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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Predictions of Boron Phase Stability Using an Efficient Bayesian Machine Learning Interatomic Potential

Thermodynamic phase stability of three elemental boron allotropes, i.e., α-B, β-B, and γ-B, was investigated using a Bayesian interatomic potential trained via a sparse Gaussian process (SGP). SGP potentials trained with datasets from on-the-fly active learning achieve quantum mechanical level accuracy when employed in molecular dynamics simulations to predict wide-ranging thermodynamic, structural, and vibrational properties. The simulated phase diagram (500~1400 K and 0~16 GPa) agrees with experimental measurements. The SGP-based MD simulations also successfully predicted that the B13 defect is critical in stabilizing β-B below 700 K. At higher temperatures, the entropy becomes the dominant factor, making β-B the more stable phase over α-B. Furthermore, this Letter demonstrates that SGP potentials based on a training set consisting of defect-free-only systems could make correct predictions of defect-related phenomena in solid-state crystals, paving the path to investigate crystal phase stability and transitions.

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

Investigating the Impacts of Aqueous-Phase Processing on Organic Aerosol Chemical Climatology Using ARM and ASR Observations

This project improved understanding of how atmospheric aerosol particles form and evolve, with a focus on the role of water-driven (aqueous-phase) chemical reactions in the atmosphere. These processes occur in clouds, fog, and humid air and can significantly change the composition and properties of airborne particles, known as aerosols, which influence air quality and climate. By combining field measurements, laboratory experiments, and advanced analytical techniques, the project identified key chemical signatures that allow scientists to distinguish particles formed through aqueous processes from those formed in the gas phase. Observations from multiple environments, including wildfire smoke, urban regions, and cloud-influenced areas, show that aqueous chemistry is an important pathway for particle formation and aging. The project also developed new measurement approaches using uncrewed aerial systems (UAS) to capture how aerosol composition varies with altitude, providing critical insights into how particles interact with clouds. In addition, new data analysis frameworks were created to better interpret long-term aerosol measurements and improve characterization of particle sources and transformations. These results have been integrated into a global database of aerosol measurements and used to support atmospheric modeling efforts. Overall, the project provides important tools and knowledge to improve predictions of how aerosols affect climate and air quality, particularly through their interactions with radiation and clouds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Cost optimization of high recovery single stage gap membrane distillation

Estimating the cost of membrane distillation (MD) processes at scale is confounded by the vast array of possible process configurations, module configurations, and operating conditions and the extreme sensitivity of MD costs to these parameters. This work develops a comprehensive MD cost optimization framework for minimizing the levelized cost of water (LCOW) as a function of feed salinity and temperature. Our single stage gap MD model fully captures solution properties, heat transfer, and mass transfer relationships in the modules to determine the cost optimal design as a function of multiple decision variables, including process configuration (feed location and connectivity between the hot and cold-side), the gap type (air, permeate, conductive), equipment sizes (membrane, heater, chiller, and heat exchanger units), and operating conditions (flow rates, hot and cold side temperatures, and heating and cooling duties). We estimate that the minimum LCOW of conductive gap MD ranges between $\$$10 and $\$$16 per m 3 for feed salinities of 25 g/L to 200 g/L and water recoveries of 30%–75%. Further, we update several heuristic assumptions for cost optimal system operation, including: increased thermal conductivity of the gap decreases system costs; cost optimal MD operates at the highest allowable temperature; and the LCOW is a strong function of brine salinity. Finally, we find that improving membrane properties and lowering heating and cooling costs will provide only modest reductions in costs (<25%).

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

Molecular dipole moment learning via rotationally equivariant derivative kernels in molecular-orbital-based machine learning

This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree–Fock computations. A MOB pairwise decomposition of the correlation part of the dipole moment is applied, and these pair dipole moments could be further regressed as a universal function of MOs. The dipole MOB features consist of the energy MOB features and their responses to electric fields. An interpretable and rotationally equivariant derivative kernel for Gaussian process regression (GPR) is introduced to learn the dipole moment more efficiently. The proposed problem setup, feature design, and ML algorithm are shown to provide highly accurate models for both dipole moments and energies on water and 14 small molecules. To demonstrate the ability of MOB-ML to function as generalized density-matrix functionals for molecular dipole moments and energies of organic molecules, we further apply the proposed MOB-ML approach to train and test the molecules from the QM9 dataset. The application of local scalable GPR with Gaussian mixture model unsupervised clustering GPR scales up MOB-ML to a large-data regime while retaining the prediction accuracy. In addition, compared with the literature results, MOB-ML provides the best test mean absolute errors of 4.21 mD and 0.045 kcal/mol for dipole moment and energy models, respectively, when training on 110 000 QM9 molecules. The excellent transferability of the resulting QM9 models is also illustrated by the accurate predictions for four different series of peptides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Real-time insight into the multistage mechanism of nanoparticle exsolution from a perovskite host surface

In exsolution, nanoparticles form by emerging from oxide hosts by application of redox driving forces, leading to transformative advances in stability, activity, and efficiency over deposition techniques, and resulting in a wide range of new opportunities for catalytic, energy and net-zero-related technologies. However, the mechanism of exsolved nanoparticle nucleation and perovskite structural evolution, has, to date, remained unclear. Herein, we shed light on this elusive process by following in real time Ir nanoparticle emergence from a SrTiO 3 host oxide lattice, using in situ high-resolution electron microscopy in combination with computational simulations and machine learning analytics. We show that nucleation occurs via atom clustering, in tandem with host evolution, revealing the participation of surface defects and host lattice restructuring in trapping Ir atoms to initiate nanoparticle formation and growth. These insights provide a theoretical platform and practical recommendations to further the development of highly functional and broadly applicable exsolvable materials.

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

Transport coefficient sensitivities in a semi-analytic model for magnetized liner inertial fusion

Performance of magnetized liner inertial fusion (MagLIF) experiments is highly dependent on transport processes including magnetized heat flows and magnetic flux losses. Magnetohydrodynamic simulations used to model these experiments require a choice of model for the transport coefficients, which are the constants of proportionality relating driving terms, such as temperature gradients and currents, to the associated heat and magnetic field transport. The coefficients have been the subject of repeated recalculation using various methods throughout the years. Using a semi-analytic MagLIF model, we compare models for the transport coefficients. The choice of model modifies magnetic-flux losses caused by the Nernst thermoelectric effect and thermal conduction losses. We present simulated results from parameter scans conducted in order to compare the effects of the different models on parameters of interest in MagLIF. In some regions of parameter space, discrepancies of up to 38% are found in integrated quantities like the fusion yield. These results may serve as a guide for experimental validation of the various models, particularly as laser preheat energies and initial axial field strengths are increased on MagLIF experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

How to Decarbonize Our Energy Systems: Process‐Informed Design of New Materials for Carbon Capture

Decarbonisation from a variety of industrial and power emission sectors highlights a marked need for capture technologies that can be optimized for different CO 2 sources and integrated into an equally diverse range of applications of captured CO 2 as a feedstock. Some capture technologies are already operated at an industrial scale but may not be optimal for all required applications. Advanced tailored sorbent-based technologies allow flexible operation and reduced costs as they offer higher capture capacities and significantly lower energy penalties than the state-of-the-art systems. To accelerate the discovery, development, and deployment of novel advanced materials, it is critically important that efforts between experimentalists, theoreticians, and process engineers are coordinated. The PrISMa project addresses this challenge by integrating materials design with process design and environmental considerations to allow for tailor-making carbon capture solutions optimally tuned for local sources and sinks. In this article, we highlight some of the recent results obtained with the PrISMa platform.

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

Learning the solution operator of parametric partial differential equations with physics-informed DeepONets

Partial differential equations (PDEs) play a central role in the mathematical analysis and modeling of complex dynamic processes across all corners of science and engineering. Their solution often requires laborious analytical or computational tools, associated with a cost that is markedly amplified when different scenarios need to be investigated, for example, corresponding to different initial or boundary conditions, different inputs, etc. In this work, we introduce physics-informed DeepONets, a deep learning framework for learning the solution operator of arbitrary PDEs, even in the absence of any paired input-output training data. We illustrate the effectiveness of the proposed framework in rapidly predicting the solution of various types of parametric PDEs up to three orders of magnitude faster compared to conventional PDE solvers, setting a previously unexplored paradigm for modeling and simulation of nonlinear and nonequilibrium processes in science and engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Identification of engine oil-derived ash nanoparticles and ash formation process for a gasoline direct-injection engine

Engine oil-derived ash particles emitted from internal combustion (IC) engines are unwanted by-products, after oil is involved in in-cylinder combustion process. Since they typically come out together with particulate emissions, no detail has been reported about their early-stage particles other than agglomerated particles loaded on aftertreatment catalysts and filters. To better understand ash formation process during the combustion process, here differently formulated engine oils were dosed into a fuel system of a gasoline direct injection (GDI) engine that produces low soot mass emissions at normal operating conditions to increase the chances to find stand-alone ash particles separated from soot aggregates in the sub-20-nm size range. In addition to them, ash/soot aggregates in the larger size range were examined using scanning transmission electron microscopy (STEM)-X-ray electron dispersive spectroscopy (XEDS) to present elemental information at different sizes of particles from various oil formulations. The STEM-XEDS results showed that regardless of formulated oil type and particle size, Ca, P and C were always contained, while Zn was occasionally found on relatively large particles, suggesting that these elements get together from an early stage of particle formation. The S, Ca and P K-edge X-ray absorption near edge structure (XANES) analyses were performed for bulk soot containing raw ash. The linear combination approach & cross-checking among XANES results proposed that Ca 5 (OH)(PO 4 ) 2 , Ca 3 (PO 4 ) 2 and Zn 3 (PO 4 ) 2 are potentially major chemical compounds in raw ash particles, when combined with the STEM-XEDS results. Despite many reports that CaSO 4 is a major ash chemical when ash found in DPF/GFP systems was examined, it was observed to be rarely present in raw ashes using the S K-edge XANES analysis, suggesting ash transformation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Core hole processes in x-ray absorption and photoemission by resonant Auger-electron spectroscopy and first-principles theory

Electron–core hole interactions are critical for proper interpretation of core-level spectroscopies commonly used as analytical tools in materials science. Here we utilize resonant Auger-electron spectroscopy to uniquely identify exciton, shake, and charge-transfer processes that result from the sudden creation of the core hole in both x-ray-absorption and photoemission spectra. These effects are captured for the transition-metal compounds SrTiO 3 and MoS 2 by fully ab initio, combined real-time cumulant, and Bethe-Salpeter equation approaches to account for core hole dynamics and screening. Atomic charges and excited-state electron-density fluctuations reflect materials’ solid-state electronic structure, loss of translational symmetry around the core hole, and breakdown of the sudden approximation. They also demonstrate competition between long- and short-range screening in a solid.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Process-based modeling of soil nitrous oxide emissions from United States corn fields under different management and climate scenarios coupled with evaluation using regional estimates

Direct emissions of soil nitrous oxide during a growing season (N 2 O gs ) can be quantified with process-based models considering interactions between management, climate, and soil moisture when key data are available. We used an adapted “parameterized CENTURY/DAYCENT-model” ( p CENTURY) calibrated with crop growth and soil organic matter decay coefficients at the county-level for the estimation of N 2 O gs in the United States Corn Belt. Model estimated N 2 O-emissions from corn-based biofuels scenarios considering crop rotation, fertilizer inputs, tillage, and weather were compared against meta-summary of field observations from 55 studies. Both model and meta-summary ranked N 2 O gs -emissions to be corn > wheat > soybean phase while model likely underestimated cover crop N 2 O gs -emissions. The N 2 O gs -emissions and the associated emission factors (EFs) were modeled and summarized to be greater after anhydrous ammonia than urea application and from conventional tilled than non-tilled fields. Modeled and observed N 2 O gs -emissions after organic and inorganic fertilizer amendment did not differ due to high variability associated with the treatments. However, the organic fertilizer associated EFs were greater according to meta-summary data because of N input rates. Regionalized weather scenarios indicate hotspots for N 2 O gs -emissions can occur where crop N uptake is limited during dry years and in eastern states also during normal or wet seasons. The p CENTURY-derived N 2 O gs EFs (0.91 ± 0.19%) for counties investigated were only slightly lower than literature (1.07 ± 0.57%) or Tier-1 (1%) values. Our preliminary evaluation of regional soil moisture estimates showed reasonable agreement between monthly soil moisture estimates and the North American Soil Moisture Dataset during the growing season, but overestimation of soil moisture in winter-spring can influence the estimates of annual N 2 O emissions so future work is needed to calibrate soil moisture-associated model parameters. Our work provided scenario-based estimates of climate and management impacts on soil N 2 O gs -emissions together with valuable spatial insights into EFs that will be improved by more accurate information of fertilizer inputs and more temporally refined model evaluation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Development of an Attenuated Total Reflectance–Ultraviolet–Visible Probe for the Online Monitoring of Dark Solutions

Optical spectroscopy is a valuable tool for on-line monitoring of a variety of processes. Ultraviolet-visible (UV-vis) spectroscopy in particular, can monitor the concentration of analytes as well as identify speciation and oxidation state. However, it can be difficult to impossible to employ UV-vis based sensors on chemical systems that are very dark (i.e., high optical density) as exceedingly short pathlengths are required (for transmission approaches) or effective means of backscattering are needed (for reflectance approaches). Examples of processes that would benefit significantly from the use of optical sensors and encounter these challenges include used nuclear fuel recycling and molten salts with high concentrations of dissolved uranium. Utilizing an attenuated total reflectance (ATR) UV-vis approach can overcome these challenges and allow for the measurement of solutions orders of magnitude more concentrated than transmission UV-vis. However, determining ideal sensor specifications for varied processes can be time consuming and expensive. Here, in this study, we evaluate the ability for a novel ATR-UV-vis probe to measure very concentrated solutions of Co(II) and Ni(II) nitrate as well as organic dyes (methylene blue, acid red 1, and crystal violet). This sensor design provides a modular method for exploring possible “pathlengths” by altering the exposed ATR fiber length. Also studied were approaches to loading and measuring the sensor cell. These results are compared to a traditional 1 cm cuvette measured by transmission UV-vis. It was found that the ATR-UV-vis probe was capable of measuring solutions 600 times more concentrated than the 1 cm cuvette. Advanced data analysis in the form of multivariate curve resolution (MCR) was used to analyze the speciation of methylene blue over a large concentration range. The application of this novel ATR-UV-vis probe to the interrogation of dark solutions is a promising avenue for use in on-line monitoring of nuclear processes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Tuning crosslinking of hybrid preceramic polymers in vat photopolymerization toward controlled ceramic yields

Control of preceramic polymer crosslinking for UV-curable processing is essential for fine 3D printing with high ceramic conversion for sustainable polymer-derived ceramics (PDC) engineering. While various factors influencing ceramic yield have been studied, the systematic exploration of the relationship between crosslinking and ceramic yield, especially when crosslinking increases volatile elements, remains open for further investigation. This study addresses this gap by utilizing vat photopolymerization (VP) additive manufacturing (AM) as a versatile platform for controlling preceramic crosslinking and ceramic yield. By rationally designing and tuning the photochemical crosslinking through digital light processing (DLP), we demonstrate that the ceramic yield can be enhanced from 64% to over 86%, even with added volatile elements. We reveal that the post-pyrolysis ceramic yield can be closely correlated with the pre-pyrolysis crosslinking of the preceramic network represented by its stiffness. This correlation thereby suggests a fast, energy-efficient, non-destructive methodology to predict and improve ceramic yield. Combined with these findings, our one-pot thiol-ene hybridization of polycarbosilane and polycarbosiloxane via DLP offers an exemplary method to generate hybrid preceramic polymers with tailored material properties toward target applications, and potentially even higher ceramic yields. This study thus contributes to achieving better resource- and energy-efficient preceramic polymer and PDC processing routes toward sustainable, advanced organic–inorganic materials manufacturing.

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

Results of an interlaboratory study on the working curve in vat photopolymerization II: Towards a standardized method

The working curve measurement in photopolymer additive manufacturing is a ubiquitous measure of the cure depth of a printing resin as a function of radiant exposure of light. The fit parameters from this measurement (the depth of light penetration D p and the critical exposure E c ) are used to evaluate and report a resin’s printability, optimize processing parameters, and inform print and resin quality control. Despite its widespread use, the working curve lacks a standard measurement method. Here, following up on our paper “Results of an Interlaboratory Study on the Working Curve in Vat Photopolymerization” from last year, an interlaboratory study on the working curve was performed using calibrated, reproducible, bandpass filtered light sources. With these light sources, the variability between labs in measured working curves was dramatically reduced from the initial interlaboratory study. Aggregate data from this experiment produced reliable D p and E c measurements at 385 nm of 39.2 ± 3.7 µm and 12.3 ± 3.0 mJ cm −2 , respectively. At 405 nm the values of D p and E c are 69.3 ± 3.8 µm and 17.9 ± 2.3 mJ cm −2 , respectively. The results are agnostic to the thickness measurement tool utilized by participants, ensuring broad applicability across laboratories. We also tested the generalizability of the proposed method of using a filtered light source by filtering a commercial 405 nm light source and obtaining a working curve in agreement with the aggregate data from the interlaboratory study. This interlaboratory study provides a basis for a documentary standard for the working curve, so that the entire photopolymer additive manufacturing industry can share reproducible and interoperable working curve data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Near-field models and simulations of pellet ablation in tokamaks

Here, detailed numerical studies of the ablation of a single neon pellet in the plasma disruption mitigation parameter space have been performed. Simulations were carried out using FronTier, a hydrodynamic/low magnetic Reynolds number MHD code with explicit tracking of material interfaces. FronTier's physics models resolve the pellet surface ablation and the formation of a dense, cold cloud of ablated material, the deposition of energy from hot plasma electrons, the expansion of the ablation cloud along magnetic field lines, and the radiation losses. A local thermodynamic equilibrium model based on Saha equations has been used to resolve atomic processes in the cloud and Redlich–Kwong corrections to the ideal gas equation of state for cold and dense gases, which have been used near the pellet surface. The FronTier pellet code is a next generation of the code described. It has been validated against the semi-analytic improved neutral gas shielding model in the 1D spherically symmetric approximation. Main results include quantification of the influence of atomic processes and Redlich–Kwong corrections on the pellet ablation in spherically symmetric approximation and verification of analytic scaling laws in a broad range of pellet and plasma parameters. Using axially symmetric MHD simulations, the properties of ablation channels and the reduction of pellet ablation rates in magnetic fields of increasing strength have been studied. While the main emphasis has been given to neon pellets for the plasma disruption mitigation, selected results on deuterium fueling pellets have also been presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Fission Evaluation Tools and Analytics (FETA)

This living document presents the Python package FETA. FETA computes observables resulting from the fission process. This document provides the definition of these observables as well as the physics models that are implemented to compute them. Some of these models are used to determine the initial conditions of fission fragments, e.g., the excitation energy E* and spin distribution p(J, π) at scission for prompt decay, while others are related to the nuclear structure and decay properties of the fragments, e.g. the ground-state properties, level density and low-lying excitation spectrum, γ-strength functions and electromagnetic transitions, and neutron transmission coefficients. The end goal for FETA is to enable users to substitute every one of these models by their own files providing these quantities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Combinatorial Synthesis of Oxysulfides in the Lanthanum–Bismuth-Copper System

Establishing synthesis approaches for a target material constitutes a grand challenge in materials research, which is compounded with use-inspired specifications on the format of the material. Solar photochemistry using thin film materials is a promising technology for which many complex materials are being proposed, and the present work describes application of combinatorial methods to explore the synthesis of predicted La–Bi–Cu oxysulfide photocathodes, in particular alloys of LaCuOS and BiCuOS. The variation in concentration of three cations and two anions in thin film materials, and crystallization thereof, is achieved by a combination of reactive sputtering and thermal processes including reactive annealing and rapid thermal processing. Composition and structural characterization establish composition-processing-structure relationships that highlight the breadth of processing conditions required for synthesis of LaCuOS and BiCuOS. The relative irreducibility of La oxides and limited diffusion indicate the need for high temperature processing, which conflicts with the temperature limits for mitigating evaporation of Bi and S. Collectively the findings indicate that alloys of these phases will require reactive annealing protocols that are uniquely tailored to each composition, motivating advancement of dynamic processing capabilities to further automate discovery of synthesis routes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Laser Ablation Plasmas and Spectroscopy for Nuclear Applications

The development of measurement methodologies to detect and monitor nuclear-relevant materials remains a consistent and significant interest across the nuclear energy, nonproliferation, safeguards, and forensics communities. Optical spectroscopy of laser-produced plasmas is becoming an increasingly popular diagnostic technique to measure radiological and nuclear materials in the field without sample preparation, where current capabilities encompass the standoff, isotopically resolved and phase-identifiable (e.g., UO and UO[Formula: see text]) detection of elements across the periodic table. These methods rely on the process of laser ablation (LA), where a high-powered pulsed laser is used to excite a sample (solid, liquid, or gas) into a luminous microplasma that rapidly undergoes de-excitation through the emission of electromagnetic radiation, which serves as a spectroscopic fingerprint for that sample. This review focuses on LA plasmas and spectroscopy for nuclear applications, covering topics from the wide-area environmental sampling and atmospheric sensing of radionuclides to recent implementations of multivariate machine learning methods that work to enable the real-time analysis of spectrochemical measurements with an emphasis on fundamental research and development activities over the past two decades. Background on the physical breakdown mechanisms and interactions of matter with nanosecond and ultrafast laser pulses that lead to the generation of laser-produced microplasmas is provided, followed by a description of the transient spatiotemporal plasma conditions that control the behavior of spectroscopic signatures recorded by analytical methods in atomic and molecular spectroscopy. High-temperature chemical and thermodynamic processes governing reactive LA plasmas are also examined alongside investigations into the condensation pathways of the plasma, which are believed to serve as chemical surrogates for fallout particles formed in nuclear fireballs. Laser-supported absorption waves and laser-induced shockwaves that accompany LA plasmas are also discussed, which could provide insights into atmospheric ionization phenomena from strong shocks following nuclear detonations. Furthermore, the standoff detection of trace radioactive aerosols and fission gases is reviewed in the context of monitoring atmospheric radiation plumes and off-gas streams of molten salt reactors. Finally, concluding remarks will present future outlooks on the role of LA plasma spectroscopy in the nuclear community.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗