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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Ionic-content-driven restructuring of spirobisindane ionene networks: implications for mechanics, self-healing, and gas transport

Polymers of intrinsic microporosity (PIMs) offer exceptional gas permeability but remain brittle and susceptible to physical aging, limiting their durability in separation applications. Here, we introduce a reconfigurable microporous polymer network that uniquely integrates permanent PIM microporosity with autonomous, intrinsic self-healing driven by imidazolium-based ionic motifs. Spirobisindane units generate the intrinsic free-volume architecture, while an imidazolium-containing polyamide ionene supplies dynamic ionic and hydrogen-bonding interactions that reorganize under mild activation. Incorporation of imidazolium-based ionic liquids further tunes cohesion, mobility, and densification, enabling the network to relax, re-associate, and retain microporosity without structural collapse. Through a comprehensive multiscale approach combining spectroscopy, scattering, thermal and mechanical characterization with all-atom molecular dynamics and density functional theory calculations, we elucidate how ionic content, as a single control parameter that reshapes free-volume distributions, modulates local coordination environments, and governs relaxation and healing kinetics. At intermediate ionic loadings, the networks achieve rapid, repeatable self-healing while maintaining CO$_2$ selectivity, demonstrating an optimal balance between segmental mobility and structural integrity. By establishing how hierarchical ionic interactions couple structure, dynamics, and transport in microporous ionene networks, this work provides generalizable design rules for adaptive soft-matter systems that require simultaneous mechanical resilience, reconfigurability, and selective gas transport.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Catalytic Bio-crude Production in a Novel, Short-Contact Time Reactor

RTI has developed a catalytic fast pyrolysis (CFP) process to produce biomass-based hydrocarbon replacement fuels. From a technology perspective, this advanced biofuels process produces liquid transportation fuels that can leverage capital expenditures in the existing petroleum distribution infrastructure. From a business perspective, this technology does not face potential market limitations caused by oxygenate blend limitations for light duty vehicle applications. A promising catalyst has been developed with excellent potential for deoxygenation to produce a hydrocarbon-rich biocrude that is more suitable for upgrading with traditional hydroprocessing technology. The role of the catalyst is to control the chemistry during biomass pyrolysis to produce a biocrude in a single step that has lower oxygen content and is more thermally stable than conventional biomass fast pyrolysis oil. Catalyst properties are optimized to minimize gas and coke production and improve catalytic deoxygenation and biocrude yields. Results from this project have demonstrated that biocrudes from catalytic fast pyrolysis contain <20 wt% oxygen, with the organic fraction as low as 12% oxygen. These oxygen numbers are > 50% lower compared to traditional fast pyrolysis oils. The biocrude produced from our CFP process can be upgraded to gasoline range hydrocarbons. A very conservative approach for upgrading included a preliminary stabilization step followed by a hydroprocessing step. The oxygen content in the upgraded product was decreased by 97% (to less than 3wt %) from the oxygen content of the biocrude sample. Additionally, the acid content was reduced by 99% from the acid content of the biocrude sample. In an optimized integrated process, the CFP step and the hydroprocessing (upgrading) step need to be carefully balanced to maximize the input biomass energy that is recovered in the finished biofuels. Hydrogen utilization and carbon recovery during both catalytic biomass pyrolysis and biocrude upgrading need to be optimized to maximize energy recovery in the finished biofuel. A nominal 1 ton per day (TPD) catalytic biomass pyrolysis system was designed based on the laboratory experimental data. The system was designed to achieve the short residence times (0.5–2 sec) and high heat transfer rates required for maximum liquid biocrude yields while optimizing process integration to maintain catalyst activity by continuous regeneration. The catalytic biomass pyrolysis reactor is a continuously circulating single-loop transport reactor design that is flexible enough to allow sensitivity studies around temperature, residence time, biomass feed rate, and catalyst-to-biomass ratio (i.e., catalyst circulation rate) for process optimization. For a nominal 100 lb/hr feed rate of dry biomass, the products include ~25 lb/hr char and coke, ~20 lb/hr permanent gases, and ~55 lb/hr condensable vapors. Pyrolysis temperatures are 350–600°C, and the system is operated close to ambient pressure. Production of the catalyst developed within the project was scaled up to provide enough material (200kg) for bench-scale testing. The 1TPD bench-scale unit was fabricated, installed and commissioned with this project. Biocrude samples from wood were produced in this unit with the CFP catalyst with material balance > 90%. The intermediate biocrude yields and chemical composition compare well with previous laboratory results and a hydroprocessing strategy for upgrading the resulting biocrude was developed. The experimental results obtained within this project were used to develop a process model for a 2000 TPD commercial facility. Detailed flow sheets were developed for all major unit operations in the process and provided a basis for an economic analysis to develop capital and operating costs. Estimates of the biofuel production costs were used to develop internal rates of return for the yields and compositions measured for the current state of technology at the end of this project and for the yields anticipated for an optimized (nth plant) scenario as additional process improvements are made.

09 BIOMASS FUELS↗
DOE OSTI2022

Quantifying Northern High Latitude Gross Primary Productivity (GPP) Using Carbonyl Sulfide (OCS)

The northern high latitude (NHL, 40°N to 90°N) is where the second peak region of gross primary productivity (GPP) other than the tropics. The summer NHL GPP is about 80% of the tropical peak, but both regions are still highly uncertain. Carbonyl sulfide (OCS) provides an important proxy for photosynthetic carbon uptake. Here we optimize the OCS plant uptake fluxes across the NHL by fitting atmospheric concentration simulation with the GEOS-CHEM global transport model to the aircraft profiles acquired over Alaska during NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (2012–2015). We use the empirical biome-specific linear relationship between OCS plant uptake flux and GPP to derive the six plant uptake OCS fluxes from different GPP data. Such GPP-based fluxes are used to drive the concentration simulations. We evaluate the simulations against the independent observations at two ground sites of Alaska. The optimized OCS fluxes suggest the NHL plant uptake OCS flux of -247 Gg S year -1 , about 25% stronger than the ensemble mean of the six GPP-based OCS fluxes. GPP-based OCS fluxes systematically underestimate the peak growing season across the NHL, while a subset of models predict early start of season in Alaska, consistent with previous studies of net ecosystem exchange. The OCS optimized GPP of 34 PgC yr -1 for NHL is also about 25% more than the ensembles mean from six GPP data. Further work is needed to fully understand the environmental and biotic drivers and quantify their rate of photosynthetic carbon uptake in Arctic ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Probabilistic Groundwater Modeling of the RDX Plume at Los Alamos National Laboratory to Support Risk Assessment - 20359

A plume of the contaminant hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) with concentrations greater than the New Mexico tap water drinking standard (9.66 ppb) is present in the regional aquifer near the southwestern boundary of Los Alamos National Laboratory (LANL). A risk assessment is performed for exposure to regional aquifer groundwater, with long-term predictions of RDX concentrations provided by a calibrated, probabilistic, numerical fate and transport model. The structure of the model is hierarchical, with the RDX Regional Aquifer (RA) groundwater model acting as the primary tool for analysis of downgradient RDX concentrations. The RA model is deeply informed by the conceptual site model (CSM) and is calibrated using site RDX concentration data and hydraulic head measurements, along with other analyses. The model is calibrated using data through December 2019. Where data are scarce other lines of evidence are used to inform inputs, including the multiphase RDX Vadose Zone (VZ) model and Pipe and Disk analytical screening tool. Model inputs are described with informative prior distributions using a robust approach to development that incorporates all available lines of evidence for every parameter used as an input to the RA model. Calibration is performed using a classical nonlinear optimization routine, which is then used to initialize a Bayesian calibration. The Bayesian calibration constrains the uncertainty in the classical calibration, ultimately providing posterior distributions for all model parameters. The challenges of the calibration include high-dimensional parameter space, including spatially heterogeneous hydraulic conductivities, comparatively sparse data, and low RDX concentrations. Posterior parameter distributions developed in the calibration process are then used for stochastic predictive model runs into the future. The result of the forward runs is spatially and temporally explicit estimates of head and concentration with uncertainty at all points in space and time. The probabilistic modeling approach presented here includes innovative computational and statistical methods that leverage high-performance computing (HPC) resources. It makes use of prior modeling work performed at the chromium plume site in the central LANL area, with extensive updates. The risk assessment will ultimately be used to support decision-making at the site, using multiple appropriately weighted sources of information, as well as uncertainty, sensitivity, and value-of-information analyses. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Optimization of a Mixed Fleet of Aerial Drones for Medical Supplies: A Case Study of Blood Delivery Logistics

Aerial drones have emerged as an innovative solution for faster transportation of time-sensitive items (e.g., emergency medical supplies), potentially reducing the transmission of contagious diseases and enhancing healthcare availability through contactless autonomous delivery. We study fleet sizing and efficient scheduling of a mixed fleet of drones for delivering time-sensitive medical items having distinct release and due times to minimize the required fleet size and fleet composition, the required number of additional batteries, and the total energy consumption. We continuously track the remaining battery energy of drones to determine the optimal timing for battery replacement, rather than replacing the battery at each node. Using actual drone flight test data, we employed a machine learning (ML) method to estimate the energy consumption of different drone types during flight segments for different operating parameters. We present a novel mixed-integer programming model to efficiently formulate the problem that integrates the estimated energy consumption functions from ML. We propose a new greedy heuristic (GH) algorithm and a customized genetic algorithm (GA) for solving large-scale instances of this problem faster. Results demonstrate that the GH algorithm is substantially faster than the accelerated CPLEX and the GA, while sacrificing the solution quality by a small amount. Results based on an actual blood sample delivery case study from Pendleton, Oregon, United States, show that using a mixed fleet of drones reduces the total cost and total energy consumption up to 18.18% and 28.7%, respectively, compared to using a homogeneous fleet.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2024

Confinement performance predictions for a high field axisymmetric tandem mirror

This paper presents Hammir tandem mirror confinement performance analysis based on Realta Fusion's first-of-a-kind model for axisymmetric magnetic mirror fusion performance. This model uses an integrated end plug simulation model including, heating, equilibrium, and transport combined with a new formulation of the plasma operation contours (POPCONs) technique for the tandem mirror central cell. Using this model in concert with machine learning optimization techniques, it is shown that an end plug utilizing high temperature superconducting magnets and modern neutral beams enables a classical tandem mirror pilot plant producing a fusion gain Q > 5. The approach here represents an important advance in tandem mirror design. The high fidelity end plug model enables calculations of heating and transport in the highly non-Maxwellian end plug to be made more accurately. The detailed end plug modelling performed in this work has highlighted the importance of classical radial transport and neutral beam absorption efficiency on end plug viability. The central cell POPCON technique allows consideration of a wide range of parameters in the relatively simple near-Maxwellian central cell, facilitating the selection of more optimal central cell plasmas. These advances make it possible to find more conservative classical tandem mirror fusion pilot plant operating points with lower temperatures, neutral beam energies, and end plug performance requirements than designs in the literature. Despite being more conservative, it is shown that these operating points have sufficient confinement performance to serve as the basis of a viable fusion pilot plant provided that they can be stabilized against MHD and trapped particle modes.

FOS: Physical sciences↗
DOE OSTI2023

Linking Phase Behavior to Performance Parameters in Non-Fullerene Acceptor Solar Cells

Performance of nonfullerene-based organic solar cells is largely dependent on the donor:acceptor blend morphology, which is initially tuned by composition. However, morphological design rules are difficult to identify due to the complex relationship between composition-dependent phase behavior and performance properties. In this study, the authors are able to establish a direct link between PM6:Y6 film morphology and device properties by combining grazing-incidence wide-angle X-ray scattering with highly sensitive Fourier transform photocurrent spectroscopy. By analyzing properties across the full composition range, interfacial microstructure and the corresponding charge transfer (CT) states are identified and direct structure–property relationships are established. Here the results indicate that open-circuit voltage is controlled by the CT state at amorphous-PM6:crystalline-Y6 interfaces. Crystalline Y6 is found to increase both transport and non-radiative recombination, suggesting that morphological tradeoffs may be necessary for overall device optimization.

14 SOLAR ENERGY↗
DOE OSTI2023

Designing a GIS-based supply chain for producing carinata-based sustainable aviation fuel in Georgia, USA

Carinata is a potential crop for sustainable aviation fuel (SAF) production in the southern USA. However, as a novel crop, the cost-effectiveness and environmental feasibility of carinata feedstock are unknown, and there are questions about the optimal supply chain configuration for carinata-based SAF production. This study aims to design a supply chain model for carinata-based SAF production by optimizing the location of farms and facilities (e.g. storage units, crushing mills, biorefineries) for a minimum transportation cost under a set of supply and demand conditions. An integrated mixed-integer linear programming (MILP) model was combined with geographical information system (GIS) analysis to design a spatially explicit supply chain configuration. The GIS-based network analysis considered all of the counties in Georgia to set the candidate locations of carinata farms and facilities, and determined minimum cost and emission routes between those counties and the airport using existing transportation networks and modes (e.g. road, rail and pipeline). The MILP model determined the final selection of the farms and the number of facilities and their locations over those minimum-cost routes. With this supply chain configuration, the minimum price of SAF was $\$$0.92 L –1 , which is $\$$0.44 higher than conventional aviation fuel (CAF). The associated carbon intensity of SAF was estimated at 940.7 g CO 2 e L –1 , a reduction of 66% relative to the carbon intensity of equivalent CAF. The study found that a carbon tax (or subsidy) of $\$$230.48 t CO 2 e –1 would be needed to overcome the cost differential with CAF and promote carinata-based SAF in Georgia.

09 BIOMASS FUELS↗
DOE OSTI2020

Refueling infrastructure planning in intercity networks considering route choice and travel time delay for mixed fleet of electric and conventional vehicles

The range anxiety has been a major factor that affects the market acceptance of electric vehicles. Even with the recent development of battery technologies, a lack of charging stations and range anxiety are still significant concerns, specifically for intercity trips. This calls for more investments in building charging stations and advancing battery technologies to increase the market share of electric vehicles and improve sustainability. This study suggests a configuration for plug-in electric vehicle charging infrastructure to support long-distance intercity trips of electric vehicles at the network level. A model is proposed to minimize the total system cost including infrastructure investment (building charging stations/spots) and travel time delays (charging time, waiting time in the queue, and detour time to access charging stations). This study fills existing gaps in the literature by capturing realistic patterns of travel demand and considering flow-dependent charging delays at charging stations. Furthermore, the proposed model, which is formulated as a mixed-integer program with nonlinear constraints, solves the optimization problem at the network level. At the network level, impacts of charging station locations on the traffic assignment problem with a mixed fleet of electric and conventional vehicles need to be considered. To this end, a traffic assignment module is integrated with a simulated annealing algorithm. The numerical experiments show a satisfactory application of the model for a full-scale case study (intercity network in Michigan). The solution quality and efficiency of the proposed solution algorithm are evaluated against those of an enumeration approach for a small case study. The results suggest that even for the current market share and charging stations’ setting, a significant investment is needed to support intercity trips without range anxiety issues and with acceptable delays. Additionally, through sensitivity analyses, the required infrastructure and battery investments to support intercity trips with acceptable delays are established for hypothetical increased market shares and battery size in the future.

42 ENGINEERING↗
DOE OSTI2022

Low-Tortuosity Thick Electrodes with Active Materials Gradient Design for Enhanced Energy Storage

The ever-growing energy demand of modern society calls for the development of high-loading and high-energy-density batteries, and substantial research efforts are required to optimize electrode microstructures for improved energy storage. Low-tortuosity architecture proves effective in promoting charge transport kinetics in thick electrodes; however, heterogeneous electrochemical mass transport along the depth direction is inevitable, especially at high C-rates. In this work, we create an active material gradient in low-tortuosity electrodes along ion-transport direction to compensate for uneven reaction kinetics and the nonuniform lithiation/delithiation process in thick electrodes. The gradual decrease of active material concentration from the separator to the current collector reduces the integrated ion diffusion distance and accelerates the electrochemical reaction kinetics, leading to improved rate capabilities. Further, the structure advantages combining low-tortuosity pores and active material gradient offer high mass loading (60 mg cm –2 ) and enhanced performance. Comprehensive understanding of the effect of active material gradient architecture on electrode kinetics has been elucidated by electrochemical characterization and simulations, which can be useful for development of batteries with high-energy/power densities.

25 ENERGY STORAGE↗
DOE OSTI2019

The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

58 GEOSCIENCES↗
DOE OSTI2025

ELM–Wet: Inclusion of a Wet–Landunit With Sub–Grid Representation of Eco–Hydrological Patches and Hydrological Forcing Improves Methane Emission Estimations in the E3SM Land Model (ELM)

Wetlands are the largest emitters of biogenic methane (CH 4 ) and represent the highest source of uncertainty in global CH 4 budgets. Here, we aim to improve the realism of wetland representation in the U.S. Department of Energy's Exascale Earth System Model land surface model, ELM, thereby reducing uncertainty of CH 4 flux predictions. We develop an updated version, ELM-Wet, where we activate a separate landunit for wetlands that handles multiple wetland-specific eco-hydrological patch functional types. We introduce more realistic hydrological forcing through prescribing site-level constraints on surface water elevation, which allows resolving different sustained inundation depth for different patches, and if data exists, prescribing inundation depth. We modified the calculation of aerenchyma transport diffusivity based on observed conductance per leaf area for different vegetation types. We use Bayesian Optimization to parameterize CO 2 and CH 4 fluxes in the developed wet-landunit. Site-level simulations of a coastal non-tidal freshwater wetland in Louisiana were performed with the updated model. Eddy covariance observations of CO 2 and CH 4 fluxes from 2012 to 2013 were used to train the model and data from 2021 were used for validation. Patch-specific chamber flux observations and observations of CH 4 concentration profiles in the soil porewater from 2021 were used for evaluation of the model performance. Our results show that ELM-Wet reduces the model's CH 4 emission root mean squared error by up to 33% and is able to represent inter-daily CO 2 and CH 4 flux variability across the wetland's eco-hydrological patches, including during periods of extreme dry or wet conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Closed-loop optimization of fast trapped-ion shuttling with sub-quanta excitation

Shuttling ions at high speed and with low motional excitation is essential for realizing fast and high-fidelity algorithms in many trapped-ion-based quantum computing architectures. Achieving such performance is challenging due to the sensitivity of an ion to electric fields and the unknown and imperfect environmental and control variables that create them. Here we implement a closed-loop optimization of the voltage waveforms that control the trajectory and axial frequency of an ion during transport in order to minimize the final motional excitation. The resulting waveforms realize fast round-trip transport of a trapped ion across multiple electrodes at speeds of 0.5 electrodes per microsecond (35 m∙s -1 for a one-way transport of 210 μm in 6 s) with a maximum of 0.36 ± 0.08 mean quanta gain. This sub-quanta gain is independent of the phase of the secular motion at the distal location, obviating the need for an electric field impulse or time delay to eliminate the coherent motion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Analyses of circular solutions for advanced plastics waste recycling

A circular plastics economy can leverage the lightweight, strong and durable characteristics of macromolecular materials, while simultaneously reducing the negative environmental impacts associated with polymer waste. Advanced recycling technologies provide an opportunity to valorize plastics waste and extend the lifespan of these materials by converting waste into new monomers, polymers or specialty chemicals. Although many advanced technologies appear promising, assessments of economic and environmental sustainability are often not conducted in a standardized fashion and neglect factors such as plastics waste transportation, sorting and pretreatment. These shortcomings can lead to inaccurate or misleading predictions, reduce opportunities for optimization and limit industrial relevance. In this Review, we highlight select industrial case studies to underscore the notable consequences of underestimating the complexity of real-life consumer plastics waste. In addition, the current challenges associated with the assessment of the industrial viability of laboratory-scale processes are explored. Here, by discussing relevant analysis frameworks and system boundaries, along with potential analytical pitfalls, future research will be guided beyond chemical considerations and toward impactful circular solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory

The ability to track simulated cancer cells through the circulatory system, important for developing a mechanistic understanding of metastatic spread, pushes the limits of today's supercomputers by requiring the simulation of large fluid volumes at cellular-scale resolution. To overcome this challenge, we introduce a new adaptive physics refinement (APR) method that captures cellular-scale interaction across large domains and leverages a hybrid CPU-GPU approach to maximize performance. Through algorithmic advances that integrate multi-physics and multi-resolution models, we establish a finely resolved window with explicitly modeled cells coupled to a coarsely resolved bulk fluid domain. In this work we present multiple validations of the APR framework by comparing against fully resolved fluid-structure interaction methods and employ techniques, such as latency hiding and maximizing memory bandwidth, to effectively utilize heterogeneous node architectures. Collectively, these computational developments and performance optimizations provide a robust and scalable framework to enable system-level simulations of cancer cell transport.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

High-temperature low-humidity proton exchange membrane with “stream-reservoir” ionic channels for high-power-density fuel cells

The perfluorosulfonic acid (PFSA) proton exchange membrane (PEM) is the key component for hydrogen fuel cells (FCs). We used in situ synchrotron scattering to investigate the PEM morphology evolution and found a “stream-reservoir” morphology, which enables efficient proton transport. The short-side-chain (SSC) PFSA PEM is fabricated under the guidance of morphology optimization, which delivered a proton conductivity of 193 milliSiemens per centimeter [95% relativity humidity (RH)] and 40 milliSiemens per centimeter (40% RH) at 80°C. The improved glass transition temperature, water permeability, and mechanical strength enable high-temperature low-humidity FC applications. Performance improvement by 82.3% at 110°C and 25% RH is obtained for SSC-PFSA PEM FCs compared to Nafion polymer PEM devices. The insights in chain conformation, packing mechanism, crystallization, and phase separation of PFSAs build up the structure-property relationship. In addition, SSC-PFSA PEM is ideal for high-temperature low-humidity FCs that are needed urgently for high-power-density and heavy-duty applications.

25 ENERGY STORAGE↗
DOE OSTI2023

Comparative analysis of the functional properties of human and mouse ferroportin

Ferroportin (Fpn)—expressed at the plasma membrane of macrophages, enterocytes, and hepatocytes—mediates the transfer of cellular iron into the blood plasma. Under the control of the iron-regulatory hormone hepcidin, Fpn serves a critical role in systemic iron homeostasis. Although we have previously characterized human Fpn, a great deal of research in iron homeostasis and disorders uses mouse models. By way of example, the flatiron mouse, a model of classical ferroportin disease, bears the mutation H32R in Fpn and is characterized by systemic iron deficiency and macrophage iron retention. The flatiron mouse also appears to exhibit a manganese phenotype, raising the possibility that mouse Fpn serves a role in manganese metabolism. At odds with this observation, we have found that human Fpn does not transport manganese, so we considered the possibility that a species difference could explain this discrepancy. We tested the hypothesis that mouse but not human Fpn can transport manganese and performed a comparative analysis of mouse and human Fpn. We examined the functional properties of human Fpn, mouse Fpn, and mutant mouse Fpn by using radiotracer assays in RNA-injected Xenopus oocytes. We found that neither mouse nor human Fpn transports manganese. Mouse and human Fpn share identical properties with respect to substrate profile, calcium dependence, optimal pH, and hepcidin sensitivity. We have also demonstrated that Fpn is not an ATPase pump. Our findings validate the use of mouse models of ferroportin function in iron homeostasis and disease.

Cell Biology↗
DOE OSTI2024

Deep Learning Image Segmentation for Atmospheric Rivers

Abstract The identification of atmospheric rivers (ARs) is crucial for weather and climate predictions as they are often associated with severe storm systems and extreme precipitation, which can cause large impacts on society. This study presents a deep learning model, termed ARDetect, for image segmentation of ARs using ERA5 data from 1960 to 2020 with labels obtained from the TempestExtremes tracking algorithm. ARDetect is a convolutional neural network (CNN)-based U-Net model, with its structure having been optimized using automatic hyperparameter tuning. Inputs to ARDetect were selected to be the integrated water vapor transport (IVT) and total column water (TCW) fields, as well as the AR mask from TempestExtremes from the previous time step to the one being considered. ARDetect achieved a mean intersection-over-union (mIoU) rate of 89.04% for ARs, indicating its high accuracy in identifying these weather patterns and a superior performance than most deep learning–based models for AR detection. In addition, ARDetect can be executed faster than the TempestExtremes method (seconds vs minutes) for the same period. This provides a significant benefit for online AR detection, especially for high-resolution global models. An ensemble of 10 models, each trained on the same dataset but having different starting weights, was used to further improve on the performance produced by ARDetect, thus demonstrating the importance of model diversity in improving performance. ARDetect provides an effective and fast deep learning–based model for researchers and weather forecasters to better detect and understand ARs, which have significant impacts on weather-related events such as floods and droughts.

Galea, Daniel↗