∼400 deep convective cells near Houston, observed by a dual-polarization C-band radar during the Tracking Aerosol Convection Interactions Experiment (TRACER) intensive observation period (June–September 2022). Cells are categorized by lifetime into short-lived (<40 min), intermediate-lived (40–80 min), and long-lived (80+ min) groups. Long-lived cells were broader (∼13.2 km at 2–4-km height) and deeper (∼11.4 km) than short-lived cells (∼6.4-km width, ∼7.31-km height). Using random forest (RF) modeling and correlation analyses, precipitable water vapor (PWV), 2–6-km lapse rate, 0–8-km bulk shear, and fine aerosol mass concentration (Mass_f) are identified as key predictors of cell lifetime. Higher PWV is associated with significantly longer convective cell lifetimes compared to the low-PWV group, particularly within low 2–6-km temperature lapse rate (LR_26km), moderate-to-higher 0–8-km bulk shear (BS_08km), and low-to-moderate Mass_f environments. RF analysis also identifies low-level (0–2 km) equivalent potential temperature, PWV, Mass_f, and surface latent heat flux as key predictors for cell width and height. Short-lived cells have higher aerosol number concentrations (500–1000-nm size range), linked to onshore wind conditions and marine aerosols; however, their low concentration suggests the sensitivity may reflect associated meteorological regimes rather than a direct aerosol effect. Long-lived cells have higher concentrations of organic and sulfate aerosols, while short-lived cells exhibit higher black carbon concentrations. These results highlight the intricate dependence of convective cell lifetimes and structure on environmental moisture, thermodynamics, wind shear, and aerosol characteristics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations

We provide a global, long-term carbon flux dataset of gross primary production and ecosystem respiration generated using meta-learning, called MetaFlux. The idea behind meta-learning stems from the need to learn efficiently given sparse data by learning how to learn broad features across tasks to better infer other poorly sampled ones. Using meta-trained ensemble of deep models, we generate global carbon products on daily and monthly timescales at a 0.25-degree spatial resolution from 2001 to 2021, through a combination of reanalysis and remote-sensing products. Site-level validation finds that MetaFlux ensembles have lower validation error by 5–7% compared to their non-meta-trained counterparts. In addition, they are more robust to extreme observations, with 4–24% lower errors. We also checked for seasonality, interannual variability, and correlation to solar-induced fluorescence of the upscaled product and found that MetaFlux outperformed other machine-learning based carbon product, especially in the tropics and semi-arids by 10–40%. Overall, MetaFlux can be used to study a wide range of biogeochemical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Development of observation-based global multilayer soil moisture products for 1970 to 2016

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5∘ resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from −0.044 to 0.033 m3 m−3, root mean square errors from 0.076 to 0.104 m3 m−3, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Ultrafast electron diffraction: Visualizing dynamic states of matter

Since the discovery of electron-wave duality, electron scattering instrumentation has developed into a powerful array of techniques for revealing the atomic structure of matter. Beyond detecting local lattice variations in equilibrium structures with the highest possible spatial resolution, recent research efforts have been directed toward the long-sought-after dream of visualizing the dynamic evolution of matter in real time. The atomic behavior at ultrafast timescales carries critical information on phase transition and chemical reaction dynamics, the coupling of electronic and nuclear degrees of freedom in materials and molecules, and the correlation among structure, function, and previously hidden metastable or nonequilibrium states of matter. Ultrafast electron pulses play an essential role in this scientific endeavor, and their generation has been facilitated by rapid technical advances in both ultrafast laser and particle accelerator technologies. Here this review presents a summary of the noteworthy developments in this field in the last few decades. The physics and technology of ultrafast electron beams is presented with an emphasis on the figures of merit most relevant for ultrafast electron diffraction experiments. Recent developments in the generation, manipulation, and characterization of ultrashort electron beams aimed at improving the combined spatiotemporal resolution of these measurements are discussed. The fundamentals of electron scattering from atomic matter and the theoretical frameworks for retrieving dynamic structural information from solid-state and gas-phase samples is described. Essential experimental techniques and several landmark works that have applied these approaches are also highlighted to demonstrate the widening applicability of these methods. Ultrafast electron probes with ever-improving capabilities, combined with other complementary photon-based or spectroscopic approaches, hold tremendous potential for revolutionizing our ability to observe and understand energy and matter at atomic scales.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Climate lags and genetics determine phenology in quaking aspen ( Populus tremuloides )

Spatiotemporal patterns of phenology may be affected by mosaics of environmental and genetic variation. Environmental drivers may have temporally lagged impacts, but patterns and mechanisms remain poorly known. Here, we combine multiple genomic, remotely sensed, and physically modeled datasets to determine the spatiotemporal patterns and drivers of canopy phenology in quaking aspen, a widespread clonal dioecious tree species with diploid and triploid cytotypes. We show that over 391 km 2 of southwestern Colorado: greenup date, greendown date, and growing season length vary by weeks and differ across sexes, cytotypes, and genotypes; phenology has high phenotypic plasticity and heritabilities of 31–61% (interquartile range); and snowmelt date, soil moisture, and air temperature predict phenology, at temporal lags of up to 3 yr. Our study shows that lagged environmental effects are needed to explain phenological variation and that the effect of cytotype on phenology is obscured by its correlation with topography. Phenological patterns are consistent with responses to multiyear accumulation of carbon deficit or hydraulic damage.

carbon allocation↗
NASA NTRS2003

The relation of motion sickness to the spatial-temporal properties of velocity storage

Tilting the head in roll to or from the upright while rotating at a constant velocity (roll while rotating, RWR) alters the position of the semicircular canals relative to the axis of rotation. This produces vertical and horizontal nystagmus, disorientation, vertigo, and nausea. With recurrent exposure, subjects habituate and can make more head movements before experiencing overpowering motion sickness. We questioned whether promethazine lessened the vertigo or delayed the habituation, whether habituation of the vertigo was related to the central vestibular time constant, i.e., to the time constant of velocity storage, and whether the severity of the motion sickness was related to deviation of the axis of eye velocity from gravity. Sixteen subjects received promethazine and placebo in a double-blind, crossover study in two consecutive 4-day test series 1 month apart, termed series I and II. Horizontal and vertical eye movements were recorded with video-oculography while subjects performed roll head movements of approx. 45 degrees over 2 s to and from the upright position while being rotated at 138 degrees /s around a vertical axis. Motion sickness was scaled from 1 (no sickness) to an endpoint of 20, at which time the subject was too sick to continue or was about to vomit. Habituation was determined by the number of head movements that subjects made before reaching the maximum motion sickness score of 20. Head movements increased steadily in each session with repeated testing, and there was no difference between the number of head movements made by the promethazine and placebo groups. Horizontal and vertical angular vestibulo-ocular reflex (aVOR) time constants declined in each test, with the declines being closely correlated to the increase in the number of head movements. The strength of vertiginous sensation was associated with the amount of deviation of the axis of eye velocity from gravity; the larger the deviation of the eye velocity axis from gravity, the more severe the motion sickness. Thus, promethazine neither reduced the nausea associated with RWR, nor retarded or hastened habituation. The inverse relationship between the aVOR time constants and number of head movements to motion sickness, and the association of the severity of motion sickness with the extent, strength, and time of deviation of eye velocity from gravity supports the postulate that the spatiotemporal properties of velocity storage, which are processed between the nodulus and uvula of the vestibulocerebellum and the vestibular nuclei, are likely to represent the source of the conflict responsible for producing motion sickness.

NASA Discipline Neuroscience↗
DOE OSTI2026

A machine learning pipeline for identifying infiltration managed aquifer recharge locations from satellite imagery in the San Joaquin Valley, California

This study focuses on an agricultural region in California’s Central Valley, USA, where Managed Aquifer Recharge (MAR) is widely implemented to mitigate groundwater depletion under increasing water demand and climate variability. A deep learning and machine learning framework was developed to identify infiltration-MAR locations using satellite imagery and environmental data. The framework integrates surface water detection from Sentinel-2 imagery, geospatial delineation of water bodies, spatiotemporal tracking of water body dynamics, and supervised classification using meteorological, environmental, and topographic variables. The framework was applied to a 2379 km² study area southwest of Fresno, where 765 water bodies were detected, including 139 identified MAR sites based on publicly available datasets and expert knowledge. The classification model achieved an accuracy of 0.94 and an F1 score of 0.85. Feature importance analysis indicates that cropland, normalized difference vegetation index (NDVI), and evaporation are among the most influential predictors for infiltration-MAR. Notably, the framework suggests that engineered water management in infiltration-MAR systems can disrupt or even reverse the expected positive correlation between surface water extent and precipitation. These findings provide physically interpretable insights into the characteristics of existing infiltration-MAR facilities and demonstrate the potential of the proposed framework as a reproducible, interpretable, and potentially transferable tool for data-driven infiltration-MAR identification and inventory development under growing climatic and hydrological uncertainty.

Classification↗
DOE OSTI2024

Geophysical methods reveal the soil architecture and subsurface stratigraphic heterogeneities across land-lake interfaces along Lake Erie

Abstract Purpose The land-lake interface is a unique zone where terrestrial and aquatic ecosystems meet, forming part of the Earth’s most geochemically and biologically active zones. The unique characteristics of this interface are yet to be properly understood due to the inherently high spatiotemporal variability of subsurface properties, which are difficult to capture with the traditional soil sampling methods. Geophysical methods offer non-invasive techniques to capture variabilities in soil properties at a high resolution across various spatiotemporal scales. Methods We combined electromagnetic induction (EMI), electrical resistivity tomography (ERT), and ground penetrating radar (GPR) with data from soil cores and in situ sensors to investigate hydrostratigraphic heterogeneities across land-lake interfaces along the western basin of Lake Erie. Results EMI revealed high spatial heterogeneities in ECa distribution across the land-lake interfaces, with higher values in the wetland and transition zones compared to the upland zone. Soil ECa maps matched soil maps from a public database with the hydric soil units delineated as high conductivity zones (ECa > 40 mS/m). ERT and GPR showed vertical variation in soil properties with clear stratigraphic boundaries, and correlation of ERT profiles with lithologs from piezometers revealed the stratigraphic units of silt–clay and till sequence down to 3.5 m depth which are consistent with the surficial geology of the study area. Conclusions These results validate the use of multiple geophysical methods for extrapolating soil properties and mapping stratigraphic structures at land-lake interfaces, thereby providing the missing information required to improve the earth system model (ESM) of coastal interfaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Spatiotemporal and Statistical Mapping of Transition Metal Equilibria in Alkaline Media

Transition metal dissolution and redeposition (D/R) kinetics in alkaline media play a critical role in various chemical and electrochemical processes. Competitive reaction kinetics between different transition metals can modulate individual metal behavior in these processes. To date, these phenomena have remained largely unmeasured, and even when captured, they are difficult to statistically characterize due to their dynamic nature, simultaneous occurrence, and spatially heterogeneous nature. Here, in this study, we develop a statistical analysis framework based on in situ and operando X-ray fluorescence microscopy (XFM) to investigate the relative D/R kinetics of multiple transition metals in alkaline media. By employing statistical analysis, we quantify the spatial distribution of D/R species and assess the rate at which the system reaches equilibrium under varying reaction conditions. We show that pH does not simply change the rate of dissolution and redeposition, but reorganizes the cross-element kinetic correlations among Ni, Fe, and Mn and accelerates the spatial equilibration of D/R events, as quantified through correlation analysis, reaction-rate estimation, probability function distributions, and texture-based monitoring statistics. Additionally, we demonstrate how modifying the solvent environment can influence D/R kinetics, providing a pathway for tuning materials synthesis and process optimization. Our study offers valuable insights into the complex interplay between different transition metals and provides a reliable statistical framework for spatial analysis of diverse imaging data sets, enabling deeper extraction of latent information across multiple modalities.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Lethality is Local, but Survival is Systemic: Temporal and Multi-Organ Responses to Chlorine Gas Exposure in a Murine Model

Chlorine gas (Cl2) is a highly toxic chemical associated with both localized lung injury and systemic health effects. While pulmonary damage has been well characterized, the systemic inflammatory and metabolic responses remain poorly understood. We aimed to define the temporal and multi-organ responses to Cl2 exposure in a murine model, with a focus on identifying spatiotemporal inflammation and its impact on survival and lethality. SKH1 mice were exposed for 10 min to varying concentrations of Cl2 (94.4–810 ppm, representative of non-lethal, LD10, and LD50 doses) and monitored for respiratory function, perfusion, and acidosis using organ-specific imaging. At multiple time points (40 min, 6 h, 24 h, and 7 d), we measured phosphoproteins, cytokines, chemokines, growth factors, and metabolic hormones in the lungs, heart, cortex, and plasma. Statistical modeling and logistic regression were used to identify biomarkers associated with lethality and survival. We found that lung injury was the primary cause of potential lethality, particularly via early phosphoprotein signaling disruptions. However, survival correlated with early systemic coordination of inflammatory and metabolic signals across organs. Perfusion and acidosis imaging were strongly associated with chemokine and hormone responses. Key survival-associated plasma biomarkers included decreased insulin, increased ghrelin, and decreased eotaxin. While potential lethality from Cl2 exposure is locally driven by pulmonary injury, survival depends on systemic, multi-organ responses that occur rapidly post-exposure. Within this model, our findings identify a potential therapeutic window to enhance survival and suggest candidate biomarkers that may be explored translationally for both triage and treatment of chlorine-related incidents.

chlorine gas↗
DOE OSTI2022

Laser-Assisted Synthesis of Monolayer 2D MoSe 2 Crystals with Tunable Vacancy Concentrations: Implications for Gas and Biosensing

We report tuning the structural and electronic properties of atomically thin two-dimensional (2D) materials via defect and vacancy engineering is the key to enabling their potential use in various applications, including electronics, energy, and sensing devices. Vacancies are, for instance, becoming highly promising for the enhanced interaction of gases and biomolecules with 2D materials in energy and sensing applications. However, the deterministic generation of desirable vacancies with tunable concentrations remains a challenge in 2D materials due to the limitations in the current growth methods, such as the complex reaction chemistries and gas flow dynamics. Therefore, engineering defects and vacancies in 2D materials have been mainly limited to destructive top-down processes such as heating, ion bombardments, and laser postprocessing. Here, we introduce a single-step bottom-up synthesis approach for the growth of monolayer MoSe 2 crystals with tunable vacancy concentrations. This method utilizes the spatiotemporal properties and adjustable power densities of the lasers to control the vaporization dynamics of the stoichiometric MoSe 2 powders. Such a mechanism in the vaporization allows us to grow tunable stoichiometry monolayer MoSe 2–x crystals on the substrates. The localized and time-controlled (250 ms to 2 s) vaporization of the MoSe 2 powder by a CO 2 laser enables the formation of monolayer crystals with controlled vacancy concentrations ranging from ~1 to 20%. The effects of laser power, laser irradiation time, and background pressure on the tuning range and subsequent properties of the crystals are investigated and quantified using Raman and photoluminescence spectroscopy, scanning transmission electron microscopy (STEM), and time-correlated single-photon counting (TCSPC). This bottom-up synthesis is a promising approach that allows the deterministic vacancy tuning for future electronics and, in particular, gas and biosensing applications without the need for further postprocessing and potential structural disruption of the crystals.

2D materials↗
DOE OSTI2023

Investigating the impact of environmental factors on West Nile virus human case prediction in Ontario, Canada

West Nile virus is the most common mosquito borne disease in North America and the leading cause of viral encephalitis. West Nile virus is primarily transmitted between birds and mosquitoes while humans are incidental, dead-end hosts. Climate change may increase the risk of human infections as climatic variables have been shown to affect the mosquito life cycle, biting rate, incubation period of the disease in mosquitoes, and bird migration patterns. We develop a zero-inflated Poisson model to investigate how human West Nile virus case counts vary with respect to mosquito abundance and infection rates, bird abundance, and other environmental covariates. We use a Bayesian paradigm to fit our model to data from 2010–2019 in Ontario, Canada. Our results show mosquito infection rate, temperature, precipitation, and crow abundance are positively correlated with human cases while NDVI and robin abundance are negatively correlated with human cases. We find the inclusion of spatial random effects allows for more accurate predictions, particularly in years where cases are higher. Our model is able to accurately predict the magnitude and timing of yearly West Nile virus outbreaks and could be a valuable tool for public health officials to implement prevention strategies to mitigate these outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Methane and nitrous oxide porewater concentrations and surface fluxes of a regulated river

Greenhouse gas (GHG) emissions from rivers are a critical missing component of current global GHG models. Their exclusion is mainly due to a lack of in-situ measurements and a poor understanding of the spatiotemporal dynamics of GHG production and emissions, which prevents optimal model parametrization. We combined simultaneous observations of porewater concentrations along different beach positions and depths, and surface fluxes of methane and nitrous oxide at a plot scale in a large regulated river during three water stages: rising, falling, and low. Our goal was to gain insights into the interactions between hydrological exchanges and GHG emissions and elucidate possible hypotheses that could guide future research on the mechanisms of GHG production, consumption, and transport in the hyporheic zone (HZ). Results indicate that the site functioned as a net source of methane. Surface fluxes of methane during river water stages at three beach positions (shallow intermediate and deep) correlated with porewater concentrations of methane. However, fluxes were significantly higher in the intermediate position during low water stage, suggesting that low residence time increased methane emissions. Vertical profiles of methane peaked at different depths, indicating an influence of the magnitude and direction of the hyporheic mixing during the different river water stages on methane production and consumption. The site acted as either a sink or a source of nitrous oxide depending on the elevation of the water column. Nitrous oxide porewater concentrations peaked at the upper layers of the sediment throughout the different water stages. River hydrological stages significantly influenced porewater concentrations and fluxes of GHG, probably by influencing heterotrophic respiration (production and consumption processes) and transport to and from the HZ. Our results highlight the importance of including dynamic hydrological exchanges when studying and modeling GHG production and consumption in the HZ of large rivers.

54 ENVIRONMENTAL SCIENCES↗