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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Comprehensive computational design of ordered peptide macrocycles

Mixed-chirality peptide macrocycles such as cyclosporine are among the most potent therapeutics identified to date, but there is currently no way to systematically search the structural space spanned by such compounds. Natural proteins do not provide a useful guide: Peptide macrocycles lack regular secondary structures and hydrophobic cores, and can contain local structures not accessible with l -amino acids. Here, we enumerate the stable structures that can be adopted by macrocyclic peptides composed of l - and d -amino acids by near-exhaustive backbone sampling followed by sequence design and energy landscape calculations. We identify more than 200 designs predicted to fold into single stable structures, many times more than the number of currently available unbound peptide macrocycle structures. Nuclear magnetic resonance structures of 9 of 12 designed 7- to 10-residue macrocycles, and three 11- to 14-residue bicyclic designs, are close to the computational models. Our results provide a nearly complete coverage of the rich space of structures possible for short peptide macrocycles and vastly increase the available starting scaffolds for both rational drug design and library selection methods.

Hosseinzadeh, Parisa (ORCID:0000000231287433)↗
DOE OSTI2021

The Relationship Between Topography, Bedrock Weathering, and Water Storage Across a Sequence of Ridges and Valleys

Abstract Bedrock weathering regulates nutrient mobilization, water storage, and soil production. Relative to the mobile soil layer, little is known about the relationship between topography and bedrock weathering. Here, we identify a common pattern of weathering and water storage across a sequence of three ridges and valleys in the sedimentary Great Valley Sequence in Northern California that share a tectonic and climate history. Deep drilling, downhole logging, and characterization of chemistry and porosity reveal two weathering fronts. The shallower front is ∼7 m deep at the ridge of all three hillslopes, and marks the extent of pervasive fracturing and oxidation of pyrite and organic carbon. A deeper weathering front marks the extent of open fractures and discoloration. This front is 11 m deep under two ridges of similar ridge‐valley spacing, but 17.5 m deep under a ridge with nearly twice the ridge‐valley spacing. Hence, at ridge tops, the fraction of the hillslope relief that is weathered scales with hillslope length. In all three hillslopes, below this second weathering front, closed fractures and unweathered bedrock extend about one‐half the hilltop elevation above the adjacent channels. Neutron probe surveys reveal that seasonally dynamic moisture is stored to approximately the same depth as the shallow weathering front. Under the channels that bound our study hillslopes, the two weathering fronts coincide and occur within centimeters of the ground surface. Our findings provide evidence for feedbacks between erosion and weathering in mountainous landscapes that result in systematic subsurface structuring and water routing.

58 GEOSCIENCES↗
DOE OSTI2025

New Era Towards Autonomous Additive Manufacturing: A Review of Recent Trends and Future Perspectives

Abstract The Additive Manufacturing (AM) landscape has significantly transformed in alignment with Industry 4.0 principles, primarily driven by the integration of Artificial Intelligence (AI) and Digital Twin (DT). However, current Intelligent Additive Manufacturing (IAM) systems face limitations such as fragmented AI tool usage and suboptimal human-machine interaction (HMI). This paper reviews existing IAM solutions, emphasizing control, monitoring, process autonomy, and end-to-end integration, and identifies key limitations, such as the absence of a high-level controller for global decision-making. To address these gaps, we propose a transition from IAM to Autonomous Additive Manufacturing (AAM), featuring a hierarchical framework with four integrated layers: knowledge, generative solution, operational, and cognitive. In the cognitive layer, AI agents notably enable machines to independently observe, analyze, plan, and execute operations that traditionally require human intervention. These capabilities streamline production processes and expand the possibilities for innovation, particularly in sectors like in-space manufacturing (ISM). Additionally, this paper discusses the role of AI in self-optimization and lifelong learning, positing that the future of AM will be characterized by a symbiotic relationship between human expertise and advanced autonomy, fostering a more adaptive, resilient manufacturing ecosystem.

Fan, Haolin↗
NASA NTRS2006

Spatial Growth Modeling and High Resolution Remote Sensing Data Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 60 percent of the world s population will live in cities. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes within an urban ecosystems perspective. A reduction in air quality over cities is a major result of these impacts. Because of its complexity, the urban landscape is not adequately captured in air quality models such as the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to a meteorological/air quality modeling system focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include business as usual and smart growth scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the CMAQ modeling schemes. Use of these data has been found to better characterize low density/suburban development as compared with USGS lkm land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the Georgia Environmental Protection Division to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rational decisions on urban growth and sustainability for the metropolitan area in the future.

Quattrochi, Dale A.↗
DOE OSTI2024

Mapping and Characterizing the Visual Impacts of the Existing US Wind Turbine Fleet

Visual impacts of wind turbines have been a persistent concern for wind energy development in the United States (US) for decades and remain a major source of project delays and cancellations. Assessments of visual impacts are frequently performed at a local scale for individual projects, but a comprehensive understanding of broader geographic patterns in visual impacts across the US is lacking. This paper presents a visual impact assessment of the existing land-based wind turbine fleet of the contiguous United States (CONUS). The assessment combines geographic information systems and 3D simulation methods to account for key factors driving the visual magnitude of impacts from the installed turbines. The results indicate that, despite the deployment of approximately 70,000 turbines and over 144 gigawatts of land-based wind in the CONUS, the visual impacts are very small when measured as a proportion of land area, population, and sensitive visual resources. Nonetheless, visual impacts are not evenly distributed: people experience a concentrated share in a small number of natural settings, primarily including plains, prairies, and deserts. Finally, we find that although increased density of wind development consistently leads to visual impacts across a greater proportion of land, it does not always lead to impacts to a greater share of the population. These findings suggest that visual impacts from wind energy are generally well-mitigated across the CONUS to date but also highlight the need for a deeper understanding of landscape sensitivity and individual perceptions of wind turbines in the most heavily impacted natural settings.

3D simulation↗
DOE OSTI2021

Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer

The U.S. Department of Agriculture’s (USDA) Cropland Data Layer (CDL) is a 30 m resolution crop-specific land cover map produced annually to assess crops and cropland area across the conterminous United States. Despite its prominent use and value for monitoring agricultural land use/land cover (LULC), there remains substantial uncertainty surrounding the CDLs’ performance, particularly in applications measuring LULC at national scales, within aggregated classes, or changes across years. To fill this gap, we used state- and land cover class-specific accuracy statistics from the USDA from 2008 to 2016 to comprehensively characterize the performance of the CDL across space and time. We estimated nationwide area-weighted accuracies for the CDL for specific crops as well as for the aggregated classes of cropland and non-cropland. We also derived and reported new metrics of superclass accuracy and within-domain error rates, which help to quantify and differentiate the efficacy of mapping aggregated land use classes (e.g., cropland) among constituent subclasses (i.e., specific crops). We show that aggregate classes embody drastically higher accuracies, such that the CDL correctly identifies cropland from the user’s perspective 97% of the time or greater for all years since nationwide coverage began in 2008. We also quantified the mapping biases of specific crops throughout time and used these data to generate independent bias-adjusted crop area estimates, which may complement other USDA survey- and census-based crop statistics. Our overall findings demonstrate that the CDLs provide highly accurate annual measures of crops and cropland areas, and when used appropriately, are an indispensable tool for monitoring changes to agricultural landscapes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Energetic basis on interactions between ferredoxin and ferredoxin NADP{sup +} reductase at varying physiological conditions

In spite of a number of studies to characterize ferredoxin (Fd):ferredoxin NADP{sup +} reductase (FNR) interactions at limited conditions, detailed energetic investigation on how these proteins interact under near physiological conditions and its linkage to FNR activity are still lacking. We herein performed systematic Fd:FNR binding thermodynamics using isothermal titration calorimetry (ITC) at distinct pH (6.0 and 8.0), NaCl concentrations (0–200 mM), and temperatures (19–28 °C) for mimicking physiological conditions in chloroplasts. Energetically unfavorable endothermic enthalpy changes were accompanied by Fd:FNR complexation at all conditions. This energetic cost was compensated by favorable entropy changes, balanced by conformational and hydrational entropy. Increases in the NaCl concentration and pH weakened interprotein affinity due to the less contribution of favorable entropy change regardless of energetic gains from enthalpy changes, suggesting that entropy drove complexation and modulated affinity. Effects of temperature on binding thermodynamics were much smaller than those of pH and NaCl. NaCl concentration and pH-dependent enthalpy and heat capacity changes provided clues for distinct binding modes. Moreover, decreases in the enthalpy level in the Hammond's postulate-based energy landscape implicated kinetic advantages for FNR activity. All these energetic interplays were comprehensively demonstrated by the driving force plot with the enthalpy-entropy compensation which may serve as an energetic buffer against outer stresses. We propose that high affinity at pH 6.0 may be beneficial for protection from proteolysis of Fd and FNR in rest states, and moderate affinity at pH 8.0 and proper NaCl concentrations with smaller endothermic enthalpy changes may contribute to increase FNR activity. - Highlights: • Energetics of Fd:FNR binding were examined by considering physiological conditions. • NaCl and pH affect energetically Fd:FNR binding with minimal effects of temperature. • Enthalpy and heat capacity may modulate binding kinetics and modes for FNR activity. • Entropy drives complexation by overcoming unfavorable enthalpy and tunes affinity. • Driving force plot reveals condition-dependent energetic interplays for complexation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

Remote Sensing and Spatial Growth Modeling Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 80 percent of the world s population will live in cities. Directly aligned with the expansion of cities is urban sprawl. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes. A reduction in air quality over cities is a major result of these impacts. Strategies that can be directly or indirectly implemented to help remediate air quality problems in cities and that can be accepted by political decision makers and the general public are now being explored to help bring down air pollutants and improve air quality. The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how ozone and air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rationale decisions on urban growth and sustainability for the metropolitan area in the future.

Quattrochi, Dale A.↗
DOE OSTI2018

Using a spatially-distributed hydrologic biogeochemistry model with a nitrogen transport module to study the spatial variation of carbon processes in a Critical Zone Observatory

Terrestrial carbon processes are affected by soil moisture, soil temperature, nitrogen availability and solar radiation, among other factors. Most of the current ecosystem biogeochemistry models represent one point in space, and have limited characterization of hydrologic processes. Therefore these models can neither resolve the topographically driven spatial variability of water, energy, and nutrient, nor their effects on carbon processes. A spatially-distributed land surface hydrologic biogeochemistry model, Flux-PIHM-BGC, is developed by coupling the Biome-BGC model with a physically-based land surface hydrologic model, Flux-PIHM. In the coupled system, each Flux-PIHM model grid couples a 1-D Biome-BGC model. In addition, a topographic solar radiation module and an advection-driven nitrogen transport module are added to represent the impact of topography on nutrient transport and solar energy distribution. Because Flux-PIHM is able to simulate lateral groundwater flow and represent the land surface heterogeneities caused by topography, Flux-PIHM-BGC is capable of simulating the complex interaction among water, energy, nutrient, and carbon in time and space. The Flux-PIHM-BGC model is tested at the Susquehanna/Shale Hills Critical Zone Observatory. Model results show that distributions of carbon and nitrogen stocks and fluxes are strongly affected by topography and landscape position, and tree growth is nitrogen limited. The predicted aboveground and soil carbon distributions generally agree with the macro patterns observed. Although the model underestimates the spatial variation, the predicted watershed average values are close to the observations. Lastly, the coupled Flux-PIHM-BGC model provides an important tool to study spatial variations in terrestrial carbon and nitrogen processes and their interactions with environmental factors, and to predict the spatial structure of the responses of ecosystems to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Amplicon Sequencing Reveals Microbiological Signatures in Spent Nuclear Fuel Storage Basins

Water quality is an important determinant for the structural integrity of alloy cladded fuels and assemblies during long-term wet storage. Detailed characterization of a water filled storage basin for spent nuclear reactor fuel was performed following the formation and proliferation of an amorphous white flocculent. White precipitant was sampled throughout the storage basin for chemical and spectroscopic characterization, and eDNA was extracted for pyrosequencing of bacterial rRNA gene diversity. Accordingly, spectroscopic analyses indicated the precipitant to be primarily amorphous to crystalline aluminum (oxy) hydroxides with minor associated elemental components including Fe, Si, Ti, and U. High levels of dissolved carbon were co-localized with the precipitant relative to bulk water. Bacterial densities were highly variable between sampling locations and with depth; cell numbers (log scale) ranged from 5.6 to 4.89 cells / mL. Bacterial diversity that was physically associated with the aluminum (oxy) hydroxide complexes exceeded an estimated 4,000 OTUs / amplicon library (3% cutoff) and the greatest percent majority of sequences were aligned to the families Burkholderiales (23%), Nitrospiraceae (23%), Hyphomicrobiaceae (17%), and Comamonadaceae (6%). We surmise that episodic changes in the physical and chemical properties of the basin contribute to the polymerization of aluminum (oxy) hydroxides, which in turn can chemisorb nutrients, carbon ligands and bacterial cells from the surrounding bulk aqueous phase. As such, these precipitants should establish favorable microhabitats for bacterial colonization and growth. Comparative analyses of 16S rRNA gene amplicon libraries across diverse environmental landscapes were performed and microbiological signatures unique to the spent nuclear fuel storage basin environment were revealed. These insights could spur the development of tractable bioindicators that are specific of and diagnostic for water quality at discrete locations and finer scales of resolution, marking an important contribution for improved water quality and management of spent nuclear fuel storage facilities.

aluminum↗
DOE OSTI2025

A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases

Human diseases are characterized by intricate cellular dynamics. Single-cell transcriptomics provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in silico drug interventions. Here we introduce UNAGI, a deep generative neural network tailored to analyse time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modelling and screening. When applied to a dataset from patients with idiopathic pulmonary fibrosis, UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation using proteomics reveals the accuracy of UNAGI’s cellular dynamics analysis, and the use of the fibrotic cocktail-treated human precision-cut lung slices confirms UNAGI’s predictions that nifedipine, an antihypertensive drug, may have anti-fibrotic effects on human tissues. UNAGI’s versatility extends to other diseases, including COVID, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond idiopathic pulmonary fibrosis, amplifying its use in the quest for therapeutic solutions across diverse pathological landscapes.

Neural Network↗
DOE OSTI2023

HLA-Clus: HLA class I clustering based on 3D structure

In a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria. The HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved. The HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository (https://github.com/yshen25/HLA-Clus).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

An approach to combine neutron and ion irradiation data to accelerate material qualification for nuclear reactors

Next-generation nuclear power plants are generally characterized by higher operating temperatures, increased neutron fluences and energies, and distinct corrosive coolant environments versus the existing light water reactor fleet. Whether using existing materials in new environments, newly developed materials tailored for these environments, or new manufacturing methods, the traditional decades-long approach for materials qualification does not facilitate rapid deployment. Ion irradiation has demonstrated success in reproducing material microstructure and select property evolution resulting from neutron irradiation with three to four orders of magnitude reduction in time and cost, making it an ideal candidate for accelerated irradiation testing. Because microstructure has a large impact on bulk material properties, limited neutron irradiation data at lower damage levels can in principle be combined with accelerated ion testing results and modeling and simulation to form an accurate prediction of microstructure evolution and select properties under different neutron irradiation conditions and at higher damage levels. The objective of this work is to present a conceptual framework of specific steps to fulfill several technical challenges associated with qualifying materials for performance in radiation environments on an accelerated time frame. A brief review of the regulatory landscape for materials in nuclear environments is presented, followed by additional overviews to understand the current state of the art for correlation of materials properties across radiation environments using experimental and computational methodologies. Finally, the roles of academia, national laboratories, and industry in the advancement of this accelerated materials qualification framework are discussed as a path forward, with possible case studies presented.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Mapping the conformational landscape of a dynamic enzyme by multitemperature and XFEL crystallography

Determining the interconverting conformations of dynamic proteins in atomic detail is a major challenge for structural biology. Conformational heterogeneity in the active site of the dynamic enzyme cyclophilin A (CypA) has been previously linked to its catalytic function, but the extent to which the different conformations of these residues are correlated is unclear. Here we compare the conformational ensembles of CypA by multitemperature synchrotron crystallography and fixed-target X-ray free-electron laser (XFEL) crystallography. The diffraction-before-destruction nature of XFEL experiments provides a radiation-damage-free view of the functionally important alternative conformations of CypA, confirming earlier synchrotron-based results. We monitored the temperature dependences of these alternative conformations with eight synchrotron datasets spanning 100-310 K. Multiconformer models show that many alternative conformations in CypA are populated only at 240 K and above, yet others remain populated or become populated at 180 K and below. These results point to a complex evolution of conformational heterogeneity between 180-–240 K that involves both thermal deactivation and solvent-driven arrest of protein motions in the crystal. The lack of a single shared conformational response to temperature within the dynamic active-site network provides evidence for a conformation shuffling model, in which exchange between rotamer states of a large aromatic ring in the middle of the network shifts the conformational ensemble for the other residues in the network. Together, our multitemperature analyses and XFEL data motivate a new generation of temperature- and time-resolved experiments to structurally characterize the dynamic underpinnings of protein function.

Keedy, Daniel A. (ORCID:0000000291847586)↗
DOE OSTI2025

Summertime Carbonaceous Aerosol in Interior Versus Coastal Northern Alaska

Abstract Rapid warming is likely increasing primary production and wildfire occurrence in the Arctic. Projected changes in carbonaceous aerosols during the summer will impact atmospheric chemistry and climate, but our understanding of these processes is limited by sparse observations. Here, we characterize carbonaceous aerosol in Alaska, USA: Toolik Field Station in the Interior and the Atmospheric Radiation Measurement facility at Utqiaġvik on the Arctic coast, during the summers of 2022 and 2023. We estimated PM 2.5 and PM 10 concentrations using laser light scattering (PurpleAir sensors) and examined total carbon (TC) and its organic carbon (OC) and elemental carbon (EC) fractions in total suspended particles (TSP). We investigated the dominant sources of carbonaceous aerosol using air mass backward‐trajectories from the NOAA HYSPLIT model and radiocarbon source apportionment of TC. TC concentrations were about twice as high in the Interior compared to the coast, with contemporary sources dominating at both Toolik (95%–99%) and Utqiaġvik (86%–89%) over minor contributions from fossil sources. Elevated PM, TC, OC, and EC concentrations coincided with major boreal forest fire activity in North America that brought smoke to the region. The radiocarbon signature of EC measured at Toolik during these wildfire events indicated that over 90% of the EC came from contemporary sources. Our measurements demonstrate the potential for Arctic aerosol concentrations to respond significantly to climate warming‐induced changes to the landscape and emphasize the need for continuous atmospheric monitoring to advance our understanding of this rapidly changing environment.

Welch, Allison M. [Department of Earth System Scie↗
DOE OSTI2023

Dinitrogen Emissions Dominate Nitrogen Gas Emissions From Soils With Low Oxygen Availability in a Moist Tropical Forest

Lowland tropical forest soils are relatively N rich and are the largest global source of N 2 O (a powerful greenhouse gas) to the atmosphere. Despite the importance of tropical N cycling, there have been few direct measurements of N 2 (an inert gas that can serve as an alternate fate for N 2 O) in tropical soils, limiting our ability to characterize N budgets, manage soils to reduce N 2 O production, or predict the future role that N limitation to primary productivity will play in buffering against climate change. We collected soils from across macro‐ and micro‐topographic gradients that have previously been shown to differ in O 2 availability and trace gas emissions. We then incubated these soils under oxic and anoxic headspaces to explore the relative effect of soil location versus transient redox conditions. No matter where the soils came from, or what headspace O 2 was used in the incubation, N 2 emissions dominated the flux of N gas losses. In the macrotopography plots, production of N 2 and N 2 O were higher in low O 2 valleys than on more aerated ridges and slopes. In the microtopography plots, N 2 emissions from plots with lower mean soil O 2 (5%–10%) were greater than in plots with higher mean soil O 2 (10%–20%). We estimate an N gas flux of ∼37 kg N/ha/yr from this forest, 99% as N 2 . These results suggest that N 2 fluxes may have been systematically underestimated in these landscapes, and that the measurements we present call for a reevaluation of the N budgets in lowland tropical forest ecosystems.

54 ENVIRONMENTAL SCIENCES↗