SEARCH · Search NASA

Results for “interface structure prediction”

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

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Simulation insights into wetting properties of hydrogen-brine-clay for hydrogen geo-storage

Hydrogen geo-storage is attracting substantial interdisciplinary interest as a cost-effective and sustainable option for medium- and long-term storage. Hydrogen can be stored underground in diverse formations, including aquifers, salt caverns, and depleted oil and gas reservoirs. The wetting dynamics of the hydrogen-brine-rock system are critical for assessing both structural and residual storage capacities, and ensuring containment safety. Through molecular dynamics simulations, we explore how varying concentrations of cushion gases (CO 2 or CH 4 ) influence the wetting properties of hydrogen-brine-clay systems under geological conditions (15 MPa and 333 K). We employed models of talc and the hydroxylated basal face of kaolinite (kaoOH) as clay substrates. Our findings reveal that the effect of cushion gases on hydrogen-brine-clay wettability is strongly dependent on the clay-brine interactions. Notably, CO 2 and CH 4 reduce the water wettability of talc in hydrogen-brine-talc systems, while exerting no influence on the wettability of hydrogen-brine-kaoOH systems. Detailed analysis of free energy of cavity formation near clay surfaces, clay-brine interfacial tensions, and the Willard-Chandler surface for gas-brine interfaces elucidate the molecular mechanisms underlying wettability changes. Our simulations identify empirical correlations between wetting properties and the average free energy required to perturb a flat interface when clay-brine interactions are less dominant. Here, our thorough thermodynamic analysis of rock-fluid and fluid-fluid interactions, aligning with key experimental observations, underscores the utility of simulated interfacial properties in refining contact angle measurements and predicting experimentally relevant properties. These insights significantly enhance the assessment of gas geo-storage potential. Prospectively, the approaches and findings obtained from this study could form a basis for more advanced multiscale simulations that consider a range of geological and operational variables, potentially guiding the development and improvement of geo-storage systems in general, with a particular focus on hydrogen storage.

25 ENERGY STORAGE↗

DOE OSTI2025

Initial assessment of alternative carbon fiber geometries for design of cost-effective compressive performance: Size effect studies

Carbon fiber provides opportunity to reduce weight in structural composites, including wind turbine blades, due to the material's superior specific stiffness and specific strength compared to alternatives. Despite these advantages, cost and compressive performance are considered weaknesses for carbon fiber products available today. Studies to produce low-cost carbon fiber alternatives, including the use of textile-derived precursor systems, have shown progress and merit through the DOE/ORNL low-cost carbon fiber initiatives. Here, this work focuses on enabling increases in compressive strength through design of the carbon fiber geometry, applicable to both textile and conventional precursor systems, while also providing opportunities to reduce carbon fiber processing costs. Fiber-resin interface and fiber alignment are among the most frequently cited factors controlling composite compressive performance. However, it is believed that there is opportunity in traditionally unexplored routes to increasing compressive strength through alteration of the carbon fiber geometry by increasing the fiber area moment of inertia and/or the fiber perimeter and interfacial area. This paper presents initial results from manufacturing carbon fiber materials to assess the impacts of carbon fiber size on tested composite compressive performance with projected neutral or even beneficial impact on fiber and composite manufacturing economics. Carbon fiber systems with increasing size illustrate a favorable correlation for compressive performance greater than predicted from a micromechanical failure model. The manufacturing and mechanical test results support the hypothesis of this work that alterations to fiber geometry can be used to produce improvements of the compressive strength of carbon fiber reinforced polymers and provide incentive for related work in designing alternative shapes to further enhance compressive performance.

42 ENGINEERING↗

DOE OSTI2026

Understanding Adsorption and Reactions at Aqueous Oxide Interfaces with Neural Network Potential Molecular Dynamics

Chemical processes at metal oxide−water interfaces are of central importance in geochemistry, biology, and energy technologies. A better understanding of these processes would allow us to make a significant step toward optimizing and controlling them, which could in turn lead to broader impacts. Computational modeling is indispensable to accomplishing this task because complexity and disorder often make it difficult to extract atomistic information from experiments. Balancing computational cost and accuracy, simulation schemes based on efficient machine learning representations of the potential energy surface (PES) predicted by ab initio calculations have become increasingly popular over the past decade. In particular, several studies have demonstrated the ability of machine learning models to accurately reproduce the complex ab initio PESs of aqueous oxide interfaces, allowing simulations of systems and processes that are not accessible with ab initio methods. In this Account, we review our recent efforts to understand adsorption processes and reactions at aqueous oxide interfaces using deep potential molecular dynamics (DPMD), a simulation scheme employing deep neural networks (DNNs), which has proven to be quite successful in accurately describing many different systems in the condensed phase. After summarizing the DPMD methodology, we first review our work on the acid−base chemistry of oxide surfaces in contact with water, a fundamental characteristic that controls proton transfer and surface charge at the interface. We focus on the aqueous interface of rutile IrO 2 , an oxide material thus far considered the best catalyst for the oxygen evolution reaction (OER). We show that this interface is characterized by a large fraction of dissociated water and a strong Brønsted acidity of the surface sites, in good agreement with the experimentally measured value of the point of zero proton charge. In our second example, we investigate how the adsorption of organic species from ambient air or water affects the structure and wettability of the aqueous interfaces of TiO 2 , a prototypical photocatalytic material. This is a question that is relevant to understanding the UV-induced hydrophilicity of TiO 2 surfaces, a property at the basis of self-cleaning windows and related applications. Specifically focusing on formic and acetic acids, the two most common atmospheric organic acids, our simulations reveal that these acids control the wettability of TiO 2 largely through acid−base chemistry at the interface rather than chemisorption on the oxide surface, a finding that could help improve the design of self-cleaning surfaces and photocatalytic devices. Finally, we review our recent study of methanol at TiO 2 −water interfaces, a system whose interest is largely motivated by the role of methanol in enhancing photocatalytic hydrogen evolution on TiO 2 . Our simulations provide mechanistic insights into the coupled roles of the organic adsorbate and water at the TiO 2 interface, with implications for how methanol enhances the activity of H 2 evolution.

adsorption↗

DOE OSTI2025

Experimental evidence of disordered crystalline premixing in sputter-deposited Ni(V)/Al multilayers

The sputter deposition of alternating layers of Ni(V) and Al forms a reactive multilayer known to undergo self-propagating formation reactions when ignited. The sequential deposition process leads to nanometer-scale premixing of reactants at each included interface, which ultimately affects multilayer exothermicity. This work performs the direct measurement of a disordered face-centered cubic (FCC) solid solution premixed phase at the interfaces of Ni(V)/Al multilayers via scanning transmission electron microscopy. The crystallinity of the observed phase differs from previously reported a priori predictions of an amorphous interlayer. The disordered FCC phase retains its symmetry after annealing for 16 h at 135 ± 5 °C, but the lattice parameter shifts consistently with an Al-rich composition. The existence of a crystalline premix in Ni(V)/Al is attributed to the electronic contribution to the entropy of crystallization. The importance of electronic entropy to the phase formation of energetic materials motivates its inclusion when constructing digital twins for atomistic kinetics and ignition sensitivity.

Crystal structure↗

DOE OSTI2024

Dissecting neurofilament tail sequence-phosphorylation-structure relationships with multicomponent reconstituted protein brushes

Neurofilaments (NFs) are multisubunit, bottlebrush-shaped intermediate filaments abundant in the axonal cytoskeleton. Each NF subunit contains a long intrinsically disordered tail domain, which protrudes from the NF core to form a “brush” surrounding each NF. Precisely how the tails’ variable charge patterns and repetitive phosphorylation sites mediate their conformation within the brush remains an open question in axonal biology. We address this problem by grafting recombinant NF tail protein constructs NF-Light, -Medium, and -Heavy (NFL, NFM, and NFH) to surfaces, yielding protein brushes of defined stoichiometry that can be phosphorylated in vitro. Atomic force microscopy measurements reveal that brush height depends on composition monotonically but not always linearly for binary NFL:NFM or NFL:NFH systems, and that NFM-based brushes are highly extended, while brushes incorporating the much larger NFH are surprisingly compact even after multisite phosphorylation. Complementary self-consistent field theory (SCFT) predicts multilayer brush morphologies for NFM and phosphorylated NFH brushes. Further experiments and SCFT analysis with designed mutants reveal that N-terminal negative charges in the NFH tail repel phosphorylated residues to generate the multilayer morphology, while the C-terminal charge-neutral region contributes to multilayer brush morphology but not total brush height. Charge-shuffled NFM variants show that charge segregation promotes brush collapse near physiological ionic strengths. Collectively, this study supports a role for NFM in establishing a dynamic range for NF brush conformation, lending insight into previous in vitro and in vivo findings. More broadly, this work establishes a platform for dissecting contributions of disordered protein sequence to conformation at interfaces.

Science & Technology - Other Topics↗

DOE OSTI2025

Challenges and approaches to interpretive modeling of boundary plasma and neutral transport in a closed, pumped divertor

An experimental discharge from the DIII-D tokamak is modeled using the SOLPS-ITER code suite and compared against measurements in the pumped and relatively closed upper divertor. Uncertainties of boundary plasma simulations are identified by attempting to match code inputs to experimental conditions, including iteratively solving transport coefficients to match upstream experimental profiles using varying quantities of core particle flux, different pumping models, and various assumptions of ion thermal transport. Simulated boundary conditions for particle injection at the core interface are shown to be relevant to the plasma solution at the divertor targets, even if upstream transport is modified so that plasma profiles are comparatively similar, although seperatrix density is not held constant. When upstream plasma profiles are matched to experimental measurements by varying diffusive transport coefficients, using either poloidally symmetric or ballooning structure, the model finds a majority of injected energy being transported radially off the computational domain, in conflict with experimental radiated power measurements and heat flux measurements at the divertor target. Imposing a maximum thermal diffusivity or radially shifting the experimental separatrix location of the fitted profiles to increase power conducted to the targets by increasing the upstream electron temperature does not significantly modify this result. Including a thermalizing plenum volume in the simulation domain is shown to maintain the experimental volumetric pumping rate without knowing the neutral energy distribution incident on the pump duct a priori. By modifying transport parameters to match different assumptions for ion temperature, downstream neutral pressure changes by more than a factor of two, suggesting that attention to ion thermal transport may be a critical parameter for simulations to accurately resolve recycling and neutral transport, particularly in a closed divertor geometry. In addition to quantifying various modeling uncertainties, this work motivates both further experimental study and modeling improvements to improve predictive capabilities.

divertor↗

DOE OSTI2024

Elucidating Phosphate and Cadmium Cosorption Mechanisms on Mineral Surfaces with Direct Spectroscopic and Modeling Evidence

The simultaneous sorption of cations and anions at the mineral–water interface can substantially alter their individual sorption characteristics; however, this phenomenon lacks a mechanistic understanding. Our study provides direct spectroscopic and modeling evidence of the molecular cosorption mechanisms of the cadmium ion (Cd 2+ ) and phosphate (P) on goethite and layered manganese (Mn) oxide of birnessite, through in situ attenuated total reflection Fourier-transform infrared (ATR-FTIR), P K-edge X-ray absorption near-edge structure (XANES) spectroscopy, and surface complexation modeling. Phosphate synergistically cosorbed with Cd on goethite predominantly through P-bridged ternary complexes (≡Fe–P–Cd) and electrostatic interactions at wide pH conditions. Likewise, P and Cd exhibited synergistic cosorption on birnessite by forming P-bridged ternary complexes (≡Mn–P–Cd) and weak competitive sorption at the layer edge sites. As pH and Cd loading increased, the surface P species transitioned from a binary complex to a ternary complex and/or Cd 3 (PO 4 ) 2 precipitate for both goethite and birnessite. Compared to that in solution at pH 8, the formation of Cd 3 (PO 4 ) 2 was inhibited by the presence of goethite and birnessite, ascribed to the specific adsorption of P and Cd, more pronounced in birnessite due to the stronger sorption of Cd at its vacant sites. Finally, the discovered cosorption mechanisms of P and Cd have important implications for understanding and predicting their mobility and availability in Cd-contaminated settings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Composition-Dependent Reconstructions of Titanium Oxide Clusters and Cu(111) Support via Cluster-Adatom Interactions

Mass-selected cluster deposition and scanning tunneling microscopy (STM) were used to investigate the surface structure and thermal stability of Ti 3 O y (y = 5, 6) clusters on a Cu(111) surface. STM imaging shows that the substoichiometric Ti 3 O 5 clusters aggregate into large dendritic assemblies at step edges, while the stoichiometric Ti 3 O 6 clusters are highly dispersed as single or small groupings of clusters. Cluster deposition also results in extensive reconstruction of the Cu(111) surface. The Ti 3 O 5 /Cu(111) surface exhibits one Cu-layer deep pits that are ringed by cluster assemblies that nucleate and grow at the perimeter step edges. By comparison, the Ti 3 O 6 /Cu(111) surface consists of irregular step edges and small one Cu-layer islands that are decorated with clusters. The restructuring of the Cu(111) surface is attributed to strong Cu-cluster interactions that release Cu adatoms from the step edges. This mechanism is supported by density functional theory (DFT) calculations, which predict that the Cu adatom attachment to the clusters is energetically favorable. Annealing the surfaces at elevated temperatures (400-700 K) results in higher densities of irregular steps, pits and isolated Cu islands as well as increased cluster aggregation. In conclusion, this work highlights adatom-cluster interactions as another manifestation of strong cluster-support interactions that can significantly alter the nature of the oxide-metal interface.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

On plasticity-enhanced interfacial toughness in bonded joints

Here, the performance and reliability of many structures and components depend on the integrity of interfaces between dissimilar materials. Interfacial toughness Γ is the key material parameter that characterizes resistance to interfacial crack growth, and Γ is known to depend on many factors including temperature. For example, previous work showed that the toughness of an epoxy/aluminum interface decreased 40 % as the test temperature was increased from -60 °C to room temperature (RT). Interfacial integrity at elevated temperatures is of considerable practical importance. Recent measurements show that instead of continuing to decrease with increasing temperature, Γ increases when test temperature is above RT. Cohesive zone finite element calculations of an adhesively bonded, asymmetric double cantilever beam specimen of the type used to measure Γ suggest that this increase in toughness may be a result of R-curve behavior generated by plasticity-enhanced toughening during stable subcritical crack growth with interfacial toughness defined as the critical steady-state limit value. In these calculations, which used an elastic-perfectly plastic epoxy model with a temperature-dependent yield strength, the plasticity-enhanced increase in Γ above its intrinsic value Γ o depended on the ratio of interfacial strength σ* to the yield strength σyb of the bond material. There is a nonlinear relationship between Γ/Γ o and σ*/σ yb with the value Γ/Γ o increasing rapidly above a threshold value of σ*/σ yb . The predicted increase in toughness can be significant. For example, there is nearly a factor of two predicted increase in Γ/Γ o during micrometer-scale crack-growth when σ*/σ yb = 2 (a reasonable choice for σ*/σ yb ). Furthermore, contrary to other reported results, plasticity-enhanced toughening can occur prior to crack advance as the cohesive zone forms and the peak stress at the tip of the original crack tip translates to the tip of the fully formed cohesive zone. These results suggest that plasticity-enhanced toughening should be considered when modeling interfaces at elevated temperatures.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Multiscale analysis of large twist ferroelectricity and swirling dislocations in bilayer hexagonal boron nitride

With its atomically thin structure and intrinsic ferroelectric properties, heterodeformed bilayer hexagonal boron nitride (hBN) has gained prominence in next-generation non-volatile memory applications. However, studies to date have focused almost exclusively on small-twist bilayer hBN, leaving the question of whether ferroelectricity can persist under small heterostrain and large heterodeformation entirely unexplored. In this work, we establish the crystallographic origin of ferroelectricity in bilayer hBN configurations heterodeformed relative to high-symmetry configurations such as AA-stacking and 21.786789° twisted configurations (Σ7), using Smith normal form bicrystallography. We then demonstrate out-of-plane ferroelectricity in bilayer hBN across configurations vicinal to both the AA and Σ7 stackings. Atomistic simulations reveal that AA-vicinal systems support ferroelectricity under both small twist and small strain, with polarization switching in the latter governed by the deformation of swirling dislocations rather than the straight interface dislocations seen in the former. For Σ7-vicinal systems, where existing interatomic potentials underperform particularly under extreme out-of-plane compression, we develop a density-functional-theory-informed continuum framework—the bicrystallography-informed frame-invariant multiscale (BFIM) model, which captures out-of-plane ferroelectricity in heterodeformed configurations vicinal to Σ7 stacking. Interface dislocations in these large heterodeformed bilayer configurations exhibit markedly smaller Burgers vectors compared to interface dislocations in small-twist and small-strain bilayer hBN. The BFIM model reproduces experimental results and provides a powerful, computationally efficient framework for predicting ferroelectricity in large-unit-cell heterostructures where atomistic simulations are prohibitively expensive.

Ahmed, Md Tusher [Univ. of Illinois at Urbana-Cham↗

DOE OSTI2025

Quantifying and visualizing the microscopic degrees of freedom of grain boundaries in the Wigner–Seitz cell of the displacement-shift-complete lattice

Here we introduce a grain boundary (GB) translation vector, t ws , to describe and quantify the domain of the microscopic degrees of freedom of GBs. It has long been recognized that for fixed macroscopic degrees of freedom of a GB there exists a large multiplicity of states characterized by different relative grain translations. More recently another degree of freedom, [n], the number of GB atoms, has emerged and is now recognized as an equally important component of GB structural multiplicity. In this work, we show that all GB microstates can be uniquely characterized by their value of t ws , which is located within the Wigner–Seitz (WS) cell of the Displacement-Shift-Complete lattice (DSCL) of the GB. The GB translation vector captures information about both the translation state and the number of GB atoms. We show that the density of GB microstates inside the WS cell of the DSCL is not uniform and can form clusters that correspond to different GB phases. Further, the vectors connecting the centers of the clusters correspond to the Burgers vectors of GB phase junctions, which can be predicted without building the junctions. Using t ws , we quantify GB excess shear and argue that it is defined up to a DSCL vector, which has implications for thermodynamic equilibrium conditions. Additionally, this work generalizes the definition of the number of GB atoms [n] to asymmetric boundaries.

36 MATERIALS SCIENCE↗

DOE OSTI2024

BioRT‐HBV 1.0: A Biogeochemical Reactive Transport Model at the Watershed Scale

Abstract Reactive Transport Models (RTMs) are essential tools for understanding and predicting intertwined ecohydrological and biogeochemical processes on land and in rivers. While traditional RTMs have focused primarily on subsurface processes, recent watershed‐scale RTMs have integrated ecohydrological and biogeochemical interactions between surface and subsurface. These emergent, watershed‐scale RTMs are often spatially explicit and require extensive data, computational power, and computational expertise. There is however a pressing need to create parsimonious models that require minimal data and are accessible to scientists with limited computational background. To that end, we have developed BioRT‐HBV 1.0, a watershed‐scale, hydro‐biogeochemical RTM that builds upon the widely used, bucket‐type HBV model known for its simplicity and minimal data requirements. BioRT‐HBV uses the conceptual structure and hydrology output of HBV to simulate processes including advective solute transport and biogeochemical reactions that depend on reaction thermodynamics and kinetics. These reactions include, for example, chemical weathering, soil respiration, and nutrient transformation. The model uses time series of weather (air temperature, precipitation, and potential evapotranspiration) and initial biogeochemical conditions of subsurface water, soils, and rocks as input, and output times series of reaction rates and solute concentrations in subsurface waters and rivers. This paper presents the model structure and governing equations and demonstrates its utility with examples simulating carbon and nitrogen processes in a headwater catchment. As shown in the examples, BioRT‐HBV can be used to illuminate the dynamics of biogeochemical reactions in the invisible, arduous‐to‐measure subsurface, and their influence on the observed stream or river chemistry and solute export. With its parsimonious structure and easy‐to‐use graphical user interface, BioRT‐HBV can be a useful research tool for users without in‐depth computational training. It can additionally serve as an educational tool that promotes pollination of ideas across disciplines and foster a diverse, equal, and inclusive user community.

Sadayappan, Kayalvizhi↗

DOE OSTI2025

dynamics of organic-mineral interactions at the metal oxide-solution interface as studied via binding energetics (Final report)

This project focused on addressing longstanding fundamental and experimental uncertainties on how dissolved organic substances (DOS) interact with metal oxide surface under environmentally relevant conditions. By leveraging a custom-built real-time, in-tandem flow adsorption microcalorimetry-UV-Vis/fluorescence spectroscopy platform, we characterized the binding energetics, kinetics and mechanistic pathways driving DOS-metal oxide interactions at temporal resolution on the order of 1-5 seconds. We studied a diverse suite of model organic compounds/substances – including monocarboxylates (e.g. acetate and benzoate), di-carboxylates (oxalate and succinate), amino acids, amino-based nanparticles and natural organic matter – interacting at the mineral-water interface of structurally- and/or chemically distinct metal oxides (including SiO2, boehmite, ferrihydrite, and γ-Al2O3). Our results indicated that DOS-metal oxide interactions are governed by multi-step reaction pathways, often switching between distinct, resolvable enthalpy- and entropy-driven non-electrostatic or electrostatic configurations. To quantify these interactions, we developed and implemented an analytical workflow that integrates peak deconvolution and Monte-Carlo based error propagation to determine site-specific thermodynamic and kinetic parameters for individual binding/debinding events. In addition to resolving apparent first-order rate constants of each event, we were able to quantify associated apparent equilibrium constants as well as free energy, enthalpy and entropy contribution to the activation and subsequent progression of the binding/debinding process across compounds, compound class and metal oxide surfaces. The kinetic-thermodynamic data produced in this study captured how the interplay between oxide surface reactivity and DOS molecular structure jointly drives binding-debinding dynamics. Notably, that at pH below PZC of the oxide surface, neutral species were heavily involved in monocarboxylate binding, while anionic species drove dicarboxylate binding. Also, that among amino acids 1) positional isomers show distinctive binding characteristics to each other while enantiomers show no significant differences in binding characteristics, 2) molecules that bind via outer-sphere complexation show a larger entropic shift between binding and debinding with no impact on oxide surface while 3) inner-sphere interactions increased anion exchange capacity of the oxide surface. The new insights and data from this work has great potential for improving predictive modeling of carbon dynamics and specifically organic-mineral interactions in environmental and industrial systems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Predictive dynamic wetting, fluid–structure interaction simulations for braze run-out

Brazing and soldering are metallurgical joining techniques that use a wetting molten metal to create a joint between two faying surfaces. Here, the quality of the brazing process depends strongly on the wetting properties of the molten filler metal, namely the surface tension and contact angle, and the resulting joint can be susceptible to various defects, such as run-out and underfill, if the material properties or joining conditions are not suitable. In this work, we implement a finite element simulation to predict the formation of such defects in braze processes. This model incorporates both fluid–structure interaction through an arbitrary Eulerian–Lagrangian technique and free surface wetting through conformal decomposition finite element modeling. Upon validating our numerical simulations against experimental run-out studies on a silver-Kovar system, we then use the model to predict run-out and underfill in systems with variable surface tension, contact angles, and applied pressure. Finally, we consider variable joint/surface geometries and show how different geometrical configurations can help to mitigate run-out. This work aims to understand how brazing defects arise and validate a coupled wetting and fluid–structure interaction simulation that can be used for other industrial problems.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Integrated Molten Salt Reactor Modeling Capabilities in NEAMS Thermal Hydraulics Tools

The DOE neams program supports a full range of computational thermal fluids analysis capabilities and code developments for a broad range of advanced reactor concepts. The research and development approach under the thermal fluids technical area synergistically combines three length and time scales in a hierarchical multi-scale approach. To enable multi-scale thermal fluids capability using these codes, a key joint effort has been underway to develop an integrated system- and engineering-scale thermal fluids analysis capability, through integration of SAM and Pronghorn codes, both based on the MOOSE framework. This report summarizes recent advances in developing an integrated system- and engineering-scale modeling capability for the msr concept, which has gained significant interest in recent years. A consistent framework was established by coupling Pronghorn and SAM through the Saline interface, with thermophysical properties provided by the Molten Salt Thermal Property Database (MSTDB-TP). Further improvements were made to the coupling schemes and domain-overlapping strategies, enhancing the stability and robustness of multi-code simulations. Verification and validation efforts demonstrate the accuracy of this integration across a range of benchmark problems, including one-dimensional heated pipe flows, three-dimensional natural convection loops with evolving isotopic compositions, and \gls{msre} demonstration cases. Within Pronghorn, new capabilities were introduced to model corrosion and noble-metal plating phenomena, supported by an extended thermal-hydraulics framework and refined turbulence treatments. To capture two-phase flow behavior, a multiphase Euler–Euler model was implemented in Pronghorn, including advanced closure relations, high-resolution advection techniques, and capillary force reconstruction. Preliminary verification cases confirm the fidelity of the approach, while planned validation efforts target canonical multiphase benchmarks and application to msr components such as the msre pump bowl. Finally, updates to SAM’s msr mass transfer modeling were extended to consider noble gas migration into porous structures like graphite. The point kinetics model was updated to include reactivity feedback contributions from any defined species, such as xenon. The gas transport model was expanded for applicability to gas mixtures, bubble efflux phenomena, and species transport between liquid and gas phases. A selection of multi-scale Sherwood number correlations from MOSCATO/NekRS and multi-phase correlations from literature have been added for improved accuracy in calculating mass transfer coefficients. A companion effort on developing system-level redox corrosion has also been incorporated into SAM. Collectively, these enhancements strengthen the predictive capability of SAM and Pronghorn for simulating MSR thermal-hydraulics, corrosion, multiphase behavior, and fission-product transport, providing a more complete toolset for design, safety analysis, and licensing support of next-generation \gls{msr}s.

42 - ENGINEERING↗

DOE OSTI2025

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2024

Using interfaces to: create strongly-coupled magnetic-ferroelectrics

Starting at the level of electrons and atoms our long-term goal is to rationally design complex oxide heterostructures and interface-materials with targeted emergent behaviors. This is not a matter of simply optimizing material parameters, but rather begins with understanding a mechanism to control the interplay between the diverse microscopic degrees of freedom prevalent in complex oxides in order to create targeted macroscopic phenomena and ends with the design of new material realizations. These realizations are in turn created with atomic-layer precision, structurally assessed to see that they are the intended realization, and finally their relevant properties are measured. During this program we have developed the scientific ideas necessary to apply this materials-by-design paradigm to the creation of materials offering electrical control of magnetism. At the heart of our approach lies a partnership between theory, synthesis, and characterization. These are the areas of expertise of the three co-PIs, who have worked closely together since their arrival at Cornell. During this project bold ideas on where to put the atoms to provide electrical control of magnetism were met with the latest methods of synthesis science and electron microscopy in an attempt create targeted new multiferroics. Our work applying this materials-by-design methodology to multiferroics has enjoyed multiple successes. The Co-PIs have invented new mechanisms for multiferroics, which provide strong coupling between polarization and magnetism, and reduced them to practice. Specifically, we pioneered multiferroics based on spin-phonon coupling, rotation-driven multiferroicity, and novel geometric multiferroics. Importantly, the latter two of these mechanisms enable the deterministic switching of magnetism by an applied electric field, which is key to the application of multiferroics to future device technologies. Most recently, we performed detailed theoretical analysis on a system involving the combination of magnetoelectric and colossal magnetoresistance behaviors and predict that electrical control of a metal-insulator transition is possible in appropriately strained SmBaMn2O6. The research enabled by this grant led to 63 publications in leading refereed journals including Nature, Nature Materials, Physical Review Letters, and Advanced Materials demonstrating a materials-by-design paradigm to the creation of materials offering electrical control of magnetism.

36 MATERIALS SCIENCE↗