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Results for “behavioral sciences”
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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Nanometer-Scale Correlations in Aqueous Salt Solutions
The intermediate-range (1–4 nm) correlation of cations, an- ions, and water in aqueous alkaline earth salt solutions is measured using synchrotron X-ray diffraction. We differentiate from the entire solution structure factor, S(Q), a separate region at low Q (<1 °A-1) containing local diffraction maxima (pre-peaks) that indicate nanometer-scale oscillatory behavior. These features are quantitatively reproduced with classical molecular dynamics simulations. At high concentrations, the pre-peaks emerge from correlations arising from existence of small quasi close-packed lattice- like structures comprised of cation hydration spheres. We also analyze the concentration dependence of the pre-peak and discuss the overall results in light of the rich literature dealing with intermediate-range correlations underlying universal phenomena in concentrated electrolytes. The authors were supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences. Pacific Northwest National Laboratory (PNNL) is operated by Battelle for the U.S. Department of Energy under Contract No. DE-AC05-76RL01830. Computing resources were generously allocated by PNNL's Institutional Computing program. DOE Contract No. DEAC02- 06CH11357 supports operation of the Advanced Photon Source at Argonne National Laboratory.
Origin of Ferroelectric Phase Stabilization via the Clamping Effect in Ferroelectric Hafnium Zirconium Oxide Thin Films
The presence of the top electrode on hafnium oxide-based thin films during processing has been shown to drive an increase in the amount of metastable ferroelectric orthorhombic phase and polarization performance. This “Clamping Effect,” also referred to as the Capping or Confinement Effect, is attributed to the mechanical stress and confinement from the top electrode layer. However, other contributions to orthorhombic phase stabilization have been experimentally reported, which may also be affected by the presence of a top electrode. In this study, it is shown that the presence of the top electrode during thermal processing results in larger tensile biaxial stress magnitudes and concomitant increases in ferroelectric phase fraction and polarization response, whereas film chemistry, microstructure, and crystallization temperature are not affected. Through etching experiments and measurement of stress evolution for each processing step, it is shown that the top electrode locally inhibits out-of-plane expansion in the HZO during crystallization, which prevents equilibrium monoclinic phase formation and stabilizes the orthorhombic phase. This study provides a mechanistic understanding of the clamping effect and orthorhombic phase formation in ferroelectric hafnium oxide-based thin films, which informs the future design of these materials to maximize ferroelectric phase purity and corresponding polarization behavior.
Site-Projected Thermal Conductivity: Application to Defects, Interfaces, and Homogeneously Disordered Materials
With the rapid advance of high-performance computing and electronic technologies, understanding thermal conductivity in materials has become increasingly important. This study presents a novel method: the site-projected thermal conductivity that quantitatively estimates the local (atomic) contribution to heat transport, leveraging the Green–Kubo thermal transport equations. The effectiveness of this approach on disordered and amorphous graphene, amorphous silicon, and grain boundaries in silicon–germanium alloys is demonstrated. Amorphous graphene reveals a percolation behavior for thermal transport. The results highlight the potential of the method to provide new insights into the thermal behavior of materials, offering a promising avenue for materials design and performance optimization.
Deformation and Fracture Behavior of Additively Manufactured 316L Stainless Steel
Tensile deformation and fracture behavior of an additively manufactured (AM) 316L stainless steel (SS) in the as-built, stress-relieved, and solution-annealed conditions was investigated using in situ tensile testing in a scanning electron microscope with an electron back scattering diffraction (SEM–EBSD) detector. Analyses were performed to discuss the characteristic deformation and fracture process of the fine-grained AM 316L SS with and without relaxation heat treatment. The as-built 316L showed the highest strength, and both post-build heat treatments lowered the strength of the alloy. Regardless of the post-build processing, the AM 316L SS showed overall higher strength but slightly lower ductility when compared to the wrought (WT) 316L SS. Analysis of EBSD data indicated that the characteristic microstructural features from AM, such as the complex and fine grain morphology, dislocation network, pores, and silicon-rich oxides, evolved and exerted various roles during the tensile deformation and fracture processes. Further, it was obvious that the interaction of dislocation slips with oxide particles and cavities (or pores) resulted in an accelerated cracking in AM 316L SS. Overall, however, their influence on mechanical behavior was limited, as the genuinely high ductility of 316L SS could help avoid any premature or brittle fracture.
Transport Upscaling under Flow Heterogeneity and Matrix-Diffusion in Three-Dimensional Discrete Fracture Networks
For this work, we investigate the combined effects of network scale flow variability and retention due to matrix-diffusion on the scaling behavior of transport through fractured media. Two of the principal mechanisms controlling the transport of solutes through fractured low-permeability media are broad distributions of flow velocities and retention times in the solid matrix. We study the relative impact of these two processes under different initial conditions using a set of three-dimensional discrete fracture network simulations. We use these simulations to develop and calibrate an upscaled continuous time random walk (CTRW) approach for advective transport based on an Ornstein-Uhlenbeck model for the particle velocities that accounts for the fracture-matrix coupling using a compound Poisson process. This CTRW model can be conditioned on the initial solute distribution and allows to observe late-time scaling behavior at distances beyond what is feasible to observe using high-fidelity direct numerical simulations. We determine that the initial distribution of particles leads to marked differences in the persistent long-term scale behavior in the solute travel time distributions, even those undergoing retention due to matrix diffusion through implementation and analysis of the model.
Comparing the interactions between particulate matter and cloud properties over two populated cities in Texas using WRF-Chem fine-resolution modeling
Accurate modeling of aerosol-cloud interactions is essential for reliable weather and air quality simulations, given their significant impact on precipitation patterns, cloud dynamics, and aerosol distributions. This study employed the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to examine the impact of enhanced meteorological simulations, achieved through advanced microphysics parameterization supported by data assimilation techniques, on air quality across Texas on August 19 and 20, 2022. We tested four distinct configurations: (1) the Morrison two-moment bulk microphysics scheme, (2) Morrison's with observation nudging, (3) the Spectral Bin Microphysics (SBM), and (4) SBM with observation nudging. While the SBM scheme is known for its detailed representation of aerosol-cloud interactions, our focus was on how improvements in meteorological accuracy translate to more precise air quality simulations. Our findings demonstrated a progressive improvement in simulation accuracy, starting with the Morrison's scheme and further enhanced by adopting the SBM scheme, complemented by incorporating observation nudging. Specifically, the combination of the SBM scheme and the nudging substantially enhanced the model's ability to capture convective precipitation events, as shown by better alignment with NEXRAD radar reflectivity, with R increasing from –0.21 to 0.82, IOA from 0.10 to 0.87, and NMB decreasing from 99% to 34% in Houston. The enhanced meteorology translated into more accurate PM 2.5 concentration simulations, particularly through the more accurate representation of aerosol washout during precipitation events. In Houston, the SBM scheme with nudging improved the model's PM 2.5 simulations significantly, with NMB decreasing from –20% to 5% and IOA improving from 0.43 to 0.61. In San Antonio, improvements were also notable, with NMB improved from –27% to –22%, R increased from 0.48 to 0.82, and IOA increased from 0.66 to 0.86. Furthermore, our results underscore the crucial role of accurate meteorological simulations in refining our understanding of aerosol behaviors in relation to precipitation patterns, directly enhancing the reliability and effectiveness of air quality modeling.
The impact of anisotropic thermal expansion on the isothermal annealing of polycrystalline α -uranium
Although grain growth impacts microstructural evolution in a wide variety of materials systems, the effect of anisotropic thermal expansion on grain boundary mobility and texture evolution has not been widely studied. Anisotropic thermal expansion occurs in multiple non-cubic metals, and the thermomechanical processing behavior of these materials can be better understood with further study into the impact of thermal expansion on grain boundary mobility and texture evolution. In this work, we develop a mesoscale phase field model of grain growth that includes the effect of anisotropic thermal expansion, which is applied to study polycrystalline α-uranium, a highly anisotropic metal. Three-dimensional simulations on polycrystalline a-uranium with and without thermal expansion eigenstrains are performed to study the grain boundary mobility and texture evolution as a function of temperature. A strain-free temperature of 933 K is selected, and the system is studied at within the range of 873 – 933 K at intervals of ten degrees, resulting in increasing thermal eigenstrain with decreasing temperature. We also estimate a grain boundary mobility prefactor and activation energy based on existing experimental data of isothermal annealing of α-uranium. The grain boundary mobility is found to display significant deviation from Arrhenius behavior with the inclusion of thermal expansion eigenstrain as the amount of thermal eigenstrain (and thus elastic strain energy within the system) increases. This result explains an experimentally observed grain boundary mobility deviation from Arrhenius behavior. Furthermore, the texture evolution is affected, such that the grain orientations become less random with increasing thermal eigenstrain, which could explain experimentally observed texture behavior. These results indicate that the effect of thermal expansion should be considered when predicting the thermomechanical processing behavior of α-uranium and other materials with anisotropic thermal expansion.
Effects of fiber dosage, loading orientation and stress on frequency response of enhanced Carbon Nano-Fiber Aggregates
Nanomaterial additives have been broadly used in cement and concrete-based sensors to measure the stress and strain in the structure. Most of the studies focused on measuring the resistivity of sensors with Direct Current (DC) or relatively low-frequency. Here in this work, the impedance responses of enhanced Carbon Nano-Fiber Aggregates (CNFAs) at different frequencies are rigorously studied and utilized as a tool for real-time structural health monitoring (SHM). The CNFA impedance measurements at different measurement frequencies provide an extra dimension (frequency dimension) to allow the characterization of different phenomena and multimodal measurements simultaneously. First, a comprehensive physical model is established to understand the frequency responses of the CNFA impedance. Second, COMSOL Multiphysics is used to simulate the frequency responses of the CNFA impedance, which provides more insights into the electric field and current density distribution. The impedance of CNFAs decreased with the increased dosage of carbon nanofibers (CNFs). Third, experimental studies are reported in detail. The electrical impedance variation (EZV) of the CNFA in parallel orientation is 151% higher than that of perpendicular orientation at the frequency of 2612 Hz and uniaxial compression stress of 4.65 MPa. The strain-EZV curve obtained from the response spectrum is linear for frequencies ranging from 98 Hz to 463.9 kHz. The strain-EZV curve for 5625 Hz has a linear fit with the gauge factor of 147.78. At 1000 Hz, the CNFA exhibited a repeatable behavior up to 9.35 MPa and detection limit up to the stress of 18.62 MPa.
Glassy correlated state induced by disorder in the frustrated antiferromagnet Tb 2 Zr 2 O 7
Here, we study the low-temperature thermomagnetic properties of the polycrystalline material Tb 2 Zr 2 O 7 through ac susceptibility and specific heat measurements. This zirconate displays a defect-fluorite structure in which the magnetic Tb 3+ and nonmagnetic Zr 4+ cations sit randomly on the same metal sublattice. No long-range magnetic order is found down to 100 mK, although dominant antiferromagnetic interactions are observed and the spins remain dynamic down to the lowest temperatures investigated. We observed a frequency-dependent peak around 2.5 K which is well described by many models of a canonical spin-glass transition. In-field specific heat measurements and the recovered entropy of the system R ln(4) suggest a two doublets ground state separated by 7 K. Comparisons to the pyrochlores Tb 2 Ti 2 O 7 and Tb 2 Hf 2 O 7 reinforce the collective spin-glass-type behavior and the opening for discussions of an exotic Coulomb phase in this material.
Heterogeneous microstructural evolution during hydrodynamic penetration of a high-velocity copper microparticle impacting copper
Microparticle hydrodynamic penetration (HDP) may be associated with the erosion regime in cold spray processing and other high-velocity impact events. Here, in an experimental approach where we can individually launch particles and study the impact sites, we explore copper microparticles impacted on copper substrates at velocities above 900 m/s where HDP begins. We lift cross-sectional lamellae from the impact sites with a focused-ion beam for further microstructural characterization using electron backscatter diffraction and scanning transmission electron microscopy. Due to the gradients of strain, strain rate, and temperature associated with HDP, heterogeneous microstructures result. The structural evolution processes observed include deformation twinning and multiple dislocation-mediated grain recrystallization mechanisms—geometric dynamic recrystallization (gDRX), discontinuous DRX (dDRX), and meta DRX (mDRX). The higher strains at the interface lead to the most significant structural changes and complex mechanisms. In contrast, there is a gradient to more conventional dislocation plasticity away from the interface (on either the particle or substrate side). Here, these microstructural observations are consistent with the deformation map for copper and extend the observations of impact-induced recrystallization across new regimes of behavior.
Neural network interatomic potential-driven analysis of phase stability in Ti–V alloys at the atomistic scale
The evolution of the ω phase in titanium–vanadium (Ti–V) alloys is critical for their mechanical properties, particularly in aerospace and biomedical applications. Here, this study employs a Rapid Artificial Neural Network (RANN) potential to model the ω phase evolution at the atomistic level, demonstrating a high degree of consistency with experimental observations, unlike the Modified Embedded Atom Method (MEAM), which fails to capture this phase transformation accurately. RANN simulations replicate key phenomena such as the nucleation of α precipitates at ω/β interfaces and accurate lattice orientations, enhancing our understanding of phase stability and transformation kinetics. The findings affirm that RANN potentials can significantly improve the prediction accuracy of complex material behaviors, offering a powerful tool for designing advanced materials with tailored properties such as solute effect in various stacking fault energies. This approach not only bridges the gap between theoretical predictions and empirical data but also sets a new direction for future research in materials science, emphasizing the integration of machine learning techniques in the development and optimization of new alloys.
Synthesis and Characterization of Lignin-grafted-poly(ε-caprolactone) from Different Biomass Sources
Modification of lignin with poly(ε-caprolactone) is a promising approach to valorize industrial low-value lignins and to advance the bioeconomy. We have synthesized lignin grafted poly(ε-caprolactone) (lignin-g-PCL) copolymers via ring-opening polymerization of ε-caprolactone with different types of lignins of varying botanical sources (G-type pine lignin, S/G-type poplar lignin, and C-type Vanilla seeds lignin) and lignin extraction methods (Kraft and ethanol organosolv pulping). The lignin-g-PCL copolymer showed remarkably improved compatibility and dispersion in acetone, chloroform, and toluene in comparison to non-modified lignins. The structure and thermal properties of the lignin-g-PCL were investigated using Fourier-transform infrared spectroscopy (FTIR), 31 P nuclear magnetic resonance (NMR), 2D heteronuclear single quantum correlation (HSQC) NMR, gel permeation chromatography (GPC), and differential scanning calorimetry (DSC). Furthermore, we have found that all the technical lignins were reactive to the copolymerization reaction regardless of their plant source and isolation methods. The molecular weights of the synthesized lignin-g-PCL copolymers were positively correlated with the content of aliphatic lignin hydroxyls, suggesting that the copolymerization reaction tends to occur preferentially at the aliphatic hydroxyls rather than the phenolic hydroxyls of lignin. Thermal analyses of the lignin-g-PCL copolymers were studied, and in general, a reduction of melting temperature and crystallinity percentage in comparison to the neat PCL was observed. However, the thermal behavior of lignin-g-PCL copolymers varied depending on the lignin feedstocks employed in the copolymerization reaction.
Formate-Induced Dissolution and Reprecipitation of a Copper Electrocatalyst during Electrochemical CO 2 Reduction Reaction
Catalyst size, morphology, and crystal structure play crucial roles in determining the activity and selectivity of electrochemical CO 2 reduction reactions, which are known to change during the reaction process. A comprehensive understanding of how, when, and why these parameters evolve under operational conditions is essential for developing stable, efficient, and selective catalysts. In this study, we reveal that formate, one of the reaction products, contributes to the degradation of copper catalysts through a ligand-assisted dissolution mechanism. Utilizing in situ electrochemical atomic force microscopy and ex-situ scanning and transmission electron microscopies, we observed a significant reduction in the size of copper nanoparticles, which decreased from over 30 nm to less than 10 nm in diameter within 60 min of CO 2 RR. The temporal production of formate correlated with the particle size changes. Furthermore, analysis of the electrolyte using inductively coupled plasma optical emission spectroscopy confirmed the dissolution of copper nanoparticles. Control experiments involving various reaction products (H 2 , CO, and HCOO – ) demonstrated that formate significantly promotes copper dissolution, thereby highlighting its role in the ligand-assisted dissolution mechanism of copper electrocatalysts. In conclusion, our findings provide critical insights into copper catalyst behavior during electrochemical CO 2 reduction, facilitating the design of more resilient and effective electrocatalysts.
Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning
Drought is a pressing issue for the Colorado River Basin (CRB) due to the social and economic value of water resources in the region and the significant uncertainty of future drought under climate change. Here, we use climate simulations from various Earth System Models (ESMs) to force the Variable Infiltration Capacity hydrologic model and project multiple drought indicators for the sub-watersheds within the CRB. We apply an unsupervised machine learning (ML) based on Non-Negative Matrix Factorization using K-means clustering (NMFk) to synthesize the simulated historical, future, and change in drought indicators. The unsupervised ML approach can identify sub-watersheds where key changes to drought indicator behavior occur, including shifts in snowpack, snowmelt timing, precipitation, and evapotranspiration. While changes in future precipitation vary across ESMs, the results indicate that the Upper CRB will experience increasing evaporative demand and surface-water scarcity, with some locations experiencing a shift from a radiation-limited to a water-limited evaporation regime in the summer. Large shifts in peak runoff are observed in snowmelt-dominant sub-watersheds, with complete disappearance of the snowmelt signal for some sub-watersheds. The work demonstrates the utility of the NMFk algorithm to efficiently identify behavioral changes of drought indicators across space and time and to quickly analyze and interpret hydro climate model results.
Three‐Dimensional Permeability Inversion Using Convolutional Neural Networks and Positron Emission Tomography
Abstract Quantification of heterogeneous multiscale permeability in geologic porous media is key for understanding and predicting flow and transport processes in the subsurface. Recent utilization of in situ imaging, specifically positron emission tomography (PET), enables the measurement of three‐dimensional (3‐D) time‐lapse radiotracer solute transport in geologic media. However, accurate and computationally efficient characterization of the permeability distribution that controls the solute transport process remains challenging. Leveraging the relationship between local permeability variation and solute advection behavior, an encoder‐decoder based convolutional neural network (CNN) is implemented as a permeability inversion scheme using a single PET scan of a radiotracer pulse injection experiment as input. The CNN can accurately capture the 3‐D spatial correlation between the permeability and the radiotracer solute arrival time difference maps in geologic cores. We first test the inversion accuracy using synthetic test datasets and then test the accuracy on a suite of experimental PET imaging datasets acquired on four different geologic cores. The network‐predicted permeability maps from the geologic cores are used to parameterize forward numerical models that are directly compared with the experimental PET imaging data. The results indicate that a single trained network can generate robust 3‐D permeability inversion maps in seconds. Numerical models parameterized with these permeability maps closely capture the experimentally observed solute arrival time behavior. This work provides an unprecedented approach for efficiently characterizing multiscale permeability heterogeneity in complex geologic samples.
Quantifying the Resolution Sensitivity of the Kain–Fritsch Scheme Across the Gray Zone by Isolating Interactions: A TWP‐ICE Case Study
The resolution sensitivity of the Kain–Fritsch (KF) convection scheme and the role of interactions between the physics and dynamics within the gray zone (<10 km) were investigated using the Separate Physics and Dynamics Experiment (SPADE) framework. Two groups of experiments were conducted using the Weather Research and Forecasting (WRF) model via traditional (Tradition) runs and SPADE runs with resolutions of 1, 2, 4, and 8 km during the wet period of the Tropical Warm Pool–International Cloud Experiment (TWP‐ICE). Results show that the KF scheme simulates the weakened convective processes well as the resolution increases in both groups, and the changes in the convective variables with resolution in SPADE are smaller than in the Tradition group. This indicates the important effects of interactions between model components on convection parameterizations as the resolution changes. Additionally, the microphysics variables remain nearly unchanged with resolution in SPADE and weaken slightly in Tradition as the resolution decreases, suggesting the relatively weaker influences of model interactions for the resolved‐cloud parameterization. Therefore, the scale‐aware behavior of KF scheme is further strengthened in Tradition runs, primarily through inhibiting the strength of stratiform processes through physics–dynamics interactions and physical components.
Orbital selective Kondo effect in heavy fermion superconductor UTe2
Abstract Heavy fermion systems emerge from the collective Kondo effect, and their superconductivity can serve as a promising platform for realizing next-generation quantum technologies. However, it has been a great challenge to explore many-body effects in heavy fermion systems with ab-initio approaches. We computed the electronic structure of UTe 2 without purposive judgements, such as intentional selection of on-site Coulomb interaction and disregarding spin-orbit coupling. We show that U-5 f electrons are highly localized in the paramagnetic normal state, giving rise to the Kondo effect. It is also found that the hybridization between U-5 f and U-6 d predominantly in the orthorhombic a b -plane is responsible for the high-temperature Kondo effect. In contrast, the hybridization between U-5 f and Te-5 p along the c -axis manifests the Kondo scattering at a much lower temperature, which could be responsible for the low-temperature upturn of the c -axis resistivity. Our results show that the electron correlation in UTe 2 is orbital selective, which naturally elucidates the recent experimental observations of anomalous temperature dependence of resistivity. Furthermore, we suggest that the Kondo effect is suppressed at high pressure owing to weak localization of magnetic moments, which results from enhanced U-5 f electron hopping. Our discovery provides significant insight toward understanding anisotropic quantum behavior including selective re-entrant superconductivity in heavy fermion UTe 2 .