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At least 55 records · Page 3

Neutrino Beam Monitoring

Accelerator facilities produce neutrino beams from meson decays in a decay volume. Experiments measure event rates that depend on flux, cross sections, and detector response, so the flux is predicted using hadron production and beamline modeling and constrained by beam instrumentation, since near detectors alone cannot separate flux from cross section. Proton, hadron, and muon monitors can track the parent particle distributions and beam conditions, providing the inputs needed for flux predictions in long-baseline experiments such as NOvA, T2K, and DUNE. This talk reviews how beam monitors are used in practice to understand neutrino flux. Proton beam monitors tell where the beam hits the target and how stable it is. Farther downstream, hadron and muon monitors sample particles produced in meson decays. Because those muons come from the same parents as the neutrinos, their profiles reveal focusing, alignment shifts, and other changes in the beam, and they are routinely used to detect problems and guide flux predictions. The muon information can be used more quantitatively; for example, to infer the parent meson phase space, and fast radiation-hard timing detectors can add sensitivity to the momentum dependence of the focusing. These developments show both how tightly beam measurements can constrain the flux and where the current limits still lie. These approaches complement monitored-beam concepts, in which the decay region is instrumented to detect charged leptons from meson decays and to measure the neutrino flux directly.

Ganguly, Sudeshna [Fermilab] (ORCID:00000003163482

Neutrino Beam Monitoring

Accelerator facilities produce neutrino beams from meson decays in a decay volume. Experiments measure event rates that depend on flux, cross sections, and detector response, so the flux is predicted using hadron production and beamline modeling and constrained by beam instrumentation, since near detectors alone cannot separate flux from cross section. Proton, hadron, and muon monitors can track the parent particle distributions and beam conditions, providing the inputs needed for flux predictions in long-baseline experiments such as NOvA, T2K, and DUNE. This talk reviews how beam monitors are used in practice to understand neutrino flux. Proton beam monitors tell where the beam hits the target and how stable it is. Farther downstream, hadron and muon monitors sample particles produced in meson decays. Because those muons come from the same parents as the neutrinos, their profiles reveal focusing, alignment shifts, and other changes in the beam, and they are routinely used to detect problems and guide flux predictions. The muon information can be used more quantitatively; for example, to infer the parent meson phase space, and fast radiation-hard timing detectors can add sensitivity to the momentum dependence of the focusing. These developments show both how tightly beam measurements can constrain the flux and where the current limits still lie. These approaches complement monitored-beam concepts, in which the decay region is instrumented to detect charged leptons from meson decays and to measure the neutrino flux directly.

Ganguly, Sudeshna [Fermilab] (ORCID:00000003163482

Understanding the causes of satellite–model discrepancies in aerosol–cloud interactions using near-LES simulations of marine boundary layer clouds

Aerosol–cloud interactions (ACI) remain the largest source of uncertainty in model estimates of anthropogenic radiative forcing, primarily because of deficiencies in representing aerosol–cloud microphysical processes that lead to inconsistent cloud liquid water path (LWP) responses to aerosol perturbations between observations and models. To investigate this discrepancy, we conducted a series of large-eddy-scale simulations driven by realistic meteorology over the eastern North Atlantic, and evaluated LWP susceptibility, precipitation processes, and boundary layer thermodynamics using satellite and ground-based observations. Simulated LWP responses show a strong dependence on cloud state. Non-precipitating thin clouds exhibit a modest LWP decrease with increasing cloud droplet number concentration (N d ), consistent in sign but weaker in magnitude than satellite estimates, reflecting enhanced turbulent mixing and evaporation. The largest model-observation discrepancy occurs in non-precipitating thick clouds, where simulated LWP susceptibilities are strongly positive (+0.32) while observations indicate large negative values (−0.69). This discrepancy stems from excessive precipitation driven by underestimated entrainment, overly active accretion, and overly broad drop-size distributions in polluted conditions. While our high-resolution setup mitigates the excessive drizzling common in coarser models and captures key regime transitions, these biases persist – highlighting that improved parameterizations of cloud-top processes, precipitation, and aerosol effects are needed beyond simply increasing model resolution. Additionally, misrepresented moisture inversions in reanalysis introduce a moist bias in cloud-top relative humidity, further amplifying positive LWP susceptibility. Our results also suggest that large negative N d –LWP relationships in observations may reflect internal cloud processes rather than true ACI effects.

Aerosol-cloud interaction

Morphological Characterization of Uranyl Fluoride Particles via Atomic Force Microscopy

Uranium hexafluoride (UF 6 ) undergoes a rapid hydrolysis reaction when exposed to atmospheric water. In addition to producing hazardous HF gas, the hydrolysis reaction produces uranyl fluoride (UO 2 F 2 ), a radioactive solid phase particulate material. Because of the technological utility of UF 6 in the nuclear fuel cycle, understanding the transport properties of UO 2 F 2 aerosol produced via UF 6 hydrolysis is important for accident scenarios. Moreover, the fundamental chemical and physical properties of the UF 6 hydrolysis reaction are not completely understood. Recently, several experiments on the aerosol phase properties of UO 2 F 2 produced in this way have shown that under most relevant conditions, the particle size distribution (PSD) of UO 2 F 2 can be extremely small, approximately 3 to 5 nm, which is well below the threshold that can be routinely observed via scanning electron microscopy (SEM). Although readily observable in the aerosol phase, observation of nanometer-sized particles in the condensed phase (i.e. deposited on surfaces) remains a challenge. Here, in this study, we have used atomic force microscopy (AFM) to study the PSD and morphological characteristics of UO 2 F 2 deposited at low and high concentrations under different humidity conditions, a primary variable in the hydrolysis reaction. Here, we find strong agreement between PSD measured in the aerosol phase via scanning mobility particle sizing and PSD measured via AFM, with particle sizes peaked below 4 nm for low-humidity conditions. At higher humidity, the distribution is centered around 5 to 10 nm but extends up to 20 nm. These results are in stark contrast to previous measurements using SEM that show PSD on the order of 300- to 1000-nm particle sizes; moreover, these are the first direct measurements of individual particles of UO 2 F 2 having been produced via UF 6 hydrolysis deposited on surfaces. These measurements, therefore, open a new avenue for collecting and detecting UO 2 F 2 in the condensed phase and further refine the PSD, which is critical for environmental transport determinations.

Uranium hexafluoride

Time-series elemental imaging reveals CAX-dependent redistribution patterns for anoxia recovery

Flooding-induced oxygen deprivation (anoxia) is a challenge to plant survival, necessitating adaptive mechanisms for recovery. This study investigated elemental redistribution during anoxia recovery using time-series elemental imaging to show changes in nutrient distribution. Focusing on the role of Cation/H + Exchangers (CAXs) in Arabidopsis thaliana, we show how mutants deficient in specific CAX transporters (cax1 and the cax1-4 quadruple mutant) respond to anoxia and metal stress. Mutants showed reduced lipid peroxidation and increased expression of flood-tolerance proteins during recovery. X-ray fluorescence microscopy and laser ablation–inductively coupled plasma mass spectrometry were used to show elemental redistribution over time. In wild-type plants (Col-0), post-anoxia elemental distribution resembled the elemental distribution of CAX mutants under normoxic conditions, suggesting that CAX-mediated elemental distribution before anoxia enables faster recovery post-anoxia, rather than affecting remobilization post-anoxia. Although CAX mutants had altered tolerance to excess manganese and copper, leaf metal distribution during metal stress was not altered. Here, these findings introduce the potential utility of time-series elemental imaging to show stress-response phenotypes and the importance of elemental distribution to recovery after anoxia. The novelty of this work lies in resolving spatial distribution patterns in a non-static system to gain insight into mechanisms of stress resilience in plants.

36 MATERIALS SCIENCE

A novel conditional formulation of the Vlasov–Ampère equations: a conservative, positivity, asymptotic and Gauss law preserving scheme

We propose a novel reformulation of the Vlasov–Ampère equations for plasmas that reveals discrete symmetries that enables simultaneous conservation of mass, momentum and energy; preservation of Gauss’s law; positivity of the distribution function; and consistency with quasi-neutral asymptotics. The approach employs variable and coordinate transformations to yield a coupled system comprising a modified Vlasov equation and associated moment–field equations. The modified Vlasov equation advances a conditional distribution function that excludes mass, momentum and energy densities, which are instead evolved through moment equations enforcing the relevant symmetries, conservation laws and involution constraints. This reformulation aligns naturally with a recent slow-manifold reduction technique, which separates fast electron time scales and simplifies the treatment of the quasi-neutral limit within the reduced moment–field subsystem. Using this framework, we develop a numerical method for the reduced 1D1V subsystem that, for the first time in the literature, satisfies all key physical constraints while maintaining a quasi-neutral asymptotic behaviour. The advantages of the method are demonstrated on canonical electrostatic test problems, including the multiscale ion acoustic shock wave.

1D1V

A two-and-a-half dimensional symplectic space-charge solver

The nonlinear space-charge effect plays a significant role in high-intensity accelerators and has been extensively studied using multi-particle tracking methods. In this paper, we present a novel 2.5- dimensional symplectic space-charge solver specifically designed for long beam bunches. We begin by detailing its application to a transverse Gaussian density distribution under open boundary conditions in a straight system, where a semi-analytical expression is derived. We then demonstrate the solver’s adaptation to arbitrary distributions in open space, as well as within rectangular and round conducting pipes. Finally, we discuss the extension of this solver to circular accelerator systems. This study shows that the fast 2.5-dimensional solver can be a good approximation to the fully three-dimensional solver for long bunches in large circular accelerators.

Beam code development & simulation techniques

Generative unfolding with distribution mapping

Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We show how to extend two morphing techniques, Schrödinger Bridges and Direct Diffusion, in order to ensure that the models learn the correct conditional probabilities. This brings distribution mapping (DM) to a similar level of accuracy as the state-of-the-art conditional generative unfolding methods. Numerical results are presented with a standard benchmark dataset of single jet substructure as well as for a new dataset describing a 22-dimensional phase space of Z+2 -jets.

Butter, Anja

Graph-based Simulation Framework for Power Resilience Estimation and Enhancement

The increasing frequency of extreme weather events poses significant risks to power distribution systems, leading to widespread outages and severe economic and social consequences. This paper presents a novel simulation framework for assessing and enhancing the resilience of power distribution networks under such conditions. Resilience is estimated through Monte Carlo simulations, which simulate extreme weather scenarios and evaluate the impact on infrastructure fragility. Due to the proprietary nature of power network topology, a distribution network is synthesized using publicly available data. To generate the weather scenarios, an extreme weather generation method is developed. To enhance resilience, renewable resources such as solar panels and energy storage systems (batteries in this study) are incorporated. A customized Genetic Algorithm is proposed to determine the optimal locations and capacities for solar panels and battery installations, maximizing resilience while balancing cost constraints. Experiment results demonstrate that on a large-scale synthetic distribution network with more than 300,000 nodes and 300,000 edges, the proposed framework can efficiently evaluate the resilience, and enhance the resilience through the installations of distributed energy resources (DERs), providing utilities with valuable insights for community-level power system resilience estimation and enhancement.

Wang, Xuesong [Wayne State Univ., Detroit, MI (Uni

Conditional diffusion machine-learning framework for mapping valence electron distribution from convergent beam electron diffraction

Quantitative convergent beam electron diffraction (CBED) enables determination of aspherical valence electron distributions through refinement of low-order structure factors, which are highly sensitive to chemical bonding and charge density variations. However, conventional quantitative CBED (QCBED) requires solving a highly nonlinear inverse problem with many coupled parameters, and computationally intensive dynamical diffraction calculations, making it time-consuming and difficult to apply to complex systems. More broadly, reconstructing charge density and orbital electron distribution from diffraction data has long been a central challenge in both x-ray and electron crystallography. Here, in this study, we introduce an artificial-intelligence (AI)-based framework that replaces traditional refinement with a data-driven inverse solver. Using a large synthetic CBED dataset generated by Bloch-wave simulations, we train a conditional diffusion model to directly infer crystal structural parameters and multipole density formalism parameters, and hence valence electron distributions, from CBED patterns alone. By learning from forward simulations across realistic parameter space, the model effectively solves the inverse problem. Compared with direct regression approaches, the diffusion-based framework provides posterior parameter distributions for rigorous uncertainty quantification while preserving quantitative fidelity and reducing analysis time by orders of magnitude. By eliminating the need for external single-crystal x-ray diffraction data and complex nonlinear refinement, this approach enables practical, high-throughput, and in situ quantitative CBED, enabling real-time mapping of valence electron distributions and their correlation with functional responses in quantum and energy materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND

Seeding Advanced Treated Wastewater for Purposes of Direct Potable Reuse

Direct potable reuse (DPR) is a promising solution to address water scarcity. However, a better understanding of how introducing advanced treated water (ATW) affects microbial communities present in distribution systems is needed. Here, in this study, we measured changes to the microbial water quality in simulated distribution systems that were conditioned using treated, unimpaired surface water (SW) and then transitioned to ATW. In addition, we investigated whether adding a biological filtration step would seed the microbial community of the ATW and whether the influence would persist in the simulated distribution systems. We found that the bulk water in the ATW-fed distribution systems had lower cell counts and ATP concentrations and a distinct microbial community (based on 16S amplicon sequencing) compared to the SW-fed or the seeded ATW-fed systems. However, biofilm community composition and biomass remained consistent regardless of the feedwater. Increased microbial biomass and diversity were present in the seeded ATW, with several amplicon sequence variants identified as being introduced by the biological filter. Our results suggest that directly introducing ATW to distribution systems could disturb the existing microbial community. Preparing ATW for distribution via biological filtration may deliver more predictable and stable microbial water quality than introducing unseeded ATW.

16S

Automated Gold Nanorod Spectral Morphology Analysis Pipeline

The development of a colloidal synthesis procedure to produce nanomaterials with high shape and size purity is often a time-consuming, iterative process. This is often due to quantitative uncertainties in the required reaction conditions and the time, resources, and expertise intensive characterization methods required for quantitative determination of nanomaterial size and shape. Absorption spectroscopy is often the easiest method for colloidal nanomaterial characterization. However, due to the lack of a reliable method to extract nanoparticle shapes from absorption spectroscopy, it is generally treated as a more qualitative measure for metal nanoparticles. This work demonstrates a gold nanorod (AuNR) spectral morphology analysis tool, called AuNR-SMA, which is a fast and accurate method to extract quantitative structural information from colloidal AuNR absorption spectra. To demonstrate the practical utility of this model, we apply it to three distinct applications. First, we demonstrate this model's utility as an automated analysis tool in a high-throughput AuNR synthesis procedure by generating quantitative size information from optical spectra. Second, we use the predictions generated by this model to train a machine learning model to predict the resulting AuNR size distributions under specified reaction conditions. Third, we apply this model to spectra extracted from the literature where no size distributions are reported and impute unreported quantitative information on AuNR synthesis. This approach can potentially be extended to any other nanocrystal system where absorption spectra are size dependent, and accurate numerical simulation of absorption spectra is possible. In addition, this pipeline could be integrated into automated synthesis apparatuses to provide interpretable data from simple measurements, help explore the synthesis science of nanoparticles in a rational manner, or facilitate closed-loop workflows.

36 MATERIALS SCIENCE

Reaction Pathways over ZnZrO 2 -Based Catalysts and Catalytic Sorbents

Reactive capture and conversion (RCC) is a process intensification approach that integrates CO 2 capture and hydrogenation within a single unit, removing the CO 2 purification and storage steps of traditional process flow schemes. This alters the catalytic step from a traditional steady-state (SS) flow process to a transient capture and conversion cycle, which could lead to product distributions distinct from those observed in conventional SS experiments. Such differences are investigated in the combined capture and hydrogenation of carbon dioxide to methanol over a ZnZrO 2 catalyst and a ZnZrO 2 + NaNO 3 /Mg 3 AlO x catalytic sorbent (CS) using fixed-bed kinetic measurements, in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), and steady-state isotopic transient kinetic analysis-DRIFTS (SSITKA-DRIFTS). Under SS conditions, ZnZrO 2 produced methanol through sequential hydrogenation of HCOO* and CH 3 O* intermediates. On the contrary, CO was attributed primarily to CO 2 dissociation at oxygen vacancies, as supported by isotopic shifts and measured reaction orders. For the CS, isotopic switching experiments suggested that monodentate carbonate species (CO 3 2− , abbreviated as m-CO 3 2− ) act as active intermediates that can be hydrogenated to HCOO* and subsequently to CH 3 O. Under RCC conditions, in situ DRIFTS and isotopic experiments reveal that m-CO 3 2− species formed during the CO 2 capture step follow two competing routes upon H 2 exposure: (i) direct hydrogenation to methane on the sorbent domain or (ii) migration of m-CO 3 2− to the ZnZrO 2 domain, where they are hydrogenated to methanol through the HCOO pathway. Overall, RCC enables carbonate hydrogenation routes not observed under SS cofeed conditions. Thus, the reaction pathways and rates during RCC can be different from operation under conventional SS conditions, and the product distribution is determined here by competition between carbonate hydrogenation on sorbent sites and migration to ZnZrO 2 for methanol synthesis.

CCUS

Planning for Seattle at the Convergence of Resilience and Energy

Grid reliability metrics obscure important temporal, spatial, and categorical considerations for increasing energy resilience. Systemwide or feeder-level outage metrics do not identify which kinds of services are affected by outages, where, and for how long. These outage metrics indicate the impacts of outages but cannot measure the consequences to customers that could result from those outages. The consequences of power outages for surrounding community members are caused by disruptions to electricity-dependent critical services, rather than to electricity itself. Power outages can decrease a community's access to healthcare, fuel, safe indoor temperatures, and provisions like food and water. This project developed critical service access, a new consequence-focused resilience metric that quantifies the relative access to critical services provided to households by distribution infrastructure during normal conditions and major disruptions. We use a spatially granular grid analysis that facilitates targeted resilience interventions; dividing feeders into isolatable sections and combining those sections with the critical service access metric allows us to identify where energy improvements like solar-plus-storage microgrids could create the most benefits for community members by protecting access to food, fuel, health, shelter, and public safety services. We identify locations in a South Seattle study area that could be high priorities for resilience investment and summarize their potential neighborhood-scale resilience benefits. This analysis was complemented by direct feedback from study area residents collect through a survey and focus groups. Results can help utilities understand how and where long power outages can create real consequences for customers, set strategic targets based on that understanding, and measure the potential benefits of energy resilience upgrades for more informed decisions.

14 SOLAR ENERGY

Toward Efficient Entropic Recycling by Mastering Ring–Chain Kinetics

Traditional chemical recycling approaches for condensation polymers suffer compounding energy losses and CO 2 emissions across multiple polymerization and depolymerization cycles. Entropic recycling can address these energy losses by entrapping free energy within the deconstruction products. Entropic recycling involves depolymerization to macrocyclic monomers, but such processes have not been feasible due to the high dilutions typically required to generate macrocyclic compounds. Here, we leverage selective catalysis to allow entropic recycling at concentrations 20–2000× higher than typical for macrocyclization reactions. We find that Ru-based olefin metathesis catalysts containing bulky iodine ligands significantly bias the ring–chain kinetic product distribution during ring-closing metathesis (RCM) toward the formation of oligomeric cycloalkenes. Further improvements in reaction concentration and macrocycle yield are obtained by using high catalyst loadings and by predisposing the alkene substrates to undergo favorable macrocyclization. These RCM optimizations translate effectively to cyclodepolymerization (CDP) of an olefin-containing polymer, with RCM and CDP affording similar macrocycle product distributions under identical reaction conditions. Macrocycle polymerization by entropy-driven ring-opening metathesis provides much higher molecular weight polymers than condensation polymerization of linear analogues, reducing the time to achieve high molecular weight from hours to minutes and enabling polymerization at room temperature. Finally, our findings re-emphasize the importance of energy consumption during a polymer’s lifecycle and provide a framework for the design of efficient entropic recycling systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Characterization of Organosulfates and Organonitrates in Vertically Resolved Aerosols over the Southern Great Plains Field Campaign Report

Aerosol is the largest individual source of uncertainty in assessing the Earth’s radiative balance due to poorly understood aerosol processes and aerosol-cloud interactions. Thus, to constrain aerosol’s climate impacts, it is critical to understand aerosol compositions at high elevations. Organic material accounts for about 20-80% of total aerosol mass in the atmosphere and is known to play a key role in aerosol processes and cloud formation. In prior research, characterization of aerosol molecular composition has been mostly carried out at ground level. In these ground-level measurements, total speciated organosulfates and organonitrates may contribute significantly to organic aerosol mass on the order of 10-40%, most of which are from abundant biogenic volatile organic compounds such as isoprene and monoterpenes. Organosulfates and organonitrates have also been shown to be present in substantial amounts in cloud water, suggesting that they can be transported and/or directly formed at high elevations. On the other hand, the less common and more expensive aircraft studies have performed high-altitude measurements, but were often limited in the number of different altitudes sampled and in sample collection under consistent conditions. Therefore, the vertical distributions of key aerosol components such as organosulfates and organonitrates are not well understood and have not been extensively studied. Nevertheless, it is important to understand how organosulfates and organonitrates in aerosols are distributed, transformed, participate in aqueous-phase processes, and interact with high-altitude clouds.

54 ENVIRONMENTAL SCIENCES

Legacy Metallic Fuel U(Pu)Zr Data Qualification

The Metallic Fuels Irradiation and Physics Database (FIPD) is an organized collection of legacy metallic fuel U(Pu)Zr data, measurements, and reactor conditions from the Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF). The database provides a wealth of information and is easily accessed and utilized by the U.S. nuclear industry and the Nuclear Regulatory Commission (NRC). The database contains three categories of data. The first category covers fuel pin fabrication specifications, including fuel slug diameter and length, cladding diameter, smeared density, etc. The second consists of operational conditions, which include axial distributions of power, temperature, fluence, burnup, isotopic density, etc. The third category contains post-irradiation examination (PIE) results, largely collected at facilities now part of the Idaho National Laboratory (INL), formerly part of Argonne National Laboratory -West (ANL-W) and at the Alpha-Gamma Hot Cell Facility (AGHCF) at Argonne National Laboratory, comprising fission gas release and chemistry analysis, profilometry measurements, neutron radiography data, etc. All these data have been organized and preserved in FIPD.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS

Dual-band Ion-scale Waves in the Solar Wind: Statistical Properties and Comparison with Single-band Ion-scale Waves

We present a comprehensive statistical analysis of ion-scale waves including dual-band ion-scale waves (DBIWs) observed by the Parker Solar Probe in the solar wind. DBIWs are characterized by the simultaneous occurrence of distinct left- and right-handed polarized wave packets at higher and lower frequencies, respectively, in the spacecraft frame, implying the presence of bidirectionally propagating L-mode waves. We identify and compare DBIWs to single-band ion-scale waves (SBIWs) and investigate their spatial distributions, background magnetic field conditions, and associated inertial range turbulence characteristics. We find that DBIWs exhibit a strong preferential occurrence at heliocentric distances between 0.2 and 0.25 au (equivalently 43 and 54 R S ) in contrast to SBIWs, the occurrence rate of which peaks closer to the Sun and decreases with the heliocentric distance. DBIWs are also preferentially observed under conditions of strong Alfvénic turbulence, characterized by low magnetic compressibility and perpendicular-dominant fluctuations. These findings support a scenario in which stochastic ion heating via sufficiently large perpendicular Alfvénic fluctuation generates the ion temperature anisotropies required to excite bidirectional ion cyclotron waves. This work advances our understanding of kinetic wave generation in the solar wind and its connection to large-scale turbulence.

79 ASTRONOMY AND ASTROPHYSICS