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At least 1,315 records · Page 73

Toward white light emission from plasmonic-luminescent hybrid nanostructures

We study the light emission of plasmonic-luminescent hybrid nanostructures consisting of Ag nanoparticles (NPs) embedded in europium oxide (EuO X ). The Ag NPs present a bidimensional organization in the nanostructures and they optically behave as oblate spheroids. The photoluminescence (PL) spectral response of the nanostructures evolves from a narrow red emission characteristic of Eu 3+ ions in absence of Ag NPs to a broad blue-green emission band associated with Eu 2+ ions when the layer of Ag NPs is present. This behavior is not related to a change in the Eu 2+ /Eu 3+ ratio, which is verified by compositional analysis. Instead, a detailed investigation of the PL emission of the nanostructures suggests that the coupling of the Ag NPs to the Eu 2+ ions present in the EuO X layer, which manifests itself in an efficient sensitization of these ions, enhances their broad visible emission. In particular, the longitudinal mode of the Ag NPs surface plasmon is considered to be responsible for the efficient energy transfer for the non-normal incidence excitation PL configuration used. Finally, the use of a capping amorphous Al 2 O 3 layer allows improving the robustness of hybrid nanostructures and further enhances their PL emission. These findings provide a new path to actively control the selective excitation of Eu 2+ and Eu 3+ ions via a controlled coupling with the surface plasmon resonance modes of the Ag NPs and points to these nanostructures as promising building blocks for the development of integrable white light sources.

36 MATERIALS SCIENCE↗

Dynamically tunable second-harmonic generation using hybrid nanostructures incorporating phase-change chalcogenides

Nonlinear metasurfaces with high conversion efficiencies have been vastly investigated. However, strong dynamic tunability of such devices is limited in conventional passive plasmonic and dielectric material platforms. Germanium antimony telluride (GST) is a promising phase-change chalcogenide for the reconfiguration of metamaterials due to strong nonvolatile changes of the real and imaginary parts of the refraction index through amorphous-crystalline phase change. The orderly structured GST has an even higher potential in tunable second-harmonic generation (SHG) with a non-centrosymmetric crystal structure at the crystalline phase, while the amorphous phase of GST does not exhibit bulk second-order nonlinearity. Here, we experimentally demonstrate SHG switches by actively controlling the crystalline phase of GST for a GST-based hybrid metasurface featuring a gap-surface plasmon resonance, and a quarter-wave asymmetric Fabry–Perot (F–P) cavity incorporating GST. We obtain SHG switches with modulation depths as high as ~ 20 dB for the wavelengths at the on-state resonance. We also demonstrate the feasibility of multi-level SHG modulation by leveraging three controlled GST phases, i.e., amorphous, semi-crystalline, and crystalline, for the gap-surface plasmon hybrid device, which features stronger light–matter interaction and has higher resonant SHG efficiencies than the asymmetric F–P cavity device at respective GST phases. This research reveals that GST-based dynamic SHG switches can be potentially employed in practical applications, such as microscopy, optical communication, and photonic computing in the nonlinear regime.

42 ENGINEERING↗

Electron-induced chemical transformation of vapor-phase synthesized hybrid resist materials for EUV and beyond EUV lithography

Here, in this study, we utilize an in situ Fourier transform infrared (FTIR) spectroscopy chamber equipped with an electron flood gun to comprehensively investigate the electron-induced chemical transformations in advanced inorganic–organic hybrid resist thin films. The characteristics of the electron gun were assessed, showing a detectable current decrease with increasing Faraday cup bias voltage, confirming that the electron energies impacting the sample were within the expected range. This unique FTIR system was applied to study two hybrid resist thin-film systems, Al–HQ and Zn–HQ, both exhibiting saturation growth characteristics at 150 °C. Results revealed that Al–HQ demonstrated superior electron sensitivity compared to Zn–HQ at energies ≤ 100 eV. Chemical changes observed were consistent with previous findings, underscoring the potential of this methodology to elucidate exposure mechanisms. This research contributes to the fundamental understanding necessary for the development of advanced resist materials for the next-generation nanolithography applications.

36 MATERIALS SCIENCE↗

Zero-bias conductance peaks at zero applied magnetic field due to stray fields from integrated micromagnets in hybrid nanowire quantum dots

Many recipes for realizing topological superconductivity rely on broken time-reversal symmetry, which is often attained by applying a substantial external magnetic field. Alternatively, using magnetic materials can offer advantages through low-field operation and design flexibility on the nanoscale. Mechanisms for lifting spin degeneracy include exchange coupling, spin-dependent scattering, spin injection – all requiring direct contact between the bulk or induced superconductor and a magnetic material. Here, we implement locally broken time-reversal symmetry through dipolar coupling from nearby micromagnets to superconductor-semiconductor hybrid nanowire devices. Josephson supercurrent is hysteretic due to micromagnets switching. At or around zero external magnetic field, we observe an extended presence of Andreev bound states near zero voltage bias. We also show a zero-bias peak plateau of a non-quantized value. Our findings largely reproduce earlier results where similar effects were presented in the context of topological superconductivity in a homogeneous wire, and attributed to more exotic time-reversal breaking mechanisms [Nat. Phys. 17, 43 (2020)]. In contrast, our stray field profiles are not designed to create Majorana modes, and our data are compatible with a straightforward interpretation in terms of trivial states in quantum dots. At the same time, the use of micromagnets in hybrid superconductor-semiconductor devices shows promise for future experiments on topological superconductivity.

Jiang, Luyao [Univ. of Pittsburgh, PA (United Stat↗

Ultra-Light Hybrid Composite Door Design, Manufacturing and Demonstration

New Corporate Average Fuel Economy regulations require improved fuel efficiency of the future vehicle fleet. Weight reduction is key to achieving these targets. Replacing metallic body and chassis components with carbon fiber-reinforced composites offers the most weight reduction potential at up to 70%. Introduction of the BMW i3 and i8 in 2014 required mass production processes to meet 20,000+ units per year. Preforming with High Pressure Resin Transfer Molding has been implemented and meets rate, cost, and performance requirements. We will advance these technologies to develop an ultra-light driver’s side door for the vehicle with production rates of 40,000 units annually per plant in Detroit, Michigan. This project sought to employ a comprehensive systems approach for designing, manufacturing, and demonstrating an ultra-light hybrid composite automotive door. Composite structural components are integrated with other functional systems to reduce part count and full-system weight by a minimum of 42.5%. The approach will be demonstrated on a driver’s- side front door and will consider all fit, functional, safety, and cost requirements. These concepts will be evaluated using structural and crash simulations and linked to a manufacturing feasibility study, resulting in producible designs of an integrated door system. A cost model will evaluate part of the cost for the various designs and will ensure the cost target of less than $5 per pound of weight saved is met. Hybrid composites consisting of carbon, glass, and metal components will be included in our trade-off studies to satisfy these cost and performance goals.

02 PETROLEUM↗

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Environmental fatigue modeling is a complex problem due to multiple failure modes and their intermixing. The failure modes are function of various underlying causes in addition to the corrosive effect of reactor coolant environment. Some of the major causes are time-dependence of material associated with cyclic loading, load sequence effect associated with random/variable amplitude loading, effect of strain amplitude and rates, effect of varying temperature (along both temporal and spatial directions) and the effect of mean strain and stress. The nonlinear intermixing of failure modes associated with above mentioned causing parameters makes the environmental fatigue modeling is a challenging task. Because of this challenge, fatigue is traditionally being modeled based on experimental data. However, test based empirical approach often requires hundreds of fatigue tests to model the above-mentioned intermixing failure causes even for a single material system. The problem is further exaggerated for reactor component made from multi-material systems such as made from both carbon and stainless-steel base metals and their similar and dissimilar metal welds. With the difficulty of conducting hundreds of fatigue tests to capture the above-mentioned intermixing failure causes, fatigue modeling approaches often depends on empirical models based on limited available test data such as available through ASME code and NUREG 6909. However, these limited test-data-based models may not be enough to accurately predict the life of reactor components. Accurate prediction of life of reactor component would become a necessity, particularly when the license of the reactors to be extended for long-term-operation (LTO) that is for well beyond its original design life of 40 years. The requirement of extending the license of reactor under LTO requires hundreds of fatigue tests to be conducted to understand the mechanism associated with the above-mentioned interdependent failure causes. However, conducting large number of fatigue tests is not a feasibility due to the cost involved. To address this issues Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program trying to develop a hybrid predictive modeling approach. This is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics based modeling tools. The hybrid approach not-only can improve the accuracy of the existing stress analysis and fatigue modeling approach but also can reduce the over-dependency on test-based approach. Towards this goal following are some of the major contributions based on ANL’s FY-20 environmental fatigue modeling activities: 1) A cyclic plasticity material model database for 82/182 dissimilar metal weld, which can be readily shared with US nuclear industry and regulatory agency on request. 2) A well validated analytical modeling methodology to perform cycle-by-cycle stress prediction under both constant amplitude fatigue loading and variable amplitude fatigue loading (with load-sequence effect). 3) An AI/ML/DL based methodology to predict unmeasurable cyclic strain based on other available sensor signals. This type of approach can be used for estimating strain in real reactor components from other sensor readings. 4) An AI/ML based approach to improve the US capability on environmental fatigue testing. This is by improving ANL’s existing environmental fatigue testing capacity to conduct ASME required strain-controlled tests (by controlling strain amplitudes and its rate), while not measuring the strain (due to the difficulty of placing an extensometer in a narrow autoclave in a PWR-water-test system). 5) A simulation and experiment based probabilistic modeling methodology for time-series fatigue state and life estimation of reactor metal such as dissimilar metal weld.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Electronic Structure Calculations for Improving Catalyst Design for Use in the Hybrid Sulfur Cycle

The hybrid sulfur cycle is a promising means for efficient production of large quantities of hydrogen with a small carbon footprint. However, many challenges remain in designing catalysts that are stable at high temperatures and have high sulfur tolerance. Theoretical calculations can provide fundamental insights at the electronic scale to improve our understanding of reaction mechanisms and surface energetics, thereby facilitating improved catalyst design. Here, density functional theory was used to explore the reaction energetics of the hybrid sulfur cycle on Pt, Au, and Pt - Au surfaces. A database of information has been built that contains calculations from which adsorption energetics and density of states information can be extracted.

36 MATERIALS SCIENCE↗

Mechanical Behavior of Hybrids with Hyper-Connected Molecular Networks

Our research is focused on fundamental studies related to the molecular design, synthesis, characterization and modeling of molecular-reinforced hybrid glass films for superior mechanical and fracture properties. Molecular-reinforced hybrid glass films exhibit unique electro-optical properties while maintaining excellent thermal stability. They have important technological application for emerging nanoscience and energy technologies including anti-reflective and ultra-barrier layers in photovoltaics, display technologies, as well as quantum technologies. However, they are often inherently brittle in nature, do not adhere well to adjacent substrates, and exhibit poor mechanical properties. Furthermore, a robust understanding of the underlying mechanisms of deformation, fracture and fatigue has been lacking particularly given their mechanically fragile nature. The related lack of structure-property relationships between molecular structure, mechanical properties and processing represents a fundamental challenge to their integration into device technologies, compromises their thermomechanical reliability and has thwarted efforts to improve mechanical and, particularly, fracture properties.

36 MATERIALS SCIENCE↗

A Hybrid Approach to Estimating the Economic Value of Enhanced Power System Resilience

The costs that power interruptions impose on customers and society have emerged as essential considerations for decision making about power system reliability and resilience. There is well-established literature on the direct costs of localized and relatively short-duration power interruptions. However, far less is known about the costs of widespread and long-duration (WLD) power interruptions, especially the indirect costs and related economy-wide impacts of these events. As a result, utility planning activities generally incorporate these costs incompletely or not at all. This report proposes a new approach to estimating WLD power interruption costs to support utility resilience planning studies. Specifically, the paper describes a hybrid method that integrates: (1) empirical surveys of region- and sector-specific inherent and adaptive customer behaviors during and after power interruptions, and (2) computable general equilibrium (CGE) modeling, which estimates the direct and indirect impacts of WLD power interruptions. The report provides both a rationale for the proposed hybrid approach and a roadmap outlining how it could be implemented.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Exotic Meson Photoproduction at GlueX – Search for the hybrid candidate Y

Understanding the hadron spectrum is one of the primary goals of non-perturbative QCD. Many predictions have experimentally been confirmed, others still remain under experimental investigation. Of particular interest is how gluonic excitations give rise to states with constituent glue. One class of such states are hybrid mesons that are predicted by theoretical models and Lattice QCD calculations. Searching for and understanding the nature of these states is a primary physics goal of the GlueX experiment at the CEBAF accelerator at Jefferson Lab. A search for a JPC = 1-- hybrid meson candidate, the Y (2175), in phi(1020)pi+pi+ and phi(1020)f0(980) channels in photoproduction on a proton target has been conducted. A first measurement of non-resonant phi(1020)pi+pi+ and phi(1020)f0(980) total cross sections in photoproduction has been performed. An upper limit on the resonance production cross section for the Y (2175) -> phi(1020)pi+pi+ and Y (2175) -> phi(1020)f0(980) channels are estimated. Since the analysis essentially depends on the quality of the charged kaon identification, also an optimization of particle identification through an improvement of the energy loss estimation in the CDC by a truncated mean method has been investigated.

Hamdi, Abdennacer↗

Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)

Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?

54 ENVIRONMENTAL SCIENCES↗

Machine-Learning-Assisted Hybrid Earth System Modelling

We propose a novel hybrid modeling approach that combines a physics-based, numerical model of the Earth system with a machine learning model. We also envision to integrate the hybrid model training with a data assimilation cycle, so that new observations update both the state estimate used as initial conditions and the machine learning model parameters.

58 GEOSCIENCES↗

Garnet/PVDF-HFP Hybrid Membranes for Li-Metal Batteries: Cooperative Research and Development Final Report, CRADA Number CRD-19-00807

Hazen Research, Inc., in collaboration with University of Colorado, Boulder (CU-B) and National Renewable Energy Laboratory (NREL), will develop and demonstrate a ceramic-polymer hybrid membrane based on garnet (Li7La3Zr2O12) and PVDF (Poly Vinylidene Fluoride)-HFP (Hexafluoropropylene) adaptable to Li-metal anodes. Under this CRADA, NREL will test the developed membranes providing feedback and advice to demonstrate hybrid membranes with Li-ion conductivity >10-4 S/cm at room temperature that are stable with Li-metal, followed by demonstration of full cells with Li-metal anode.

33 ADVANCED PROPULSION SYSTEMS↗

Research on Remote and Hybrid Scientific Work: A Literature Review

This literature review is divided into two parts. The first part looks at new developments and emerging research specifically related to remote work since the start of the COVID-19 pandemic. This encompasses research and reporting on the impacts of COVID-19 on the workplace, more speculative writing on the possible future of remote and hybrid work, and impacts of remote work during COVID-19 for diversity, equity, and inclusion. The second part focuses on more fundamental research done on remote collaboration and remote work tools prior to COVID-19, which addresses in more detail how different types, aspects, or stages of work can best be supported using virtual collaboration tools. In both sections, we review literature that directly focuses on remote scientific collaboration, which is somewhat limited, as well as the broader literature on remote and hybrid work, which is relevant to a wide variety of workplaces, including scientific ones.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

A Hybrid Method for Tensor Decompositions that Leverages Stochastic and Deterministic Optimization

In this paper, we propose a hybrid method that uses stochastic and deterministic search to compute the maximum likelihood estimator of a low-rank count tensor with Poisson loss via state-of-theart local methods. Our approach is inspired by Simulated Annealing for global optimization and allows for fine-grain parameter tuning as well as adaptive updates to algorithm parameters. We present numerical results that indicate our hybrid approach can compute better approximations to the maximum likelihood estimator with less computation than the state-of-the-art methods by themselves.

97 MATHEMATICS AND COMPUTING↗

Hybrid Al Casting + AM Components for Automotive Applications

Although casting Aluminum (Al) alloys is widely used for automotive application, the conventional casting process is showing limitations for modern vehicle production. A hybrid manufacturing method that is capable of fabricating automotive structures with tailored functionality and tuned attributes at a high production rate is proposed in this project, utilizing the merits of conventional casting methods and additive manufacturing (AM) methods. Ford is interested in supplementing conventional casting processes (e.g., shape casting, high pressure die casting) with local modifications to geometry and microstructure using additive manufacturing techniques. Two additive manufacturing methods, wire-arc and laser hot-wire directed energy deposition AM process, were evaluated by deposition of 4043 filler wire onto high-pressure die cast and permanent mold cast Al-Si alloys. The feasibility of this hybrid manufacturing method was determined through trial deposition and evaluation of defects, microstructure, and mechanical properties. Laser hot-wire deposition was found to be beneficial for this application with low porosity, a refined microstructure, and mechanical properties similar to those reported in the literature for 4043. The mechanical properties of the interface between the A356 casting and deposited 4043 were found to be controlled by the weaker deposit material.

36 MATERIALS SCIENCE↗

Accelerating Biomimetic Solar - Energy Harvesting: Mapping the Interaction Landscape of Plasmonic-Excitonic Hybrid Nanosystems (Final Report)

In general, excitonic and plasmonic nanoscale materials in close proximity show high potential for significant breakthroughs in energy related materials research. The interactions between these two kinds of materials result in coupled optical transitions (plexcitons), distinct from those of both the individual exciton and plasmon as well as from those of the sum of their constituents (synergistic effects). By linking together materials-research and physical-research approaches, this project contributes to a concerted approach on nanomaterials energy research. The project’s overall goal is to accelerate the development of well-defined plexcitonic model systems consisting of carefully engineered plasmonic and excitonic nanomaterial— essential for both gaining a fundamental understanding of plexcitonic nanomaterials and the development of novel design principles for biomimetic solar energy harvesting. During the 3-year project period and the terminal renewal with limited support for a 12-month period, we successfully synthesized and characterized (1) a robust excitonic nanomaterial and (2) a library of plasmonic nanoparticles as well as developed (3) a microfluidic platform for homogenous nanosynthesis as summarized below: (1) Robust Excitonic Nanomaterial. Supramolecular assemblies are Nature’s most successful material system for solar energy harvesting. However, photovoltaic devices based on artificial supramolecular assemblies continue to be stymied by disappointing efficiencies and poor stability. The conceptual failure may lie in current solar cell architectures, which rely on solidifying supramolecular assemblies as an ensemble into a solid matrix, neglecting the intrinsic fragility of the assemblies’ internal structure, thus disrupting or even destroying their delicate optoelectronic properties, that is, delicate Frenkel excitonic properties. Supramolecular assemblies may finally serve as usable light harvesting material systems for solar energy conversion technologies, only if they meet the following criteria: (a) Stability, that is, the fragile structure including its delicate Frenkel excitonic character needs to be stable, (b) Robustness, that is, resistant against elevated and fluctuating temperatures, and (c) Viability for device integration, that is, capable of being immobilized onto solid substrates. Here, by developing a nanocomposite via a tunable, cage-like scaffold design, we successfully provided stable supramolecular nanocomposites, that inhabit robust Frenkel excitons despite harming environmental conditions such as extreme heat stress. (2) Library of Plasmonic Nanoparticles. Naturally, current models describing plasmonic hybrid quantum states—plasmonic hybridizations—parallel those developed for molecular orbitals, equating individual plasmonic nanostructures with “atoms” and the plasmonic nanoassemblies with “molecules.” In analogy to organic synthesis, a suitably robust fabrication method would allow for “atom-like” manipulation of “molecule-like” plasmonic nanoassemblies; of high value for next-generation energy nanotechnologies. Despite this frequent comparison, current plasmonic nanoassembly fabrication methods favor top-down templating over wet-chemical synthesis, however, achieving precise control over nanostructure’s geometry and surface characteristics remain an art and a scientific challenge. The conceptual failure may lie in the current wet-chemical synthesis paradigm, as it relies on the accessibility of a multi-dimensional synthesis parameter space through limited, rather one-dimensional synthesis procedures by employing step-by-step approaches. Solution-based nanoarchitectonics for rational design of precisely built plasmonic nanoassemblies via solution-based fabrication may finally be possible only if multi-dimensional syntheses approaches are available that allow for comprehensive control over the plasmonic nanomaterials’ (a) Structural Properties and (b) Surface Properties. Here, by developing an innovative multidimensional 1,3-propanediol based polyol synthesis, we successfully provided control over the plasmonic building-block’s geometry (size and shape) together with its surface characteristics. Our results present a critical step toward the vision of a “periodic table-like” system for plasmonic materials based on straightforward wet-chemical syntheses for energy nanotechnologies. Developing deliberate modifications on this synthesis, we generated a library of plasmonic nanostructures covering the vast parameter space—opening the door for fundamental investigation of plexcitonic model systems. (3) Microfluidic Platform for Homogenous Nanosynthesis. Control over structural properties of plexcitonic nanocomposites remains a challenge due to current limitations in nanosynthesis techniques. Slight variations in nanostructure’s geometry impact their optoelectronic properties, demanding precise synthesis beyond the capabilities of solution-based (batch) synthesis processes. In contrast, the small, confined liquid volumes used in microfluidics—a reaction technique where the manipulation of fluids takes place in channels with dimensions of tens of micrometers—allows for homogenous synthesis conditions, providing excellent control of the reaction and, as a result, of the materials’ geopmetry and composition. However, thus far, the majority of plexcitonic systems has been developed via batch synthesis. Here, by successfully developing a two-channel microreactor, our microfluidic-supported synthesis approach combines the advantages of both microfluidics and batch platforms, allowing for precise spatio-temporal control over all synthesis parameters opening the possibility for homogenous nanosythnesis of well-defined plexcitonic model systems.

14 SOLAR ENERGY↗

Surface Complexation/Ion Exchange Hybrid Model for Radionuclide Sorption to Clay Minerals (M4SF-23LL010301062)

This progress report (Level 4 Milestone Number M4SF-23LL010301062) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite International Collaborations Activity Number SF-23LL01030106. The activity is focused on our long-term commitment to engaging our partners in international nuclear waste repository research. The focus of this milestone is the establishment of international collaborations for surface complexation modeling and the associated impacts of unlocking larger, community-based datasets. More specifically, we are developing a database framework for Spent Fuel and Waste and Science Technology (SFWST) that is aligned with the Helmholtz Zentrum Dresden Rossendorf (HZDR) sorption database development group in support of the database needs of the SFWST program. In our FY22 effort, we described a detailed analysis of U(VI) sorption to quartz through both traditional surface complexation modeling and through a hybrid ML framework. In FY23, effort was placed on publication of these results and expansion of the LLNL surface complexation and ion exchange database (L-SCIE) in order to assess mineral-based radionuclide retardation under a wider variety of geochemical conditions (e.g., ionic strength, varying electrolyte compositions). Efforts were initiated to expand L-SCIE to include radionuclide surface complexation and ion exchange to clays that are relevant to subsurface geochemical processes occurring at nuclear waste repositories. In particular, a large source of sorption data for clays resides at the Paul Scherrer Institute (PSI) (work primarily by Bradbury and Baeyens) and we initiated discussions on how to retrieve those data and apply FAIR principles to those datasets. In addition to L-SCIE development, two hybrid models that incorporate AI/ML were investigated and compared to discern the most promising approaches for accurate and precise estimations of radionuclide retardation. Key considerations for future model development include (1) the ability to reduce computational burden on determining retardation coefficients for PA and (2) the ability to quantify and predict radionuclide-mineral partitioning at a more efficient, rapid pace due to automated workflows. Upon the careful consideration of the most effective modeling approaches, we are identifying ways to implement these approaches into PA. Ultimately, the data science-based workflows will provide a major incentive for other institutions to adopt a FAIR-formatted, interoperable database. LLNL will play a key role in disseminating sorption data and acting as good data stewards by updating the database in a consistent format and assessing the quality of the newly assimilated data in an organized fashion. To this end, all data and workflows are open access and made available on the LLNL Seaborg research website (https://seaborg.llnl.gov/resources/geochemical-databases-modeling-codes).

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗