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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Low-index surface energies, cleavage energies, and surface relaxations for crystalline NiAl from first-principles calculations

NiAl surfaces frequently serve as a platform for studying a broad range of physical and chemical phenomena including chemisorption, catalysis, oxidation, alloy growth, and surface nanostructure formation. Knowledge of precise values for low-index surface energies of NiAl, the most fundamental quantities characterizing surface thermodynamics, is often invaluable for understanding of these phenomena. In all previous analyses for NiAl(100) and NiAl(111), Ni- and Al-terminations are not distinguished, and half of the cleavage energy (or equivalently, the average of the surface energies of two differently terminated surfaces) is always identified as the “surface energy”. No values are available for individual surface energies of Ni- or Al-terminated NiAl(100) or NiAl(111) surfaces, whereas knowledge of only cleavage energy is often insufficient for analyzing surface-associated behavior. As such, in this work we perform extensive first-principles density-functional-theory (DFT) calculations for surface energies and cleavage energies of NiAl(110), NiAl(100) and NiAl(111) by considering the chemical-potential-based formulations to clarify the ambiguity in their surface energies and cleavage energies. We obtain a surface-energy phase diagram for these three low-index surfaces versus the relevant chemical potential, as well as the chemical-potential-dependent Wulff plots for NiAl crystal equilibrium shapes. We also provide the surface-relaxation information from our DFT calculations for comparison with previous experimental data.

42 ENGINEERING↗
DOE OSTI2023

Wetting Behavior of A -block- (B- random -C) Copolymers with Equal Block Surface Energies on Surfaces Functionalized with B- random -C Copolymers

To form nanopatterns with self-assembled block copolymers (BCPs), it is desirable to have through-film domains that are oriented perpendicular to the substrate. The domain orientation is determined by the interfacial interactions of the BCP domains with the substrate and with the free surface. Here, we use thin films of two different sets of BCPs with A-block-(B-random-C) architecture matched with a corresponding B-random-C copolymer nanocoating on the substrate to demonstrate two distinct wetting behaviors. The two sets of A-b-(B-r-C) BCPs are made by using thiol–epoxy click chemistry to functionalize polystyrene-block-poly(glycidyl methacrylate) with trifluoroethanethiol (TFET) and either 2-mercaptopyridine (2MP) or methyl thioglycolate (MTG). For each set of BCPs, the composition ratio of the two thiols in the BCP (φ 1 ) is found that results in the two blocks of the modified BCP having equal surface energies (Δγ air = 0). The corresponding B-r-C random copolymers were synthesized and used to modify the substrate, and the composition ratio (φ 2 ) values that resulted in the two blocks of the BCP having equal interfacial energy with the substrate (Δγ sub = 0) were determined with scanning electron microscopy. The correlation between each block’s γ sub value and the interaction parameter, χ, is employed to explain the different wetting behaviors of the two sets of BCPs. For the thiol pair 2MP and TFET, the values of φ 1 and φ 2 that lead to Δγ air = 0 and Δγ sub = 0, respectively, are significantly different. A similar difference was observed between the φ 1 and φ 2 values that lead to Δγ air = 0 and Δγ sub = 0 for the BCPs made with the thiol pair MTG and TFET. In the latter case, for Δγ sub = 0 two windows of φ 2 are identified, which can be explained by the thermodynamic interactions of the specific thiol pair and the A-b-(B-r-C) architecture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
DOE OSTI2021

Potential energy surfaces for high-energy N + O 2 collisions

Potential energy surfaces for high-energy collisions between an oxygen molecule and a nitrogen atom are useful for modeling chemical dynamics in shock waves. In the present work, we present doublet, quartet, and sextet potential energy surfaces that are suitable for studying collisions of O 2 ( 3 Σ$^{–}_{g}$) with N( 4 S) in the electronically adiabatic approximation. Two sets of surfaces are developed, one using neural networks (NNs) with permutationally invariant polynomials (PIPs) and one with the least-squares many-body (MB) method, where a two-body part is an accurate diatomic potential and the three-body part is expressed with connected PIPs in mixed-exponential-Gaussian bond order variables (MEGs). We find, using the same dataset for both fits, that the fitting performance of the PIP-NN method is significantly better than that of the MB-PIP-MEG method, even though the MB-PIP-MEG fit uses a higher-order PIP than those used in previous MB-PIP-MEG fits of related systems (such as N 4 and N 2 O 2 ). However, the evaluation of the PIP-NN fit in trajectory calculations requires about 5 times more computer time than is required for the MB-PIP-MEG fit.

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

Between Harmonic Crystal and Glass: Solids with Dimpled Potential-Energy Surfaces Having Multiple Local Energy Minima

Solids with dimpled potential-energy surfaces are ubiquitous in nature and, typically, exhibit structural (elastic or phonon) instabilities. Dimpled potentials are not harmonic; thus, the conventional quasiharmonic approximation at finite temperatures fails to describe anharmonic vibrations in such solids. At sufficiently high temperatures, their crystal structure is stabilized by entropy; in this phase, a diffraction pattern of a periodic crystal is combined with vibrational properties of a phonon glass. As temperature is lowered, the solid undergoes a symmetry-breaking transition and transforms into a lower-symmetry phase with lower lattice entropy. Here, we identify specific features in the potential-energy surface that lead to such polymorphic behavior; we establish reliable estimates for the relative energies and temperatures associated with the anharmonic vibrations and the solid–solid symmetry-breaking phase transitions. We show that computational phonon methods can be applied to address anharmonic vibrations in a polymorphic solid at fixed temperature. To illustrate the ubiquity of this class of materials, we present a range of examples (elemental metals, a shape-memory alloy, and a layered charge-density-wave system); we show that our theoretical predictions compare well with known experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Surface Energies of LaMnO 3 High-Index Surfaces Obtained from Density-Functional Theory

Here, the first six cubic LaMnO 3 high-index surfaces, (210), (211), (221), (310), (311), and (320), are examined for the first time. The unrelaxed and relaxed surface energies of the surfaces are computed as a function of surface model thickness using unrelaxed and relaxed surface models, respectively. The (210), (320), (211), and (221) surfaces are found to be more stable than the low-index (011) and (111) surfaces. Helping to explain this result, the surface terminations of the (210), (320), (211), or (221) surface are seen to exhibit a rotational relaxation of the MnO 6 or oxygen octahedra extending from the surface into the bulk. By contrast, the relaxed surface energies of the (310) and (311) surfaces are concluded to be difficult to determine due to a structural transformation or phase change in the surface models of the surfaces away from cubic LaMnO 3 . The finding of a phase change is important. It indicates that a phase change in the surface models of a surface for another material could occur if the surface is relatively open and a different phase of the material is stable at low temperature. When modeling such a surface, two steps are suggested to be taken.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Surface Energy Balance System (SEBS) Instrument Handbook

A Surface Energy Balance System (SEBS) has been installed collocated with each deployed ECOR system at the Southern Great Plains (SGP), North Slope of Alaska (NSA), Tropical Western Pacific (TWP), ARM Mobile Facility 1 (AMF1), and ARM Mobile Facility 2 (AMF2). The surface energy balance system consists of upwelling and downwelling solar and infrared radiometers within one net radiometer, a wetness sensor, and soil measurements. The SEBS measurements allow the comparison of ECOR sensible and latent heat fluxes with the energy balance determined from the SEBS and provide information on wetting of the sensors for data quality purposes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impacts of Biologically Induced Degradation on Surface Energy, Wettability, and Cohesion of Corn Stover

The impacts of biological degradation on surface area, surface energy, wettability, and cohesion of anatomically fractionated (i.e., leaf, stalk, and cob) and bulk corn stover are presented in this study. The physical, thermal and chemical properties of corn stover are critical material attributes that not only influence the mechanical processing and chemical conversion of corn stover, but also the bulk solids handling and transport. The measured surface areas were observed to be dependent on the degree of biological degradation (mild vs. moderate vs. severe) and on the anatomical fraction. The surface area of the bulk corn stover samples increased with the degree of biological degradation. The leaf fraction was the most sensitive to biological degradation, resulting in an increase in surface area from 0.5 m 2 /g (mildly degraded) to 1.2 m 2 /g (severely degraded). In contrast, the surface area of the cob fraction remained relatively unaffected by the degree of biological degradation (i.e., mildly degraded–0.55 m 2 /g, severely degraded–0.40 m 2 /g. All biologically degraded samples resulted in significant changes to the surface chemistry (evidenced by an increase in surface energy. As a general trend, the surface energy of bulk corn stover increased with the degree of biological degradation—the same trend was observed for the leaf and stalk anatomical fractions; however, the surface energy for the cob fraction remained unchanged. Wettability, calculated from surface energy, for bulk corn stover samples did not reveal any discernable trend with the degree of biological degradation. However, trends in wettability were observed for the anatomical fractions, with wettability increasing for the stalk and leaf fractions, and decreasing for the cob fraction. Excluding the cob fraction, the work of cohesion increased with the degree of biological degradation. Understanding the impacts of biological degradation on the physical, chemical and thermal properties of corn stover offers insights to improve the overall operational reliability, efficiency and economics of integrated biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2020

A Nonequilibrium Thermodynamic Approach for Surface Energy Balance Closure

Abstract The surface energy imbalance, viz. that the turbulent dissipation does not fully account for available energy, has for long been an outstanding challenge in geophysical studies. In this study, we developed a novel approach based on nonequilibrium thermodynamics by representing the atmospheric boundary layer as a heat engine. In addition, an analytically tractable approach was used to estimate the ground heat flux based on Green's function approach, which in turn determines the available energy that drives the atmospheric heat engine. The proposed model was evaluated using heat fluxes measured by eight AmeriFlux eddy covariance towers with atmospheric temperature profiles recorded at adjacent radiosonde sites. The surface energy balance closure can be improved by ~11% over various landscapes, by including the estimated power production from the atmospheric heat engine.

Li, Peiyuan↗
DOE OSTI2020

Nonequilibrium Solvent Effects during Photodissociation in Liquids: Dynamical Energy Surfaces, Caging and Chemical Identity

In the gas phase, potential energy surfaces can be used to provide insight into the details of photochemical reaction dynamics. In solution, however, it is unclear what potential energy surfaces, if any, can be used to describe even simple chemical reactions such as the photodissociation of a diatomic solute. In this paper, we use mixed quantum/classical (MQC) molecular dynamics (MD) to study the photodissociation of $Na$ $^+_2$ in both liquid Ar and liquid tetrahydrofuran (THF). We examine both the gas-phase potential surfaces and potentials of mean force (PMF), which assume that the solvent remains at equilibrium with the solute throughout the photodissociation process and show that neither resemble a nonequilibrium dynamical energy surface that is generated by taking the time integral of work. For the photodissociation of $Na$ $^+_2$ in liquid Ar, the dynamical energy surface shows clear signatures of solvent caging, and the degree of caging is directly related to the mass of the solvent atoms. For $Na$ $^+_2$ in liquid THF, local specific interactions between the solute and solvent lead to changes in chemical identity that create a kinetic trap that effectively prevents the molecule from dissociating. Here, the results show that nonequilibrium effects play an important role even in simple solution-phase reactions, requiring the use of dynamical energy surface to understand such chemical events.

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

Significant slowdown of plasma-optimized surface energy deactivation by vacuum sealing for efficient adhesive bonding

This work proposes an approach to minimize surface energy deactivation of plasma-treated metal and carbon fiber-reinforced polymer (CFRP) surfaces by vacuum sealing. Plasma treatments enhance adhesive wettability on post-treated surfaces for adhesive joints, but the treated surfaces deactivate quickly in air. The surface energy of aluminum alloy AA6061 and carbon fiber-reinforced polymer-polyamide (CFRP-PA66) optimally treated by a blown air plasma instrument returns to the original surface energy within one hour. Vacuum sealing of AA6061 and CFRP-PA66 reduced the surface energy deactivation over 7 days by at least 230 times and 970 times compared to in air. Double Cantilever Beam (DCB) tests performed on adhesively-bonded AA6061/CFRP-PA66 joints showed that the total energy release and energy dissipation before failure of plasma-treated and vacuum-sealed materials was up to 60% more than plasma-treated materials without vacuum sealing and up to 125% more than non-plasma-treated materials.

42 ENGINEERING↗
DOE OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
DOE OSTI2026

The two radiative states of the Arctic atmosphere and their impacts on the surface energy budget of sea ice

The surface energy budget (SEB) is a central regulator of Arctic climate and sea ice evolution, yet its processes remain poorly constrained due to sparse observations and complex, coupled surface-atmosphere interactions. This study leverages year-long measurements from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) to provide the most comprehensive assessment to date of the central Arctic SEB and its modulation by atmospheric variability. Ship- and ice-based observations from October 2019 to September 2020 were used to directly measure or tightly constrain each term of the SEB, leading to exceptional energetic closure with the seasonal snow and ice mass balance. The analysis reveals strong seasonal transitions in atmosphere-surface energy transfer that are modulated by the atmospheric state and constrained by the ability of the surface temperature to respond. Classification of the atmosphere into its two dominant radiative states—the semi-transparent (ST) and opaque (OP)—highlights the central role of synoptic-scale variability in clouds. The ST atmospheric state dominated the long winter ice growth season, with limited cloudiness supporting persistent surface radiative cooling and ice growth. The OP state, associated with liquid-containing or thick ice clouds, became dominant in spring, with the combination of increased solar heating and cloud surface longwave warming driving ice and snow melt. Eddy covariance versus bulk approaches for deriving surface turbulent heat fluxes provide vastly different perspectives on the role of turbulence in modulating the SEB. These results establish a high-quality benchmark dataset for Arctic SEB studies and demonstrate how the balance of atmospheric radiative states exerts a first-order control on the annual evolution of the sea ice. The findings have broad implications for advancing observing technologies, understanding Arctic amplification, improving climate models, and predicting future sea ice change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗
DOE OSTI2019

Determining Surface Energy of Porous Substrates by Spray Ionization

In this work, we have developed a new spray-based method for characterizing surface energies of planar, porous substrates. Distinct spray modes (electrospray versus electrostatic spray), from the porous substrates, occur in the presence of an applied DC potential after wetting with solvents of different surface tension. The ion current resulting from the spray process is maximized when the surface energy of the porous substrate approaches the surface tension of the wetting solvent. By monitoring the selected ion current (e.g., benzoylecgonine, m/z 290 → 168) with a mass spectrometer or the total ion current with an ammeter, we determined the solvent surface tension yielding the maximum ion current to indicate the surface energy of the solid. Detailed evaluations using polymeric substrates of known surface energies enabled effective calibration of the approach that resulted in the correct estimation of the surface energy of hydrophobic paper substrates prepared by gas-phase silanization. A three-parameter empirical model suggests that the experimentally observed ion current profile is governed by differential partitioning of analyte controlled by the interfacial forces between the wetting solvent and the porous substrate.

36 MATERIALS SCIENCE↗