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DOE OSTI2025

Periodic Korteweg–de Vries soliton potentials generate quasisymmetric magnetic field strength in a finite plasma- β equilibrium

Quasisymmetry (QS) is a hidden symmetry of the magnetic field strength, B , that enables effective confinement of charged particles in a fully three-dimensional (3D) toroidal plasma equilibrium. Such equilibria are typically modeled by the ideal magnetohydrostatic (MHS) equations. The nonlinear, overdetermined nature of the QS MHS equations severely complicates our understanding of the interplay between 3D shaping, equilibrium properties such as pressure and rotational transform, and B . Progress has been made through expansions near the magnetic axis; however, a more comprehensive theory is desirable. Using a combination of analysis and regression on a large dataset of numerically optimized quasisymmetric stellarators, we demonstrate that there is a hidden lower dimensionality of B on a magnetic flux surface with connections to the theory of periodic solitons. We show that B on a flux surface is determined by three or at most four flux functions, each of which determines a critical value of the derivative of B along the field line. While being consistent with the near-axis models, our results are global and hold even on the last closed flux surface.

Differential geometry↗

DOE OSTI2025

Cloud-atmosphere impacts on the central Arctic surface energy budget (Final Report)

This project investigated how clouds and atmosphere-sea ice interactions shape the central Arctic surface energy budget using observations from the MOSAiC expedition and coordinated model experiments. Analyses focused on three themes. First, a new method applied to thermistor string data revealed that snow thermal conductivity is systematically higher than assumed in most models, strongly influencing conductive heat flux and sea ice growth, especially under clear skies. Second, a full annual cycle of surface energy budget observations, combined with detailed cloud microphysical retrievals, demonstrated how cloud regimes regulate radiative, turbulent, and conductive fluxes, driving seasonal contrasts in sea ice energy balance: in winter, radiative forcing elicits strong turbulent and conductive responses, while in summer excess energy is primarily partitioned into surface melt. Third, these observations were used to evaluate and improve regional and global forecast models. While some models captured key Arctic cloud-radiation states, most exhibited persistent deficiencies in representing liquid-containing clouds, snow-on-sea-ice processes, and flux responses to radiative forcing. Together, these results provide unprecedented benchmarks for understanding coupled Arctic processes, improve parameterizations of snow, sea ice, and cloud interactions, and highlight critical pathways by which atmospheric variability drives sea ice change in a rapidly evolving Arctic system. Lastly, the project was a resounding success, resulting in 38 peer-reviewed publications and dozens of presentations, while also supporting an early career scientist.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Search for baryon junctions in photonuclear processes and isobar collisions at RHIC

During the early development of quantum chromodynamics, it was proposed that baryon number could be carried by a non-perturbative Y-shaped topology of gluon fields, called the gluon junction, rather than by the valence quarks as in the QCD standard model. A puzzling feature of ultra-relativistic nucleus-nucleus collisions is the apparent substantial baryon excess in the mid-rapidity region that could not be adequately accounted for in most conventional models of quark and diquark transport. The transport of baryonic gluon junctions is predicted to lead to a characteristic exponential distribution of net-baryon density with rapidity and could resolve the puzzle. In this context we point out that the rapidity density of net-baryons near mid-rapidity indeed follows an exponential distribution with a slope of –0.61 ± 0.03 as a function of beam rapidity in the existing global data from A+A collisions at AGS, SPS and RHIC energies. To further test if quarks or gluon junctions carry the baryon quantum number, we propose to study the absolute magnitude of the baryon vs. charge stopping in isobar collisions at RHIC. We also argue that semi-inclusive photon-induced processes (γ + p/A) at RHIC kinematics provide an opportunity to search for the signatures of the baryon junction and to shed light onto the mechanisms of observed baryon excess in the mid-rapidity region in ultra-relativistic nucleus-nucleus collisions. Such measurements can be further validated in A+A collisions at the LHC and e + p/A collisions at the EIC.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

DESI DR2 Galaxy luminosity functions

We present galaxy luminosity functions (LFs) for the Dark Energy Spectroscopic Instrument (DESI) DR2 Bright Galaxy Survey (BGS) in the g, r, z, and $w1$ bands over $0.002\lt z\lt 0.6$. Our analysis uses updated k-corrections and evolutionary corrections, including new polynomial kcorrection fits derived from BGS Year 1 data that supersede earlier GAMA-based prescriptions. Exploiting the statistical power of DESI, we measure LFs to very faint magnitudes, reaching $^{0.1}M_r-5\log h\sim -10$. Independent measurements from the North and South survey regions show excellent agreement around the LF knee, but the very small statistical uncertainties reveal that simple analytic forms fail to capture the full LF shape. The bright end departs from a pure exponential decline, while the faint end exhibits complex, non-powerlaw behaviour, including a pronounced upturn at $^{0.1}M_r-5\log h\gtrsim -15$, which is stronger for red galaxies than for blue. We show that our LFs are largely complete for galaxies with surface brightness $\mu _{50}\lt 25$, and that an apparent steepening fainter than $-13$ is driven primarily by local overdensity and fragmentation of large galaxies. A systematic North–South offset at the brightest magnitudes is traced to red galaxies and may reflect shallower North photometry underestimating extended earlytype profiles, although this remains inconclusive. We therefore also provide LFs based on model Petrosian magnitudes. Redshift splitting reveals small but significant residuals, indicating limitations of a simple global evolutionary model. Using the redshift limits of J. Loveday et al. (2012), we find excellent agreement with GAMA, with substantially reduced statistical errors. These measurements provide a precise reference for studies of environmental and population-dependent LFs and for testing galaxy formation models.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Density limit in peeling-limited pedestals at and above the Greenwald value in DIII-D high poloidal beta plasmas

High pressure, peeling limited pedestals with pedestal normalized beta β N,ped >2 and pedestal top density n ped at or above the Greenwald density nG have been achieved in DIII-D high poloidal beta plasmas, with high global normalized beta β N >3 and energy confinement H 98 ~1.2-1.7. Higher β N allows higher pedestal density above the Greenwald value and higher pedestal pressure, even with a low injected torque. MHD modeling confirms that the experimental profiles lie near the peeling-mode unstable boundary with high normalized pressure gradient βMHD and high edge current density. Experimental analysis and stability calculations indicate that the high poloidal beta with strong Shafranov shift, high βMHD and weak/negative magnetic shear improves the pedestal stability by decoupling the peeling and ballooning modes and stabilizing the ballooning modes, thus facilitating access to the second stable region of peeling-ballooning mode. The access to the second stable peeling-ballooning stability region opens Super-H-like channels without extremely strong shaping or strong torque injection. The high-pressure peeling pedestal allows the pedestal density to go beyond the Greenwald limit with strong ExB shear maintained: pedestal pressure increases with pedestal density even when n ped >n G , until reaching the ideal MHD instability boundary, where giant ELMs occur. The giant ELMs are dominated by a strong n=1 component and cause a large reduction of the edge pressure, but a negligible change of the core pressure, consistent with kink/peeling-mode induced instability. The pedestal recovers from the collapse and typically sustains a high baseline density, around the Greenwald limit, during the whole discharge duration. Experiments also found that internal transport barriers and n ped ~0.9nG, peeling limited pedestals could be simultaneously achieved in high β N plasmas, while an internal feedback between ITB strength and pedestal performance is found.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗

DOE OSTI2024

Why Do Oceanic Nonlinearities Contribute Only Weakly to Extreme El Niño Events?

Abstract Extreme El Niño events have outsized global impacts and control the El Niño Southern Oscillation (ENSO) warm/cold phases asymmetries. Yet, a consensus regarding the relative contributions of atmospheric and oceanic nonlinearities to their genesis remains elusive. Here, we isolate the contribution of oceanic nonlinearities by conducting paired experiments forced with opposite wind stress anomalies in an oceanic general circulation model, which realistically simulates extreme El Niño events and oceanic nonlinearities thought to contribute to ENSO skewness (Tropical Instability Waves (TIWs), Nonlinear Dynamical Heating (NDH)). Our findings indicate a weak contribution of oceanic nonlinearities to extreme El Niño events in the eastern Pacific, owing to compensatory effects between lateral (NDH and TIWs) and vertical processes. These results hold across different vertical mixing schemes and modifications of the upper‐ocean heat budget mixed layer criterion. Our study reinforces previous research underscoring the pivotal role of atmospheric nonlinearities in shaping extreme El Niño events.

Geology↗

DOE OSTI2024

Forest carbon sequestration on the west coast, USA: Role of species, productivity, and stockability

Forest ecosystems store large amounts of carbon and can be important sources, or sinks, of the atmospheric carbon dioxide that is contributing to global warming. Understanding the carbon storage potential of different forests and their response to management and disturbance events are fundamental to developing policies and scenarios to partially offset greenhouse gas emissions. Projections of live tree carbon accumulation are handled differently in different models, with inconsistent results. We developed growth-and-yield style models to predict stand-level live tree carbon density as a function of stand age in all vegetation types of the coastal Pacific region, US (California, Oregon, and Washington), from 7,523 national forest inventory plots. We incorporated site productivity and stockability within the Chapman-Richards equation and tested whether intensively managed private forests behaved differently from less managed public forests. We found that the best models incorporated stockability in the equation term controlling stand carrying capacity, and site productivity in the equation terms controlling the growth rate and shape of the curve. RMSEs ranged from 10 to 137 Mg C/ha for different vegetation types. There was not a significant effect of ownership over the standard industrial rotation length (~50 yrs) for the productive Douglas-fir/western hemlock zone, indicating that differences in stockability and productivity captured much of the variation attributed to management intensity. Our models suggest that doubling the rotation length on these intensively managed lands from 35 to 70 years would result in 2.35 times more live tree carbon stored on the landscape. These findings are at odds with some studies that have projected higher carbon densities with stand age for the same vegetation types, and have not found an increase in yields (on an annual basis) with longer rotations. We suspect that differences are primarily due to the application of yield curves developed from fully-stocked, undisturbed, single-species, “normal” stands without accounting for the substantial proportion of forests that don’t meet those assumptions. The carbon accumulation curves developed here can be applied directly in growth-and-yield style projection models, and used to validate the predictions of ecophysiological, cohort, or single-tree style models being used to project carbon futures for forests in the region. Our approach may prove useful for developing robust models in other forest types.

Chisholm, Paul J. (ORCID:0000000238784707)↗

DOE OSTI2025

Improving Projections of Antarctic Ice Sheet Contribution to Sea‐Level Change Through 2300 by Capturing Gravitational, Rotational, and Deformational Effects

Ice-mass change induces regionally varying patterns of sea-level change due to gravitational, rotational, and deformational (GRD) effects, which in turn influence marine-based ice stability in Antarctica. For improved projection of the Antarctic Ice Sheet (AIS), there is a need for including GRD effects in modeling and improving understanding of basin-by-basin sensitivity of ice evolution to GRD effects under a range of climate scenarios. We couple a high-resolution, higher-order ice-sheet model with a 1D global sea-level model that fully captures GRD effects, and simulate ice evolution in Antarctica under the Ice Sheet Model Intercomparison Project for CMIP6 experiments. We perform two sets of coupled simulations incorporating 1D Maxwell solid Earth structure suitable for West and East Antarctica and show that the Amundsen Sea Embayment (ASE) in West Antarctica has the highest sensitivity to GRD effects—in high-emission scenarios, grounding-line retreat accelerates by hundreds of kilometers by 2300 without GRD effects, but GRD effects delay this retreat on a timescale of decades. However, we find that delay times do not show a clear relationship to the strength of climate forcing alone. Furthermore, GRD effects can influence ice-sheet dynamics more than the choice of climate model for a given emissions scenario. In contrast, East Antarctica exhibits minimal sensitivity to GRD effects throughout the study period. These findings underscore the critical role of GRD effects in shaping future West AIS evolution, highlighting the importance of constraining the regional 3D Earth structure and bed topography in West Antarctica, particularly the ASE.

58 GEOSCIENCES↗

DOE OSTI2026

Evolutionary constraints and climate variability jointly shape starch–sugar balance in woody plants

Nonstructural carbohydrates (NSC) buffer plants against carbon imbalances, yet their partitioning between storage and soluble pools remains elusive at global scales. Here, we compiled a dataset of starch to soluble sugar ratio (St : Su) for 308 woody species across 220 sites world-wide and introduce a dimensionless index that integrates storage and demand while minimizing methodological artifacts. St : Su was strongly associated with growth, identifying it as a key axis of carbon allocation. Foliage consistently exhibited lower St : Su than lignified organs, reflecting a division between transient and conservative pools. Conifers accumulated more starch in foliage but less in stems relative to angiosperms, while leaf habits and mycorrhizal associations further modulated organ-specific strategies. Contrary to expectation, foliar and root St : Su varied little among biomes, but stems exhibited higher ratios in tropical rainforests than in boreal or arid regions, reflecting differences in species composition and adaptive storage under disturbance. Phylogeny constrained stem storage, whereas climatic variability, rather than mean conditions, dominated allocation in leaves and roots. These findings establish St : Su as a robust functional trait linking allocation strategies, growth, and resilience, which can be used to improve vegetation model prediction of forest productivity and mortality under climate variability.

Li, Weibin [Lanzhou Univ. (China)] (ORCID:00000001↗

DOE OSTI2024

Leaf area index (LAI), Teller site, Seward Peninsula, Alaska, 2022

Leaf area index (LAI) sampled from locations on the Teller MM27 NGEE-Arctic site, Seward Peninsula, Alaska. These data were collected in support of the ongoing NGEE-Arctic and NASA ABoVE data synthesis work. Samples were collected in July 2022 at 100 locations that cover the down slope half of Teller MM27 hillslope. This data package includes sample information and LAI data (.csv), an ESRI shape file (.shp) and Keyhole Markup Language (.kml) that define the location and area of each LAI measurement. Metadata files include data descriptions (_dd.csv) for tabular data, file level metadata (.csv), and methods and the instrument manual (.pdf). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Global metagenomics reveals plastid diversity and unexplored algal lineages

Photosynthetic organelles in eukaryotes originated through primary endosymbiosis with a cyanobacterium, an event that profoundly shaped the evolutionary landscape of the eukaryotic tree of life. Primary plastids in Archaeplastida, especially in cultivable plants and algae, contribute most to known plastid diversity. Secondary and higher-order endosymbiosis, involving eukaryotic hosts and algal endosymbionts, further spread photosynthesis among protists within the CASH lineages (Cryptophyta, Alveolata, Stramenopila, and Haptophyta). Despite various hypotheses explaining secondary plastid evolution and distribution, empirical support remains limited. Here, we employ cultivation-independent global metagenomics to expand plastid diversity and investigate plastid origins. We capture 1,027 plastid sequences, including 300 novel sequences belonging to previously unsequenced plastids and representing yet-to-be described microeukaryotes. This includes a new lineage that offers insights into plastid evolution in haptophytes and cryptophytes. Our results confirm that Archaeplastida plastids originate from an early branching cyanobacterial lineage closely related to Gloeomargaritales and identify the closest extant relative of Paulinella plastids. Additionally, our findings suggest two independent origins of secondary red-algal plastids, contributing to plastid diversity in CASH lineages and challenging the prevailing model of single secondary plastid origin. Our study highlights the importance of metagenomic data in uncovering biological diversity and advancing understanding of plastid relationships across photosynthetic eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

CRADA Final Report: CRADA Number NFE-22-09330 with General Fusion

General Fusion is developing a magnetized target fusion (MTF) approach that involves compressing an initial magnetically confined plasma inside a cavity formed in liquid metal. This approach builds from concepts initially developed under the Linus program at the U.S. Naval Research Laboratory and combines it with advances from compact toroid experiment (CTX) and sustained spheromak plasma experiment (SSPX) in compact toroid plasmas and coaxial Marshall gun systems. Modeling the tokamak during compression is central to designing a successful MTF device. The plasma is formed by coaxial helicity injection in the General Fusion device. Immediately after formation, the plasma has a diverted tokamak configuration with a single null. As the wall moves inwards, the plasma is repelled from the conducting surface and driven inwards by currents induced by its magnetic field in the liquid metal wall. As the liquid metal closes (or bridges) the opening of the coaxial plasma injector, the magnetic field topology alters to remove the null. Due to this, the plasma moves from a diverted to a wall-limited configuration. The liquid metal liner continues to close in and change shape, reducing in radius by a factor of ten at the peak of plasma compression. A model of the MTF plasma must be able to handle this continually varying geometry, and to be predictive, it must faithfully include the real imperfections arising in the process. In this project, we pursued a Monte Carlo approach to closures for MHD by computing kinetic electron trajectories in an MHD plasma background from simulations of GF devices. This requires enhancing the capabilities of the KORC-T code for running large ensembles of kinetic trajectories by porting it to GPU architectures and enabling workflows for large ensembles on OLCF machines. With these capabilities, it is possible to produce a large library of kinetic calculations of electron orbits evolving in plasma configurations spanning the magnetic configurations and plasma density profiles, including non-axisymmetry, arising in the General Fusion’s existing PI3 spherical tokamak device. Using ensembles will capture particles passing a single point in space in a given magnetic configuration, and the entire dataset will cover a range of global magnetic field geometries. By sampling around many starting points, this dataset will capture the spatial dependence of the plasma parameters. From this large dataset, it is possible to produce a reduced model for the kinetic effects not captured in MHD.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Online and Offline Data Quality Monitoring for the Mu2e Calorimeter

This thesis presents the design, implementation, and validation of a calorimeter Data Quality Monitoring (DQM) toolchain for the Mu2e experiment at Fermilab. Mu2e searches for charged lepton flavor violation via coherent muon-to-electron conversion in the field of an aluminum nucleus, $\mu^- Al \rightarrow e^-Al$, a process whose observation would constitute clear evidence of physics beyond the Standard Model. Achieving target sensitivity requires stringent control of detector performance and data integrity during acquisition, as subtle issues in readout configuration, data formatting, or electronics behavior can compromise reconstruction and bias downstream analyzes. To address these challenges, this work develops a multi-layer DQM approach spanning both raw data validation and reconstructed digi-level diagnostics. At the low level, a fragment analysis component performs word- and bit-field decoding of calorimeter readout blocks, enabling sanity checks of the expected structure and producing detailed error and integrity statistics useful for commissioning and troubleshooting. At the digi level, the CaloDigiDQM analyzer is implemented within the art framework and transforms each CaloDigiCollection into a structured hierarchy of ROOT histograms designed for fast drill-down diagnostics. The module generates coherent monitoring views at global, disk, board, and channel granularity, including occupancy, waveform-derived features (baseline, RMS, peak amplitude and position), and left-right sensor consistency metrics. Detector-aware channel-to-electronics mapping is performed through the conditions system (CaloDAQMap), ensuring that diagnostics remain aligned with hardware identifiers used in operations. For end-to-end testing without reliance on live DAQ data, a synthetic CaloDigi producer is developed to generate realistic waveforms with controlled noise and pulse shapes. The resulting system supports both offline ROOT-file production and online operation, including optional histogram streaming through otsdaq via ots::HistoSender. This toolchain provides a practical and scalable foundation for calorimeter commissioning and stable data collection, enabling early detection of anomalies and reducing operational risk for Mu2e.

Vakulenko, Mark [Drew U.] (ORCID:0009000276197818)↗

DOE OSTI2025

Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m −2 ) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m −2 (mean ≈ 350 g m −2 ), while predicted values ranged from 0 to ∼4000 g m −2 (mean ≈ 275 g m −2 ), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m −2 and R 2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.

Climate change↗

DOE OSTI2026

Model‐Based Interpretation of Solute Exports and Carbon Partitioning During Shale Weathering in a Mountainous Hillslope

The weathering of sedimentary rocks in high-elevation catchments influences freshwater quality and the global carbon cycle. While individual biogeochemical mechanisms involved in this process are relatively well understood, quantifying their contributions to solute export and carbon fluxes under natural, transient conditions remains challenging. Here, we implement a numerical multidimensional and multiphase model to simulate coupled hydrological and biogeochemical processes in a shale-underlain, snow-dominated hillslope in the Rocky Mountains, Colorado. The model captures the dynamic interplay between soil respiration, mineral weathering, and climate-driven hydrological forcing, reproducing observed soil CO 2 dynamics, groundwater chemistry, and subsurface flow. Our results reveal that seasonal snowmelt enhances carbonate weathering by promoting the infiltration of CO 2 -rich water to depth, while pyrite oxidation is primarily sensitive to low water saturation that facilitates O 2 diffusion through the regolith. Topography modulates the spatial distribution of shale weathering, as steeper slopes enhance lateral drainage, favoring the delivery of reactants to greater depths. While shale weathering at our site acts as a transient carbon sink, with silicates and carbonates buffering acidity and promoting atmospheric CO 2 consumption (1% of soil-derived CO 2 ), the exported dissolved inorganic carbon is predominantly geogenic (∼73%). Consequently, when accounting for long-term marine carbonate precipitation. The current weathering regime represents a net source of carbon to the atmosphere. The oxidation of pyrite and petrogenic organic carbon together release approximately 0.9 mol·m −2 ·yr −1 of CO 2 . Our findings highlight the role of topography, hydroclimate, and the coupling between acid-base reactions in shaping the carbon balance and the solute exports in mountainous critical zones.

carbon cycling↗

DOE OSTI2024

Potential applications of microbial genomics in nuclear non-proliferation

As nuclear technology evolves in response to increased demand for diversification and decarbonization of the energy sector, new and innovative approaches are needed to effectively identify and deter the proliferation of nuclear arms, while ensuring safe development of global nuclear energy resources. Preventing the use of nuclear material and technology for unsanctioned development of nuclear weapons has been a long-standing challenge for the International Atomic Energy Agency and signatories of the Treaty on the Non-Proliferation of Nuclear Weapons. Environmental swipe sampling has proven to be an effective technique for characterizing clandestine proliferation activities within and around known locations of nuclear facilities and sites. However, limited tools and techniques exist for detecting nuclear proliferation in unknown locations beyond the boundaries of declared nuclear fuel cycle facilities, representing a critical gap in non-proliferation safeguards. Microbiomes, defined as “characteristic communities of microorganisms” found in specific habitats with distinct physical and chemical properties, can provide valuable information about the conditions and activities occurring in the surrounding environment. Microorganisms are known to inhabit radionuclide-contaminated sites, spent nuclear fuel storage pools, and cooling systems of water-cooled nuclear reactors, where they can cause radionuclide migration and corrosion of critical structures. Microbial transformation of radionuclides is a well-established process that has been documented in numerous field and laboratory studies. These studies helped to identify key bacterial taxa and microbially-mediated processes that directly and indirectly control the transformation, mobility, and fate of radionuclides in the environment. Expanding on this work, other studies have used microbial genomics integrated with machine learning models to successfully monitor and predict the occurrence of heavy metals, radionuclides, and other process wastes in the environment, indicating the potential role of nuclear activities in shaping microbial community structure and function. Results of this previous body of work suggest fundamental geochemical-microbial interactions occurring at nuclear fuel cycle facilities could give rise to microbiomes that are characteristic of nuclear activities. These microbiomes could provide valuable information for monitoring nuclear fuel cycle facilities, planning environmental sampling campaigns, and developing biosensor technology for the detection of undisclosed fuel cycle activities and proliferation concerns.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Grid-Ready Energy Analytics Training with Data (“GREAT with Data”)

GridEd is a collaborative educational initiative consisting of the Electric Power Research Institute (EPRI), 5 Partner Universities (Stony Brook University, The University of Texas at Austin, University of California – Riverside, Virginia Tech, Washington State University), and participating industry sponsors. This educational initiative focuses on developing and training the next generation of power engineers so they can help shape the electric grid of the future by anticipating and fulfilling the needs of changing electric industry requirements. GridEd is leveraging electric industry research to educate a future electric grid workforce by empowering new and continuing education students, not only to become competent and well-informed engineers, but also to participate and influence major technological, social, and policy decisions that address critical global challenges. GridEd’s activities are centered around four core pillars: Enhancement of university power systems engineering curricula; Professional development and training for a diverse electric industry workforce; Stimulating students to join the movement for the next generation of power engineers, and; Improve workforce development efforts in the electric utility industry. Major accomplishments over the course of the project were: Over 50 unique professional short courses were delivered by more than 30 instructors across the GridEd network; Over 3,600 unique learners, many who took multiple courses, received more than 27,000 professional development hours (PDH) and over 1,000 certificates of completion; Approximately 2,900 unique learners undertook a course that was offered LIVE online or in-person; Approximately 700 unique learners undertook a course that was offered as computer-based training (CBT); Over 35 unique university courses were delivered by more than 30 instructors to 1,500 university students across the GridEd network; One-hundred-and-fifty-seven (157) students were funded to completed 43 student projects in topics of power systems and data science, and; Six (6) Historically Black Colleges and Universities (HBCUs) recruited as Affiliate Universities via Utility Partners. The professional training initiative and workforce development activities launched by this project will be sustained through EPRI’s collaborative business model with industry. Stimulating students to join the power engineering workforce of the future and the enhancement of tertiary training may continue to need government support.

14 SOLAR ENERGY↗