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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Toward Rational Design of Nickel Catalysts for Thermocatalytic Decomposition of Methane for Carbon Dioxide-Free Hydrogen and Value-Added Carbon Co-Product: A Review

Thermocatalytic decomposition of methane provides opportunities for hydrogen (H2) production with no emission of carbon dioxide. However, high-value carbon products need to be produced for economic deployment of thermocatalytic decomposition and to achieve a minimum H 2 selling price below the U.S, Department of Energy target of $ 1/kg H 2 . In this review, we re-evaluate data on catalyst development reported in the literature and propose correlations between catalyst characteristics, catalytic stability, and properties of carbon co-products. In the first part of the review, growth mechanisms for carbon nanotubes using state-of-the-art chemical vapor deposition are reviewed to catalog the effects of catalyst characteristics, the influence of carbon sources, interactions between metal particles and supports, and metal particle sizes on carbon growth. In the second part, representative developments in mono-, bi-, and tri-metallic nickel catalysts are highlighted. We present kinetic analysis of reactions catalyzed by mono-metallic nickel catalysts, which generates a correlation between metal particle size and catalyst stability. Rational design of Ni-based catalysts for TCD of methane requires attention to the size of the metal particle and effective normalization of the reaction rates. Further attention to the distribution of the metal particle sizes may help identify catalyst properties that contribute longevity and selectivity to processes that use them. While it is tempting to focus on the highest valued carbon products (e.g., CNTs and CFs), analysis of the markets for other carbon products suggests that a more flexible approach may generate comparable returns without the risk associated with specialization.

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

Machine learning assisted phase and size-controlled synthesis of iron oxide particles

Synthesis of iron oxides with specific phases and particle sizes is a crucial challenge in various fields, including materials science, energy storage, biomedical applications, environmental science, and earth science. However, despite significant advances in this area, much of the current palette of particle outcomes has been based on time-consuming trial-and-error exploration of synthesis conditions. The present study was designed to explore a very different approach to 1) predict the outcome of synthesis from specified reaction parameters based on using machine learning (ML) techniques, and 2) correlate sets of parameters to obtain products with desired outcomes by a newly designed recommendation algorithm. To achieve this, four ML algorithms were tested, namely random forest, logistic regression, support vector machine, and k-nearest neighbor. Among the models, random forest outperformed the others, attaining 96% and 81% accuracy when predicting the phase and size of iron oxide particles in the test dataset. Surprisingly, the permutation feature importance analysis revealed that volume, which may strongly relate to pressure, was one of the important features, along with precursor concentration, pH, temperature, and time, influencing the phase and size of iron oxide particles during synthesis. To verify the robustness of the random forest models, prediction and experimental results were compared based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models agreed well with the experimental results. Furthermore, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with the desired phase and particle size from previous studies in the dataset. Furthermore, this study lays the foundation for a closed-loop approach in materials synthesis and preparation, beginning with suggesting potential reaction parameters from the dataset and predicting potential outcomes, followed by conducting experiments and analyses, and ultimately enriching the dataset.

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

Effect of solvothermal synthesis parameters on the crystallite size and atomic structure of cobalt iron oxide nanoparticles

We here investigate how the synthesis method affects the crystallite size and atomic structure of cobalt iron oxide nanoparticles. By using a simple solvothermal method, we first synthesized cobalt ferrite nanoparticles of ca. 2 and 7 nm, characterized by Transmission Electron Microscopy (TEM), Small Angle X-ray scattering (SAXS), X-ray and neutron total scattering. The smallest particle size corresponds to only a few spinel unit cells. Nevertheless, Pair Distribution Function (PDF) analysis of X-ray and neutron total scattering data shows that the atomic structure, even in the smallest nanoparticles, is well described by the spinel structure, although with significant disorder and a contraction of the unit cell parameter. These effects can be explained by the surface oxidation of the small nanoparticles, which is confirmed by X-ray near edge absorption spectroscopy (XANES). Neutron total scattering data and PDF analysis reveal a higher degree of inversion in the spinel structure of the smallest nanoparticles. Neutron total scattering data also allow magnetic PDF (mPDF) analysis, which shows that the ferrimagnetic domains correspond to ca. 80% of the crystallite size in the larger particles. A similar but less well-defined magnetic ordering was observed for the smallest nanoparticles. Finally, we used a co-precipitation synthesis method at room temperature to synthesize ferrite nanoparticles similar in size to the smallest crystallites synthesized by the solvothermal method. Structural analysis with PDF demonstrates that the ferrite nanoparticles synthesized via this method exhibit a significantly more defective structure compared to those synthesized via a solvothermal method.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Particle size influences decay rates of environmental DNA in aquatic systems

Abstract Environmental DNA (eDNA) analysis is a powerful tool for remote detection of target organisms. However, obtaining quantitative and longitudinal information from eDNA data is challenging, requiring a deep understanding of eDNA ecology. Notably, if the various size components of eDNA decay at different rates, and we can separate them within a sample, their changing proportions could be used to obtain longitudinal dynamics information on targets. To test this possibility, we conducted an aquatic mesocosm experiment in which we separated fish‐derived eDNA components using sequential filtration to evaluate the decay rate and changing proportion of various eDNA particle sizes over time. We then fit four alternative mathematical decay models to the data, building towards a predictive framework to interpret eDNA data from various particle sizes. We found that medium‐sized particles (1–10 μm) decayed more slowly than other size classes (i.e., <1 and > 10 μm), and thus made up an increasing proportion of eDNA particles over time. We also observed distinct eDNA particle size distribution (PSD) between our Common carp and Rainbow trout samples, suggesting that target‐specific assays are required to determine starting eDNA PSDs. Additionally, we found evidence that different sizes of eDNA particles do not decay independently, with particle size conversion replenishing smaller particles over time. Nonetheless, a parsimonious mathematical model where particle sizes decay independently best explained the data. Given these results, we suggest a framework to discern target distance and abundance with eDNA data by applying sequential filtration, which theoretically has both metabarcoding and single‐target applications.

Brandão‐Dias, Pedro F. P.↗
DOE OSTI2022

Microstructural heterogeneity of the buffer layer of TRISO nuclear fuel particles

Tristructural isotropic (TRISO) nuclear fuel particles contain a layered spherical shell designed to retain fission products; however, failure occurs in rare cases—commonly initiated in the porous pyrocarbon buffer layer. Achieving a comprehensive understanding of the buffer-initiated failure mechanisms requires detailed characterization of the buffer porosity and its heterogeneous distribution across multiple length scales. Here we performed FIB-SEM tomography across the buffer layer thickness (~100 µm) to produce 3D reconstructions of the buffer microstructure with 50 nm spatial resolution. We found an average overall porosity of ~14%, which does not solely account for the low density of the buffer (50% of the theoretical density). Additionally, the local porosity and its fluctuation increase from the kernel interface towards the inner pyrocarbon (IPyC) layer, which we attribute to the chemical vapor deposition process conditions during the TRISO particle fabrication. Detailed characterization of the porous microstructure—including analysis of the pore size, distribution, shape, and orientation—provides insight into the process-structure-property-performance relations of TRISO nuclear fuel particles and will inform multiscale models designed to predict the failure of TRISO particles under irradiation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Galvanostatic Intermittent Titration Technique Reinvented: Part I. A Critical Review

The galvanostatic intermittent titration technique (GITT), introduced in 1977 by Weppner and Huggins, provided a readily accessible means to measuring the chemical diffusion coefficient of electrochemical electrode materials. The method continues to be widely used today, but the reported diffusivity values are highly inconsistent, ranging as much as four orders of magnitude for some Li layered oxide compositions. Even qualitative trends of diffusivity are inconsistent, suggesting significant flaws in the implementation of the method. Other variants of the GITT method also suffer from similar inconsistency problems. Here we identify numerous sources of significant error including composition-dependent reaction overpotentials, mathematical flaws in the relaxation analysis methods, finite-size and non-planar geometry effects, inter-particle inhomogeneity issues, early transient effects, and surface area uncertainties. We propose a simple relaxation analysis scheme using the time variable t relax + τ - t relax , where t relax is relaxation time and τ is the galvanostatic pulse duration. We also propose to use dense diffusion-limited samples to isolate the bulk-diffusion process in the time domain. Chemical diffusivity can be extracted much more reliably with this improved implementation of the GITT method.

25 ENERGY STORAGE↗
DOE OSTI2026

The impact of optical measurement techniques on measured aerosol particle size distributions

Ambient aerosol particle size distributions measured by the Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) at various sites around the world exhibit modes at optical diameters near 600 nm and 850 nm. These modes are not present in concurrent measurements with the Grimm 11-D Optical Particle Counter (OPC). Here, in this study, we argue that these modes result from the optical measurement technique itself, and we explain why they appear in measurements by the UHSAS but not in those by the Grimm OPC. We construct computer models of the UHSAS and Grimm (“digital UHSAS” and “digital Grimm”) and use these to investigate the size distribution that would result from measurements of artificial aerosol particle size distributions that do not contain modes. The appearance of modes for the structureless incoming size distributions sampled by the digital UHSAS is explained by the nonlinear behavior of partial scattering cross sections of uniform spherical particles as a function of their diameter. The absence of modes in the digital Grimm is explained by the coarser size resolution of that instrument. Detailed analysis of the relationship between optical and geometric diameters for uniform spherical particles reveals two important results. First, these diameters generally have different numerical values for the same particle, and second, the relationship is nonlinear; thus, widths of size bins in terms of optical diameter differ from those in geometric diameter. These results explain the modes observed in the ambient size distributions and highlight concerns with attempts to create a merged size distribution by combining measurements from different instruments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Particle Image Velocimetry and Total Temperature Characterization of Arc-Heated Mach 4.5 Free Jet

Particle image velocimetry (PIV) measurements were conducted in the Arc-Heated Combustion Tunnel (ACT-II) on a high-enthalpy Mach 4.5 nozzle flow. The primary motivation for the current work is to establish the feasibility of PIV as a high-quality flow diagnostic technique in hypersonic arc-heated flows and produce a high-resolution hypersonic nozzle flow characterization, including nozzle free jet shear layer velocity profiles. Due to the uncertain nature of the gas composition in the flowfield of interest, PIV measurements of a supersonic wedge flow were used to couple the flow velocities to the oblique shock relations. This enabled a PIV-derived total temperature calculation that does not require a priori assumptions of the exact gas composition. A PIV expansion module was developed for this study, and proper seed sizing and material were selected for the present flow. An analysis of the seed particle response from the PIV measurements provided an initial validation of the seed performance while revealing potential issues with consistent seed sizing. These measurements and derived conditions represent a means of acquiring high-resolution velocimetry and temperature datasets that consider uncertainties in the experimental generation of hypersonic high-enthalpy flows.

Engineering↗
DOE OSTI2026

Reaction Optimization for Enzymatic Deconstruction of Industrially Relevant Nylon Composites

Plastics such as polyamides (PAs) possess unique physicochemical properties that make them indispensable in modern society. However, their energy‐intensive production and challenging end‐of‐life management highlight the urgent need for efficient recycling or remanufacturing solutions. Enzymatic depolymerization offers a promising route toward circular recycling, yet remains constrained by limited enzyme characterization, lack of validation under industrially relevant conditions and substrates, and overall performance. Here, we optimized the reaction conditions for three recently discovered nylon‐degrading enzymes. One of them, Nyl12, achieved product titers with PA6 and PA66 that exceed previously reported values, without enzyme engineering or substrate pretreatment. We further demonstrated the scalability of the process and its application to complex PA‐based materials used in microelectronic components. Analysis of substrate features, including surface area and particle size, revealed key parameters governing enzymatic activity and provided a framework for future pretreatment and process optimization efforts. In combination, these efforts provide a new benchmark for enzymatic nylon recycling.

nylon↗
DOE OSTI2025

On the bulk compaction of brittle granular materials, Part III: Brittle‐to‐ductile transition and yield strength

A new and simple method is presented that enables the estimation of the yield strength (σ y ) of brittle materials (e.g., ceramics, glasses). It results from the combination of sufficiently high-stress compaction of their granular form, postmortem analysis of the crushed particles to identify the critical particle size corresponding to their brittle-to-ductile transition, and the use of a developed and simple analytical expression. Here, this method was an outcome from Part I of this three-paper series. To execute it, a granular brittle material is compacted to a sufficiently high stress, whereby the acting comminution produces both a fraction of particles having a sufficiently small size formed by ductile or plastic-like deformation and a remaining fraction of larger particles formed from brittle fracture. Postmortem microscopy is then used to identify the smallest particle size whose morphology indicates it formed from brittle fracture (d B2D ). The brittle material's σ y can then be estimated using a combination of the d B2D , Kendall's and Griffith's theories, a priori knowledge of the material's fracture toughness (K Ic ), and a fracture mechanics shape factor constant (Y) using σ y = √((32 π K Ic 2 )/(3 Y 2 d B2D )). The method's development and its use to estimate σ y for several vitreous silicates, α-quartzes, and NaCl are provided.

Compaction↗
DOE OSTI2023

Nonequilibrium Behavior in Isoprene Secondary Organic Aerosol

Recent studies have shown that instantaneous gas-particle equilibrium partitioning assumptions fail to predict SOA formation, even at high relative humidity (~85%), and photochemical aging seems to be one driving factor. In this study, we probe the minimum aging time scale required to observe nonequilibrium partitioning of semivolatile organic compounds (SVOCs) between the gas and aerosol phase at ~50% RH. Seed isoprene SOA is generated by photo-oxidation in the presence of effloresced ammonium sulfate seeds at <1 ppbv NO x , aged photochemically or in the dark for 0.3–6 h, and subsequently exposed to fresh isoprene SVOCs. Our results show that the equilibrium partitioning assumption is accurate for fresh isoprene SOA but breaks down after isoprene SOA has been aged for as short as 20 min even in the dark. Modeling results show that a semisolid SOA phase state is necessary to reproduce the observed particle size distribution evolution. The observed nonequilibrium partitioning behavior and inferred semisolid phase state are corroborated by offline mass spectrometric analysis on the bulk aerosol particles showing the formation of organosulfates and oligomers. Here, the unexpected short time scale for the phase transition within isoprene SOA has important implications for the growth of atmospheric ultrafine particles to climate-relevant sizes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Ice Particle and Aggregate Simulator (IPAS). Part III: Verification and Analysis of Ice–Aggregate and Aggregate–Aggregate Collection for Microphysical Parameterization

Abstract The Ice Particle and Aggregate Simulator (IPAS) is used to theoretically represent the aggregation process of ice crystals. Aggregates have a variety of formations based on initial ice particle size, shape, and falling orientation, all of which influence water phase partitioning. Aggregate dimensional properties and density changes are calculated for monomer–monomer (MON–MON), monomer–aggregate (MON–AGG), and aggregate–aggregate (AGG–AGG) collection to be used by ice-microphysical models for improvement in aggregation parameterizations. Aggregates are chosen from a database of 9 744 000 preformed combinations to be further collected (see Part II). AGG–AGG collection results in more extreme and a smaller range of aggregate aspect ratios than MON–AGG collection. A majority of aggregates are closer to prolate than oblate spheroids, regardless of collection type, except for quasi-horizontally oriented particles that have extreme aspect ratios to begin with. MON–AGG collection frequently results in an increase in density upon collection, whereas MON–MON and AGG–AGG collection almost always result in particle density decreases, with extreme reductions near 99% for MON–MON collection. MON–MON collection results in the greatest decreases in density but then quickly becomes unaffected by the addition of more monomers due to inherent size differences between monomers and aggregates. Finally, a holistic analysis to in situ observations of cloud particle images is presented. IPAS 2D aspect ratios surround a median value of 0.6 and closely follow that of previous studies while varying by no more than ≈12% on average from observed aggregates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Chemical Composition and Mixing State of Wintertime Aerosol from the European Arctic Site of Ny-Ålesund, Svalbard

The Arctic is rapidly warming, and aerosols play an increasingly important role by scattering and absorbing sunlight and by participating in cloud formation. Their optical and cloud-forming properties depend on the mixing state and chemical composition, but observations of these features remain limited. This study comprehensively characterizes 25,254 individual particles collected at Ny-Ålesund, Svalbard (November −December 2020), using microspectroscopy techniques to investigate their size, morphology, mixing state, and chemical composition. Fresh sea salt aerosols (SSA) were identified as the most abundant (∼85%), of the total observed aerosol population, with potential sources from sea spray and blowing snow. Air masses originating from the Arctic Ocean surrounding Svalbard likely contribute to increased concentrations of sub-micrometer “Fresh SSA” particles. “Aged SSA” particles (7.4%) are enriched in sulfur and nitrogen, compared to “Fresh SSA”. These elevated ratios may result from various atmospheric aging processes including the uptake of sulfuric and nitric acids. Here, our results suggest that aged SSA, with sizes larger than 300 nm, likely underwent chlorine depletion by sulfuric and nitric acids during transport. Additionally, elemental analysis reveals that both fresh and aged SSA can mix with dust particles, regardless of the SSA size (49.9% in sub-micrometer size and 50.1% in super-micrometer size, respectively). Dust particles are efficient ice-nucleating particles (INPs), and SSA is known to act as cloud condensation nuclei (CCN), and therefore, their mixtures may inherit both properties. The non-negligible number (4.4%) of SSA-dust mixtures underscores the importance of these particles as potential sources of CCN and INP in the Arctic atmosphere.

Arctic↗
DOE OSTI2023

Numerical Investigation of the characteristics of pressurized biomass-oxy-combustion

As a second generation of oxy-fuel combustion technologies, pressurized oxy-combustion has a potential to improve the process efficiency and economy. With the modern decarbonization needs, biomass combustion is considered to be nearly CO2 neutral, and thus co-firing biomass with coal in an oxy-combustion process can achieve a negative CO2 balance. The size of the biomass fuel particles is a key parameter in an entrained flow combustor because large biomass particles, exceeding some threshold size, may only partially be burnt due to the restrictions on the heat transport, thereby diminishing the combustion efficiency. While such a threshold particle size has been scrutinized at an atmospheric pressure, the present study extends the analysis to elevated pressures. Specifically, two scenarios were set up, for experimental-scale and full-scale pressurized oxy-combustors, with the threshold sizes of biomass particles appearing 0.5 mm and 5 mm, respectively, without temperature gradient in biomass particles. With temperature gradient, the parametric study included the particles in the range from 0.15 mm to 5 mm. It is shown that the thermal-thin model is applicable to spherical biomass particles not exceeding 0.15 mm. The impact of the heating rate on biomass particle devolatilization was also investigated and the biomass devolatilization could happen in less than 1 second; the same amount of heat energy could induce different volatile release rates.

Li, Lei↗
DOE OSTI2024

Insight into the interfacial microstructure and chemistry of hot isostatically pressed AA6061-AA6061 bonds for U-10Mo fuel cladding application

Here, in the present study, hot isostatic pressing (HIP) bonding was carried out using AA6061-AA6061 plates for U-10Mo monolithic fuel cladding application. HIP was performed at 560°C with an applied isostatic pressure of 103 MPa for 90 min. Interfacial microstructure characterizations were performed using scanning electron microscopy (SEM) combined with electron backscatter diffraction (EBSD) to quantify precipitate fractions and study recrystallization, grain growth, and orientation relationships across the interface. EBSD results confirmed that no interfacial grain growth occurred during the HIP process. Transmission electron microscopy (TEM) was also employed to quantify composition and identify phases of various precipitates and an oxide layer that formed at the interface during HIP bonding. Microstructural analyses revealed the formation of a large fraction of Mg 2 Si precipitate and a ~50 nm thick Mg-oxide layer at the interface. Further TEM analysis confirmed that the oxide layer was not continuous, and Mg 2 Al 2 O 5 oxide particles with an average size of 18 nm were present in the oxide layer. Finite element modeling analysis was performed to study the stress distribution at the interface during HIP processing.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Complete miscibility of immiscible elements at the nanometre scale

Understanding the mixing behaviour of elements in a multielement material is important to control its structure and property. When the size of a multielement material is decreased to the nanoscale, the miscibility of elements in the nanomaterial often changes from its bulk counterpart. However, there is a lack of comprehensive and quantitative experimental insight into this process. Here, in this study, we explored how the miscibility of Au and Rh evolves in nanoparticles of sizes varying from 4 to 1 nm and composition changing from 15% Au to 85% Au. We found that the two immiscible elements exhibit a phase-separation-to-alloy transition in nanoparticles with decreased size and become completely miscible in sub-2 nm particles across the entire compositional range. Quantitative electron microscopy analysis and theoretical calculations were used to show that the observed immiscibility-to-miscibility transition is dictated by particle size, composition and possible surface adsorbates present under the synthesis conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗