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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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Universal Two-Component Dynamics in Supercritical Fluids
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Polyethylene in Dead-End Silica Nanopores: Forces and Mobility from Non-Equilibrium Statistical Mechanics and Exchange Spectroscopy Nuclear Magnetic Resonance
Billions of tons of plastic have been produced, and only a small fraction of this has been recycled. Tennakoon et al. [Nature Catalysis 3, 893 (2020)] developed a catalyst that repeatedly cleaves C 10 –C 30 hydrocarbons from the end of a polyethylene chain. The reaction occurs at a Pt nanoparticle at the base of a cylindrical silica mesopore with a diameter of 2 nm and a length of 110 nm. Portions of the polymer situated inside the pore can be differentiated from those outside using 13 C nuclear magnetic resonance (NMR), allowing the dynamics and extent of polymer threading to be monitored using two-dimensional (2D) exchange spectroscopy NMR. Here we construct a Fokker–Planck equation for the polymer dynamics by assuming a reptation diffusivity and a graduated adsorption free energy that depends linearly on the depth of polymer penetration in the pore. The solutions allow us to predict the intensities of the 2D NMR resonances as a function of time. We use the solutions to extract a polymer diffusivity at each temperature and estimate the per-segment desorption free energy, enthalpy, and entropy. Random and systematic errors are examined to test key assumptions in the theory and interpretation of the experiments.
X-ray Free Electron Laser-Induced Synthesis of ε -Iron Nitride at High Pressures
The ultrafast synthesis of ε-Fe 3 N 1+x in a diamond-anvil cell (DAC) from Fe and N 2 under pressure was observed using serial exposures of an X-ray free electron laser (XFEL). When the sample at 5 GPa was irradiated by a pulse train separated by 443 ns, the estimated sample temperature at the delay time was above 1400 K, confirmed by in situ transformation of α- to γ-iron. Ultimately, the Fe and N 2 reacted uniformly throughout the beam path to form Fe 3 N 1.33 , as deduced from its established equation of state (EOS). We thus demonstrate that the activation energy provided by intense X-ray exposures in an XFEL can be coupled with the source time structure to enable exploration of the time-dependence of reactions under high-pressure conditions.
Hexagonal BC 3 : A Robust Electrode Material for Li, Na, and K Ion Batteries
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Identifying Water–Anion Correlated Motion in Aqueous Solutions through Van Hove Functions
Electrolyte solutions are ubiquitous in materials in daily use and in biological systems. However, the understanding of their molecular and ionic dynamics, particularly those of their correlated motions, are elusive despite extensive experimental, theoretical, and numerical studies. Here we report the real-space observations of the molecular/ionic-correlated dynamics of aqueous salt (NaCl, NaBr, and NaI) solutions using the Van Hove functions obtained by high-resolution inelastic X-ray scattering measurement and molecular dynamics simulation. Our results directly depict the distance-dependent dynamics of aqueous salt solutions on the picosecond time scale and identify the changes in the anion–water correlations. This study demonstrates the capability of the real-space Van Hove function analysis to describe the local correlated dynamics in aqueous salt solutions.
Atomic-Scale Mapping and Quantification of Local Ruddlesden–Popper Phase Variations
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Machine Learning Screening of Metal-Ion Battery Electrode Materials
Here, in this work we present deep neural network regression machine learning models (ML) for predicting the average voltage and the percentage change in volume of battery electrodes upon charging and discharging with metal ions. Our models exhibit good performance as measured by the average mean absolute error obtained from a 10-fold cross-validation as well as on independent test sets. We further assess the robustness our ML models by investigating their screening potential beyond the training database. We produce novel Na-ion electrodes by systematically replacing Li-ions in the original database by Na-ions, and then selecting a set of 22 electrodes that exhibit a good performance in energy density as well as small volume variations upon charging and discharging, as predicted by the machine learning model. The ML predictions for these new materials are then compared to quantum-mechanics based calculations. Our results reaffirm the significant role of machine learning techniques in the exploration of materials for battery applications.
Electrostatic Dissipation in 3D-Printable Silicone
Here, in this report, we describe the incorporation of single-walled carbon nanotubes (CNTs) into 3D printable siloxane elastomers for electrostatic dissipation. The composite was characterized, focusing on how rheological and mechanical properties of the siloxane are affected at various CNT loading levels. Electrical properties were also characterized to develop materials with effective electrostatic dissipation. We demonstrate that low loadings (<1 wt %) of CNTs can be sufficiently dispersed into silicone resins that can be 3D printed, and the resulting material shows a significant improvement in electrostatic dissipation through the reduction in electrical resistivity with minimal effect on its mechanical properties.
Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries
Machine-learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work, we develop a tool based on ML models to predict voltages of electrode materials for metal-ion batteries. To this end, we use deep neural network, support vector machine, and kernel ridge regression as ML algorithms in combination with data taken from the Materials Project database, as well as feature vectors from properties of chemical compounds and elemental properties of their constituents. We show that our ML models have predictive capabilities for different reference test sets and, as an example, we utilize them to generate a voltage profile diagram and compare it to density functional theory calculations. In addition, using our models, we propose nearly 5000 candidate electrode materials for Na- and K-ion batteries. We also make available a web-accessible tool that, within a minute, can be used to estimate the voltage of any bulk electrode material for a number of metal ions. Here, these results show that ML is a promising alternative for computationally demanding calculations as a first screening tool of novel materials for battery applications.
Nanoscale Surface Structure of Nanometer-Thick Ferroelectric BaTiO 3 Films Revealed by Synchrotron X-ray Scanning Tunneling Microscopy: Implications for Catalytic Adsorption Reactions
Ferroelectric nanomaterials are of interest in catalysis, non-volatile memory, and neuromorphic computing among other applications because of their switchable structure that can alter electronic and interface properties of a single material. The investigation of the role of polarization on the surface structure and chemistry of ferroelectric nanomaterials is a longstanding challenge, as it ideally requires a combination of both nanoscale imaging and chemical spectroscopy. In this work we study a model ferroelectric BaTiO 3 thin film by synchrotron X-ray scanning tunneling microscopy (SX-STM), a unique method which integrates nanoscale surface imaging and chemically sensitive spectroscopy. We find that polarization switching from downward to upward in (001)single crystalline BaTiO 3 thin films increases the intensity of X-ray absorption across Ba M, Ti L and O K edges. Chemical mapping of nanometer sized domains further demonstrates the modulation of surface structures upon polarization switching, as well as confirming the trends observed in single point experiments across the surface. Here, we complement these measurements with ab-initio computational absorption spectroscopy to elucidate the effect of polarization switching on the core-hole excitations using the Bethe-Salpeter equation approach. Our experimental and theoretical results thus confirm a stronger binding strength for the upward polarized surface with molecular O 2 as a model reactant, offering mechanistic evidence that supports previous reports. This work advances the understanding of the surface chemistry and electronic structure of ferroelectrics, which can ultimately aid strategies to design interfaces with tailored properties.
Catalytic Hydrogenolysis of Polyethylene Under Reactive Separation
Deconstruction of polyolefins by catalytic hydrogenolysis is typically accompanied by the generation of undesired light gases. At reaction temperatures, the desired liquid products also tend to be volatile. Secondary cleavage of these liquid products contributes to light gas formation. The latter process was mitigated by reactive separation, continuously separating the liquid products from the catalyst throughout the experiment. At equivalent conversion, the yield and selectivity for oligomeric liquid species are increased under reactive separation, even though the carbon–carbon bond cleavage rate is slower than that in sealed experiments. More light gas is formed in the sealed reactor. Under 1 atm H 2 partial pressure, alkenes accompany the typical alkane hydrogenolysis products. Further, the alkene yield is higher, with greater selectivity for valuable α-olefins under reactive separation. These results provide the mechanistic insight that terminal alkenes are primary products of carbon–carbon bond cleavage during hydrogenolysis under experimental conditions, and secondary deconstruction of these species produces light gases.
Size-Resolved Density Measurements of Particulate Emissions from an Advanced Combustion Diesel Engine: Effect of Aggregate Morphology
We report the first in situ size-resolved density measurements of particles produced by premixed charge compression ignition (PCCI) and compare these with conventional diesel particles. The densities of size-classified particles were determined by measurements with a differential mobility analyzer (DMA) and an aerosol particle mass analyzer (APM). Particle masses of the different size classes were evaluated with a proposed DMA-APM response function for aggregates. Our results indicate that the effective densities of PCCI and conventional diesel particles were approximately the same for 50 and 100 nm electrical mobility diameters (0.9 and 0.6 g/cc, respectively), but the PCCI particle effective density (0.4 g/cc) was less than the conventional (0.5 g/cc) for 150 nm. The lowest effective particle densities were observed for exhaust gas recirculation (EGR) levels somewhat less than that required for PCCI operation. The inherent densities of conventional particles in the 50 and 100 nm size classes were 1.22 and 1.77 g/cc, which is in good agreement with Park et al. (2004). PCCI inherent particle densities for these same size classes were higher (1.27 and 2.10 g/cc), suggesting that there may have been additional adsorbed liquid hydrocarbons. For 150 nm particles, the inherent densities were nearly the same for PCCI and conventional particles at 2.20 g/cc. We expect that the lower effective density of PCCI particles may improve particulate emissions control with diesel particulate filters (DPFs). The presence of liquid hydrocarbons may also promote oxidation in DPFs.