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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Effect of static local distortions vs. dynamic motions on the stability and band gaps of cubic oxide and halide perovskites

Ternary ABX 3 perovskites made of corner-sharing BX 6 octahedra have long featured prominently in solid-state chemistry and condensed matter physics. Still, the joint understanding of their two main subgroups—halides and oxides—has not been fully developed. Indeed, unlike the case in simpler compounds having a single, robust repeated motif (“monomorphous”), certain cubic perovskites can manifest a non-thermal (= intrinsic) distribution of local motifs (“polymorphous networks”). Such static deformations can include positional degrees of freedom (e.g., atomic displacements and octahedral tilting) or magnetic moment degrees of freedom in paramagnets. Unlike thermal motion, such static distortions do not time-average to zero, being an expression of the intrinsic symmetry breaking preference of the chemical bonding. Here, the present study compares electronic structure features of oxide and halide perovskites starting from the static polymorphous distribution of motifs described by Density Functional Theory (DFT) minimization of the internal energy, continuing to finite temperature thermal disorder modeled via finite temperature DFT molecular dynamics. We find that (i) different oxide vs. halide ABX 3 compounds adopt different energy-lowering symmetry-breaking modes. The calculated pair distribution function (PDF) of SrTiO 3 from the first-principles agrees with recently measured PDF. (ii) In both oxides and halides, such static distortions lead to band gap blueshifts with respect to undistorted cubic Pm-3m structure. (iii) For oxide perovskites, high-temperature molecular dynamics simulations initiated from the statically distorted polymorphous structures reveal that the thermally-induced distortions can lead to a band gap redshift. (iv) In contrast, for cubic halide perovskite CsPbI 3 , both the intrinsic distortions and the thermal distortions contribute in tandem to band gap blueshift, the former, intrinsic effect being dominant. (v) In the oxide SrTiO 3 and CaTiO 3 (but not in halide) perovskites, octahedral tilting leads to the emergence of a distinct Γ–Γ direct band gap component as a secondary valley minimum to the well-known indirect R–Γ gap. Understanding such intrinsic vs. thermal effects on oxide vs. halide perovskites holds the potential for designing target electronic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Advancing uncertainty characterization for understanding projected water scarcity in multi-sector, multi-actor river basins across scales.

Invited Talk at AGU Fall Meeting 2023 Modeling how human institutions and infrastructure interact with the water cycle is essential to better understand the vulnerability, and resilience of water resources systems from the local to the global scale. This is especially true when investigating multi-sector, multi-actor responses to the effects of long-term changes and short-term shocks. It is also well recognized that uncertainty present throughout the modeling cycle (e.g., in data, functional relations, and model coupling approaches) limits our ability to trace the interactive dynamics of human-water systems, as well as to quantify their implications for management and planning. Systems with large numbers of diverse stakeholders further compound this challenge, as uncertain drivers and complex dynamics might have very disparate effects on water users. The work presented is conducted through the Integrated Multisector Multiscale Modeling (IM3) Science Focus Area, which explores how human and natural systems co-evolve in response to change. Through this multi-year effort, we have developed exploratory modeling methods to better characterize how the uncertain human and natural drivers of water scarcity yield consequential vulnerabilities in institutionally complex, multi-sectoral systems. This talk will specifically present on a series of uncertainty characterization experiments performed in the Upper Colorado River Basin, a sub-basin of the Colorado with thousands of water users. These complementary and systematic experiments are aimed at understanding: How are various uncertain stressors (e.g., climate change, demand growth) affecting the diverse water users of this basin in terms of water shortage? What are the key drivers of this shortage for each user? Methods and results from this work are used to address additional questions on the ability of adaptation to modulate the effects of these uncertain drivers, and on their compounding effects across spatial scales and through sectoral interactions.

Hadjimichael, Antonia↗
DOE OSTI2025

Density functional theory calculations of the mixing enthalpy of ternary uranium carbide compounds

The high melting point of uranium-zirconium carbides (U,Zr)C makes them an ideal fuel for nuclear thermal propulsion (NTP) reactors. Gaps remain in the current understanding of the U-Zr-C system due to the difficulty of conducting thermodynamic experiments at NTP operation conditions. Density functional theory calculations using the Hubbard U model (DFT+U) were performed using orbital matrix occupation (OMC) to obtain the mixing enthalpy for UC and ZrC for (U,Zr)C ternary compounds. Similarly, DFT+U calculations were also carried out for the (U,Nb)C and (U,Ta)C systems. In conclusion, the DFT results are envisioned to be used in thermodynamic assessments of the uranium carbide systems based on the CALPHAD approach to supplement the lack of experimental data for the mixing thermodynamics.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

New Synthetic Route to 4,6‐Diamino‐5,7‐dinitro‐benzo‐furazan, Important Decomposition Product of 1,3,5‐Triamino‐2,4,6‐trinitrobenzene

The important 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) decomposition byproduct, 4,6-diamino-5,7-dinitro-benzo-furazan (F 1 ), was prepared in a four-step reaction sequence starting with commercially available materials through a key intermediate, 4,6-dichloro-5,7-dinitro-benzo-furazan. The introduction of azido to 4,6-dichloro-benzo-furazan, followed by nitration and amination gave F 1 in good yield. This new method showed a greatly improved yield and ease of purification than previous methods (the total yield was 30%–40% in seven steps). F 1 is a stable yellow solid with a melting point of 308.7°C and a thermal decomposing temperature of 310.3°C (differential scanning calorimetry at 10°C/min heating rate). The solid-state structure was solved by single-crystal X-ray analyses at low temperatures (100 K). The compound crystallizes in a P 2 1 c space group with the formula C 6 H 4 N 6 O 5 •H 2 O with 12 molecules of F 1 /H 2 O in the unit cell. This compound has eluded complete characterization for almost 50 years. With this information, more advanced decomposition models can be created to further improve critical safety margins in handling the energetic material, TATB.

decomposition↗
DOE OSTI2021

Insights into the thermal decomposition of plutonium(IV) oxalate – a DFT study of the intermediate structures

The thermal decomposition of plutonium oxalate to oxide is one of the most studied reactions in actinide chemistry but the intermediates have been the subject of debate for decades. Recent experimental data suggest that the decomposition of Pu(IV) oxalate in air undergoes dehydration first, then reduction to Pu(III) oxalate. The precise structural modifications that take place are unknown as experiments have not been able to fully characterize the intermediates at the microscopic level. To rectify this, we employed solid state density functional theory calculations at the PBE-D3 level with a Hubbard U correction to model the structures and energetics of potential dehydrated Pu(IV) and Pu(III) oxalate intermediate compounds. Based on the theoretical study presented here, the anhydrous analogues of the known hydrated Pu(IV) and Pu(III) oxalates are the preferred crystal structures formed through an overall exothermic reaction process. However, decomposition could proceed through the formation of a higher energy, more complicated 3D lattice structure with frustrated oxalate binding. It is expected that the intermediates presented here could be identified using spectroscopic techniques to enable further insight into the reaction mechanism.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

A Comparative Study of Physics‐Informed and Data‐Driven Neural Networks for Compound Flood Simulation at River‐Ocean Interfaces: A Case Study of Hurricane Irene

Simulating compound flooding (CF) at the river-ocean interface within large-scale Earth System Models (ESMs) presents significant challenges due to complex interactions between river discharge, storm surge, and tides. This study assesses the comparative advantages of physics-informed and data-driven machine learning (ML) approaches for enhancing local ESM performance. We systematically compare data-driven neural network models (i.e., CNNs, U-Net, Long Short-Term Memory (LSTM), Gated Recurrent Unit), and physics-informed neural network (PINN) models, including vanilla PINN and a finite-difference-based PINN (FD-PINN). Specifically, FD-PINN is introduced to enhance computational efficiency, accelerating vanilla PINNs by ∼6.5 times while improving accuracy. To enhance data-driven model training, a new data-generation approach is developed to sample historical fluvial and coastal flood events, which ensures a robust data set for extreme event prediction. The models are evaluated using a realistic one-dimensional river domain extracted from an ESM's river mesh and the Hurricane Irene event as an independent test case. Results show that FD-PINN achieves accurate predictions with significantly reduced computational costs relative to vanilla PINNs. Among data-driven models, the best overall performance is achieved by a CNN-LSTM hybrid, which balances accuracy and efficiency. While a fully connected CNN (CNN-FC) provides the best accuracy, it incurs high computational cost. Architectures lacking strong temporal modeling tend to underperform on unseen events. These findings highlight the importance of sequence-aware designs for robust generalization. This study reveals the trade-offs between physics-informed and data-driven models and proposes an adaptive hybrid framework for integrating ML into ESMs to enhance local flood simulations.

Earth Systems Modeling↗
DOE OSTI2023

Computational analysis of metal–metal bonded dimetal tetrabenzoate redox potentials in the context of ammonia oxidation electrocatalysis

Metal–metal bonded complexes are promising candidates for catalyzing redox transformations. Of particular interest is the oxidation of ammonia to dinitrogen, an important half reaction for the potential utilization of ammonia as a fuel or hydrogen carrier. This work computationally explores 30 different metal–metal bonded dimers (5 different metal centers and 6 different benzoate ligand derivatives) to explore the tunability of the redox potential when ammonia is bound to the complexes as an axial ligand, modeling the first step in ammonia oxidation electrocatalysis. We calculate the redox potentials of these compounds, making reference to experimental data when appropriate, identifying two degrees of tunability: a coarse adjustment, changing the metal center, allows for a wide range of redox potentials to be accessed (from +1.0 to –2.0 V vs. ferrocene/ferrocenium in acetonitrile solution) and a fine adjustment, the para-substituent of the benzoate derivative, which affects the redox potential in a smaller range based on the electron donating/withdrawing effects of the substituent. Ruthenium and osmium tetrabenzoate catalysts are prime candidates for next generation ammonia oxidation catalysts because their redox potentials fall within the direct ammonia fuel cell “viability zone” bracketed by the thermodynamic potentials of oxygen reduction (ORR) and nitrogen reduction (NRR). Rhodium tetrabenzoate species fall above the ORR potential, suggesting ammonia oxidation promoted by Rh 2 catalysts could instead be used to facilitate hydrogen production through coupling to hydrogen evolution at a cathode. The redox potentials of rhenium and iridium tetrabenzoate catalysts fall below the NRR potential suggesting that these compounds could be further investigated in the context of electrochemical ammonia synthesis. Each redox event studied involves electron transfer from the M–M δ* orbital regardless of choice of metal or benzoate ligand derivative; this leads us to believe that the chemical reactivity of the various studied compounds will be similar in the context of ammonia oxidation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Solving the one-dimensional penetration problem for the fission channel in the statistical Hauser-Feshbach theory

Here we solve the Schrödinger equation for an arbitrary one-dimensional potential energy to calculate the transmission coefficient in the fission channel of compound nucleus reactions. We incorporate the calculated transmission coefficients into the statistical Hauser-Feshbach model calculation for neutron-induced reactions on 235,238 U and 239 Pu. The one-dimensional model reproduces the evaluated fission cross section data reasonably well considering the limited number of model parameters involved. A resonance-like structure appears in the transmission coefficient for a double-humped fission barrier shape that includes an intermediate well, which is understood to be a quantum mechanical effect in the fission channel. The calculated fission cross sections for the neutron-induced reactions on 235,238 U and 239 Pu all exhibit a similar structure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Production of protons and light nuclei in Au +Au collisions at $\sqrt{s_{NN}}$ = 3 GeV with the STAR detector

We report the systematic measurement of protons and light nuclei production in Au +Au collisions at $\sqrt{s_{NN}}$ = 3 GeV by the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The transverse momentum (𝑝 𝑇 ) spectra of protons (𝑝) , deuterons (𝑑) , tritons (𝑡) , 3 He , and 4 He have been measured from midrapidity to target rapidity for different collision centralities. We present the rapidity and centrality dependence of particle yields (𝑑⁢𝑁/𝑑⁢𝑦), average transverse momentum (⟨𝑝 𝑇 ⟩), yield ratios (𝑑/𝑝, 𝑡/𝑝, 3 He/𝑝, 4 He/𝑝), as well as the coalescence parameters (𝐵 2 ,𝐵 3 ). The 4⁢𝜋 yields for various particles are determined by utilizing the measured rapidity distributions, 𝑑⁢𝑁/𝑑⁢𝑦. Furthermore, we present the energy, centrality, and rapidity dependence of the compound yield ratios (𝑁 𝑝 ×𝑁 𝑡 /𝑁$^{2}_{d}$) and compare them with various model calculations. Furthermore, the physics implications of these results on the production mechanism of light nuclei and the QCD phase structure are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Pre-Equilibrium De-Excitations in Neutrino-Nucleus Interactions

Pre-equilibrium de-excitation is a well-established stage of nucleon-induced nuclear reactions but has not previously been incorporated into neutrino event generators. This work implements the Koning-Duijvestijn exciton model within the MARLEY neutrino event generator to simulate energy redistribution and particle emission before compound nucleus equilibrium. The implementation includes modular calculations of particle-hole state densities, internal transition rates, emission rates, and Monte Carlo cascade sampling, and is validated against the TALYS-2.2 nuclear reaction code. A new event record is also proposed to enable future uncertainty reweighting studies. This work establishes the first framework for studying pre-equilibrium effects in neutrino-induced reactions, with the goal of improving predictions of particle multiplicities, γ-ray production, and MeV-scale detector signals relevant to experiments such as DUNE.

Visser, Erin [Michigan State U., East Lansing (mai↗
DOE OSTI2023

Manipulation of glycogen and sucrose synthesis increases photosynthetic productivity in cyanobacteria

Photosynthetic productivity is limited by low energy conversion efficiency in naturally evolved photosynthetic organisms, via multiple mechanisms that are not fully understood. Here we show evidence that extends recent findings that cyanobacteria use “futile” cycles in the synthesis and degradation of carbon compounds to dissipate ATP. Reduction of the glycogen cycle or the sucrose cycle in the model cyanobacterium Synechocystis 6803 led to redirection of cellular energy toward faster growth under simulated outdoor light conditions in photobioreactors that was accompanied by higher energy charge [concentration ratio of ATP/(ATP + ADP)]. Such manipulation of energy metabolism may have potential in engineering microalgal chassis cells to increase productivity of biomass or target metabolites.

09 BIOMASS FUELS↗
DOE OSTI2025

Adsorptive denitrogenation of model aviation fuel using mesoporous silica in a packed bed adsorption system

This study aims to understand the effects of system process parameters such as flow rate, adsorbent particle size, and use of recycled adsorbent on denitrogenation performance of a model fuel using mesoporous silica gel. The goal is to reduce the nitrogen content of the model fuel from 1500 parts per million to single-digit ppm to meet ASTM specifications for drop-in fuels. This work was done with the intent of applying adsorptive denitrogenation to sustainable aviation fuel (SAF) product fractions produced via hydrothermal liquefaction (HTL). The adsorption performance of the silica is evaluated via packed column breakthrough data, with data generated from collecting from the column outlet and quantifying nitrogen content via gas chromatography. Select experiments use a significantly larger (2.5x column diameter and length) column to demonstrate linear scalability of the process. Thermogravimetric analysis data is collected to evaluate the effects of thermal calcination as a sorbent regeneration method. Effects of a more complex feed are also investigated using a known reference fuel with additional added nitrogen containing compounds. The results presented in this work successfully demonstrate up to 99.8 % removal of NCCs from a model fuel fraction at an original NCC concentration of approximately 1500 ppm to single-digit parts per million after treatment. We also examine calcination of sorbent materials to remove the adsorbed species to enable sorbent reuse and minimize waste generation and show that the calcined material can be reused up to 5 cycles with reduced adsorption capacity. Overall, this work indicates that adsorptive denitrogenation using silica gel is a viable solution to enable the integration of HTL-derived aviation fuels into existing fuel infrastructure.

Adsorption techniques↗
DOE OSTI2020

Exploration of oxidative chemistry and secondary organic aerosol formation in the Amazon during the wet season: explicit modeling of the Manaus urban plume with GECKO-A

The GoAmazon 2014/5 field campaign took place in Manaus, Brazil, and allowed the investigation of the interaction between background-level biogenic air masses and anthropogenic plumes. We present in this work a box model built to simulate the impact of urban chemistry on biogenic secondary organic aerosol (SOA) formation and composition. An organic chemistry mechanism is generated with the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to simulate the explicit oxidation of biogenic and anthropogenic compounds. A parameterization is also included to account for the reactive uptake of isoprene oxidation products on aqueous particles. The biogenic emissions estimated from existing emission inventories had to be reduced to match measurements. The model is able to reproduce ozone and NO x for clean and polluted situations. The explicit model is able to reproduce background case SOA mass concentrations but does not capture the enhancement observed in the urban plume. The oxidation of biogenic compounds is the major contributor to SOA mass. A volatility basis set (VBS) parameterization applied to the same cases obtains better results than GECKO-A for predicting SOA mass in the box model. The explicit mechanism may be missing SOA-formation processes related to the oxidation of monoterpenes that could be implicitly accounted for in the VBS parameterization.

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

Surface Crust Formation Controls Evaporation Kinetics of Secondary Organic Aerosols

Gas-particle partitioning is critical for the evolution of secondary organic aerosols (SOA) in the atmosphere. SOA particles evaporate more slowly than expected at nearly size-independent rates, but the underlying mechanism remains controversial. Here, in this study, we apply kinetic multilayer modeling to simulate evaporation of α-pinene SOA, demonstrating that surface crust formation, emerging from accumulation of low-volatility compounds at the particle surface, leads to slow evaporation and reduced size dependence of the evaporation rate. While evaporation induced by decomposition of oligomers would naturally lead to size-independent evaporation rates, we observe and simulate nearly size-independent slow evaporation of polyethylene glycol mixture particles containing polymeric species that do not decompose, confirming the relevance of composition-dependent diffusivity for size-independent, slow evaporation. Slow evaporation of limonene SOA was also observed in environmental chamber experiments, and model simulations demonstrate strong surface crust formation with bulk diffusivity being depressed by up to 5 orders of magnitude compared to the inner bulk. We present experimental evidence using a surface-based mass spectrometry technique that shows that the particle surface becomes enriched in high molecular weight compounds upon evaporation of monomers. Our findings imply that viscous surface crusts may also limit the growth and chemical transformation of SOA particles, influencing their impacts on air quality and climate.

heterogeneous chemistry↗
DOE OSTI2020

An integrated computational materials engineering framework to analyze the failure behaviors of carbon fiber reinforced polymer composites for lightweight vehicle applications

A bottom-up multi-scale modeling approach is used to develop an Integrated Computational Materials Engineering (ICME) framework for carbon fiber reinforced polymer (CFRP) composites, which has the potential to reduce development to deployment lead time for structural applications in lightweight vehicles. In this work, we develop and integrate computational models comprising of four size scales to fully describe and characterize three types of CFRP composites. In detail, the properties of the interphase region are determined by an analytical gradient model and molecular dynamics analysis at the nano-scale, which is then incorporated into micro-scale unidirectional (UD) representative volume element (RVE) models to characterize the failure strengths and envelopes of UD CFRP composites. Then, the results are leveraged to propose an elasto-plastic-damage constitutive law for UD composites to study the fiber tows of woven composites as well as the chips of sheet molding compound (SMC) composites. Subsequently, the failure mechanisms and failure strengths of woven and SMC composites are predicted by the meso-scale RVE models. Finally, building upon the models and results from lower scales, we show that a homogenized macro-scale model can capture the mechanical performance of a hat-section-shaped part under four-point bending. Along with the model integration, we will also demonstrate that the computational results are in good agreement with experiments conducted at different scales. The present work illustrates the potential and significance of integrated multi-scale computational modeling tools that can virtually evaluate the performance of CFRP composites and provide design guidance for CFRP composites used in structural applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exchange-correlation functional challenges in modeling quaternary chalcogenides

The development of next-generation quaternary chalcogenides, such as Cu2ZnSnS4 (CZTS) and Cu2ZnGeS4 (CZGS), for solar energy and thermoelectric applications hinges upon both careful experimentation and accurate quantum mechanical modeling. To address the latter, many have turned to density functional theory (DFT), which offers several choices for the approximate treatment of electron exchange and correlation (XC). Popular XC functionals include the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) and the recently developed strongly constrained and appropriately normed (SCAN) meta-GGA. Extensions of DFT functionals, such as adding a Hubbard U correction and introducing a fraction of the Fock exchange (hybrid functionals), have been used widely to model systems containing 3d metal ions. However, no studies yet have compared comprehensively PBE(+U) and SCAN(+U) in the quality of their predictions of the bulk and defect thermodynamics of quaternary chalcogenides, which play a critical role in device fabrication and performance. Hence, here we calculate the (i) 0 K formation energies of bulk Ge compounds and (ii) neutral defect formation energies including charge-balanced (e.g., CuZn + ZnCu) and charge-imbalanced (e.g., CuSn) combinations of antisites and vacancies in CZTS and CZGS using the PBE, PBE +U, SCAN, SCAN +U, and the hybrid Heyd-Scuseria-Ernzerhof XC frameworks. We find that the formation energies of charge-imbalanced defects are more sensitive to the choice of the XC functional than those of charge-balanced defects, which can be explained by the differences in the extent of penalization of defect-generated delocalized electrons/holes by PBE, PBE +U, SCAN, and SCAN +U. Additionally, our results show that SCAN systematically underbinds Ge-containing compounds, thus highlighting the need for even further improvement of XC functionals. Based on our findings, we recommend the use of SCAN for modeling quaternary chalcogenides because its errors are systematic, and it has the firmest theoretical underpinning. Our work provides guidance for future modeling of quaternary chalcogenides.

14 SOLAR ENERGY↗
DOE OSTI2021

Artificial neural network prediction of self-diffusion in pure compounds over multiple phase regimes

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. The ANNs were tested on an experimental database of 6625 self-diffusion constants for 118 different chemical compounds. The presence of multiple phases results in a heavy skew in the distribution of diffusion constants and multiple approaches were used to address this challenge. First, an ANN was developed with the raw diffusion values to assess what the main drawbacks of this direct method were. The first approach for improving the predictions involved taking the log 10 of diffusion to provide a more uniform distribution and reduce the range of target output values used to develop the ANN. The second approach involved developing individual ANNs for each phase using the raw diffusion values. Results show that the log transformation leads to a model with the best self-diffusion constant predictions and an overall average absolute deviation (AAD) of 6.56%. The resultant ANN is a generalized model that can be used to predict diffusion across all three phases and over a diverse group of compounds. The importance of each input feature was ranked using a feature addition method revealing that the density of the compound has the largest impact on the ANN prediction of self-diffusion constants in pure compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗