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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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At least 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Stochastic nucleation processes and substrate abundance explain time-dependent freezing in supercooled droplets

Atmospheric immersion freezing (IF), a heterogeneous ice nucleation process where an ice nucleating particle (INP) is immersed in supercooled water, is a dominant ice formation pathway impacting the hydrological cycle and climate. Implementation of IF derived from field and laboratory data in cloud and climate models is difficult due to the high variability in spatio-temporal scales, INP composition, and morphological complexity. We demonstrate that IF can be consistently described by a stochastic nucleation process accounting for uncertainties in the INP surface area. This approach accounts for time-dependent freezing, a wide range of surface areas and challenges phenomenological descriptions typically used to interpret IF. The results have an immediate impact on the current description, interpretation, and experiments of IF and its implementation in models. The findings are in accord with nucleation theory, and thus should hold for any supercooled liquid material that nucleates in contact with a substrate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations

The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A hybrid calorimetry-simulation model of mixing enthalpy for molten salt

Calorimetric determination of enthalpies of mixing (ΔH mix ) in multicomponent molten salts is often interpreted using empirical models that lack physically meaningful parameters. However, for improving pyrochemical separation of spent nuclear fuel, where lanthanides are major fission products and critical elements, a deeper thermodynamic understanding of the link between excess thermodynamic properties and solvation structure is critically needed. In this work, we implement a hybrid and physics-informed framework, MIVM+Calorimetry+AIMD, which integrates experimentally measured ΔH mix (via high temperature drop calorimetry) with solvation structures from ab initio molecular dynamics (AIMD). This approach is demonstrated using LaCl 3 mixed with eutectic LiCl-KCl (58 mol% – 42 mol%) at 873 K and 1133 K. MIVM-derived parameters enable extrapolation of excess Gibbs energy and La 3+ activity across compositions. In contrast, direct ΔH mix predictions from AIMD and polarizable ion model simulations deviate significantly. By incorporating experimentally benchmarked solvation structures into an interpretable thermodynamic model, the MIVM+Calorimetry+AIMD formalism achieves higher accuracy and generalizable method for studying molten salts, offering a robust path for understanding and optimizing molten salt chemistry relevant to nuclear fuel cycles and separation science.

Goncharov, Vitaliy G. [Washington State Univ., Pul↗
DOE OSTI2024

Rotation in attosecond vibronic coherence spectroscopy for molecules

Abstract Excitation or ionization of a molecule by ultrafast laser pulses can create a superposition of electronic states, whose dynamics is influenced by the interplay of electronic coherence and nuclear motion, resulting in charge migration and possibly charge transfer. Probing the vibronic coherence is therefore vital to monitoring electronic dynamics and controlling chemical reactivity, as recently demonstrated in molecules via attosecond transient absorption spectroscopy (ATAS). However, theories supporting the interpretation of ATAS experiments neglect the effects of molecular rotation, often leading to inaccurate interpretation of experimental data. Here, we develop a comprehensive theory for ATAS of molecules encompassing the entire pump-probe process. Applying the theory to N 2 , we demonstrate that the emergence of coherent signals critically depends on the consideration of molecular rotation. This work contributes to close the gap between theory and ATAS experiments, paving the way for monitoring electronic motion and controlling chemical reactivity in diverse molecular systems.

Physics↗
DOE OSTI2023

Differentiable modelling to unify machine learning and physical models for geosciences

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. Here, in this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

58 GEOSCIENCES↗
DOE OSTI2021

Neural network-based order parameter for phase transitions and its applications in high-entropy alloys

Phase transition is one of the most important phenomena in nature and plays a central role in materials design. All phase transitions are characterized by suitable order parameters, including the order–disorder phase transition. However, finding a representative order parameter for complex systems is non-trivial, such as for high-entropy alloys. Given the strength of dimensionality reduction of a variational autoencoder (VAE), we introduce a VAE-based order parameter. Here, we propose that the Manhattan distance in the VAE latent space can serve as a generic order parameter for order–disorder phase transitions. The physical properties of our order parameter are quantitatively interpreted and demonstrated by multiple refractory high-entropy alloys. Using this order parameter, a generally applicable alloy design concept is proposed by mimicking the natural mixing process of elements. Our physically interpretable VAE-based order parameter provides a computational technique for understanding chemical ordering in alloys, which can facilitate the development of rational alloy design strategies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Sulfate adenylyl transferase kinetics and mechanisms of metabolic inhibitors of microbial sulfate respiration

Abstract Sulfate analog oxyanions that function as selective metabolic inhibitors of dissimilatory sulfate reducing microorganisms (SRM) are widely used in ecological studies and industrial applications. As such, it is important to understand the mode of action and mechanisms of tolerance or adaptation to these compounds. Different oxyanions vary widely in their inhibitory potency and mechanism of inhibition, but current evidence suggests that the sulfate adenylyl transferase/ATP sulfurylase (Sat) enzyme is an important target. We heterologously expressed and purified the Sat from the model SRM, Desulfovibrio alaskensis G20. With this enzyme we determined the turnover kinetics (kcat, KM) for alternative substrates (molybdate, selenate, arsenate, monofluorophosphate, and chromate) and inhibition constants (KI) for competitive inhibitors (perchlorate, chlorate, and nitrate). These measurements enable the first quantitative comparisons of these compounds as substrates or inhibitors of a purified Sat from a respiratory sulfate reducer. We compare predicted half-maximal inhibitory concentrations (IC50) based on Sat kinetics with measured IC50 values against D. alaskensis G20 growth and discuss our results in light of known mechanisms of sensitivity or resistance to oxyanions. This analysis helps with the interpretation of recent adaptive laboratory evolution studies and illustrates the value of interpreting gene–microbe–environment interactions through the lens of enzyme kinetics.

Carlson, Hans K. (ORCID:0000000215835313)↗
DOE OSTI2015

Structural, electronic and vibrational properties of few-layer 2H-and 1T-TaSe 2

Two-dimensional metallic transition metal dichalcogenides (TMDs) are of interest for studying phenomena such as charge-density wave (CDW) and superconductivity. Few-layer tantalum diselenides (TaSe 2 ) are typical metallic TMDs exhibiting rich CDW phase transitions. However, a description of the structural, electronic and vibrational properties for different crystal phases and stacking configurations, essential for interpretation of experiments, is lacking. We present first principles calculations of structural phase energetics, band dispersion near the Fermi level, phonon properties and vibrational modes at the Brillouin zone center for different layer numbers, crystal phases and stacking geometries. Evolution of the Fermi surfaces as well as the phonon dispersions as a function of layer number reveals dramatic dimensionality effects in this CDW material. Lastly, our results indicate strong electronic interlayer coupling, detail energetically possible stacking geometries, and provide a basis for interpretation of Raman spectra.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Effect of Chain Structure on the Miscibility of Cellulose Acetate Blends. A Small-Angle Neutron Scattering Study

The miscibility of cellulose ester blends with varying degree of substitution (DS) of acetates along the chain backbone has been investigated using small-angle neutron scattering. The difference in degree of substitution (ΔDS) between the two components in the blend was systematically varied from 0.06 to 0.63 where each blend was found to be a partially miscible, two-phase system. Miscibility between the two components initially decreases as ΔDS of the blends increases. The Flory interaction parameter, χ, concurrently increases with increasing ΔDS as a result of diminishing van der Waals forces between components. The cellulose acetates with lower degree of substitution, which contain more hydroxyl substituents, however, demonstrate greater miscibility even at higher ΔDS. This is interpreted to be the result of favorable hydrogen bonding between blend components that are possible in the presence of more hydroxyl groups. FT-IR data support this interpretation, indicating an increase in hydrogen bonding in a blend having a lower DS component. These results indicate that while an increase in structural differences between cellulose acetate blend components limits miscibility, the presence of hydroxyl groups on the chain promotes mixing. This competition accentuates the significant impact specific interactions have on blend miscibility for these copolymers.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Ion counting in supercapacitor electrodes using NMR spectroscopy

19 F NMR spectroscopy has been used to study the local environments of anions in supercapacitor electrodes and to quantify changes in the populations of adsorbed species during charging. In the absence of an applied potential, anionic species adsorbed within carbon micropores (in-pore) are distinguished from those in large mesopores and spaces between particles (ex-pore) by a characteristic nucleus-independent chemical shift (NICS). Adsorption experiments and two-dimensional exchange experiments confirm that anions are in dynamic equilibrium between the in- and ex-pore environments with an exchange rate in the order of tens of Hz. 19 F in situ NMR spectra recorded at different charge states reveal changes in the intensity and NICS of the in-pore resonances, which are interpreted in term of changes in the population and local environments of the adsorbed anions that arise due to the charge-storage process. A comparison of the results obtained for a range of electrolytes reveals that several factors influence the charging mechanism. For a tetraethylammonium tetrafluoroborate electrolyte, positive polarisation of the electrode is found to proceed by anion adsorption at a low concentration, whereas increased ion exchange plays a more important role for a high concentration electrolyte. In contrast, negative polarization of the electrode proceeds by cation adsorption for both concentrations. For a tetrabutylammonium tetrafluoroborate electrolyte, anion expulsion is observed in the negative charging regime; this is attributed to the reduced mobility and/or access of the larger cations inside the pores, which forces the expulsion of anions in order to build up ionic charge. Significant anion expulsion is also observed in the negative charging regime for alkali metal bis(trifluoromethane)sulfonimide electrolytes, suggesting that more subtle factors also affect the charging mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Structure-charge relationship - the case of hematite (001)

We present a multidisciplinary study on the hematite (001)–aqueous solution interface, in particular the relationship between surface structure (studied via surface diffraction in a humid atmosphere) and the macroscopic charging (studied via surface- and zeta-potential measurements in electrolyte solutions as a function of pH). Upon aging in water changes in the surface structure are observed, that are accompanied by drastic changes in the zeta-potential. Surprisingly the surface potential is not accordingly affected. We interpret our results by increasing hydration of the surface with time and enhanced reactivity of singly-coordinated hydroxyl groups that cause the isoelectric point of the surface to shift to values that are reminiscent of those typically reported for hematite particles. In its initial stages after preparation the hematite surface is very flat and only weakly hydrated. Our model links the entailing weak water structure with the observed low isoelectric point reminiscent of hydrophobic surfaces. The absence of an aging effect on the surface potential vs. pH curves is interpreted as domination of the surface potential by the doubly coordinated hydroxyls, which are present on both surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Uncovering biologically significant lipid isomers with liquid chromatography, ion mobility spectrometry and mass spectrometry

Understanding how biological molecules are generated, metabolized and eliminated in living systems is important for interpreting processes such as immune response and disease pathology. While genomic and proteomic studies have provided vast amounts of information over the last several decades, interest in lipidomics has also grown due to improved analytical technologies revealing altered lipid metabolism in type 2 diabetes, cancer, and lipid storage disease. Liquid chromatography and mass spectrometry (LC-MS) measurements are currently the dominant approach for characterizing the lipidome by providing detailed information on the spatial and temporal composition of lipids. However, interpreting lipids’ biological roles is challenging due to the existence of numerous structural and stereoisomers (i.e. distinct acyl chain and double-bond positions), which are unresolvable using present LC-MS approaches. Here we show that combining structurally-based ion mobility spectrometry (IMS) with LC-MS measurements distinguishes lipid isomers and allows insight into biological and disease processes.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2016

Rate equation model of laser induced bias in uranium isotope ratios measured by resonance ionization mass spectrometry

Resonance Ionization Mass Spectrometry (RIMS) has been developed as a method to measure uranium isotope abundances. In this approach, RIMS is used as an element-selective ionization process between uranium atoms and potential isobars without the aid of chemical purification and separation. The use of broad bandwidth lasers with automated feedback control of wavelength was applied to the measurement of the U-235/U-238 ratio to decrease laser-induced isotopic fractionation. In application, isotope standards are used to identify and correct bias in measured isotope ratios, but understanding laser-induced bias from first-principles can improve the precision and accuracy of experimental measurements. A rate equation model for predicting the relative ionization probability has been developed to study the effect of variations in laser parameters on the measured isotope ratio. The model uses atomic data and empirical descriptions of laser performance to estimate the laser-induced bias expected in experimental measurements of the U-235/U-238 ratio. Empirical corrections are also included to account for ionization processes that are difficult to calculate from first principles with the available atomic data. Development of this model has highlighted several important considerations for properly interpreting experimental results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Functional mapping reveals mechanistic clusters for OER catalysis across (Cu–Mn–Ta–Co–Sn–Fe)O x composition and pH space

Identification of stable electrocatalysts for the oxygen evolution reaction (OER) remains a primary challenge in materials for energy. The pH-dependent activity is known for very few catalysts, prompting our exploration of a broad range of catalysts using high throughput experiments and data science. This approach enables the largest screening of OER activity and operational stability to date, as illustrated through investigation of the (Cu-Mn-Ta-Co-Sn-Fe)O x composition space as 15 unique quaternary composition spaces. In total 2121 compositions are tested between pH 3 and 13, creating an extensive dataset whose interpretation requires development and application of data science to provide insights that are both beyond the standard composition-activity relationships and beyond human interpretation due to the dimensionality of the dataset. In this work, three distinct classes of OER catalysts are identified with respect to pH-dependent activity and stability. The large-scale screening reveals a new class of Co-rich OER catalysts that can be compositionally tailored to a specified pH and perform on par with state-of-the-art acid OER catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Electron inelastic mean free path in water

Liquid phase transmission electron microscopy (LPTEM) is rapidly developing as a powerful tool for probing processes in liquid environments with close to atomic resolution. Knowledge of the water thickness is needed for reliable interpretation and modelling of analytical studies in LPTEM, and is particularly essential when using thin liquid layers, required for achieving the highest spatial resolutions. Furthermore, the log-ratio method in electron energy-loss spectroscopy (EELS) is often applied in TEM to quantify the sample thickness, which is measured relative to the inelastic mean free path ( λ IMFP ). However, λ IMFP itself is dependent on sample material, the electron energy, and the convergence and divergence angles of the microscope electronoptics. Here, we present a detailed quantitative analysis of the λ IMFP of water as functions of the EELS collection angle ( β ) at 120 keV and 300 keV in a novel nanochannel liquid cell. We observe good agreement with earlier studies conducted on ice, but find that the most widely used theoretical models significantly underestimate λ IMFP of water. We determine an adjusted average energy-loss term E m, water , and characteristic scattering angle θ E, water that improve the accuracy. The results provide a comprehensive knowledge of the λ IMFP of water (or ice) for reliable interpretation and quantification of observations in LPTEM and cryo-TEM studies.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for instance, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3–55 atoms with mean absolute errors in the range of 0.11–0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure–property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal–metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3–55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Computational investigations of Dienes defect- and vacancy-induced changes in the electronic and vibrational properties of carbon fiber structural units

Carbon fiber (CF) is a promising lightweight alternative to steel and is of significant interest for energy applications. As CF continues to find new uses and is exposed to new external conditions, a noninvasive method of monitoring its structural integrity is critical. Raman spectroscopy is a commonly used method for this monitoring; however, it is highly inferential, and the interpretation of the data is not always straightforward. In this work, we perform density functional theory (DFT) calculations to investigate changes in the vibrational properties of CF structural units (i.e., graphene and graphite) caused by monovacancy and Dienes defects as a foundation for modeling more complex defects that move our model toward that of realistic CF. Using large computational supercells, we can understand how these defects change the electronic structure and vibrational properties of graphene and graphite for interdefect distances near those of the lower experimental limit. The monovacancy opens an electronic bandgap at the K point. Although no such electronic gap is opened by the Dienes defect, both defects introduce flat defect bands near the Fermi energy. The Dienes defect creates long-range deviations of the phonons, leading to substantial broadening of the highest frequency optical modes in the band structure compared to that of the pristine material. In contrast, the phonon changes caused by the monovacancy are short range, and only minor changes in the band structure or phonon density of states were observed. These findings can assist in the interpretation of experimental results by providing atomic-scale insight into key electronic and vibrational features.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Revealing nanomechanical deformation at the interface and degradation in all-thin-film inorganic electrochromic devices

Recently, progress in electrochromic (EC) devices has been made in optimizing electrode and device configurations and performance. However, the ion insertion/de-insertion induced charge transfer (CT) nanomechanical effect has remained unexplored, i.e., repetitive electrode size changes at the nanoscale and stress/strain generated during electrochemical cycling, which is the focus of this work due to its intimate correlation with the elastic and plastic deformation at the interface. Considering the intervalence electrons, excellent electrochemical kinetics, and dramatic color changes, tungsten oxide (WO 3 ) and nickel oxide (NiO) films are configured as the EC cathode and anode materials, respectively, within a full device. Upon extended cycles (>10 000), the void generation and delamination that occurred at the interface account for performance decay. Encouraged by the findings, nanoindentation mechanical tests and electrical kelvin probe force microscopy were employed to investigate the CT induced effects at the interface. There is a dramatic increase of up to 45% in the elastic Young's modulus in colored/charged WO 3 at ~40 mC cm -2 . Here, the correlation between CT and synergistic mechanical effect is interpreted by the Lippman equation. Interestingly, despite the charged state (colored; lithiated) with a relatively flat morphology bringing an ~3.4 times higher electrostatic surface potential, the electrical work function unexpectedly decreases, arising from the dominant effect of the dipole layer potential over the chemical potential. The interatomic cohesive energy and equilibrium distance increase bury the seeds for mechanical deformation in the long run. This work provides fundamental insights into electro-chemo mechanics and interdisciplinary concerted interfacial effects at the nano/atomic level. The dependence of surface potential, stress, work function, and cohesive energy on electrochemical kinetics has been interpreted.

36 MATERIALS SCIENCE↗