SEARCH · Search NASA
Results for “parameter correlations”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Tuning Surface Adhesion Using Grayscale Electron-beam Lithography
Surface texturing of manufactured products tailors their properties, such as friction, adhesion, biocompatibility, or fluid interactions. However, advancements in this area are largely the result of trial-and-effort testing and generally lack a science-guided framework for determining the surface topography that will optimize performance. The present investigation explores grayscale electron-beam lithography as a means to create multiscale surface patterns to control surface performance. Here, we created and characterized a set of surface textures on a silicon wafer; the textures were superpositions of sine waves of varying wavelengths and amplitudes. First, the multiscale topography of the patterned surface was characterized, using profilometry and atomic force microscopy, to understand its fidelity to the designed-in pattern. The results of this analysis demonstrated how grayscale lithography accurately controlled the lateral size of features but was less precise on the vertical height of the surface, and also introduced inherent roughness below the scale of patterning. Second, a micromechanical tester was used to characterize the adhesion of the surfaces with large-scale polished silicon spheres. The results showed that adhesion could be tailored, with significant contribution from all of the designed-in length scales of topography. The strength of adhesion did not correlate with conventional roughness parameters but could be accurately modeled using simple numerical integration. Taken together, this investigation demonstrates the promise and challenges of grayscale e-beam lithography with multiscale patterns as a method for the tailoring of surface performance.
Tuning the Charge Transport Property and Photocatalytic Activity of Anthracene-Based 1D π–d Conjugated Coordination Polymers by Interlayer Stacking
One-dimensional (1D) π–d-conjugated coordination polymers (CCPs) with charge delocalization have attracted significant attention due to their potential application in energy conversion and storage. However, the fundamental understanding of the correlation of their structural parameters with photophysical and photocatalytic properties remains underexplored. Herein, we report three novel Cu-node anthracene-based 1D π–d CCPs with systematic variation of steric groups (Ph > Me > H) at the 9 and 10 position of anthracene (denoted as An Ph , An Me , and An H ), which is aimed at altering the stacking of the polymer chains and its impact on the inter-chain charge transport property. Here, by using the combination of steady-state X-ray absorption spectroscopy, optical transient absorption spectroscopy, X-ray transient absorption spectroscopy, and electrochemical impedance spectroscopy, we show that the linear ligands (An Ph , An Me , and An H ) with different degrees of steric groups (Ph > Me > H) introduced at the 9 and 10 position of anthracene can alter the stacking of the polymer chains and thus impact their crystallinity, charge separation, and charge transport property, which in turn impacts their photocatalytic performance for hydrogen evolution reaction.
CH 4 Activation over Perovskite Catalysts: True Density and Reactivity of Active Sites
The high thermal stability of perovskites has drawn attention toward their applications for catalytic CH 4 activation and conversion, typically occurring at high temperatures. The reaction rates of perovskite catalysts for CH 4 combustion, however, trail behind those of noble metal catalysts. Ways to optimize the performance of perovskite catalysts are destined to trial-and-error approaches unless their complex reconstructed surfaces are correlated with fundamental kinetic parameters. Discerning the intrinsic activity of surface catalytic sites and the density of those sites is crucial to rationally envision complex metal oxides with enhanced catalytic performance. Here, the present work presents a detailed kinetic analysis of catalytic CH 4 combustion over a set of seven perovskites (SrTiO 3 , SrZrO 3 , SrFeO 3 , LaFeO 3 , LaInO 3 , LaCoO 3 , LaMnO 3 ) with various surface terminations. Steady-state isotopic transient kinetic analysis was employed to measure turnover frequency (TOF) and density of surface intermediates (N) under operando conditions. Top surface characterization elucidated performance-structure relationships between near-monolayer surface composition and intrinsic reactivity of the catalysts. By using a chemical etching procedure to expose Fe-sites at the top surface of LaFeO 3 (LaFeO 3 , HNO 3 ), its TOF was increased 4-fold, compared with the unmodified sample, although N on the surface of LaFeO 3 , HNO 3 decreased. Density functional theory simulations corroborated that surface Fe-termination and La-Fe termination offer lower energetic barriers for CH 4 activation when compared with La-termination. In general, surface reconstruction is shown as a tool to tune TOF and N to improve reaction rates. This work fills a gap in current kinetic studies of perovskites through a careful assessment and discussion of the density and intrinsic reactivity of active sites for methane combustion over well-characterized reconstructed perovskite surfaces.
Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates
Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers (k cat s) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved k cat s. Diminishing returns epistasis prevents enzymes from developing higher k cat s in all reactions and keeps the organism far from the potential fitness optimum. Multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape allows k cat evolution to be convergent. Predicted k cat parameters show a significant correlation with experimental data, validating our modeling approach. Our analysis reveals how evolutionary forces shape modern k cat s and the whole of metabolism.
Two-dimensional non-equilibrium melting of charged colloids
Thermodynamic two-dimensional melting has been extensively studied in experiments and simulations, and is well predicted by theory. For systems in equilibrium, this transition is well described by the Kosterlitz–Thouless–Halperin–Nelson–Young theory, where melting is directly linked to the unbinding of topological defects. For driven, non-equilibrium melting and other non-equilibrium phase transitions, the picture is less clear. Here, in this work, we study the two-dimensional melting of a crystal of charged colloids. By randomly replacing some charged colloids with magnetic colloids, we can melt our system by rotating a fraction of the particles to create non-equilibrium, hydrodynamic random flows and local stresses. We can also melt it thermally by changing the particle number density. We find that an effective temperature approach cannot explain the results of our driven system. Rather, in both experiments and simulations, we observe that plotting the hexatic order parameter and the hexatic correlation’s exponent versus the density of disclinations and dislocations, respectively, yields universal curves. This implies that in our systems, two-dimensional melting depends directly on the density of topological defects and is independent of whether thermal or non-equilibrium forces generate them.
Fractal analysis of collision cascades in pulsed-ion-beam-irradiated solids [Collision cascade densities in ion pulsed ion irradiated solids { a fractal approach]
The buildup of radiation damage in ion-irradiated crystals often depends on the spatial distribution of atomic displacements within collision cascades. Although collision cascades have previously been described as fractals, the correlation of their fractal parameters with experimental observations of radiation damage buildup remains elusive. Here, we use a pulsed-ion-beam method to study defect interaction dynamics in 3C-SiC irradiated at 100 °C with ions of different masses. These data, together with results of previous studies of SiC and Si, are analyzed with a model of radiation damage formation which accounts for the fractal nature of collision cascades. Our emphasis is on the extraction of the effective defect diffusion length from pulsed beam measurements. Results show that, for both Si and SiC, collision cascades are mass fractals with fractal dimensions in the range of ~1–2, depending on ion mass, energy, and the depth from the sample surface. Within our fractal model, the effective defect diffusion length is ~10 nm for SiC and ~20 nm for Si, and it decreases with increasing cascade density. These results demonstrate a general method by which the fractal nature of collision cascades can be used to explain experimental observations and predict material’s response to radiation.
Analysis of broad-lined Type Ic supernovae from the (intermediate) Palomar Transient Factory
Here, we study 34 Type Ic supernovae that have broad spectral features (SNe Ic-BL). This is the only SN type found in association with long-duration gamma-ray bursts (GRBs). We obtained our photometric data with the Palomar Transient Factory (PTF) and its continuation, the intermediate PTF (iPTF). This is the first large, homogeneous sample of SNe Ic-BL from an untargeted survey. Furthermore, given the high observational cadence of iPTF, most of these SNe Ic-BL were discovered soon after explosion. We present K-corrected Bgriz light curves of these SNe, obtained through photometry on template-subtracted images. We analyzed the shape of the r-band light curves, finding a correlation between the decline parameter Δm 15 and the rise parameter Δm -10 . We studied the SN colors and, based on g-r, we estimated the host-galaxy extinction for each event. Peak r-band absolute magnitudes have an average of -18.6 ± 0.5 mag. We fit each r-band light curve with that of SN 1998bw (scaled and stretched) to derive the explosion epochs. We computed the bolometric light curves using bolometric corrections, r-band data, and g-r colors. Expansion velocities from Fe II were obtained by fitting spectral templates of SNe Ic. Bolometric light curves and velocities at peak were fitted using the semianalytic Arnett model to estimate ejecta mass Mej, explosion energy E K and 56Ni mass M(56Ni) for each SN. We find average values of M ej = 4 ± 3 M ⊙ , E K = (7 ± 6)×10 51 erg, and M( 56 Ni)=0.31 ± 0.16 M ⊙ . The parameter distributions were compared to those presented in the literature and are overall in agreement with them. We also estimated the degree of 56 Ni mixing using scaling relations derived from hydrodynamical models and we find that all the SNe are strongly mixed. The derived explosion parameters imply that at least 21% of the progenitors of SNe Ic-BL are compatible with massive (> 28 M ⊙ ), possibly single stars, whereas at least 64% might come from less massive stars in close binary systems.
Self-shielding of a plasma-exposed surface during extreme transient heat loads
The power deposition on a tungsten surface exposed to combined pulsed/continuous high power plasma is studied. A study of the correlation between the plasma parameters and the power deposition on the surface demonstrates the effect of particle recycling in the strongly coupled regime. Upon increasing the input power to the plasma source, the energy density to the target first increases then decreases. We suggest that the sudden outgassing of hydrogen particles from the target and their subsequent ionization causes this. This back-flow of neutrals impedes the power transfer to the target, providing a shielding of the metal surface from the intense plasma flux.
High and low frequency instabilities driven by counter-streaming electron beams in space plasmas
A four-component plasma composed of a drifting (parallel to ambient magnetic field) population of warm electrons, drifting (anti-parallel to ambient magnetic field) cool electrons, stationary hot electrons, and thermal ions is studied in an attempt to further our understanding of the excitation mechanisms of broadband electrostatic noise (BEN) in the Earth's magnetospheric regions such as the magnetosheath, plasmasphere, and plasma sheet boundary layer (PSBL). Using kinetic theory, beam-driven electrostatic instabilities such as the ion-acoustic, electron-acoustic instabilities are found to be supported in our multi-component model. The dependence of the instability growth rates and real frequencies on various plasma parameters such as beam speed, number density, temperature, and temperature anisotropy of the counter-streaming (relative to ambient magnetic field) cool electron beam are investigated. It is found that the number density of the anti-field aligned cool electron beam and drift speed play a central role in determining which instability is excited. Using plasma parameters which are closely correlated with the measurements made by the Cluster satellites in the PSBL region, we find that the electron-acoustic and ion-acoustic instabilities could account for the generation of BEN in this region.
Coherent states for nonlinear harmonic oscillator and some of its properties
A one-dimensional nonlinear harmonic oscillator is studied in the context of generalized coherent states. We develop a perturbative framework to compute the eigenvalues and eigenstates for the quantum nonlinear oscillator and construct the generalized coherent states based on Gazeau-Klauder formalism. We analyze their statistical properties by means of Mandel parameter and second order correlation function. Our analysis reveals that the constructed coherent states exhibit super-Poissonian statistics. Moreover, it is shown that the coherent states mimic the phenomena of quantum revivals and fractional revivals during their time evolution. The validity of our results has been discussed in terms of various parametric bounds imposed by our computational scheme.
Fractal and multifractal characteristics of swift heavy ion induced self-affine nanostructured BaF{sub 2} thin film surfaces
Fractal and multifractal characteristics of self-affine surfaces of BaF{sub 2} thin films, deposited on crystalline Si 〈1 1 1〉 substrate at room temperature, were studied. Self-affine surfaces were prepared by irradiation of 120 MeV Ag{sup 9+} ions which modified the surface morphology at nanometer scale. The surface morphology of virgin thin film and those irradiated with different ion fluences are characterized by atomic force microscopy technique. The surface roughness (interface width) shows monotonic decrease with ion fluences, while the other parameters, such as lateral correlation length, roughness exponent, and fractal dimension, did not show either monotonic decrease or increase in nature. The self-affine nature of the films is further confirmed by autocorrelation function. The power spectral density of thin films surfaces exhibits inverse power law variation with spatial frequency, suggesting the existence of fractal component in surface morphology. The multifractal detrended fluctuation analysis based on the partition function approach is also performed on virgin and irradiated thin films. It is found that the partition function exhibits the power law behavior with the segment size. Moreover, it is also seen that the scaling exponents vary nonlinearly with the moment, thereby exhibiting the multifractal nature.
Optical detection of strain and doping inhomogeneities in single layer MoS{sub 2}
Van der Waals single-layer materials are characterized by an inherent extremely low bending rigidity and therefore are prone to nanoscale structural modifications due to substrate interactions. Such interactions can induce excess charge concentration, conformational ripples, and residual mechanical strain. In this work, we employed spatially resolved Raman and photoluminescence (PL) images to investigate strain and doping inhomogeneities in a single layer exfoliated molybdenum disulphide crystal. We have found that correlations between the spectral parameters of the most prominent Raman bands A{sub 1}′ and E′ enable us to decouple and quantify strain and charge doping effects. In comparison with Atomic Force Microscopy (AFM) topography, we show that the spatial distribution of the position of the A{sup −} -trion PL peak is strain sensitive and its linewidth can capture features smaller than the laser spot size. The presented optical analysis may have implications in the development of high-quality devices based on two-dimensional materials since structural and electronic modifications affect considerably their carrier mobility and conductivity.
Characterization of gas-phase thorium nitride
In this work, properties of gas-phase thorium nitride, ThN, have been experimentally determined from a combined optical and microwave spectroscopic study. An intense band near 555 nm has been assigned as the [18.0]1.5–X 2 Σ + (0,0) transition and recorded at high resolution in the presence of static electric and magnetic fields. The observed optical Stark shifts were analyzed to determine permanent electric dipole moments, $\vec{μ}_{el}$ for the [18.0]1.5 and X 2 Σ + states of 4.38 ± 0.02D and 5.11 ± 0.09D, respectively. Zeeman shifts were used to determine the magnetic g-factors. The pure rotational spectrum was recorded using a separated field optical pump/probe microwave repopulation scheme and analyzed to determine the bond length and 14 N magnetic hyperfine and nuclear electric quadrupole parameters. A molecular orbital correlation diagram and ligand field electronic structure models are used to provide a qualitative interpretation of the electronic state ordering, magneto- and electro-static properties, and hyperfine interactions. Electronic structure calculations for the X 2 Σ + state were performed, and results were compared with observations. Observed trends in $\vec{μ}_{el}$ for the ThX (X = N, S, O, F, and Cl) series are discussed.
Analytic evaluation of energy first derivatives for spin–orbit coupled-cluster singles and doubles augmented with noniterative triples method: General formulation and an implementation for first-order properties
The paper reports a formulation of analytic energy first derivatives for the coupled-cluster singles and doubles augmented with noniterative triples [CCSD(T)] method with spin–orbit coupling included at the orbital level and an implementation for evaluation of first-order properties. Here, the new development extends the applicability of relativistic coupled cluster methods for heavy-element chemistry and spectroscopy. Benchmark calculations of first-order properties including dipole moments and electric-field gradients using the corresponding exact two-component property integrals are presented for heavy-element containing molecules to demonstrate the usefulness of the present analytic scheme.
Investigating boosted decision trees as a guide for inertial confinement fusion design
Inertial confined fusion experiments at the National Ignition Facility have recently entered a new regime approaching ignition. Improved modeling and exploration of the experimental parameter space were essential to deepening our understanding of the mechanisms that degrade and amplify the neutron yield. The growing prevalence of machine learning in fusion studies opens a new avenue for investigation. Here in this paper, we have applied the Gradient-Boosted Decision Tree machine-learning architecture to further explore the parameter space and find correlations with the neutron yield, a key performance indicator. We find reasonable agreement between the measured and predicted yield, with a mean absolute percentage error on a randomly assigned test set of 35.5%. This model finds the characteristics of the laser pulse to be the most influential in prediction, as well as the hohlraum laser entrance hole diameter and an enhanced capsule fabrication technique. We used the trained model to scan over the design space of experiments from three different campaigns to evaluate the potential of this technique to provide design changes that could improve the resulting neutron yield. While these data-driven model cannot predict ignition without examples of ignited shots in the training set, it can be used to indicate that an unseen shot design will at least be in the upper range of previously observed neutron yields.
Resonant dynamics of one-side multipactor on dielectric
Breakdown of dielectric radio frequency (RF) windows is an important issue for particle accelerators and high-power RF sources. One of the common reasons for RF window failure is the multipactor on a dielectric surface. The multipactor may be responsible for excessive heating of the dielectric and discharge of charges that accumulated in the ceramic due to secondary emission. In this study, comprehensive self-consistent particle-in-cell simulations with space charge effect were performed. This was to better understand the dynamic of one-side multipactor development and floating potential on the dielectric induced by the emission. The important correlations between the multipactor parameters at saturation, the secondary emission properties of dielectric, and the applied RF field parameters have been found, which led to the conclusion that the dynamics of one-side multipactor on dielectric is a resonant phenomenon.
Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics
Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calculations than ab initio methods but with compromised accuracy. By replacing these static parameters with machine-learned dynamic values inferred from the local environment, we greatly improve the accuracy of the SEQM methods. Trained on molecular energies and atomic forces, these dynamically generated Hamiltonian parameters show a strong correlation with atomic hybridization and bonding. Trained with only about 60,000 small organic molecular conformers, the resulting model retains interpretability, extensibility, and transferability when testing on much larger chemical systems and predicting various molecular properties. Overall, this work demonstrates the virtues of incorporating physics-based descriptions with ML to develop models that are simultaneously accurate, transferable, and interpretable.