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DOE OSTI2025

Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics: A DOE-NIH Joint Workshop Executive Summary

The DOE-NIH Joint Workshop on Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics, held on September 27, 2024, brought together experts from government, academia, and industry to address critical challenges in radionuclide production and clinical translation. Here, the workshop emphasized interdisciplinary collaboration, particularly between the Department of Energy (DOE) and the National Institutes of Health (NIH), to strengthen the domestic isotope supply, streamline regulatory pathways, and further integrate computational tools into radiopharmaceutical therapy (RPT). Key discussions explored the role of AI-driven modeling, machine learning, and digital twin technologies in optimizing dosimetry, dynamically personalizing treatments, and reducing time to clinical adoption. Advances in predictive computational modeling were highlighted as essential for improving radionuclide yield, purity, and synthesis efficiency. Regulatory considerations and equitable access were central themes, with participants advocating for harmonized global standards, adaptive trial designs, and expanded infrastructure for clinical implementation. DOE computational and production infrastructure was emphasized. Future priorities identified include increased investment in radionuclide production infrastructure, expanded workforce development in radiopharmaceutical sciences and computational modeling, and the creation of robust public-private partnerships. The workshop concluded that continued strategic collaboration and sustained resources will be vital for advancing next-generation radiotheranostics, ensuring safe and effective therapies accessible to all patients.

digital twins↗

DOE OSTI2020

Current Needs in Radiochemistry

There are a number of current needs in the development of the Pu and Am separations that underpin larger interest areas including nuclear waste issues, nuclear weapons, nuclear forensics, nuclear fuels and similarly related fields. In the large areas of interest, there are a number of sub-fields that include corrosion chemistry, pyrochemistry, solution-phase chemistry, rapid separations, environmental speciation, and other similar efforts. However, in all of the these fields it is important to realize that the underlying chemistry that underpins these efforts is similar in that Pu and Am are bound to ligands that can be in the solid, aqueous, or gas phase and separations occurs due to the preferential binding of the ligand to Pu or Am based on a number of factors that include pH, concentration, oxidation state, etc. In the past, most of the Pu and Am chemistry has occurred in limited scope within universities where micro-scale and bench-top scale chemistry are the dominate investigative theme. At US national laboratory and their international counterparts, larger scale reactions have been attempted such that industrial scale chemistry has been completed. Some of this technology has been harnessed in radiopharmaceuticals and nuclear fuel reprocessing. However, such bench-top research is expensive to due to the safety and security that is concerned when dealing with Pu, and scale-up of reactions is even more resource intensive. In an effort to offset this expensive, it would be of interest to explore the ability to predict selectivity based on the computational modeling of actinide chemistry. This has historically been limited by the abilities of large supercomputers, but with the emphasis on developing exascale computers within the US and similar efforts by other countries this predictive modeling has potential to produce significant results that would yield improved understanding of actinide chemistry.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2022

Enabling High-Energy-Density Physics Using X-Ray Free Electron Lasers

New High-Energy-Density (HED) Physics research facilities, such as x-ray free electron lasers (XFEL) are becoming available to the research community. These new tools can be used to address stockpile stewardship science questions in the HED regime with greater accuracy and in parameter ranges not previously accessible. Two important challenges that must be overcome before using the XFEL are developing simulation tools needed to design the experiment and developing large quantities of inexpensive targets from which the HED state is created. We developed two-dimensional simulations in a new geometry (planar) to determine basic plasma parameters that could be expected in a notional experiment. We developed a postprocessor that used the results of the simulations to predict the x-ray Thomson scattering (XRTS) signal that yield the plasma density and pressure at a specified location and time. Finally, we developed several possible target geometries that are inexpensive and easy to use. With these tools in hand, the risk in designing an experiment for an XFEL is significantly reduced.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Applicability of 1D site response analysis to shallow sedimentary basins: A critical evaluation through physics-based 3D ground motion simulations

One-dimensional site response analysis (1D SRA) remains the standard practice in considering the effect of local soil deposits and predicting site-specific ground motions, although its range of applicability to realistic seismic wavefields is still in question. In this 1D approach, horizontal and vertical ground shaking are assumed to be induced by vertically propagating shear and compressional waves, respectively. A recent study based on analytical two-dimensional (2D) plane waves and simple point source earthquake simulations has shown two mechanistic limitations in this 1D modelling technique for general inclined seismic waves, that is, systematic over-prediction of the vertical motion and wave trapping in the 1D soil column. In this article, we evaluate in detail the applicability of this 1D modelling approach to realistic three-dimensional (3D) simulated seismic wavefields in shallow sedimentary basins. Linear-viscoelastic 1D SRA predictions using two types of input motions that are commonly used in practice—rock outcrop and in-column motions, are compared with the reference true site response results from 3D earthquake simulations in terms of various measures in the frequency and time domain. It is shown that the horizontal motion in the 3D seismic wavefield exhibits dominant shear wave propagation phenomenon, while the vertical motion is a combined effect of compressional and shear waves and can be over-predicted by the 1D approach when the incident seismic waves are inclined. Direct evidence of the wave refraction process that leads to the vertical motion over-prediction is provided. 1D SRA with in-column inputs can yield motions that have significantly longer duration compared to the true 3D site response solution due to trapped waves, casting in doubt the frequent need for increased soil damping in existing site studies to compensate for wave attenuation due to scattering alone. Sensitivity investigation on the increase of soil profile damping by a multiplier D mul shows D mul values compatible with those found in the literature for both horizontal and vertical motions. It is shown that the level of D mul optimized for a best match of the spectral acceleration is dependent on the characteristic of the input motion and a larger D mul is typically required for the vertical component. In contrast, 1D SRA with outcrop motions predicts motions with shorter significant duration due to its inability to capture the basin-edge generated surface waves. A suite of ground motion simulations was performed to assess the sensitivity of the observations to the basin geologic structure including the velocity gradient, rock-basin impedance contrast and basin depth. The analysis results show that the accuracy of the simplified 1D procedure is dependent on the wavefield composition of both the input motions and the true 3D site response solution. While the horizontal motions in shallow sedimentary basins can, to the first order, be reasonably captured by the simplified 1D approach, 1D SRA for the vertical component is in general not reliable and contributions from inclined shear waves should be accounted for in site-specific evaluation of the vertical design ground motion.

1D assumption↗

DOE OSTI2026

Evidence of Z Z γ Production and Observation of 4 ℓ γ in Proton-Proton Collisions at s = 13 TeV

Evidence of the production of two Z bosons and a photon in proton-proton collisions is reported for the first time in CMS. The analysis uses data collected by the CMS experiment between 2016 and 2018 at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb - 1 . Evidence for the process p p → Z Z γ → 4 ℓ γ ( ℓ = e , μ ), with an observed (expected) significance of 3.7 (3.1) standard deviations in a fiducial region defined by p T γ > 20 GeV , | η γ | < 2.4 , Δ R ( ℓ , γ ) > 0.5 , m Z between 60 and 120 GeV, and the invariant mass of either of the two Z bosons combined with the photon ( m Z γ ) larger than 100 GeV, is reported. The measured (predicted) fiducial cross section is 60 - 22 + 27 ab ( 47.56 ± 0.04 ab ). Additionally, the inclusive production of p p → 4 ℓ γ is studied by removing the m Z γ requirement to include final state radiation where one Z boson decays to 2 ℓ γ , yielding an observed (expected) significance of 5.0 (4.2) standard deviations and a measured (predicted) fiducial cross section of 156 - 35 + 39 ab ( 99.97 ± 0.09 ab ).

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗

DOE OSTI2024

Predicting Partial Atomic Charges in Metal–Organic Frameworks: An Extension to Ionic MOFs

Molecular simulation is an invaluable tool to predict and understand the usage of metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges, commonly obtained from density functional theory (DFT) calculations, are often required to model the electrostatic interactions between the MOF and adsorbates, especially when the adsorbates have dipole or quadrupole moments, such as water and CO 2 . Machine learning (ML) models have been previously employed to predict partial charges and avoid the computational cost associated with DFT calculations. However, previous ML models suffer from small training data sets, which limit their scope of application. In this work, we introduce two novel machine learning models, PACMOF2-neutral and PACMOF2-ionic, aimed at predicting the density-derived electrostatic and chemical (DDEC6) partial atomic charges for both neutral and ionic MOFs. These models not only yield DFT-level accuracy at a fraction of the computational cost but also demonstrate a remarkable improvement in prediction of adsorption, as validated with grand canonical Monte Carlo simulations. Furthermore, the robustness and fast computational time of the PACMOF2 models, along with their transferability to other porous materials such as covalent organic frameworks and zeolites, underscores their potential in high-throughput screening of MOFs for diverse applications.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Future changes in isoprene-epoxydiol-derived secondary organic aerosol (IEPOX SOA) under the Shared Socioeconomic Pathways: the importance of physicochemical dependency

Abstract. Secondary organic aerosol (SOA) is a dominant contributor of fine particulate matter in the atmosphere, but the complexity of SOA formation chemistry hinders the accurate representation of SOA in models. Volatility-based SOA parameterizations have been adopted in many recent chemistry modeling studies and have shown a reasonable performance compared to observations. However, assumptions made in these empirical parameterizations can lead to substantial errors when applied to future climatic conditions as they do not include the mechanistic understanding of processes but are rather fitted to laboratory studies of SOA formation. This is particularly the case for SOA derived from isoprene epoxydiols (IEPOX SOA), for which we have a higher level of understanding of the fundamental processes than is currently parameterized in most models. We predict future SOA concentrations using an explicit mechanism and compare the predictions with the empirical parameterization based on the volatility basis set (VBS) approach. We then use the Community Earth System Model 2 (CESM2.1.0) with detailed isoprene chemistry and reactive uptake processes for the middle and end of the 21st century under four Shared Socioeconomic Pathways (SSPs): SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. With the explicit chemical mechanism, we find that IEPOX SOA is predicted to increase on average under all future SSP scenarios but with some variability in the results depending on regions and the scenario chosen. Isoprene emissions are the main driver of IEPOX SOA changes in the future climate, but the IEPOX SOA yield from isoprene emissions also changes by up to 50 % depending on the SSP scenario, in particular due to different sulfur emissions. We conduct sensitivity simulations with and without CO2 inhibition of isoprene emissions that is highly uncertain, which results in factor of 2 differences in the predicted IEPOX SOA global burden, especially for the high-CO2 scenarios (SSP3–7.0 and SSP5–8.5). Aerosol pH also plays a critical role in the IEPOX SOA formation rate, requiring accurate calculation of aerosol pH in chemistry models. On the other hand, isoprene SOA calculated with the VBS scheme predicts a nearly constant SOA yield from isoprene emissions across all SSP scenarios; as a result, it mostly follows isoprene emissions regardless of region and scenario. This is because the VBS scheme does not consider heterogeneous chemistry; in other words, there is no dependency on aerosol properties. The discrepancy between the explicit mechanism and VBS parameterization in this study is likely to occur for other SOA components as well, which may also have dependencies that cannot be captured by VBS parameterizations. This study highlights the need for more explicit chemistry or for parameterizations that capture the dependence on key physicochemical drivers when predicting SOA concentrations for climate studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

The updated ITPA global H-mode confinement database: description and analysis

The multi-machine ITPA Global H-mode Confinement Database has been upgraded with new data from JET with the ITER-like wall and ASDEX Upgrade with the full tungsten wall. This paper describes the new database and presents results of regression analysis to estimate the global energy confinement scaling in H-mode plasmas using a standard power law. Various subsets of the database are considered, focusing on type of wall and divertor materials, confinement regime (all H-modes, ELMy H or ELM-free) and ITER-like constraints. Apart from ordinary least squares, two other, robust regression techniques are applied, which take into account uncertainty on all variables. Regression on data from individual devices shows that, generally, the confinement dependence on density and the power degradation are weakest in the fully metallic devices. Using the multi-machine scalings, predictions are made of the confinement time in a standard ELMy H-mode scenario in ITER. The uncertainty on the scaling parameters is discussed with a view to practically useful error bars on the parameters and predictions. One of the derived scalings for ELMy H-modes on an ITER-like subset is studied in particular and compared to the IPB98(y,2) confinement scaling in engineering and dimensionless form. Transformation of this new scaling from engineering variables to dimensionless quantities is shown to result in large error bars on the dimensionless scaling. Regression analysis in the space of dimensionless variables is therefore proposed as an alternative, yielding acceptable estimates for the dimensionless scaling. The new scaling, which is dimensionally correct within the uncertainties, suggests that some dependencies of confinement in the multi- machine database can be reconciled with parameter scans in individual devices. This includes vanishingly small dependence of confinement on line-averaged density and normalized plasma pressure (β), as well as a noticeable, positive dependence on effective atomic mass and plasma triangularity. Extrapolation of this scaling to ITER yields a somewhat lower confinement time compared to the IPB98(y, 2) prediction, possibly related to the considerably weaker dependence on major radius in the new scaling (slightly above linear). Further studies are needed to compare more flexible regression models with the power law used here. In addition, data from more devices concerning possible ‘hidden variables’ could help to determine their influence on confinement, while adding data in sparsely populated areas of the parameter space may contribute to further disentangling some of the global confinement dependencies in tokamak plasmas.

Database↗

DOE OSTI2023

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Probabilistic Predictions for Fastener Failure in the Sandia Mechanics Challenge Using the Discrete-Direct Uncertainty Quantification Approach

This paper documents the blind and post-blind analysis predictions for the 2023 Sandia Mechanics Challenge (SMC), which involved predicting the behavior of a threaded fastener joint structure subjected to shock loading. Utilizing repeat sets of fastener calibration data from various experimental configurations including tension, double shear, and joint tension, we developed a library of calibrated models which were propagated through the application model using the Discrete-Direct (DD) uncertainty quantification (UQ) approach. Although the initial blind predictions did not incorporate spare-sample processing to quantify fastener failure probabilities, the analyses yielded reasonable conclusions aligned with experimental results. In the post-blind analysis phase, we focused on enhancing the fidelity of the aluminum constitutive model and innovating the DD approach to obtain probabilistic predictions for fastener failure, particularly when quantities of interest (QoIs) approach their bounds. The improved aluminum model captures the behavior of the cantilever under shock loading more accurately, predicting both partial and complete cracks, although it tends to underpredict failure propagation. The enhanced DD approach facilitates probabilistic predictions that reflect the interdependent failure mechanisms of the fasteners and the cantilever, revealing that while certain fasteners are more likely to fail, the failure does not necessarily follow a progressive pattern. Overall, the post-blind analyses significantly improved the predictive capabilities of the model, providing valuable insights into the SMC application and establishing a robust foundation for informed engineering decisions. The methodology demonstrates a cost-effective and extensible approach suitable for a wide range of applications, highlighting the importance of uncertainty quantification to provide context for engineering decision making.

42 ENGINEERING↗

DOE OSTI2022

Biomass Quality Responses to Selection for Increased Biomass Yield in Perennial Energy Grasses

Perennial grasses are candidates for biomass cropping systems that are focused on providing a wide range of ecosystem services in addition to sustainable bioenergy production. Switchgrass (Panicum virgatum L.) and big bluestem (Andropogon gerardii Vitman) are two of the dominant grasses of the tallgrass prairie and both are candidates for development of multifunctional diverse production systems. Breeding programs for both species are aimed at increasing biomass yield as a mechanism of improving economic sustainability without increasing production costs. The objective of this study was to determine if long-term selection for increased biomass yield in these two species has had any adverse impacts on biomass quality, or the ability to convert biomass into bioenergy. Check cultivars and improved breeding populations of both species were evaluated for a wide range of biomass quality traits at 13 locations in the North Central and Northeastern USA, and a subset of these populations were also subjected to more intensive and detailed fermentation analyses. In general, lignin and ferulates either remained constant or decreased following selection for increased biomass yield in the various genetic pedigrees. These changes resulted in some increases in predicted ethanol production and in vitro digestibility. Here, the prediction of increased digestibility was confirmed by higher glucose release by pretreatment and deconstruction of an advanced lowland population. However, bioreactor fermentations with two different biofuel-producing microbes showed no differences in ethanol production. Overall, these studies indicated that the improved switchgrass and big bluestem populations had greater biomass yields without significantly reducing biomass quality or conversion efficiency into ethanol, suggesting that selection can achieve increases in biomass productivity while maintaining consistent biomass quality.

09 BIOMASS FUELS↗

DOE OSTI2020

Modeling fluid phase equilibria of carbon dioxide-methanol binary system

Accurate fluid phase equilibria modeling of carbon dioxide-methanol binary system is essential for numerous industrial applications. Prior modeling studies were limited in scope in terms of temperature and pressure ranges. In this study, the phase equilibria behavior of the binary system is modeled with cubic equations-of-state (EoS) including Peng-Robinson (PR) and Soave-Redlich-Kwong with various mixing rules, Predictive Soave-Redlich-Kwong, Cubic Plus Association, and Perturbed-Chain Statistical Associating Fluid Theory. Among them, the classical PR EoS and its variants yield the overall best results in representing the phase behavior at temperature above 330 K. Furthermore, PR incorrectly predicts two liquid phases when the CO 2 mole fraction exceeds ~ 0.4 at temperature lower than 330 K. Raoult’s law with the classical nonrandom two-liquid excess Gibbs energy model is recommended for the low temperature conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Editorial: Proceedings of ICPSBBB 2018 - 2nd International Conference on Plant Synthetic Biology, Bioengineering and Biotechnology

Plant synthetic biology combines engineering principles with plant biology toward the design, construction, and testing of plants with new traits (Liu and Stewart, 2015). Synthetic biology applies engineering principles, using defined genetic parts with the aim of achieving predictable outcomes. The field aims at providing fuels, pharmaceuticals, bioactive metabolites and increased crop yield. Synthetic biology is increasingly forming a framework in genetic transformation of plants allowing us to improve traits and crop performance. The conference on Plant Synthetic Biology, Bioengineering, and Biotechnology covers broad aspects of plant synthetic biology, ranging from tools to the design of predictable functions and precision genome editing, to re-designed photosynthesis, plant bioengineering techniques, and rational design of novel plant traits. This Frontiers in Plant Science virtual issue on the “Research Topic Proceedings of ICPSBBB 2018 - 2nd International Conference on Plant Synthetic Biology, Bioengineering and Biotechnology” consists of 8 publications, including four reviews and four original research articles, which fall into the following general topics: quantitative and genetic parts, evolution, expression of transgenes and epigenetic changes, engineering of stress tolerance and bioproduction.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Ionization yield measurement in a germanium CDMSlite detector using photo-neutron sources

Two photo-neutron sources, Y 88 Be 9 and Sb 124 Be 9 , have been used to investigate the ionization yield of nuclear recoils in the CDMSlite germanium detectors by the SuperCDMS collaboration. In this study, we evaluate the yield for nuclear recoil energies between 1 and 7 keV at a temperature of ~ 50 mK . We use a geant4 simulation to model the neutron spectrum assuming a charge yield model that is a generalization of the standard Lindhard model and consists of two energy dependent parameters. We perform a likelihood analysis using the simulated neutron spectrum, modeled background, and experimental data to obtain the best fit values of the yield model. The ionization yield between recoil energies of 1 and 7 keV is shown to be significantly lower than predicted by the standard Lindhard model for germanium. There is a general lack of agreement among different experiments using a variety of techniques studying the low energy range of the nuclear recoil yield, which is most critical for interpretation of direct dark matter searches. This suggests complexity in the physical process that many direct detection experiments use to model their primary signal detection mechanism and highlights the need for further studies to clarify underlying systematic effects that have not been well understood up to this point.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Exploring scenarios for enhanced fuel compression and performance on the National Ignition Facility with machine-learning-aided design techniques

Recent fusion experiments on the National Ignition Facility (NIF) have achieved ignition, producing multi-MJ fusion yields for input laser energies of roughly 2 MJ [Abu-Shawareb et al., Phys. Rev. Lett. 132, 065102 (2024)]. Building on the success of the target designs that have achieved ignition, we explore new implosion scenarios predicted to generate significantly more compression of the dense DT ice layer and correspondingly higher yields while preserving many of the key physics characteristics of present-day ignition designs. Our main result is a novel 3-shock implosion scheme that effectively minimizes the shock-induced entropy in the dense, accelerating DT shell and maximizes the resulting fuel compression subject to a fixed leading shock strength consistent with present-day ignition experiments, which is necessary to melt the crystalline high-density carbon ablator. Compared to the first NIF experiment to fulfill Lawson's ignition criterion, shot N210808 [Abu-Shawareb et al., Phys. Rev. Lett. 129, 075001 (2022)], our design exhibits a 40% increase in simulated peak areal density (ρR) and a 5× increase in 1D fusion yield using a 4% lighter ablator and identical DT payloads. We also present a complete integrated 2D hohlraum design and laser pulse specifications capable of generating the desired 3-shock drive and maintaining control of the low-mode capsule implosion symmetry, where the increase in simulated 2D yield relative to N210808 is > 10×. This new implosion regime was discovered with help from a machine-learning-enabled capsule design optimization framework. We outline the workflow this automated tool uses to identify improved design candidates by running several rounds of capsule simulations, constructing a surrogate model mapping input variations to key physics output quantities, and querying the resulting statistical model to propose adjustments to the x-ray drive and capsule to reach a set of physics objectives prescribed by the designer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Surface Phase Stability of Fe 2 O 3 (001) in Hydrogen Reducing Environments: A DFT and XPS Analysis

Here, this study combines density functional theory (DFT) and ab initio thermodynamics calculations with X-ray photoelectron spectroscopy (XPS) investigations to identify the reduction properties of the Fe 2 O 3 (001) surface with implications for corrosion resistance, hydrogen transport, and energy safety. Ab initio thermodynamics modeling predicts fully hydroxylated surface stability across a broad range of pressures (1 × 10 –23 to 1 × 10 5 mbar) and temperatures below 700 K, consistent with previous experimental studies. Above 800 K, exposures to 1 × 10 –4 mbar H 2 , 1 × 10 –4 mbar O 2 , or 1 × 10 –4 mbar H 2 + 1 × 10 –4 mbar O 2 each yield unique XPS signals indicating a loss of −OH coverage, aligning with DFT predictions. Insight into the mechanism of reduction as a function of H 2 exposure is provided, as well as conditions that promote further reduction toward Fe 3 O 4 . Theoretical and experimental investigations indicate the ability to maintain the Fe 2 O 3 protective layer of iron oxides that have been exposed to H 2 environments by including trace amounts of aqueous O2.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

MOA-2009-BLG-319Lb: A Sub-Saturn Planet inside the Predicted Mass Desert

We present an adaptive optics (AO) analysis of images from the Keck II telescope NIRC2 instrument of the planetary microlensing event MOA-2009-BLG-319. The ∼10 yr baseline between the event and the Keck observations allows the planetary host star to be detected at a separation of 66.5 ± 1.7 mas from the source star, consistent with the light-curve model prediction. The combination of the host star brightness and light-curve parameters yields host star and planet masses of M {sub host} = 0.524 ± 0.048M {sub ⊙} and m {sub p} = 67.3 ± 6.2M {sub ⊕} at a distance of D {sub L} = 7.1 ± 0.7 kpc. The star−planet projected separation is 2.03 ± 0.21 au. The planet-to-star mass ratio of this system, q = (3.857 ± 0.029) × 10{sup −4}, places it in the predicted “planet desert” at 10{sup −4} < q < 4 × 10{sup −4} according to the runaway gas accretion scenario of the core accretion theory. Seven of the 30 planets in the Suzuki et al. sample fall in this mass ratio range, and this is the third with a measured host mass. All three of these host stars have masses of 0.5 ≤ M {sub host}/M {sub ⊙} ≤ 0.7, which implies that this predicted mass ratio gap is filled with planets that have host stars within a factor of two of 1M {sub ⊙}. This suggests that runaway gas accretion does not play a major role in determining giant planet masses for stars somewhat less massive than the Sun. Our analysis has been accomplished with a modified DAOPHOT code that has been designed to measure the brightness and positions of closely blended stars. This will aid in the development of the primary method that the Nancy Grace Roman Space Telescope mission will use to determine the masses of microlens planets and their hosts.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Hydroxyl radical yields in the heavy ion radiolysis of water

Scavenging experiments coupled with Monte Carlo track structure and kinetics simulations have been used to determining and understand the yields of hydroxyl radicals in the radiolysis of water with protons and carbon ions. Carbon dioxide production from aerated formic acid solutions with concentrations ranging from 10-3 to 1 mol./dm3 was used as a probe of the time dependence of the hydroxyl radical yield from about 5 ns to 1 ?s. Numerical extrapolation of the results at slightly different ion energies in combination with data from gamma radiolysis experiments allows a systematic analysis of both track average and track segment yields, with considerable track chemistry found to occur on the nanosecond to microsecond time scale. Track chemistry calculations utilizing Monte Carlo track structure simulations and nonhomogeneous independent reaction times diffusion-reaction modeling of product yields reproduce experimental observations. Subsequently, the track simulation methods were used to predict hydroxyl radical kinetics in deaerated and aerated water at neutral pH. Taken with literature data for helium ion beam studies, the results offer insight into the potential for indirect radiation induced damage to biological systems for light ions with very different track structures.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗