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DOE OSTI2026

Jahn–Teller distortion controls electron transfer in photoexcited Cu( I ) donor–acceptor systems

The Jahn–Teller distortion (JTD) is a defining structural response to electronic excitation in Cu( I )-based transition metal complexes, yet its role in photoinduced electron transfer (PET) remains largely unexplored. Here, we demonstrate that the JTD governs charge separation (CS) through a vibronically controlled conical intersection in heteroleptic Cu( I ) bisphenanthroline–naphthalene diimide (CuHETPHEN-NDI) donor–acceptor dyads. Using 20-fs broadband transient absorption spectroscopy combined with coherent vibrational wavepacket (CVWP) analysis and quantum chemical calculations, we directly track nuclear motions that steer the system from the metal-to-ligand charge-transfer ( 1 MLCT) state to the CS state. Steric bulkiness of the pendant groups at the 2,9-positions of the phenanthroline ligand systematically slows both JTD and CS. Short-time Fourier transformation and Fourier filtering analyses identify two key vibrational signatures: a low-frequency breathing mode (∼100 cm −1 ) via a bond distance change between Cu and ligated Ns (Cu–N) that modulates the NDI anion absorption and acts as a vibronic coupling coordinate, and a higher-frequency mode (∼313 cm −1 ) that evolves along the PET trajectory. Normal mode analysis and potential energy surface calculations show that the JTD brings the 1 MLCT and CS states into degeneracy, while the Cu–N breathing motion dynamically modulates donor–acceptor electronic coupling to enable ultrafast nonadiabatic electron transfer. Steric hindrance exerted by the groups at the 2,9 positions of the phenanthroline ligands suppresses this vibronic coupling, leading to faster CVWP decoherence for the 313 cm −1 mode and slower CS. These findings unravel JTD-controlled vibronic coupling at conical intersection as a governing factor for CS and provide insight into designing Cu-based photosensitizers by harnessing structural dynamics to control PET.

Kim, Pyosang [Argonne National Laboratory (ANL), A↗

DOE OSTI2021

Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states

We report molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. The application of Nesbet’s theorem makes it possible to recast a typical extrapolation task, training on correlation energies for small molecules and predicting correlation energies for large molecules, into an interpolation task based on the properties of orbital pairs. We demonstrate the importance of preserving physical constraints, including invariance conditions and size consistency, when generating the input for the machine learning model. Numerical improvements are demonstrated for different datasets covering total and relative energies for thermally accessible organic and transition-metal containing molecules, non-covalent interactions, and transition-state energies. MOB-ML requires training data from only 1% of the QM7b-T dataset (i.e., only 70 organic molecules with seven and fewer heavy atoms) to predict the total energy of the remaining 99% of this dataset with sub-kcal/mol accuracy. This MOB-ML model is significantly more accurate than other methods when transferred to a dataset comprising of 13 heavy atom molecules, exhibiting no loss of accuracy on a size intensive (i.e., per-electron) basis. It is shown that MOB-ML also works well for extrapolating to transition-state structures, predicting the barrier region for malonaldehyde intramolecular proton-transfer to within 0.35 kcal/mol when only trained on reactant/product-like structures. Finally, the use of the Gaussian process variance enables an active learning strategy for extending the MOB-ML model to new regions of chemical space with minimal effort. We demonstrate this active learning strategy by extending a QM7b-T model to describe non-covalent interactions in the protein backbone–backbone interaction dataset to an accuracy of 0.28 kcal/mol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Nucleation kinetics model for primary crystallization in Al–Y–Fe metallic glass

The high density of aluminum nanocrystals (>10 21 m -3 ) that develop during the primary crystallization in Al-based metallic glasses indicates a high nucleation rate (~10 18 m -3 s -1 ). Several studies have been advanced to account for the primary crystallization behavior, but none have been developed to completely describe the reaction kinetics. Recently, structural analysis by fluctuation electron microscopy has demonstrated the presence of the Al-like medium range order (MRO) regions as a spatial heterogeneity in as-spun Al 88 Y 7 Fe 5 metallic glass that is representative for the class of Al-based amorphous alloys that develop Al nanocrystals during primary crystallization. From the structural characterization, an MRO seeded nucleation configuration is established, whereby the Al nanocrystals are catalyzed by the MRO core to decrease the nucleation barrier. The MRO seeded nucleation model and the kinetic data from the delay time (τ) measurement provide a full accounting of the evolution of the Al nanocrystal density (N v ) during the primary crystallization under isothermal annealing treatments. Moreover, the calculated values of the steady state nucleation rates (J ss ) predicted by the nucleation model agree with the experimental results. Moreover, the model satisfies constraints on the structural, thermodynamic, and kinetic parameters, such as the critical nucleus size, the interface energy, and the volume-free energy driving force that are essential for a fully self-consistent nucleation kinetics analysis. In conclusion, the nucleation kinetics model can be applied more broadly to materials that are characterized by the presence of spatial heterogeneities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Investigations of plasma response associated with resonant magnetic perturbation fields using perturbation method in KSTAR H-mode plasmas

Abstract The plasma response associated with the resonant magnetic perturbation (RMP) field was investigated using the small edge perturbations induced by a modulated supersonic molecular beam injection (SMBI) in KSTAR. The modulated SMBI provides a time-varying perturbation of the plasma density source in the region just inside the last closed flux surface and a modulated flow damping rate. Radial propagation of the toroidal rotation perturbation induced by SMBI from the q = 3 surface to the q = 2 surface was observed. Theoretical analysis using the general perturbed equilibrium code of the RMP intensity profiles of the RMP field is consistent with the phase profile of the toroidal rotation perturbation.

Physics↗

DOE OSTI2023

Direct preemptive stabilization of $m,n$ = 2,1 neoclassical tearing modes by electron cyclotron current drive in the DIII-D low-torque ITER baseline scenario

We report the first preemptive direct stabilization of $m,n$ = 2,1 neoclassical tearing modes (NTMs) in DIII-D low-torque ITER baseline scenario plasmas by electron cyclotron waves. These experiments strongly support that the observed stabilization is achieved by replacing the missing bootstrap current in the island by electron cyclotron current drive (ECCD). These plasmas (with and without ECCD) are stable to 2,1 NTMs when the differential rotation between the q = 1 and q = 2 surfaces (Δf 1,2 ) is sustained above a critical value (Δf CRIT ≈ 1 kHz), but those evolving to low differential rotation (Δf 1,2 < Δf CRIT ) routinely develop a disruptive 2,1 island at the time and frequency of a sawtooth precursor. Preemptive, local and direct stabilization by ECCD is tested by scanning the current drive amplitude and location inside and outside of the q = 2 rational surface shot by shot. Analysis of only low differential rotation time windows, i.e. when stabilization from Δf 1,2 is absent, shows focused ECCD at q = 2 prevents the onset of 2,1 NTMs. These results are important, as they give the first demonstration of disruptive NTM control by ECCD in the plasma scenario planned for ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

On the role of ion temperature gradient turbulence in driving ion thermal transport in neutral beam injection-heated L-mode plasmas in a superconducting tokamak

In this paper, we report a detailed experimental study of the role of ion-temperature-gradient driven turbulence in driving ion thermal transport in neutral beam injection (NBI)-heated L-mode plasmas in Experimental Advanced Superconducting Tokamak (EAST) (Wan et al 2000 Nucl. Fusion 40 1057) coupled with linear and nonlinear gyrokinetic simulations. Significant ion-scale turbulence k ⟂ < cm −1 (k ⟂ < 1.5, where k ⟂ where is the perpendicular wavenumber and ρ s is the ion gyroradius calculated using local electron temperature T e ), measured with a microwave reflectometer, is observed in the plasma core at for r/a ≈ 0.265 and 0.5 (where r is half the diameter of the closed flux surface at a given radial position, and a is half the diameter of the last closed flux surface). Local linear stability analysis with experimental equilibrium quantities at these two radial locations using the GS2 gyrokinetic code shows that the most unstable ion-scale micro-instability is the ion temperature gradient (ITG) mode. Since the computed maximum ion-scale linear growth rates are larger than the local Waltz-Miller E × B shearing rate (Waltz and Miller 1999 Phys. Plasmas 6 4265), the E × B shear is unable to suppress ion-scale turbulence, consistent with the experimental observation of ion-scale turbulence. Experimental ion and electron thermal transports, calculated with power balance analysis, are both anomalous with ion thermal transport being the dominant channel. Nonlinear gyrokinetic simulations using the electrostatic global particle-in-cell gyrokinetic tokamak simulation (GTS) code (Weixing Wang et al 2010 Phys. Plasmas 17 072511) show good quantitative agreement between predicted ion thermal transport and that from the power balance analysis. These results demonstrate for the first time the important role of ITG turbulence in driving thermal transport in NBI-heated L-mode plasmas in EAST, showing the effectiveness of GTS code in simulating electrostatic turbulence in EAST and the need of suppressing ITG turbulence to improve energy confinement in EAST.

Sun, P. J. [Chinese Academy of Sciences (CAS), Hef↗

DOE OSTI2022

Cubit v.16.X

SAND2021-3051 O CUBIT is a full-featured software toolkit for robust generation of two- and three-dimensional finite element meshes (grids) and geometry preparation. Its main goal is to reduce the time required to generate meshes - particularly large hex meshes of complicated, interlocking assemblies. It is a solid-modeler-based preprocessor that meshes volumes and surfaces for finite element analysis. CUBIT also includes state-of-the-art smoothing algorithms and provides an extensive suite of tools for geometry decomposition and mesh generation. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Stimpson, Clint↗

DOE OSTI2024

Bridging New Observational Capabilities and Process-Level Simulation: Insights into Aerosol Roles in the Earth System

The spatial distribution of ambient aerosol particles significantly impacts aerosol–radiation–cloud interactions, which contribute to the largest uncertainty in global anthropogenic radiative forcing estimations. However, the atmospheric boundary layer and lower free troposphere have not been adequately sampled in terms of spatiotemporal resolution, hindering a comprehensive characterization of various atmospheric processes and impeding our understanding of the Earth system. To address this research data gap, we have leveraged the development of uncrewed aerial systems (UAS) and advanced measurement techniques to obtain mesoscale spatial data on aerosol microphysical and optical properties around the U.S. Southern Great Plains (SGP) atmospheric observatory. Our study also benefits from state-of-the-art laboratory facilities that include three-dimensional molecular imaging techniques enabled by secondary ion mass spectrometry and nanogram-level chemical composition analysis via micronebulization aerosol mass spectrometry. Through our study, we have developed a framework for observation–modeling integration, enabling an examination of how various assumptions about the organic–inorganic components mixing state, inferred from chemical analysis, affect clouds and radiation in observation-constrained model simulations. By integrating observational constraints (derived from offline chemical analysis of the aerosol surface using collected samples) with in situ UAS observations, we have identified a prominent role of organic-enriched nanometer layers located at the surface of aerosol particles in determining profiles of aerosol optical and hygroscopic properties over the SGP observatory. Furthermore, we have improved the agreement between predicted clouds and ground-based cloud lidar measurements. This UAS–model–laboratory integration exemplifies how these new advanced capabilities can significantly enhance our understanding of aerosol–radiation–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Matilda v1.0: An R package for probabilistic climate projections using a reduced complexity climate model

A primary advantage to using reduced complexity climate models (RCMs) has been their ability to quickly conduct probabilistic climate projections, a key component of uncertainty quantification in many impact studies and multisector systems. Providing frameworks for such analyses has been a target of several RCMs used in studies of the future co-evolution of the human and Earth systems. In this paper, we present Matilda, an open-science R software package that facilitates probabilistic climate projection analysis, implemented here using the Hector simple climate model in a seamless and easily applied framework. The primary goal of Matilda is to provide the user with a turn-key method to build parameter sets from literature-based prior distributions, run Hector iteratively to produce perturbed parameter ensembles (PPEs), weight ensembles for realism against observed historical climate data, and compute probabilistic projections for different climate variables. This workflow gives the user the ability to explore viable parameter space and propagate uncertainty to model ensembles with just a few lines of code. The package provides significant freedom to select different scoring criteria and algorithms to weight ensemble members, as well as the flexibility to implement custom criteria. Additionally, the architecture of the package simplifies the process of building and analyzing PPEs without requiring significant programming expertise, to accommodate diverse use cases. We present a case study that provides illustrative results of a probabilistic analysis of mean global surface temperature as an example of the software application.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Data & Code from Phoenix CPPP Phase 2 Analysis

This data and code package supports the analysis presented in “Beyond Surface Cooling: Comprehensive Field Assessment of Reflective Pavement Thermal Performance in Phoenix, Arizona” and provides fully reproducible workflows for evaluating the thermal performance of cool pavement treatments in a hot urban environment. The dataset integrates multi-modal field measurements collected across residential and nonresidential settings, including mobile air temperature traverses, stationary air temperature monitoring, residential mean radiant temperature (MRT) measurements, subsurface temperature profiles, and controlled testbed observations. The data package contains raw and processed datasets in comma-separated value (CSV) format, accompanying metadata files describing site characteristics and measurement protocols, and R scripts (.R files) used for data cleaning, time synchronization, spatial and temporal matching, quality control filtering, statistical comparison, and figure generation. All analyses were conducted using R (version ≥ 4.2.0) with commonly available packages (e.g., tidyverse, lubridate, data.table, ggplot2). No proprietary software is required to reproduce results. Field campaigns were designed to quantify the effects of high-reflectance pavement coatings on surface temperature, near-surface air temperature, subsurface heat propagation, and radiative heat exposure. Temporal alignment procedures include standardized timestamp conversion and nearest-neighbor matching of high-frequency sensor measurements to stop-based metadata within defined tolerance windows to ensure comparability across instruments. The workflows generate summary statistics, treatment–control contrasts, depth-dependent thermal gradients, and time-series visualizations used in the associated publication. By integrating mobile, stationary, radiative, and subsurface measurements within a unified and transparent processing framework, this package enables comprehensive evaluation of cool pavement performance across multiple thermal exposure pathways and supports reuse in future urban heat mitigation and climate resilience studies.

AIR TEMPERATURE↗

DOE OSTI2021

L-Basin microbial monitoring program - evaluation of 20 years of monitoring

This report summarizes the results from over 20-years’ of activities for microbial monitoring of the bulk water, floc analysis, and test coupon surfaces for the Savannah River Site L Basin. The Microbial Monitoring Program provides important information for the characterization of overall water quality as it impacts the continued safe storage of spent nuclear fuel in L Basin. The potential impacts to fuel storage are: 1) microbial-influenced corrosion of the fuel; and 2) biofouling of the fuel storage systems.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

2019 Megasun Illumination System. Modifications and Operating Procedure Updates [Slides]

The original operating procedure was nearly 20 years old, though it was on a semi-annual review schedule. Some electrical categories had changed, as well as their risk management assessments. The IWD did not reflect these updated guidelines. No injuries have been reported during nearly 20 years of operation (not a bad record), however, several abnormal conditions came to surface during our Hazard Analysis conversations. One of which included an oil based capacitor malfunction which cracked it’s housing and leaked oil within the 2001 system. Our training and qualifications were updated to reflect LANL’s current standards.

42 ENGINEERING↗

DOE OSTI2022

Generation of Beamline Sources – Preliminary Design (Second Target Station (STS) Project)

This report provides a short summary of how four neutron beamline sources at the US Department of Energy’s Oak Ridge National Laboratory were generated for use in shielding and activation analysis along Second Target Station (STS) neutron beamlines, inside and outside the bunker, and inside instrument caves. Analysis of these sources shows that an angular acceptance of ±2° from the axis of the neutron beamline is all that is needed to accurately model dose rates for these applications. Further analysis shows that the surface area of the tallies used to calculate these sources, and therefore the number of particles in the sources (normalization), should be scaled up to ~19 × 19 cm to provide conservative estimates of dose rate outside the bunker. The spectrum of neutrons entering beamline 11 (ST11), which has the hardest spectrum at high energies and the most neutrons below 100 eV, produces the largest prompt dose rates inside and outside the bunker and the largest number of activation products inside the bunker, therefore, producing the largest residual dose rates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

2021 GeoAI Workshop Report: The Trillion Pixel Challenge

The convergence of geospatial big data with advancements from artificial intelligence, cloud infrastructure, and high-performance computing continues to revolutionize mapping and analysis of Earth's surface in unprecedented detail. Rapid innovations in sensing technologies will soon collect geospatial data in even higher resolution and throughput. These developments offer the potential for breakthroughs in science, policy, and national security via end-to-end GeoAI systems that can provide fresh insights into how humans occupy and alter their environment over time. At the 2021 GeoAI Trillion Pixel workshop, international subject matter experts from government, academia, industry, and nonprofit organizations gathered virtually to discuss the Trillion Pixel GeoAI Challenge. The event focused on six major themes currently influencing scientific innovation and breakthroughs. Particular focus was paid to societal impacts. As an additional takeaway message, the gathering identified remaining application gaps and challenges that are in need of stronger community partnerships and collaborations.

58 GEOSCIENCES↗

DOE OSTI2022

A probabilistic inverse prediction method for predicting plutonium processing conditions

In the past decade, nuclear chemists and physicists have been conducting studies to investigate the signatures associated with the production of special nuclear material (SNM). In particular, these studies aim to determine how various processing parameters impact the physical, chemical, and morphological properties of the resulting special nuclear material. By better understanding how these properties relate to the processing parameters, scientists can better contribute to nuclear forensics investigations by quantifying their results and ultimately shortening the forensic timeline. This paper aims to statistically analyze and quantify the relationships that exist between the processing conditions used in these experiments and the various properties of the nuclear end-product by invoking inverse methods. In particular, these methods make use of Bayesian Adaptive Spline Surface models in conjunction with Bayesian model calibration techniques to probabilistically determine processing conditions as an inverse function of morphological characteristics. Not only does the model presented in this paper allow for providing point estimates of a sample of special nuclear material, but it also incorporates uncertainty into these predictions. This model proves sufficient for predicting processing conditions within a standard deviation of the observed processing conditions, on average, provides a solid foundation for future work in predicting processing conditions of particles of special nuclear material using only their observed morphological characteristics, and is generalizable to the field of chemometrics for applicability across different materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Indices of extremes: geographic patterns of change in extremes and associated vegetation impacts under climate intervention

Extreme weather events have been demonstrated to be increasing in frequency and intensity across the globe and are anticipated to increase further with projected changes in climate. Solar climate intervention strategies, specifically stratospheric aerosol injection (SAI), have the potential to minimize some of the impacts of a changing climate while more robust reductions in greenhouse gas emissions take effect. However, to date little attention has been paid to the possible responses of extreme weather and climate events under climate intervention scenarios. We present an analysis of 16 extreme surface temperature and precipitation indices, as well as associated vegetation responses, applied to the Geoengineering Large Ensemble (GLENS). GLENS is an ensemble of simulations performed with the Community Earth System Model (CESM1) wherein SAI is simulated to offset the warming produced by a high-emission scenario throughout the 21st century, maintaining surface temperatures at 2020 levels. GLENS is generally successful at maintaining global mean temperature near 2020 levels; however, it does not completely offset some of the projected warming in northern latitudes. Some regions are also projected to cool substantially in comparison to the present day, with the greatest decreases in daytime temperatures. The differential warming–cooling also translates to fewer very hot days but more very hot nights during the summer and fewer very cold days or nights compared to the current day. Extreme precipitation patterns, for the most part, are projected to reduce in intensity in areas that are wet in the current climate and increase in intensity in dry areas. We also find that the distribution of daily precipitation becomes more consistent with more days with light rain and fewer very intense events than currently occur. In many regions there is a reduction in the persistence of long dry and wet spells compared to present day. However, asymmetry in the night and day temperatures, together with changes in cloud cover and vegetative responses, could exacerbate drying in regions that are already sensitive to drought. Overall, our results suggest that while SAI may ameliorate some of the extreme weather hazards produced by global warming, it would also present some significant differences in the distribution of climate extremes compared to the present day.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

The Hawaii Meteorology, Energy and Transmission (MET) Toolkit

Reliable long-term resource adequacy and grid planning in Hawaii require precise, multi-decadal meteorological records. This paper introduces the Hawaii Meteorology, Energy and Transmission (MET) Toolkit, a 26-year (2000-2025) high-fidelity atmospheric dataset developed by the National Laboratory of the Rockies (NLR). We present a validation study of the underlying WRF model configurations, comparing the legacy MYNN PBL scheme against an alternative YSU formulation. Using vertical lidar profiles and surface buoy data, our analysis identified a foundational geometric distortion in the legacy NOW-23 Hawaii dataset caused by an incorrect grid projection. When evaluated on a corrected, zero-distortion grid, the YSU scheme demonstrated superior performance in bias and cRMSE compared to the legacy setup. To address these findings, the MET Toolkit has been re-produced as a unified 26-year record using the optimized YSU setup and corrected geometry. This dataset offers the Hawaii power sector a robust, validated, and homogenous reference for future grid resilience and energy integration.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Analysis of Tank 38H (HTF-38-22-91, -92) and Tank 43H (HTF-43-22-93, -94) Samples for Support of the Enrichment Control and Corrosion Control Programs

SRNL analyzed samples from Tank 38H and Tank 43H to support ECP and CCP. The results indicate the concentrations of most soluble species in the Tank 38H surface sample increased slightly from the previous surface sample. The Tank 38H sub-surface sample shows changes in concentration for soluble species in the solution with some increasing and some decreasing. The current Tank 38H sub-surface sample contains more sludge solids than the previous sample based on visual appearance. The small differences in the concentrations of major components between the Tank 38H surface and sub-surface samples indicate only minimal stratification of solution species within the tank. The Tank 43H surface and sub-surface samples are similar in composition to the previous samples. The similar solution compositions measured in the Tank 43H surface and sub-surface samples indicate a minimal stratification within the tank. The total uranium and plutonium in the current Tank 38H surface sample remains similar to the previous analysis. The Tank 38H sub-surface sample shows an increase in uranium and plutonium concentrations compared to the previous sample likely because of an increase in sludge solids in the current sample. The total uranium concentration in the two Tank 43H samples is essentially unchanged from previous sample results. The plutonium concentration in the Tank 43H surface sample is similar to the previous sample results while the plutonium in the Tank 43H sub-surface sample increased relative to the previous analysis. The sum of the major cations versus the sum of the major anions shows a difference of <10% for both samples from Tank 38H and for both samples from Tank 43H providing an indication of good data quality for the non-radioactive analytes in the samples. The silicon concentrations measured in the Tank 38H sub-surface sample increased compared with the previous sample results likely due to the presence of more sludge solids in the current sample. The Tank 38H surface sample silicon concentrations is similar to the previous sample results. The Tank 43H surface and sub-surface sample silicon concentrations both increased compared to the previous sample results. The samples analyzed from Tanks 38H and 43H show silicon concentrations ranging from 61.5 to 97.3 mg/L.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗