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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fe‐Triazolate Metal–Organic Frameworks as Water Oxidation Catalysts with Dual Photoanode Functionality

Artificial photosynthesis is an emerging technology that achieves renewable fuels, such as hydrogen, from sunlight. Its realization depends on finding highly active and stable catalysts of water splitting and photoactive materials for light absorption. To be scalable, these should contain only abundant elements. Here, for the first time, Fe-triazolate (Fe(ta) 2 ) and its metal substituted derivatives (Fe-Metal(ta) 2 ) Metal-organic frameworks (MOFs) are characterized as new dual-function materials for photo-absorption and water oxidation catalysis in acidic media. The materials were studied by a range of structural, spectroscopic, and computational density functional theory (DFT) techniques. Fe(ta) 2 and Fe-Mn(ta) 2 were found to be highly active and stable in chemical and photochemical water oxidation, and in addition function as photoanodes, with photo-electrocatalytic currents (∼2.00 x 10 −3 Acm −2 at + 1.4 V vs. Ag/AgCl) at pH = 1. The possibility of a unique catalytic mechanism where O─O bond formation is possible from the coupling of two adjacent Fe IV = O fragments was demonstrated by DFT analysis. Thus, Fe-triazolate MOF has been established as a new, stable, scalable, versatile, and efficient platform for sustainable energy conversion in the realm of artificial photosynthesis.

Artificial photosynthesis↗
DOE OSTI2021

Plant functional types broadly describe water use strategies in the Caatinga, a seasonally dry tropical forest in northeast Brazil

In seasonally dry tropical forests, plant functional type can be classified as deciduous low wood density, deciduous high wood density, or evergreen high wood density species. While deciduousness is often associated with drought-avoidance and low wood density is often associated with tissue water storage, the degree to which these functional types may correspond to diverging and unique water use strategies has not been extensively tested. We examined (a) tolerance to water stress, measured by predawn and mid-day leaf water potential; (b) water use efficiency, measured via foliar δ 13 C; and (c) access to soil water, measured via stem water δ 18 O. We found that deciduous low wood density species maintain high leaf water potential and low water use efficiency. Deciduous high wood density species have lower leaf water potential and variable water use efficiency. Both groups rely on shallow soil water. Evergreen high wood density species have low leaf water potential, higher water use efficiency, and access alternative water sources. These findings indicate that deciduous low wood density species are drought avoiders, with a specialized strategy for storing root and stem water. Deciduous high wood density species are moderately drought tolerant, and evergreen high wood density species are the most drought tolerant group. Our results broadly support the plant functional type framework as a way to understand water use strategies, but also highlight species-level differences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Aqueous Diels–Alder reactions for thermochemical storage and heat transfer fluids identified using density functional theory

Thermal storage and transfer fluids have important applications in industrial, transportation, and domestic settings. Current thermal fluids have relatively low specific heats, often significantly below that of water. However, by introducing a thermochemical reaction to a base fluid, it is possible to enhance the fluid's thermal properties. Here, density functional theory (DFT) is used to screen Diels–Alder reactions for use in aqueous thermal fluids. From an initial set of 52 reactions, four are identified with moderate aqueous solubility and predicted turning temperature near the liquid region of water. These reactions are selectively modified through 60 total functional group substitutions to produce novel reactions with improved solubility and thermal properties. Among the reactions generated by functional group substitution, seven have promising predicted thermal properties, significantly improving specific heat (by as much as 30.5%) and energy storage density (by as much as 4.9%) compared to pure water.

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

Hydrogen Bond Benchmark: Focal‐Point Analysis and Assessment of DFT Functionals

We performed a hierarchical, convergent ab initio benchmark study and systematically analyzed the performance of density functional approximations for describing hydrogen bonds in small neutral, cationic, and anionic complexes, as well as in larger systems involving amide, urea, deltamide, and squaramide moieties. Focal point analyses (FPA), extrapolating to the ab initio limit, were carried out using correlated wave function methods up to CCSDT(Q) for the small complexes and CCSD(T) for the larger systems, together with correlation-consistent Gaussian basis sets up to the complete basis set limit. Optimized geometries and vibrational frequencies were obtained at the CCSD(T) level. The resulting FPA hydrogen-bond energies converge within a few tenths of a kcal mol −1 . These reference data were used to evaluate 60 density functionals (including 12 dispersion-corrected), spanning the local-density approximation (LDA), generalized gradient approximations (GGAs), meta-GGAs, hybrids, meta-hybrids, double-hybrids, and range-separated hybrids. Overall, the meta-hybrid M06-2X provides the best performance for both hydrogen bond energies and geometries, while the dispersion-corrected GGAs BLYP-D3(BJ) and BLYP-D4 also yield accurate hydrogen-bond data and can serve as cost-effective options for studying large and complex systems.

coupled cluster theory↗
DOE OSTI2025

On the Sampling-Based Computation of Nash Equilibria Under Uncertainty via the Nikaido–Isoda Function

We consider the computation of an equilibrium of a stochastic Nash equilibrium problem, where the player objectives are assumed to be L 0 -Lipschitz continuous and convex, given rival decisions with convex and closed player-specific feasibility sets. To address this problem, we consider minimizing a suitably defined value function defined using the Nikaido–Isoda function. Such an avenue does not necessitate either monotonicity properties of the concatenated gradient map or potentiality requirements on the game but does require a suitable regularity requirement under which a stationary point is a Nash equilibrium. We design and analyze a sampling-enabled projected-gradient-response method, reliant on inexact resolution of a player-level best-response subproblem. Here, by deriving suitable Lipschitzian guarantees on the value function, we derive both asymptotic guarantees for the sequence of generated iterates as well as rate and complexity guarantees for computing a stationary point by appropriate choices of the sampling rate and inexactness sequence.

Nikaido-Isoda function↗
DOE OSTI2024

What is the gradient of a scalar function defined on a subspace of square matrices?

We illustrate a technique to calculate the gradient of scalar functions that are defined on any arbitrary matrix subspace. It generalizes our earlier work titled “What is the gradient of a scalar function of a symmetric matrix ?”(Indian Journal of Pure and Applied Mathematics (2022), https://doi.org/10.1007/s13226-022-00313-x), in which we considered the special case of the subspace of symmetric matrices. Extant methods to calculate the gradient in such cases have an inherent flaw which leads to spurious results that populate several publications, as well as respected textbooks and handbooks on matrix calculus. Here, we examine these sources and results in a rigorous and concrete mathematical setting of a finite-dimensional inner-product space and discover the inherent flaw and also a remedy. We demonstrate two ways to calculate the derivative/gradient and second derivative for scalar functions of matrices defined over an arbitrary matrix subspace; the first method is by considering any (differentiable) extension to the space of square matrices and projection of its gradient onto the given subspace. The second method utilizes an ordered basis and computes each component of the gradient through evaluation of the directional derivative. All the ideas presented are illustrated by non-trivial examples, namely, considering the subspace of 3 x 3 circulant and Toeplitz matrices and presenting the results of gradient-descent with both the spurious and correct gradients. Moreover, our bibliography makes it clear that a rigorous approach to matrix calculus is not common in practice, and our presentation of matrix calculus in the language of inner-product spaces will be significant and meaningful for applied mathematicians, engineers and researchers working in inter-disciplinary fields to avoid the conceptual pitfalls that exist.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Revisiting the definition of field capacity as a functional parameter in a layered agronomic soil profile beneath irrigated maize

The soil water content at the condition of field capacity (θ FC ) is a key parameter in irrigation scheduling and has been suggested to be determined by running a synthetic drainage experiment until the flux rate (q) at the bottom of the soil profile achieves a predefined negligible value (q FC ). We question the impact of q FC on the assessment of field capacity. Moreover, calculating θ FC as the integral mean of the water content profile when q is equal to q FC is strictly valid only for uniform soil profiles. By contrast, this practice is ambiguous and biased for stratified soil profiles due to the soil water content discontinuity at the layer interfaces. In this study, the concept of field capacity was revisited and adapted to practical agronomic heuristics. By resorting to the assessment of root-zone water storage capacity (W), we envision field capacity as a functional hydraulic parameter derived from synthetic irrigation scheduling scenarios to minimize drought stress, drainage, and nitrate leachate below the root zone. A functional analysis was carried out on a 135-cm-thick layered soil profile beneath maize in eastern Nebraska. On-farm irrigation scheduling applications and agricultural practices were recorded for 20 years (2001–2020) at a daily time step. Hydrus-1D was calibrated and validated with direct measurements of the soil water retention curve and soil water content data, respectively, in each soil layer. A set of functional field capacity values was derived from 24 irrigation scheduling scenarios, and the optimal water storage capacity at field capacity (W FC ) was approximately 50 cm (corresponding to about 80% saturation in the soil profile). An average irrigation amount of 217.5 mm distributed over 21 events was obtained by using optimal irrigation scheduling, which was initiated when the matric pressure head took on a value of –700 cm and the irrigation rate was set at 1.0 cm d –1 . This irrigation practice ensured water storage at approximately the same level (ideally at W FC ) by sustaining only evapotranspiration fluxes in the uppermost portion of the root zone and by limiting excessive drainage. This protocol can be transferred to other agricultural fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Phosphonate functionalized magnetic mesoporous silica for rare earth element recovery from citrate assisted solid waste extracts

The growing high demand of rare earth elements (REEs) has prompted extensive research on REE recovery from waste streams. This study reports the synthesis and evaluation of phosphonate-functionalized magnetic mesoporous silica (MMS-PP) for REE recovery from the acidic extracts of solid wastes. MMS-PP was synthesized using surfactant template and post-synthesis methods, and tested using simulated and real extraction solutions from citrate-assisted REE extracts from municipal solid waste incineration (MSWI) ash. The organic-inorganic hybrid MMS-PP was evaluated for La recovery from 50 mM simulated citrate extract, with the adsorption capacity of 13.5 mg/g compared to ∼2.5 mg/g for non-functionalized MMS. The MMS-PP material exhibited fast adsorption within 10 min, good La selectivity against competing Na + and Ca 2+ , moderate selectivity against Al 3+ and Fe 3+ , high recyclability over multiple adsorption-desorption cycles, and equivalent efficiency for La, Ce, Nd, and Y recovery. Furthermore, the MMS-PP material also demonstrated 70–95% REE recovery from real citrate extracts of MSWI ash, and the spent MMS-PP material can be regenerated and reused for multiple cycles. Functionalized mesoporous materials in combination with organic-ligand assisted extraction can potentially provide a green, effective, and tunable solid-liquid separation platform for REE recovery from complex solid waste streams.

REE recovery↗
DOE OSTI2022

Superhydrophobic to superhydrophilic wettability transition of functionalized SiO 2 nanoparticles

The superhydrophobic and superhydrophilic surfaces and their transitions are of great interest for the production of self-cleaning, anti-biofouling, or corrosion-resistant materials. This research reports the wettability transition from superhydrophobic to superhydrophilic SiO 2 nanoparticles functionalized with 1H, 1H, 2H, 2H-perfluorooctyltriethoxysilane (POTS) and induced by temperature. The functionalization of these nanoparticles was confirmed by Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy. The functionalization of SiO 2 nanoparticles with POTS resulted in superhydrophobic surfaces with water contact angles up to 157°. A sudden transition to superhydrophilic behavior with water contact angles (WCA) below 5° was observed when the sample was heat-treated at 500 °C, despite the presence of fluorine on the surface of these nanoparticles, as confirmed by XPS and transmission electron microscopy. XPS suggested that the transition was caused by the change in orientation of the fluoroalkyl molecules and its partial decomposition due to the loss of the –CF 3 group, resulting in shorter chains with a tail-end group with C–O bonds, which promoted the superhydrophilicity.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Green’s function fast multipole method for computation of micromechanical fields in heterogeneous materials

Computation of micromechanical fields in heterogeneous materials is usually performed using either the finite element method or the Green’s function method based on FFTs. The finite element method allows for accurate discretization and for non-periodic boundary conditions but is computationally expensive. On the other hand, the FFT-based method is computationally efficient but requires discretization on a regular grid of hexahedral voxels. In this paper, a Green’s function method allowing for accurate discretization using tetrahedral elements and for non-periodic boundary conditions is proposed. The convolution is computed using the fast multipole method, which provides good accuracy even for low-order expansion due to the fast decay of interactions between elements. The proposed Green’s function fast multipole method is verified by comparison with analytical and FFT-based solutions. Furthermore, the computational time is analyzed and compared to the FFT-based method for non-periodic convolution. Finally, effective properties of an elastic polycrystalline microstructure containing thin intergranular cracks are computed and analyzed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Combined density functional theory/kinetic Monte Carlo investigation of surface morphology during cycling of Li-Cu electrodes

In this work, we employed a combined density functional theory and kinetic Monte Carlo approach in order to investigate the nanoscale behavior of lithium stripping and plating on lithium-copper nanoalloys. Density functional theory calculations were used to develop an atom-by-atom understanding of the nucleation behavior of lithium on copper surfaces with different surface morphologies. We found that nanometric pits resulted in significantly more favorable lithium deposition when compared to pristine or lightly-defective Cu surfaces, which is in agreement with previous experimental studies. We then used density functional theory and constrained ab initio molecular dynamics to develop interaction and reaction parameters for a larger-scale kinetic Monte Carlo model in order to provide insight into the stripping and plating behavior of nanostructured Li-Cu nanoalloy anodes. Substantial losses in capacity and strong indications of morphological change were observed for a wide range of Cu:Li ratios over 100 cycles. We observed a complex relationship between the nanoparticle composition and the properties of the formed solid-electrolyte interphase.

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

Effect of the sample work function on alkali metal dosing induced electronic structure change

Alkali metal dosing (AMD) has been widely used as a way to control doping without chemical substitution. This technique, in combination with angle resolved photoemission spectroscopy (ARPES), often provides an opportunity to observe unexpected phenomena. However, the amount of transferred charge and the corresponding change in the electronic structure vary significantly depending on the material. Here, we report study on the correlation between the sample work function and alkali metal induced electronic structure change for three iron-based superconductors: FeSe, Ba(Fe 0.94 Co 0.06 ) 2 As 2 and NaFeAs which share a similar Fermi surface topology. Electronic structure change upon monolayer of alkali metal dosing and the sample work function were measured by ARPES. In this work, our results show that the degree of electronic structure change is proportional to the difference between the work function of the sample and Mulliken's absolute electronegativity of the dosed alkali metal. This finding provides a possible way to estimate the AMD induced electronic structure change.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Performance analysis of functionally graded multifunctional piezoelectric energy harvesting microgyroscopes

Functionally graded materials (FGMs) are composite materials with varying material properties in one or more directions. These materials possess distinct characteristics compared to their constituent components. The ability to control material distribution and compositions in FGMs offers improved constraints over the natural frequencies of the system, making them highly suitable for energy harvesting applications. In this study, we focus on a piezoelectric FGM composed of Platinum (Pt) and Lead Titanate Zirconate (PZT). By utilizing FGMs for energy harvesting, we simplify the system from a multilayer structure to a single-layer system, thereby increasing power density by reducing volume. In this work, a power law distribution is used to model the material variation throughout the thickness of the single-layered beam. The governing equations of motion and boundary conditions for FG energy harvesting microgyroscope are derived using Euler-Bernoulli beam theory, the constitutive piezoelectric principle, and the extended Hamilton's principle. To simulate the nonlinear motion of the FG energy harvester, we employ the differential quadrature method (DQM). Subsequently, an investigation is carried out to examine the effects of various factors including material distribution, platinum percentage, base rotation, DC voltage, and electrical load resistance on the energy harvesting microgyroscope with functionally graded materials. Here, the findings suggest that functionally graded energy harvesting gyroscopes can be adjusted to obtain effective energy harvesting and sensing capabilities by carefully selecting the material distribution, input voltage, and electrical load resistance.

42 ENGINEERING↗
DOE OSTI2022

Learning functional priors and posteriors from data and physics

In this work, we develop a new Bayesian framework based on deep neural networks to be able to extrapolate in space-time using historical data and to quantify uncertainties arising from both noisy and gappy data in physical problems. Specifically, the proposed approach has two stages: (1) prior learning and (2) posterior estimation. At the first stage, we employ the physics-informed Generative Adversarial Networks (PI-GAN) to learn a functional prior either from a prescribed function distribution, e.g., Gaussian process, or from historical data and physics. At the second stage, we employ the Hamiltonian Monte Carlo (HMC) method to estimate the posterior in the latent space of PI-GANs. In addition, we use two different approaches to encode the physics: (1) automatic differentiation, used in the physicsinformed neural networks (PINNs) for scenarios with explicitly known partial differential equations (PDEs), and (2) operator regression using the deep operator network (DeepONet) for PDE-agnostic scenarios. We then test the proposed method for (1) meta-learning for one-dimensional regression, and forward/inverse PDE problems (combined with PINNs); (2) PDE-agnostic physical problems (combined with DeepONet), e.g., fractional diffusion as well as saturated stochastic (100-dimensional) flows in heterogeneous porous media; and (3) spatial-temporal regression problems, i.e., inference of a marine riser displacement field using experimental data from the Norwegian Deepwater Programme (NDP). The results demonstrate that the proposed approach can provide accurate predictions as well as uncertainty quantification given very limited scattered and noisy data, since historical data could be available to provide informative priors. In summary, the proposed method is capable of learning flexible functional priors, e.g., both Gaussian and non-Gaussian process, and can be readily extended to big data problems by enabling mini-batch training using stochastic HMC or normalizing flows since the latent space is generally characterized as low dimensional.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification

In numerous applications, the integration of prior knowledge and historical information is essential, particularly for tasks requiring the solution of ordinary or partial differential equations (ODEs/PDEs) in data-sparse or noisy environments. For instance, achieving accurate solutions to time-dependent PDEs with limited initial condition measurements necessitates an effective strategy for embedding prior knowledge. Hard-parameter sharing architectures in neural networks (NNs) have demonstrated success in both traditional and scientific machine learning domains, facilitating the learning of informative representations. Here, in this study, we introduce a novel, yet efficient, method to enhance physics-informed neural networks (PINNs) by incorporating a multi-head structure that enables the learning of functional priors from both empirical data and governing physical laws. This prior information can then be used to address data sparsity and high-level noise in solving ODE/PDE problems with uncertainty quantification (UQ). The approach, termed Multi-Head PINN (MH-PINN), consists of a shared body NN and multiple head NNs, each corresponding to an individual PINN instance. Our framework for functional prior learning is carried out in two stages: (1) training the MH-PINNs to develop a shared body NN alongside multiple head NNs, and (2) employing these trained head NNs to estimate a prior distribution through a normalizing flow-based density estimator. The learned functional prior can then be applied as a regularization mechanism in deterministic contexts or as an informative prior within a Bayesian inference framework, aiding in the resolution of subsequent ODE/PDE tasks. We evaluate the efficacy of MH-PINNs across five benchmark problems, including a high-dimensional parametric PDE, all characterized by data sparsity or substantial noise levels. Our findings reveal that MH-PINNs deliver accurate solutions and robust UQ, demonstrating adaptability across a range of complex and challenging scenarios.

Bayesian inference↗
DOE OSTI2024

Formation of functionally graded steel by laser powder bed fusion via in-situ carbon doping

Additive Manufacturing (AM) enables functional integration by combining multiple components into a single part to shorten assembly time, reduce weight, and improve performance. Laser Powder Bed Fusion (LPBF) is an important AM method due to excellent spatial resolution, surface finish, and material properties without the need for extensive post-processing. Functional integration could be enhanced by spatial tuning of properties, but LPBF cannot readily vary material composition. Here, this paper addresses a method to add spatial composition control by printing small quantities of dopants via liquid carrier prior to laser fusion. The impact of carbon black suspension added to select regions of a Stainless Steel 316 L powder bed on melt pool dimension, hardness, and porosity is reported. The distribution of the carbon between the doped and plain layers and the resulting spatial variation in hardness is measured. Optical microscopy and composition analysis show that the carbon dispersed uniformly within the layer of deposition and diffused as little as 50 μm in the build direction. Keyhole conditions dramatically increase the inter-layer transport of the dopant. The added carbon increased hardness by >50 %. Porosity increased in doped regions but remained below 1.5 % for the best processing parameters. These results demonstrate that the composition of LPBF parts could be controlled in 3-dimensions using a dopant that is soluble in the melt pool. Additional work will be required to evaluate different dopant materials and optimize processing conditions for full density, but microalloying with soluble dopants appears to be a plausible solution to enhance functional integration with LPBF.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Back to functional hydrides: Effects of neutron-irradiated microstructure on hydrogen retention in yttrium hydride

Functional hydrides are promising candidates for advanced nuclear reactors, particularly in portable or transportable applications, due to their high hydrogen-retention capabilities, enabling efficient neutron moderation, and compact reactor design. However, hydrogen mobility in hydrides at elevated irradiation temperatures poses significant technological challenges, necessitating a comprehensive understanding of their irradiation behavior. Furthermore, this study investigated the microstructural and chemical stability of neutron-irradiated yttrium hydrides to assess their hydrogen-retention capacity. A targeted literature review was also conducted to contextualize neutron-irradiation effects on functional hydrides with regards to structural stability and hydrogen retention. Experimental characterizations revealed that, at high temperatures, irradiated hydrides retained their phase stability, which was likely enhanced by irradiation-induced microstructure evolution. Notably, an amorphous yttrium and oxygen -rich surface layer was present at the free surface of the hydride. Its thickness decreased while a continuous crystalline Y-O-rich layer was formed with increasing neutron damage. Additionally, the number density of dislocation loops and cavities generally increased as a function of neutron dose. First-principles calculations of hydrogen behavior within yttrium vacancy clusters in yttrium hydrides demonstrated vacancy-size-dependent hydrogen stability and configuration, highlighting the role of vacancy geometry in regulating hydrogen retention. Thus, the presence of irradiation-induced dislocation loops and cavities were hypothesized to improve hydrogen retention. Collectively, these findings advance the understanding of hydride behavior under neutron irradiation as well as their technological readiness for portable or transportable nuclear reactors.

Defect clusters↗
DOE OSTI2024

Work Function Tuning of a Weak Adhesion Homojunction for Stable Perovskite Solar Cells

Perovskite solar cells (PSCs) have demonstrated a comparable efficiency to Si-based cells. However, the buried interface with weak adhesion remains a critical issue since the ion migration enhanced by the built-in electric field at this interface might lead to instability. We report here that adjusting the energy-level alignment at the weak adhesion homojunction interface can mitigate ion migration and thereby enhance the stability and photovoltaic performance of PSCs. Functional molecules with self-assembled monolayer characteristics were introduced to the surface of the SnO2 layer using silane derivatives, which tuned the work function of the homojunction depending on the functional groups in the molecules and thereby significantly reduced the built-in electric field. The PSC exhibited a power conversion efficiency (PCE) of 25.3%. The maximum power point (MPP) tracking under continuous illumination confirmed that the device retained more than 97% of its initial PCE, even after 1,000 h.

homojunction↗