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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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We apply optimal control theory to drive a polar active fluid into new behaviors: relocating asters, reorienting waves, and on-demand switching between states. This study reveals general principles to program active matter for useful functions.
This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENviroment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report provides discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity is maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In addition to the investigative work being conducted on FMUs and FMIs, a series of updates to the hybrid repository has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and opensourcing the HYBRID repository with an integrated regression system.
Uranium mononitride (UN) is a promising nuclear fuel that combines the advantageous properties of readily used UO 2 and uranium alloys. Various properties of UN have been previously studied using different density functional theory (DFT) methodologies; however, there are still inconsistencies when it comes to the dynamical stability and defect properties of UN. We address these inconsistencies by studying the UN phonons and defect properties using DFT calculations employing two generalized gradient approximation (GGA) exchange-correlation functionals: PBE and AM05, with and without an added on-site Coulomb repulsion term (+U). Furthermore, we investigate the importance of spin-orbit coupling (SOC) when calculating the properties of UN. We use the different methodologies to determine the preference of UN to have antiferromagnetic (AFM) ordering, as seen in experiments, or ferromagnetic (FM) ordering of the uranium spins. We compare the crystallographic properties, density of states, the DFT X-ray photoelectron spectra and phonon dispersions calculated using the different methodologies. We demonstrate that GGA + U reproduces the AFM ordering in UN, but the crystal structure is dynamically unstable. We also show that magnetic ordering is important in finding the lowest energy defective structure, and that SOC has a distinct influence on the energy of the different uranium interstitial defects. Lastly, we discuss the point defect formation energies under U-rich and N-rich conditions, and the stoichiometric formation energies calculated with the different methodologies, providing insight into the observed tendency for forming hypostoichiometric UN.
The duality between color and kinematics brings many simplifications to the construction of scattering amplitudes. Here, we show that satisfaction of the tree-level Bern-Carrasco-Johansson relations can dramatically simplify loop computations even when the loop-level relations are not explicitly satisfied. We introduce an agglomerative algorithm, color-dual cut tiling, that builds the entire integrand from the simplest on-shell conditions applied to a seed of off-shell integrand information. Specifically, we demonstrate that for two-to-two scattering at three loops in the maximally supersymmetric gauge theory there is sufficient information contained in planar cuts—completely determined by positivity constraints—to generate all of the nonplanar sector. We further make use of the generalized double copy to generate a representation of maximally supersymmetric gravity as a functional of the planar SYM input. We close with comments on generalizations and possible applications of the approach. Published by the American Physical Society 2025
Iridium oxides with high oxidation states have been reported to be effective in enhancing the acidic oxygen evolution reaction (OER) performance. Herein, we develop ultrasmall IrO x nanoparticles (NPs) over titanium nitride (TiN), which undergoes surface oxidation under oxidative conditions to form oxygen-modified TiN (oxi-TiN), enabling the formation of highly oxidized Ir δ+ (δ > 4). This IrO x /oxi-TiN catalyst delivers higher Ir mass activity than commercial IrO 2 , while comparable stability is maintained. The superior OER activity of IrO x /oxi-TiN is further demonstrated in a proton exchange membrane water electrolyzer (PEMWE), requiring only 1.88 V to reach 3 A cm −2 , achieving the U.S. Department of Energy 2025 target (1.90 V at 3 A cm −2 ). In situ X-ray absorption spectroscopy (XAS) confirms that the superior OER activity of IrO x /oxi-TiN originates from highly oxidized Ir δ+ under OER conditions. Further, density functional theory (DFT) calculations reveal a general correlation between the oxidation state of Ir and OER overpotential. Specifically, the introduction of interfacial oxygen at the Ir/TiN interface increases the oxidation state of deposited Ir δ+ from δ < 4 to δ > 4, decreasing the OER overpotential. This study highlights the critical role of high oxidation states of Ir δ+ in enhancing OER activity, providing guidance for the development of advanced acidic OER catalysts.
Iridium oxides with high oxidation states have been reported to be effective in enhancing the acidic oxygen evolution reaction (OER) performance. In this work, we develop ultrasmall IrO x nanoparticles (NPs) over titanium nitride (TiN), which undergoes surface oxidation under oxidative conditions to form oxygen‐modified TiN (oxi‐TiN), enabling the formation of highly oxidized Ir δ+ (δ > 4). This IrO x /oxi‐TiN catalyst delivers higher Ir mass activity than commercial IrO 2 , while comparable stability is maintained. The superior OER activity of IrO x /oxi‐TiN is further demonstrated in a proton exchange membrane water electrolyzer (PEMWE), requiring only 1.88 V to reach 3 A cm −2 , achieving the U.S. Department of Energy 2025 target (1.90 V at 3 A cm −2 ). In situ X‐ray absorption spectroscopy (XAS) confirms that the superior OER activity of IrO x /oxi‐TiN originates from highly oxidized Ir δ+ under OER conditions. Density functional theory (DFT) calculations reveal a general correlation between the oxidation state of Ir and OER overpotential. Specifically, the introduction of interfacial oxygen at the Ir/TiN interface increases the oxidation state of deposited Ir δ+ from δ < 4 to δ > 4, decreasing the OER overpotential. This study highlights the critical role of high oxidation states of Ir δ+ in enhancing OER activity, providing guidance for the development of advanced acidic OER catalysts.
A necessary ingredient for extending the BFKL equation to next-to-next-to-leading logarithmic (NNLL) accuracy is the one-loop central emission vertex (CEV) for two gluons which are not strongly ordered in rapidity. Here we consider the one-loop six-gluon amplitude in $\mathcal{N}$ = 4 super Yang-Mills (SYM) theory in a central next-to-multi-Regge kinematic (NMRK) limit, we show that its dispersive part factorises in terms of the two-gluon CEV, and we use it to extract the one-loop two-gluon CEV for any helicity configuration within this theory. This is a component of the two-gluon CEV in QCD. Although computed in the NMRK limit, both the colour structure and the kinematic dependence of the two-gluon CEV capture much of the complexity of the six-gluon amplitudes in general kinematics. In fact, the transcendental functions of the latter can be conveniently written in terms of impact factors, trajectories, single-emission CEVs and a remainder, which is a function of the conformally invariant cross ratios which characterise the six-gluon amplitudes in planar $\mathcal{N}$ = 4 SYM. Finally, as expected, in the MRK limit the two-gluon CEV neatly factorises in terms of two single-emission CEVs.
Land surface depressions play a central role in the transformation of rainfall to ponding, infiltration and runoff, yet digital elevation models (DEMs) used by spatially distributed hydrologic models that resolve land surface processes rarely capture land surface depressions at spatial scales relevant to this transformation. Methods to generate DEMs through processing of remote sensing data, such as optical and light detection and ranging (LiDAR) have favored surfaces without depressions to avoid adverse slopes that are problematic for many hydrologic routing methods. Here, in this study, we present a new topographic conditioning workflow, Depression-Preserved DEM Processing (D2P) algorithm, which is designed to preserve physically meaningful surface depressions for depression-integrated and efficient hydrologic modeling. D2P includes several features: (1) an adaptive screening interval for delineation of depressions, (2) the ability to filter out anthropogenic land surface features (e.g., bridges), (3) the ability to blend river smoothing (e.g., a general downslope profile) and depression resolving functionality. From a case study in the Goodwin Creek Experimental Watershed, D2P successfully resolved 86% of the ponds at a DEM resolution of 10 m. Topographic conditioning was achieved with minimum impact as D2P reduced the number of modified cells from the original DEM by 51% compared to a conventional algorithm. Furthermore, hydrologic simulation using a D2P processed DEM resulted in a more robust characterization on surface water dynamics based on higher surface water storage as well as an attenuated and delayed peak streamflow.
Solid oxide electrolysis cell (SOEC) is an attractive renewable technology capable of producing synthesis gas (H 2 + CO) from H 2 O and CO 2 . An SOEC consists of a solid oxide electrolyte and two ceramic or metal-ceramic electrodes. H 2 O and CO 2 are electrolyzed at the cathode producing H 2 and CO, releasing O 2- ions, which travel through the electrolyte to the anode side and recombine to produce pure oxygen gas as the byproduct. In the present study, a La 0.7 Sr 0.2 Ni 0.2 Fe 0.8 O 3 cathode was used to co-electrolyze H 2 O and CO 2 at three different operating temperatures- 700 °C, 750 °C and 800 °C. Synthesis gas was produced at all three operating temperatures with 100% Faradaic efficiency. The composition of the syngas, however, was found to be a function of the operating temperature. In general, the H 2 /CO ratio in the syngas decreased with increased temperature. This was partially due to CO 2 electrolysis becoming more favorable compared to H 2 O electrolysis at higher temperatures.
Corrosion is an expensive and complex challenge for desalination, yet current design approaches do not explicitly account for corrosion mechanisms in process modeling and technoeconomic analysis. Here, to address this gap, we present a workflow for incorporating corrosion design constraints directly into desalination process optimization models. We develop surrogate models for general and localized corrosion metrics as functions of temperature, pH, salinity, dissolved oxygen, and material using data from OLI Systems’ Corrosion Analyzer. We then integrate these surrogates as corrosion design constraints in a cost-optimization MVC model that minimizes the levelized cost of water (LCOW). For a case study of mechanical vapor compression (MVC) treating seawater across a range of recoveries, we find dissolved oxygen (DO) is the dominant driver of localized corrosion, and thus of cost-optimal material choice and operating conditions. Reducing the DO from 8 mg/L to 0.5 mg/L reduces the LCOW by 15-35%, informing the breakeven costs for implementing DO removal or selecting highly corrosion-resistant alloys. This framework is broadly applicable across corrosion types, materials, and components and enables desalination process design that minimizes capital costs.
In his paper on net primary productivity of terrestrial communities predicted from climatological data, Rosenzweig (1968) argued that variability in productivity is well accounted for by (evapo)-transpiration, and that water from transpiration is, on global scales, the most variable component in the photosynthesis reaction. The goal of this paper is to investigate whether variability in plant growth on local scales and within species is primarily related to transpiration under several scenarios including different terrain curvature, slope aspect, soil characteristics, and climate ranges. Here, we test the hypothesis that this relationship exists because root growth into the surface soil layers (0–2 m) tends to follow paths with minima in resistance, which in turn maximizes water flow and nutrient delivery rates that regulate growth. The set of all connected paths with individual pore-to-pore flow resistances less than a critical, percolating, value forms a cluster with mass fractal dimensionality, d f . We propose that roots follow paths through the 2D percolation cluster, defining the set of all optimal flow paths, making the 2D value of d f from percolation relevant to root fractal dimensionality. The tortuosity of such optimal paths as defined in percolation theory should then relate root length to root radial extent, linking the parameters of root tortuosity and plant productivity. Our analysis of large data sets across species implies that root radial extent and tree height are both proportional to cumulative transpiration until trees approached maximum height, and their growth rates are proportional to the transpiration rate, not to the moisture content. Local variations in tree height as functions of the variables investigated appear generally consistent with deduced variations in transpiration. Here this correlation is investigated more closely in the context of studies addressing individual tree species.
Joining of high strength Al alloy (AA7099) to steel (rolled homogeneous armor) with and without AA6061 interlayer was achieved using a friction stir dovetailing (FSD) method. Detailed investigations were conducted to establish correlations between mechanical performance and microstructural evolution of FSD joints. Lap shear tensile testing revealed that incorporation of the AA6061 interlayer combined with tailored processing methodology significantly improved both mechanical interlocking and load transmission across the dovetail interface. Following incorporation of the interlayer, the maximum load carrying capacity and peak extension (of the AA7099/AA6061/steel joint) are 60% and 210% higher, respectively, than results obtained from an AA7099/steel joint without an interlayer. Without an AA6061 interlayer, formation of zinc rich intermetallic compounds led to brittle interfacial failure of the AA7099-steel FSD joint. Incorporation of novel tool and joint configuration eliminated asymmetric joint performance as a function of loading direction, which is generally observed in friction stir lap welding. The efficacy of the use of an AA6061 interlayer in joining AA7099 to steel was demonstrated through mechanical testing as well as high resolution analytical electron microscopy characterization.
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Accurate prediction of adsorption energies on heterogeneous catalyst surfaces is crucial to predicting reactivity and screening materials. Adsorption linear scaling relations have been developed extensively but often lack accuracy and apply to one adsorbate and a single binding site type at a time. These facts undermine their ability to predict structure sensitivity and optimal catalyst structure. Using machine learning on nearly 300 density functional theory calculations, we demonstrate that generalized coordination number scaling relations hold well for oxygen- and high-valency carbon-binding species but fail for others. Here we reveal that the valency and the electronic coupling of a species with the surface, along with the site type and its coordination environment, are critical for small species adsorption. The model simultaneously predicts the adsorption energy and preferred site and significantly outperforms linear scalings in accuracy. It can expose the structure sensitivity of chemical reactions and enable enhanced catalyst activity via engineering particle shape and facet defects. The generality of our methodology is validated by training the model with transition metal data and transferring it to predict adsorption energies on single-atom alloys.
For the first time, the phase transition and criticality of methane confined in nanoporous media are measured. The measurement is performed by establishing an experimental setup utilizing a differential scanning calorimeter (DSC) capable of operating under very low temperatures as well as high pressures to detect the capillary phase transition of methane inside nanopores. By performing experiments along isochoric cooling paths, both the capillary condensation and the bulk condensation of methane are detected. Here, the pore critical point (PCP) of nanoconfined methane is also determined and then used to derive the parameters of a previously developed self-consistent equation of state based on the Generalized van der Waals (vdW) partition function. Using this parameter, the equation of state can predict the capillary-condensation curves that agree well with the experimental data.
Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.
Magneto-Raman spectroscopy has been used to study spin-phonon coupling in two-dimensional (2D) magnets. Raman spectra of CrI 3 show a strong dependence on the magnetic order within a layer and between the layers. Here we carry out the first systematic theoretical investigation of the magneto-Raman effect in 2D magnets by performing density functional theory calculations and developing a generalized polarizability model. Our first-principles simulations well reproduce experimental Raman spectra of CrI 3 with different magnetic states. The model reveals how the change of spin orientation in each layer is coupled to the layer’s vibration to induce or eliminate the spin-dependent anti-symmetric off-diagonal terms in the Raman tensor for altering the selection rules. We also uncover that the correlation between phonon modes and magnetic orders is a universal phenomenon, which should exist in other phonon modes and 2D magnets. Our predictive simulations and modeling are expected to guide the research in 2D magnets.
Machine learning methods are increasingly deployed to construct surrogate models for complex physical systems at a reduced computational cost. However, the predictive capability of these surrogates degrades in the presence of noisy, sparse or dynamic data. Here, we introduce an online learning method empowered by optimizer-driven sampling that has two advantages over current approaches: it ensures that all local extrema (including endpoints) of the model response surface are included in the training data, and it employs a continuous validation and update process in which surrogates undergo retraining when their performance falls below a validity threshold. We find, using benchmark functions, that optimizer-directed sampling generally outperforms traditional sampling methods in terms of accuracy around local extrema even when the scoring metric is biased towards assessing overall accuracy. Finally, the application to dense nuclear matter demonstrates that highly accurate surrogates for a nuclear equation-of-state model can be reliably autogenerated from expensive calculations using few model evaluations.