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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Review: Recent advances of ToF-SIMS for environmental analysis and imaging

Background: Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface analysis technique, initially developed and applied in inorganic materials and semiconductors. In past decades, ToF-SIMS has attracted more attention in its analysis capabilities of organic materials, with increased applications in biology, medical, and health development. It has also become a versatile and effective tool in environmental analysis due to its high mass resolution, mass accuracy, and depth profiling. Results: In this review, we first give an overview of the principle of ToF-SIMS and follow with recent ToF-SIMS applications in exemplary environmental study cases, including atmospheric aerosol, soil, water, plant, and organic solvent analysis. Moreover, sample preparation techniques are summarized in relation to corresponding environmental applications. Specifically, we call attention to ToF-SIMS investigations showcasing studies in surface chemical compositions, images, and depth profile analysis. These findings emphasize the important role of interfacial chemistry in environmental processes and provide valuable insights into dynamic processes, such as chemical transformation, particle formation, plant biology, and microbial inspired biotechnology development. The mass spectral imaging results acquired by ToF-SIMS offer a deeper understanding of intermediate stages and transient phases for environmental specimens. Significance: In situ and operando imaging offer new possibilities in studying phenomena in real time with high spatial resolution. Furthermore, it is anticipated that more research groups will use ToF-SIMS in environmental research given recent advances in measurement capabilities and surging needs in chemical mapping of complex analytes and systems.

Aerosol↗
DOE OSTI2020

< c+a > dislocations in {101 - 2} twins in Mg: A kinematic and energetic requirement

Consideration of published TEM and molecular dynamics studies of interactions between matrix dislocations and {101¯2} twin boundaries (TB) in hexagonal close packed metals inspires a hypothesis regarding the formation of dislocations with < c+a > Burgers vectors inside the twins. It was previously reported that when basal < a > dislocations from the matrix are swept by an advancing TB, they are converted into partial dislocations bounding basal I 1 stacking faults (SFs) within the twin. In the present work, we show that this dislocation configuration near TB renders it energetically favorable for a pair of partial dislocations to constrict into a dislocation within the twin. New in situ TEM observations confirm this hypothesis by revealing long < c+a > dislocation dipoles terminated by the TB at one end and by I1 SFs within the twin at the other end. Finally, the resulting near-screw < c+a > dislocation dipoles are observed to be glissile, enabling self-annihilation in some circumstances. Thus, rather than requiring a high energy dislocation pile-up to press two < a > dislocations through a TB into a single < c+a > dislocation, it is demonstrated that < c+a > dislocations naturally form, within {101¯2} twins in Mg, as TB migrates into a matrix, which inevitably contains dislocations with < a > Burgers vectors. Furthermore, these observations help to explain how the twins themselves may subsequently deform and why they may be particularly vulnerable to plastic, shear localization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The influence of cooling rates on strain phase diagrams and domain structures of ferroelectric thin films: A case study of PbTiO 3

Strain engineering has been established as an effective approach to control phase equilibria, domain configurations, and functional properties of ferroelectric thin films. Temperature-strain phase diagrams have been used as powerful tools for providing insights into strain engineering. However, almost all existing phase diagrams established using the phase-field approach assume quenching conditions without considering actual cooling rates during the post-deposition annealing process of ferroelectric thin films. Within this work, we systematically investigate the influence of cooling rates on domain structures and the strain-phase diagram of ferroelectric thin films using phase-field simulations, taking PbTiO 3 thin films as a model system. We found that both the position of phase boundaries in the strain phase diagrams and the domain morphology are significantly influenced by the cooling rates. It is revealed that while the paraelectric-ferroelectric phase boundary remains invariant, the phase boundaries between single-phase and multi-phase regions tend to shift toward the corresponding multi-phase region as the cool rate reduces. Slow cooling generally leads to more ordered domain structures with increased domain size. Using the obtained equilibrium domain structures, we calculated effective thermal conductivities and found significant variations that can be tuned by the cooling rates. In conclusion, this work reveals an underexplored yet critical impact of cooling rates on phase equilibria and domain structures in ferroelectric thin films, which may inspire further fine-tuning of domains and domain walls in low-dimensional ferroelectrics for multifunctional applications.

Domain structures↗
DOE OSTI2020

Additive manufacturing of two-phase lightweight, stiff and high damping carbon fiber reinforced polymer microlattices

Carbon fiber reinforced polymer (CFRP) composite is known for its high stiffness-to-weight ratio and hence is of great interest in several engineering fields such as aerospace, automotive, defense, etc. However, such a composite is not suitable for energy dissipation as failure occurs with very little or no plastic deformation. Herein, we present an extendable multi-material projection microstereolithography process capable of producing carbon-fiber-reinforced cellular materials that achieve simultaneously high specific stiffness and damping coefficient. Inspired by the upper bounds of stiffness-loss coefficient in a two-phase composite, we designed and additively manufactured CFRP microlattices with soft phases architected into selected stiff-phase struts. Our results, confirmed by experimental and analytical calculations, revealed that the damping performance can be significantly enhanced by the addition of only a small fraction of the soft phase. The presented design and additive manufacturing strategy allow for optimizing mutually exclusive properties. As a result, these CFRP microlattices achieved high specific stiffness comparable to commercial CFRP, technical ceramics, and composites, while being dissipative like elastomers.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Laser pre-sintering for denudation reduction in the laser powder bed fusion additive manufacturing of Ti-6Al-4V alloy

As a fundamentally different process for creation of complex parts, laser powder bed fusion (LPBF) additive manufacturing exhibits complex behavior at the melt pool that has a direct influence on the macroscopic properties of the final material. Powder denudation, a previously studied deleterious phenomenon, is the displacement of powder due to entrainment in a jet of rapidly vaporized metal created by the process laser. To minimize the effect of denudation and inspired by methods in adjacent fields, a laser pre-sintering (LPS) method was studied in an effort to reduce denudation. LPS was found to be most effective in the 1–4 J/mm3 volumetric energy density processing regimes, minimizing the common experimentally observed problems with the LPBF process. Scanning electron microscopy of the LPS-treated powder bed revealed insight into the accelerating effect morphological irregularities along the powder particle surface have in the pre-sintering process. To further understand the process, an estimation of the sintered neck sizes necessary to eliminate denudation in LPS is made, qualitatively affirming the low energy densities needed to prevent denudation. Here, the results presented in this work represent an initial step towards understanding and implementing LPS as a quality-enhancing step in LPBF.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A time-parallel method for scalable heat transfer simulations of additive manufacturing

Here, a major challenge in simulating the thermal behavior in additive manufacturing processes is the disparate length and time scales between transport phenomena occurring in the melt pool and the component. A common simulation approach relies on spatial decomposition for parallel computing, but due to the nature of heat transfer in AM, where most of the computational expenditure is localized near the melt pool, the computational speedup from spatial parallelization saturates quickly. Therefore, additional parallelism by means of time-domain decomposition is needed to fully take advantage of high-performance computing (HPC) resources. This work introduces a time-parallel method to improve the computational scalability of additive manufacturing simulations on HPC systems, while maintaining high temporal resolution of heat transfer near the melt pool. The method, inspired by the nonlinear paraexp formalism, performs an iterative superposition of nonlinear solutions to the initial value problem, integrating the heat equation across overlapping time-parallel intervals. For a single layer of the NIST AMB2018–01 L7 benchmark problem, the method achieves a 38.51x speedup in wall-clock time with a maximum error in the global temperature solution of 0.99%. This reduces the total solution time from 196.72 min to 5.11 min on 128 nodes of the ORNL Frontier supercomputer. The tradeoff between accuracy and total wall-clock time is investigated and recommendations for time-parallel deployment for AM problems are made.

Additive manufacturing↗
DOE OSTI2023

Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO 2 -water-rock system

Rock surface wettability is a critical property that regulates multiphase flows in porous media, which can be quantified using the surface contact angle (CA). X-ray micro-computed tomography (μCT) provides an effective approach to in-situ measurements of surface CAs. However, the CA measurement accuracy depends significantly on the quality of CT image segmentation, which is the clustering of CT pixels into separate phases. Inspired by this, we developed a deep learning (DL)-based CA measurement workflow. Motivated by the recent tremendous progress in unsupervised learning techniques and aiming to avoid expensive manual data annotations, an unsupervised DL pipeline for CT image segmentation was proposed and implemented, which includes unsupervised model training and post-processing. The unsupervised model training was driven by a novel loss function constrained with feature similarity and spatial continuity and implemented by iterative forward and backward paths; the former clustered the pixel-wise feature vectors extracted by convolution neural networks, whereas the latter updated the parameters using gradient descent. An over-segmentation strategy was adopted for model training. The post-processing steps based on agglomerative hierarchical clustering (AHC) were implemented to further merge the over-segmented model output to the desired cluster number, which is intended to improve the efficiency of image segmentation. The developed unsupervised DL pipeline was compared with other commonly-used image segmentation methods using pixel-wise and physics-based evaluation metrics on a synthetic raw-image dataset, which had a known ground truth. The unsupervised DL pipeline showed the best performance. Next, the segmented images were input to an automatic CA measurement tool, and the results were validated by comparisons with manual measurements. The CA values from the manual and automatic measurements showed similar distributions and statistical properties. The automatic measurement demonstrated a wider spectrum because of the much larger number of measurement data points. The primary novelty of the unsupervised DL pipeline developed in this study lies in the novel loss function and the over-segmentation strategy associated with AHC post-processing. Finally, the workflow has been proven an efficient tool for pore-scale wettability characterization, which has a wide range of applications in fundamental studies of multiphase flows in natural porous media, which have critical implications to geological carbon sequestration, hydrocarbon energy recovery, and contaminant transport in groundwater.

42 ENGINEERING↗
DOE OSTI2024

Life-cycle analysis of offshore macroalgae production systems in the United States

Offshore macroalgae production offers the potential to provide valuable biomass for food, energy, and higher value products without the use of land or freshwater while using excess nutrients and carbon dioxide. To realize this potential, the Macroalgae Research Inspiring Novel Energy Resources program of the Advanced Research Projects Agency-Energy has initiated projects to develop advanced cultivation technologies that enable the cost- and energy-efficient production of macroalgal biomass. Here, this study addresses the life-cycle greenhouse gas emissions and energy return on investment for five U.S. offshore macroalgae production systems designed for deployment at the thousand-hectare scale using a detailed module developed within the GREET life-cycle analysis model for this study. The carbon intensity of macroalgae production system designs, expressed as kg of carbon dioxide equivalent per dry metric ton of algae harvested, vary widely from 49 to 220 and confirm that biomass productivity has the highest degree of sensitivity across the model parameters tested. Regardless of the system designs, the upstream and combustion emissions from fuel use are the key contributor (over 45 %) to carbon intensity, indicating that the use of low-carbon fuels (e.g., renewable diesel) could further reduce greenhouse gas emissions. Further studies need to specify the market opportunity and specific product slates for macroalgae to provide a complete picture of the environmental impacts of macroalgal feedstock.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Kinklike structures in an arcsin real scalar dynamics

Highlights: • Kinklike analyses in a real scalar theory with an arcsin dynamic. • First-order formalism implementation and deformation procedure in an arcsin scalar dynamics. In this paper, we analyze kink-like analytical solutions in a real scalar theory with an arcsin dynamics inspired by the arcsin electrodynamics presented in Kruglov (2015). This analysis is done by means of the first-order formalism. This formalism provides a framework where the equations of motion can be simplified by preserving the linear stability of the theory. In this work, the deformation procedure is implemented with the aim of finding exact solutions in systems with generalized dynamics. Along the paper, we explore how the first-order formalism is implemented in the arcsin kinetics and how such a term influences the kink-like solutions. As a part of the result of our paper, we show that the kink-like solutions are similar to the ones obtained in the standard scalar kinetic theory. We also show that the extra parameter, that controls the non-linearities of the model, plays an essential role in the energy densities and stability potentials. These quantities vary according to this parameter. The goals here are to show how the first-order framework is implemented in this arcsin scenario and to present the analytical kink-like solutions that can be found by means of the first-order framework and deformation method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Minimizing energy demand and environmental impact for sustainable NH 3 and H 2 O 2 production—A perspective on contributions from thermal, electro-, and photo-catalysis

There is an urgent need to provide adequate and sustainable supplies of water and food to satisfy the demand of an increasing population. Catalysis plays important roles in meeting these needs by facilitating the synthesis of hydrogen peroxide that is used in water decontamination and chemicals production, and ammonia that is used as fertilizer. However, these chemicals are currently produced with processes that are either very energy-intensive or environmentally unfriendly. This article offers the perspectives of the challenges and opportunities in the production of these chemicals, focusing on the roles of catalysis in more sustainable, alternative production methods that minimize energy consumption and environmental impact. While not intended to be a comprehensive review, the article provides a critical review of selected literature relevant to its objectives, discusses areas needed for further research, and potential new directions inspired by new developments in related fields. For each chemical, production by thermal, electro-, and photo-excited processes are discussed. Problems that are common to these approaches and their differences are identified and possible solutions suggested.

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

Developing single-site Pt catalysts for the preferential oxidation of CO: A surface science and first principles-guided approach

Here, we report a comprehensive study combining surface science, Density Functional Theory (DFT) calculations, and catalyst synthesis, characterization, and testing to investigate the preferential oxidation of CO in the presence of H 2 over single-site Pt 1 /Cu x O catalysts. Surface science studies show that while Pt 1 /Cu x O model surfaces enable low-temperature CO oxidation via a Mars-van Krevelen mechanism, there was no evidence for H 2 activation or oxidation. DFT-based calculations explain these results and demonstrate that the H 2 oxidation barrier is high as compared to H 2 desorption from Pt 1 /Cu x O. Inspired by these model catalyst studies, nanoporous Pt 1 /Cu x O catalysts were synthesized and demonstrated to be active and highly selective for the preferential oxidation of CO. This work highlights the potential of combined surface science, theory, and catalyst studies for identifying new catalytic materials, which in this case led to the development of a promising single-site Pt 1 /Cu x O catalyst for the preferential oxidation of CO.

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

LaCeO x coupled N-doped graphene/Ru single-atoms as a binary-site catalyst for efficient hydrogen evolution based on hydrogen spillover

Based on the hydrogen spillover effect, versatile binary-site catalysts are promising for the alkaline hydrogen evolution reaction (HER). In which one site can adsorb and dissociate water, while the neighboring site is favorable for liberating hydrogen. Inspired by these possibilities, oxygen vacancy (O v )-rich LaCeO x coupled N-doped graphene/Ru single-atoms (LaCeO x @NGr/Ru 1 ) was synthesized as an efficient dual-site HER catalyst. It delivered an impressive low overpotential of 22 mV at a current density of 10mAcm –2 and a slight Tafel slope of 40 mV dec –1 in an alkaline medium, outstanding the advanced Ru-based catalysts. Moreover, this promising binary-component catalyst exhibited higher mass activity and longer lasting durability than commercial Pt/C catalyst (20 wt%). Finally, experimental and theoretical investigations provided insights into the HER mechanisms of LaCeO x @NGr/Ru 1 based on three indispensable steps: water adsorption and dissociation on O v -rich LaCeO x , diffusion of generated H* species towards Ce 3+ -N-Ru 1 bridges, and hydrogen evolution on Ru 1 sites.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Convolutional neural networks for intra-hour solar forecasting based on sky image sequences

Accurate and timely solar forecasts play an increasingly critical role in power systems. Compared to longer forecasting timescales, very short-term solar forecasting has lagged behind in both research and practice. In this paper, we propose deep convolutional neural networks (CNNs) to provide operational intra-hour (10-minute-ahead to 60-minute-ahead) solar forecasts. We develop two CNN structures inspired by a widely-used CNN architecture. The CNNs are tailored to our solar forecasting regression tasks and rely solely on sky image sequences. Case studies based on six years of data (over 150,000 data points) demonstrate that the best CNN model has forecast skill scores of 20%-39% over the naive persistence of cloudiness benchmark, even at these very short timescales. The CNNs also have consistently superior performance when compared to shallow machine learning models with meteorological predictors, where the improvement averages around 7%. The sensitivity analyses show that the sky image length, resolution, and weather conditions have impacts on the deep learning model accuracy. In our intra-hour problem with specific setups, two sky images with a 10-minute 128 x 128 resolution yield the most accurate forecasts. Current limitations, future work, and deployment challenges and solutions are also discussed.

14 SOLAR ENERGY↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗
DOE OSTI2022

The W4 method: A new multi-dimensional root-finding scheme for nonlinear systems of equations

We propose a new class of method for solving nonlinear systems of equations, which, among other things, has four nice features: (i) it is inspired by the mathematical property of damped oscillators, (ii) it can be regarded as a simple extension to the Newton-Raphson (NR) method, (iii) it has the same local convergence as the NR method does, (iv) it has a significantly wider convergence region or the global convergence than that of the NR method. In this article, we present the evidence of these properties, applying our new method to some examples and comparing it with the NR method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Tailoring activity of iron phthalocyanine by edge-nitrogen sites induced electronic delocalization

Fe-N-C catalysts have been recognized as the most satisfactory candidates alternatively to Pt-based catalysts for oxygen reduction reaction (ORR). However, fine-tailoring of their intrinsic ORR catalytic activity still remains a great challenge due to the inferior accessibility and intrinsic activity of FeN x moieties. Herein, one order of magnitude activity enhancement of pristine Fe-N-C through cooperating with nitrogen-doped carbon micro-flower is achieved. The axial coordination effect between Fe active center and nitrogen atoms in support can break the electronic distribution symmetry of FeN x moieties and induce the electron delocalization on Fe active center and the electron localization on N, respectively, which favor the adsorption behavior of *OH intermediate. As a result, the catalyst exhibits a remarkable half-wave potential of 0.9 V and a high kinetic current density of 74.04 mA cm -2 at 0.85 V. In addition, when utilized as a cathode catalyst of liquid Zn-air batteries (ZABs), it possesses excellent electrochemical performance, for example, a high open circuit voltage (OCV) and peak power density of 1.59 V and 170.09 mW cm -2 , respectively. In conclusion, this work provides a new understanding into the activity enhancement mechanism of Fe-N-C catalysts, and inspires electronic delocalization of active sites for adjusting catalytic activity.

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

MADLens, a python package for fast and differentiable non-Gaussian lensing simulations

Here, we present MADLens a python package for producing non-Gaussian lensing convergence maps at arbitrary source redshifts with unprecedented precision. MADLens is designed to achieve high accuracy while keeping computational costs as low as possible. A MADLens simulation with only particles produces convergence maps whose power agrees with theoretical lensing power spectra up to within the accuracy limits of HaloFit. This is made possible by a combination of a highly parallelizable particle-mesh algorithm, a sub-evolution scheme in the lensing projection, and a machine-learning inspired sharpening step. Further, MADLens is fully differentiable with respect to the initial conditions of the underlying particle-mesh simulations and a number of cosmological parameters. These properties allow MADLens to be used as a forward model in Bayesian inference algorithms that require optimization or derivative-aided sampling. Another use case for MADLens is the production of large, high resolution simulation sets as they are required for training novel deep-learning-based lensing analysis tools. We make the MADLens package publicly available under a Creative Commons License

79 ASTRONOMY AND ASTROPHYSICS↗