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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Testing of a Line Driver With Configurable Pre-Emphasis on Lossy Transmission Lines

Rare-event physics experiments such as the Deep Underground Neutrino Experiment (DUNE) or the next Enriched Xenon Observatory (nEXO) experiment search for rare, low-energy events, detected by sensitive detectors immersed in a cryogenic noble liquid (e.g., liquid argon or xenon). Readout electronics used within such detectors must consume minimal power while operating reliably in cryogenic environments. Furthermore, in the case of nEXO, maximizing the radiopurity of the environment is vital to minimize background noise, thus placing strict limits on the volume of dielectric materials, leading to high-loss data cables spanning distances up to 12 m. Such cables cause high attenuation and intersymbol interference (ISI), resulting in a high bit-error rate (BER). These issues were addressed by developing an integrated line driver with configurable pre-emphasis in a 65-nm CMOS process. The pre-emphasis parameters can be programmed to minimize BER for specific cables and data rates under power constraints. Here, the driver was tested at both room and cryogenic temperatures. In both cases, the output BER was found to be strongly correlated with the pre-emphasis settings. Furthermore, analysis and simulation showed that adapting the pre-emphasis settings based on the incoming bit sequence can further improve performance with minimal changes to the current solution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

SPT-3G D1: Compton-$y$ maps using data from the SPT-3G and Planck surveys

We present thermal Sunyaev-Zel'dovich (tSZ) Compton-$y$ parameter maps constructed from two years (2019-2020) of observations with the South Pole Telescope (SPT) third-generation camera, SPT-3G, combined with data from the Planck satellite. Using a linear combination (LC) pipeline, we obtain a suite of reconstructions that explore different trade-offs between statistical sensitivity and suppression of astrophysical contaminants, including minimum-variance, CMB-deprojected, and CIB-deprojected $y$-maps. We validate these maps through different statistical techniques such as auto- and cross-power spectra with large-scale structure tracers as well as stacking on cluster locations. These tests are used to understand the balance between noise and astrophysical foreground residuals (such as the CIB) in combination with the recovery of the tSZ signal for different maps. For example, results from stacking at the location of clusters confirm the robustness of the recovered tSZ signal over the $\sim 1500\: {\rm deg}^2$ SPT-3G survey field used in this analysis. The high-resolution and low-noise maps produced here provide an important cosmological tool for future studies, including measurements of the Compton-$y$ map power spectrum, cross-correlations with other tracers of the large-scale structure, detailed modeling of cluster pressure profiles, and study of the thermodynamic state of the baryons in the Universe.

Maniyar, A. S. [Harvard-Smithsonian Ctr. Astrophys↗
DOE OSTI2026

Numerical Analysis of Offshore Wind-Farm-Induced Drag Effects on Coastal Upwelling Dynamics

Wind farms extract momentum from the atmospheric flow, generating wind-speed deficits both within the plant, and extending downstream. When located offshore, these deficits modulate air-sea coupling, potentially impacting coastal upwelling in sensitive regions. We investigate impacts of wind farms on coastal upwelling using kilometre-scale, three-way-coupled simulations with the coupled ocean-atmosphere-wave-sediment transport system for the US West Coast. Wind-farm effects are represented by a generalised turbine drag formulation, an idealised, height-dependent body force whose magnitude is systematically varied. This approach isolates the leading-order fluid-dynamical response in a realistic coastal configuration. The atmospheric adjustment exhibits an approximately linear relation between drag force and wind-speed deficit, with wakes that expand downstream and increase in magnitude as drag increases. An empirical orthogonal function analysis of sea-surface-temperature anomalies reveals the emergence of a canonical dipole pattern under strong drag forcing. Subsurface diagnostics show consistent shoaling of the mixed layer and suppressed upward velocities in areas near wind-farm region, accompanied by compensating enhancements of shoaling closer to the coast. These results identify turbine drag as a control parameter in assessing interactions between wind-farm wake and coastal upwelling and provide scaling relationships for understanding offshore wind-farm effects on the coastal circulation dynamics.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Measurement of the muon neutrino charged-current mesonless cross section in the NOvA near detector

NOvA is a long-baseline accelerator neutrino experiment at Fermilab. Its physics goals include precision neutrino oscillation measurements, neutrino interaction cross-section measurements and beyond Standard Model explorations. We present a measurement of muon neutrino charged-current cross section with zero mesons in the final state at the NOvA near detector. This measurement is performed as a function of the kinematics of the final state muon. Our chosen interaction channel is especially sensitive to quasielastic and meson exchange current interactions and it provides handles for constraining the cross section systematic uncertainties in oscillation analyses in present and future experiments. For particle identification, we use a convolutional neural network (CNN) trained on individual particles simulated in the NOvA Near detector. This allows us to select the desired signal while reducing the potential bias from neutrino interaction modeling. We study strategies for constraining the remaining charged-pion background via Michel electron information in a template fitting approach. The main experimental result is a two-dimensional differential cross section as a function of final-state muon kinetic energy and polar angle. The parameters of this measurement, including binning and unfolding, were optimized to reduce the expected systematic uncertainty in the total cross section. The final result shows good agreement with the main GENIE-based simulation framework that was independently fine-tuned in NOvA. We finally propose improvements and subsequent steps that build on this analysis and further dissect the final states of neutrino interactions. This work has been supported by US DOE grant DE-SC0015684.

Sánchez Falero, Sebastián Jesús [Iowa State U.]↗
DOE OSTI2026

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↗
DOE OSTI2026

PPPL Report on Reduced Modeling of Fusion Alpha Transport in ARC Burning Plasmas

We are reporting on the modeling of fusion alpha particle transport in the planned ARC fusion device being designed by the CFS (Commonwealth Fusion Systems: https://cfs.energy). The ARC tokamak is designed to operate in a burning-plasma regime characterized by a substantial population of fusion-born alpha particles. Alfvén eigenmode (AE) stability is assessed both analytically and numerically, incorporating alpha-particle drive, ion Landau, and radiative damping from thermal species and collisional damping from trapped electrons. Regions of unstable and near-threshold AE activity are mapped across ARC’s operational parameter space. Linear stability analysis with NOVA indicates multiple, often marginally unstable AEs, extending to toroidal mode numbers up to n= 30. The present report focuses on the ARC flat-top operating point prior to the sawtooth event. Alpha-particle transport on timescales exceeding the neoclassical slowing-down time is assessed using the NUBEAM module [1][2] of the TRANSP code [3], employing transport coefficients derived from the RBQ quasilinear modeling (cf. Appendix B). These global simulations identify favorable and unfavorable operating regimes with respect to alpha confinement, pressure redistribution, and overall alpha-heating efficiency. We also evaluate additional transport mechanisms—including neoclassical tearing mode (TM)–induced stochasticity, sawtooth-driven redistribution, and toroidal-field ripple using the kick model (cf. Appendix C) which makes use of the guiding-center code ORBIT, see Section 5. The kick model is integrated into TRANSP to enable self-consistent predictions of alpha-driven current formation and sustainment within the ARC scenario. Sensitivity scans are performed over the mode frequency, rational-surface alignment, island width, mode amplitude, and proximity of the limiter to the plasma. Our study provides an initial, physics-based guidance for machine design, operational planning, and equilibrium control, ensuring adequate alpha confinement and robust self-heating performance in ARC. Our simulations mostly targeted worst case scenarios, e.g. for TMs and sawteeth. Overall, we expect benign effects for the ARC scenario investigated in this work on fusion alpha confinement and losses in the presence of AEs, tearing modes and sawteeth. This report addresses three thrusts identified at the outset. The first thrust focuses on analytic estimates of the parametric dependencies of EP relaxation based on local AE stability simulations (Section 3). The second thrust involves global evaluations of AE stability using the NOVA, RBQ, and NUBEAM codes (Section 4). Finally, we investigate alpha-particle transport driven by low-frequency instabilities associated with sawteeth and tearing modes (Section 5).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

A Tutorial on Bayesian analysis of linear shock compression data

Gas gun and other shock compression experiments often produce shock wave velocity measurements that are linearly associated with particle velocity. Traditionally, this empirical relationship is quantified with a single Hugoniot curve that is estimated using least squares regression. However, for downstream modeling and simulation tasks, it is often more useful to have multiple Hugoniot curves in the pressure–volume plane that are consistent with the data. We employ Bayesian uncertainty quantification methods as a framework for propagating measurement uncertainty through to model parameters and predictions. Specifically, this Tutorial shows how to sample multiple Hugoniot curves in the pressure–volume plane that are consistent with the shock wave-particle velocity measurements in a two-step Bayesian approach. First, we obtain an analytical expression for the posterior distribution of the linear model parameters using Bayesian linear regression. Second, we propagate samples from the posterior distribution through the Rankine–Hugoniot equations to yield Hugoniot curves in the pressure–volume plane. The procedure is demonstrated with publicly available data on argon, copper, and nickel, and compared against bootstrapping and linear regression. The Bayesian procedure is shown to be interpretable, computationally inexpensive, and less sensitive than an alternative bootstrapping approach to the removal of the point in the copper dataset that has the largest particle velocity. As a Tutorial on Bayesian methodology for the shock compression community, we provide several derivations and explanations that make this paper self-contained, and make all code and data available at github.com/llnl/BALSCD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Experimental Report: Multi-Instrument Comparison of AAF Size Distribution Instruments

Aerosols are particles suspended in the atmosphere, ranging in size from nanometers to micrometers. Their size distribution affects key atmospheric processes, including nucleation, coagulation, scavenging, activation, and radiative properties (Seinfeld and Pandis 2016). Aerosol size distribution is a critical parameter in atmospheric science, influencing processes such as cloud formation and radiative forcing. Accurate representation of aerosol size distributions is essential for understanding their impact on climate, air quality, and human health. However, aerosol size and composition vary significantly across time and space due to meteorological conditions and natural or anthropogenic sources. (Wu and Boor 2021). Various instruments are used to measure aerosol size distributions, each with distinct principles, advantages, and limitations. This report begins with an in-depth overview of aerosol size-distribution comparison studies, focusing on the passive cavity aerosol spectrometer probe (PCASP), portable optical particle spectrometer (POPS), ultra-high-sensitivity aerosol spectrometer (UHSAS), aerodynamic particle sizer (APS), and scanning mobility particle sizer (SMPS). All of these instruments are used by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF), which commissioned this comparison and report. The report evaluates the strengths and weaknesses of these instruments, highlights their applications, and discusses efforts to merge data from multiple instruments for comprehensive analysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Imaging Ferroelastic Domain Walls in Hybrid Improper Ferroelectric Sr 3 Sn 2 O 7

For this work, we combined synchrotron-based near field infrared spectroscopy and atomic force microscopy to image the properties of ferroelastic domain walls in Sr 3 Sn 2 O 7 . Although frequency shifts at the walls are near the limit of our sensitivity, we can confirm semiconducting rather than metallic character and widths between 20 and 60 nm. The latter is significantly narrower than in other hybrid improper ferroelectrics like Ca 3 Ti 2 O 7 . We attribute this trend to the softer lattice in Sr 3 Sn 2 O 7 , which may enable the octahedral tilt and rotation order parameters to evolve more quickly across the wall without significantly increased strain. These findings are crucial for the understanding of phononic properties at interfaces and the development of domain wall-based devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Availability of Critical Benchmark Experiments for the Pebble Tanker Transportation Model for Nuclear Criticality Safety Validation of TRISO Pebbles

This study addresses the need for comprehensive investigations into TRi-structural ISOtropic (TRISO) fuel pebble transportation validation. In this work, an exploratory model, the pebble tanker(PT), was developed with the aim of facilitating the validation of nuclear criticality safety calculations in the context of industrial-scale transportation of TRISO fuel. The PT model was designed to investigate the availability and applicability of critical benchmark experiments crucial for assessing the transportation of these pebbles. This work incorporated sensitivity/uncertainty (S/U) similarity studies to quantify the applicability of critical benchmark experiments and to address nuclear data uncertainties in the context of TRISO transportation. Two container models were investigated: one for the Hermes-type pebble and one for the Pebble Bed Modular Reactor (PBMR)–type pebble. The models were simplified, considering fuel, containment, and either water or air, to enable a focus on the underlying physics of applications involving TRISO fuel pebbles using the PT model. A crucial aspect under consideration was the capacity of the transport package to hold pebbles while ensuring subcriticality in the flooded state. An approach in the criticality validation process involves assessing the similarity between systems through an integral index parameter evaluation. This involves calculating a correlation coefficient (referred to as c k ) based on shared nuclear data–induced uncertainty between a benchmark experiment and the application of the PT model. To facilitate this analysis, the SCALE tools, particularly the CSAS6-Shift, TSUNAMI-3D-Shift, and TSUNAMI-IP sequences, were employed for comprehensive studies in neutronics and S/U analysis. Our findings showed that there are sufficient critical experimental benchmarks to perform this validation of the PT model in the most reactive state, i.e. when the tanker is flooded. This paper provides valuable insights into validating a transport package for Generation IV TRISO fuel pebbles.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Co-optimization of fuel properties, combustion system geometry, and injection strategy for conventional diesel fuel

Here, studies have shown that fuel properties can impact an engine’s operation in several ways, including ignition delay, sooting tendency, mixture formation, and combustion temperature. In mixing-controlled compression ignition (MCCI) engines, the fuel system design and piston bowl geometry significantly affect combustion performance and emissions. Based on current information, it is difficult to draw conclusions about fuel property effects and sensitivities. The central fuel hypothesis approach used in the US Department of Energy Co-Optima program has worked well for spark ignition fuels: identifying critical fuel property ranges is sufficient to screen fuel blends that are expected to maximize efficiency and reduce pollutant emissions. However, for MCCI-relevant fuels, the information gained from past studies is not sufficient to build such a merit function or to allow for performing a similar screening of fuel blends. It is hypothesized that a co-optimization of a fuel’s physical and chemical properties, combustion system geometry, and injection strategy could leverage synergies between the effects of the fuel properties and geometries, resulting in improved performance over state-of-the-art. A machine learning–assisted unconstrained global optimization algorithm was used to explore a design space comprising 23 independent variables. The results show that physical property effects were minimal even for large variations in fuel properties, and the only interaction effect that was observed was the effect of varied fuel density parameters on fuel/air mixture formation. Nevertheless, these interactions were not sufficient in magnitude to significantly affect optimization results. Therefore, analysis of the results suggests that fuel physical properties cannot be leveraged in a co-optimization context to increase engine efficiency.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Assessment of MiniFuel Subcapsule Design Recommendations on Previous Experiments

MiniFuel describes the class of separate effects nuclear fuels irradiation experiments that have been conducted in the High Flux Isotope Reactor (HFIR) since 2018. These experiments comprise a stack of six fuel-bearing subcapsules contained in a stainless steel target housing that is in contact with HFIR coolant on its exterior. All MiniFuel targets have a near-standardized architecture, and the primary design variables that change between experiments are the radial gap size between the subcapsule and housing and the target fill gas composition. Finite element heat transfer models are used to determine the optimum gas composition and gap sizes, and recent studies were performed to identify model parameters that contribute the most uncertainty to fuel specimen temperature predictions. That work, which is referenced herein, also recommended a set of design modifications to the subcapsule internal architecture and assembly process. These modifications are intended to reduce fuel temperature uncertainty in future experiments. In this report, the subcapsule design modifications were retroactively applied to a previously conducted MiniFuel experiment to determine how these changes affect the established safety and performance envelope of the experimental capability. These effects were determined in two steps. First, the modifications were applied to the subcapsule design without any other changes to determine their isolated effect on the predicted fuel specimen temperatures. This portion of the analysis showed that fuel temperatures were modestly reduced because the implemented changes improved heat transfer efficacy. Next, traditional MiniFuel design activities (i.e., sizing the gas gaps and determining the fill gas composition) were reperformed, and they confirmed that the original desired fuel temperatures could be achieved while remaining within established safety limits. Therefore, this report demonstrates improved performance resulting from the subcapsule modifications, which mitigate uncertainty while meeting the objectives of past experiments. An additional benefit of the design changes is reduced sensitivity of the fuel temperature to the evolving flux spectrum in HFIR, leading to more stable temperatures and enhanced utility of MiniFuel as a separate effects irradiation platform.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Prognostic simulations of mixed-phase clouds with model AC-1D v1.0: the impact of aerosol types and freezing parameterizations on ice crystal budgets

Mixed-phase clouds at high latitudes contribute to the uncertainty in predicting cloud feedbacks and climate sensitivity, mainly due to the complexity of microphysical processes that influence the partitioning between the supercooled liquid and ice phases, and hence, cloud radiative effects on regional scales. Particularly in Arctic mixed-phase clouds, the activation of ice-nucleating particles (INPs) from various aerosol populations remains a leading source of uncertainty. We developed an aerosol-cloud one-dimensional (AC-1D) model, which provides a novel framework to prognostically treat INP and ice crystal budgets while explicitly accounting for polydisperse and multicomponent aerosol that activate INPs following different freezing parameterizations. The AC-1D model is informed by large-eddy simulations to probe the impact of INP representation on predicted ice crystal number concentrations (N i ) and ice crystal budgets in mixed-phase Arctic stratus. We apply three immersion freezing (IMF) parameterizations, two time-independent (singular) and one time-dependent (classical nucleation theory), to predict the evolution of the INP reservoir and resulting ice crystal budget from polydisperse mineral dust, organic (humic-like substances), and sea spray aerosol particle size distributions. Our analysis focuses on how variations in aerosol number concentration and cloud system parameters such as cloud cooling rate, cloud-top entrainment rate, and ice crystal fall speed influence the INP reservoir and ice crystal budgets. Furthermore, this study investigates the competitive ice nucleation dynamics in mixed aerosol environments and provides a process-level quantification of the INP budget terms, which directly controls ice crystal budgets. For all studied case scenarios, the aerosol types and associated particle size distributions significantly impact INP and N i , and the choice between a time-dependent and a singular freezing description yields orders-of-magnitude differences in the predicted INP and N i over the 10 h simulation time, reflecting typical cloud lifetimes. Our results show that the influence of cloud cooling, INP entrainment, and sedimentation varies significantly depending on the chosen freezing parameterization. These findings underscore the critical need for robust IMF parameterizations and precise cloud system observations to enhance the accuracy of models in predicting mixed-phase cloud structure and evolution.

Arctic clouds↗
DOE OSTI2025

Revisiting the Role of Mobile Ion Size for the Activation Barrier of Charge Transport in Single Ion Conducting Polymers

The development of polymer electrolytes for energy storage and conversion technologies requires a fundamental understanding of the material parameters controlling the energy barriers for ion transport. In glassy polymers, these activation barriers are usually extracted by using Arrhenius procedures. However, our recent studies of single ion conducting polymers reveal that this traditional Arrhenius description provides anomalously small prefactors, an issue that is widely disregarded in the literature. Here, to address it, an alternative approach was introduced for extracting the effective activation barrier E by imposing a physically valid limit (∼10 –13 s) for the ion hopping attempt time. Two consequences of this recent approach are that in polymer electrolytes (i) E is significantly (∼30–40%) lower than the barrier estimated using traditional Arrhenius fits and (ii) E displays significant temperature dependence even in the glassy state. Under these circumstances, we are revisiting the role of ion size, glass transition temperature T g , and dielectric and elastic constants of the polymer matrix on the effective activation barriers for ion transport as extracted using the recently proposed approach and highlight the differences from the picture that previously emerged based on simplistic apparent Arrhenius analysis. To this end, we investigate cation transport in three families of single ion conducting trifluoromethane sulfonimide-based polyanions with varied T g . Our results indicate that E decreases with mobile cation size and is highly sensitive to changes in the dielectric permittivity of the matrix, even for large cations. These insights call for revisions of many earlier results based on apparent Arrhenius fits as the proposed approach can provide more accurate guidance for the design of polymer electrolytes with enhanced ionic conductivity.

energy barrier↗
DOE OSTI2026

Thermal Reservoir Networks for Modularly Expandable Thermal Microgrids

The Department of Defense (DoD) faces the substantial challenge of cost-effectively retrofitting one to two installations per month, each comprising approximately 1,000 buildings, to improve resilience, reduce energy consumption, and enhance energy supply security. Achieving these objectives requires optimal system selection and effective risk mitigation during system integration. To address this need, we introduce Platform-Based Design (PBD), a structured, hierarchical methodology adapted from other industrial sectors to the domain of energy system retrofits. We demonstrate the effectiveness of PBD through a techno-economic feasibility study comparing geothermal-coupled thermal energy networks (TENs) with conventional energy systems for heating, cooling, and powering 17 buildings at Joint Base Andrews (JBA) in Maryland. Our analysis illustrates that the PBD approach enables rigorous, data-driven, sequential decision making, resulting in a family of Pareto-optimal systems, among which the TEN emerged as the most promising solution. The selected TEN design integrates geothermal borefields, heat recovery heat pumps, photovoltaic (PV) arrays, and battery storage. Compared to the baseline system – gas heating combined with air-source chillers – the proposed TEN reduces annual imported energy by 74% and peak electricity demand by 45%, achieves a levelized cost of energy of $\$0.210$/kWh, and substantially enhances resilience. Life-cycle costs increase by approximately 6%, and initial investment costs are about 2.5 times higher than the baseline. However, if central plant infrastructure, district loops, and utility-scale PV and battery systems are privately funded and operated, the initial investment would fall below the baseline system cost. Critical to achieving these significant performance improvements were detailed nonlinear dynamic simulations coupling geothermal heat transfer, energy system operation, and realistic feedback control logic. These simulations identified essential design modifications and control strategy refinements that substantially reduced energy use, peak demand, and compressor shortcycling, thereby improving durability and reliability—issues that would have been significantly more expensive to resolve during operation. Additionally, the verification step highlighted sensitivities to key design parameters that could reduce initial investment by approximately $\$2$ million and reduce annual life-cycle costs more than $\$300,000$. We recommend adopting the PBD methodology for future feasibility studies and TEN pilot projects to gain valuable operational experience. Furthermore, we recommend that DoD invest in transferring and scaling the PBD methodology to other installations. This entails developing standardized computational frameworks and component libraries as well as training industry in conducting PBD. Such investments would enable rapid, robust, reliable, and cost-effective retrofits, supporting DoD’s ambitious energy system modernization goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Overview of the Nephele Perturbed Parameter Ensemble for Aerosol‐Cloud Interactions in E3SMv3

Aerosol-cloud interactions (aci) are the leading source of uncertainty in inferring climate sensitivity from the historical record. Earth system models (ESMs) struggle to represent aci because the processes responsible for these phenomena occur at much finer time and space scales than can be resolved by any ESM. Observational constraints provide key benchmarks to test ESMs, but cannot be used alone to fully understand aci processes except in very specific cases where causality is controlled; some degree of modeling is required to infer aci and estimate radiative forcing. Here, we generate and characterize a perturbed parameter ensemble (PPE) in version 3 of the Energy Exascale ESM (E3SMv3). We perturb 25 parameters that govern aci processes over 250 members and integrate the model over present-day and preindustrial aerosol emissions. We find that the process representation in E3SMv3 is flexible and can generate global-mean effective radiative forcings due to aci (ERFaci) ranging from −3.0 to +0.9 W m −2 . The positive ERFaci values simulated by a portion of the PPE are implausible and result from parameter combinations that produce unrealistic top-of-atmosphere energy fluxes. While global-mean cloud droplet number concentration always increases in response to anthropogenic aerosol, cloud liquid water path can both increase and decrease, suggesting that precipitation suppression is not the only aerosol-cloud adjustment represented by E3SMv3. Analysis of which processes control liquid cloud adjustment in the PPE points toward stratiform precipitation processes and aerosol activation, which is consistent with many previous ESMs, as well as the new two-moment convective cloud microphysics in E3SMv3.

Nugent, Jacqueline M. [Univ. of Wyoming, Laramie, ↗
DOE OSTI2026

Charge Transport in Solvated Donor–Acceptor Functionalized Peptoids: Molecular Dynamics and Rate Theory

Scalable solar-energy conversion requires photoactive materials that combine the efficiency of natural photosynthetic systems with the stability and processability needed for practical applications. Achieving reliable charge transport in soft, self-assembled organic materials remains challenging, as structural fluctuations and environmental effects strongly influence charge-transfer (CT) rates. Here, we present a broadly applicable computational framework for evaluating CT rates in the condensed phase, combining Fermi’s golden rule rate theory with inputs from all-atom molecular dynamics (MD) simulations and first-principles electronic-structure calculations. The approach does not rely on system-specific parametrization and is applicable to a wide range of soft and disordered materials. We demonstrate the applicability and usefulness of the framework on redox-active peptoids functionalized with iron–porphyrin (Fe–P) complexes, a bioinspired platform with programmable donor–acceptor units and tunable three-dimensional organization. The calculated CT rates exhibit strong sensitivity to molecular conformation, with variations spanning several orders of magnitude. This dependence is shown to arise from the pronounced variation in diabatic electronic coupling with the relative orientations and separations of the Fe–P complexes across the conformational ensemble. The framework provides a consistent route for connecting atomistic structure to CT kinetics in the condensed phase and enables analysis of structure–rate relations in organic semiconducting systems.

Charge transfer↗
DOE OSTI2024

Variation of the Passive Film on Compositionally Concentrated Dual-Phase Al 0.3 Cr 0.5 Fe 2 Mn 0.25 Mo 0.15 Ni 1.5 Ti 0.3 and Implications for Corrosion

The passive film on a dual-phase Al 0.3 Cr 0.5 Fe 2 Mn 0.25 Mo 0.15 Ni 1.5 Ti 0.3 FCC + Heusler (L2 1 ) compositionally concentrated alloy formed during extended exposure to an applied potential in the passive range in dilute chloride solution was characterized. Each phase, with its own distinct composition of passivating elements, formed unique passive films separated by a heterophase interface. High-resolution, surface sensitive characterization enabled chemical analysis of the passive film formed over individual phases. The film formed over the L2 1 phase had a higher concentration of Al, Ni, and Ti, while the film formed over FCC phase was of similar thickness but contained comparatively higher Cr, Fe, and Mo concentrations, consistent with the differences in bulk microstructure composition. The passive film was continuous across phase boundaries and the distribution of passivating elements (Al, Cr, and Ti) indicated both phases were independently passivated. Spatially resolved analysis of the surface chemistry of the dual-phase CCA revealed that the cation with the highest composition in passive film formed on the FCC phase was Cr (52.4 at. pct) and for the L2 1 phase was Ti (53.1 at. pct) despite the bulk concentration of each element being below 20 at. pct in their respective phases. Al, Cr, and Ti were enriched in both phases within the passive film relative to their respective bulk compositions. In parallel studies, single-phase alloys with compositions representative of the FCC and L2 1 phases were synthesized to evaluate the corrosion behavior of each phase in isolation. The corrosion behavior of the dual-phase alloy showed passivity evidenced by a pitting potential of 0.615 V SCE in 0.01 M NaCl. The pitting potential and other electrochemical parameters suggested a combination of behaviors of both single-phase samples, suggesting that the global corrosion behavior may be represented by a composite theory applied to phases, their area fractions, and interphase length. However, the interphase in the dual-phase CCA was a local corrosion initiation site and may limit localized corrosion protectiveness. The alloy design implications for optimization of second phase structure and morphology are discussed.

36 MATERIALS SCIENCE↗