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At least 559 records · Page 31

Nuclear cusps and singularities in the nonadditive kinetic potential bifunctional from analytical inversion

The non-additive kinetic potential $v^{\text{NAD}}$ is a key quantity in density-functional theory (DFT) embedding methods, such as frozen density embedding theory and partition DFT. $v^{\text{NAD}}$ is a bi-functional of electron densities $\rho_{\rm B}$ and $\rho_{\rm tot} = \rho_{\rm A} + \rho_{\rm B}$. It can be evaluated using approximate kinetic-energy functionals, but accurate approximations are challenging. The behavior of $v^{\text{NAD}}$ in the vicinity of the nuclei has long been questioned, and singularities were seen in some approximate calculations. In this article, the existence of singularities in $v^{\text{NAD}}$ is analyzed analytically for various choices of $\rho_{\rm B}$ and $\rho_{\rm tot}$, using the nuclear cusp conditions for the density and Kohn-Sham potential. It is shown that no singularities arise from smoothly partitioned ground-state Kohn-Sham densities. We confirm this result by numerical calculations on diatomic test systems HeHe, HeLi$^+$, and H$_2$, using analytical inversion to obtain a numerically exact $v^{\rm NAD}$ for the local density approximation. We examine features of $v^{\rm NAD}$ which can be used for development and testing of approximations to $v^{\rm NAD}[\rho_{\rm B},\rho_{\rm tot}]$ and kinetic-energy functionals.

74 ATOMIC AND MOLECULAR PHYSICS↗

Bifurcation of equilibrium positions for ellipsoidal particles in inertial shear flows between two walls

We conducted a systematic numerical investigation of spherical, prolate and oblate particles in an inertial shear flow between two parallel walls, using smoothed particle hydrodynamics (SPH). It was previously shown that above a critical Reynolds number, spherical particles experience a supercritical pitchfork bifurcation of the equilibrium position in shear flow between two parallel walls, namely that the central equilibrium position becomes unstable, leading to the emergence of two new off-centre stable positions (Foxet al.,J. Fluid Mech., vol. 915, 2021). This phenomenon was unexpected given the symmetry of the system. In addition to confirming this finding, we found, surprisingly, that ellipsoidal particles can also return to the centre position from the off-centre positions when the particle Reynolds number is further increased, while spherical particles become unstable under this increased Reynolds number. By utilizing both SPH and the finite element method for flow visualization, we explained the underlining mechanism of this reverse of bifurcation by altered streamwise vorticity and symmetry breaking of pressure. Furthermore, we expanded our investigation to include asymmetric particles, a novel aspect that had not been previously modelled, and we observed similar trends in particle dynamics for both symmetric and asymmetric ellipsoidal particles. While further validation through laboratory experiments is necessary, our research paves the road for development of new focusing and separation methods for shaped particles.

Mechanics↗

TNet: A Model-Constrained Tikhonov Network Approach for Inverse Problems

Deep learning (DL), in particular deep neural networks, by default is purely data-driven and in general does not require physics. This is the strength of DL but also one of its key limitations when applied to science and engineering problems in which underlying physical properties—such as stability, conservation, and positivity—and accuracy are required. DL methods in their original forms are often not capable of respecting the underlying mathematical models or achieving desired accuracy even in big-data regimes. On the other hand, many data-driven science and engineering problems, such as inverse problems, typically have limited experimental or observational data, and DL would overfit the data in this case. Leveraging information encoded in the underlying mathematical models, we argue, not only compensates for missing information in low data regimes but also provides opportunities to equip DL methods with the underlying physics, hence promoting better generalization. This paper develops a model-constrained DL approach and its variant TNet—a Tikhonov neural network—which are capable of learning not only information hidden in the training data but also in the underlying mathematical models to solve inverse problems governed by partial differential equations in low data regimes. We provide the constructions and some theoretical results for the proposed approaches for both linear and nonlinear inverse problems. Since TNet is designed to learn inverse solutions with Tikhonov regularization, it is interpretable: in fact it recovers Tikhonov solutions for linear cases while potentially approximating Tikhonov solutions for nonlinear inverse problems. We also prove that data randomization can enhance not only the smoothness of the networks but also their generalizations. Comprehensive numerical results confirm the theoretical findings and show that with even as little as 1 training data sample for one-dimensional (1D) deconvolution, 5 for an inverse 2D heat conductivity problem, 100 for inverse initial conditions for a time-dependent 2D Burgers’s equation, and 50 for inverse initial conditions for 2D Navier–Stokes equations, TNet solutions can be as accurate as Tikhonov solutions while being several orders of magnitude faster. Furthermore, this is possible owing to the model-constrained term, replications, and randomization.

97 MATHEMATICS AND COMPUTING↗

Influence of Realistic, Cyclic Atmospheric Cycles on the Pitting Corrosion of Austenitic Stainless Steels

Pitting corrosion was evaluated on stainless steels 304H, 304, and 316L the surfaces of which had ASTM seawater printed on them as a function of surface roughness after exposure to an exemplar realistic atmospheric diurnal cycle for up to one year. Methods to evaluate pitting damage included optical imaging, scanning electron microscopy imaging, profilometry analysis, and polarization scans. The developed cyclic exposure environment did not significantly influence pitting morphology nor depth in comparison to prior static exposure environments. Cross-hatching was observed in a majority of pits for all material compositions with the roughest surface finish (#4 finish) and in all surface finishes for the 304H composition. Evidence is provided that cross-hatched pit morphologies are caused by slip bands produced during the grinding process for the #4 finish or by material processing. Additionally, micro-cracking was observed in pits formed on samples with the #4 surface finish and was greatly reduced or absent for pits formed on samples with smooth surface finishes. This suggests that both a low RH leading to an MgCl 2 -dominated environment and a rough surface containing significant residual stress are necessary for micro-cracking. Finally, the use of various characterization techniques and cross sectioning was employed to both qualitatively and quantitatively assess pitting damage across all SS compositions and surface finishes.

36 MATERIALS SCIENCE↗

Quantifying uncertainties and correlations in the nuclear-matter equation of state

We perform statistically rigorous uncertainty quantification (UQ) for chiral effective field theory (χ EFT) applied to infinite nuclear matter up to twice nuclear saturation density. The equation of state (EOS) is based on high-order many-body perturbation theory calculations with nucleon-nucleon and three-nucleon interactions up to fourth order in the χ EFT expansion. From these calculations our newly developed Bayesian machine-learning approach extracts the size and smoothness properties of the correlated EFT truncation error. Furthermore, we then propose a novel extension that uses multitask machine learning to reveal correlations between the EOS at different proton fractions. The inferred in-medium χ EFT breakdown scale in pure neutron matter and symmetric nuclear matter is consistent with that from free-space nucleon-nucleon scattering. These significant advances allow us to provide posterior distributions for the nuclear saturation point and propagate theoretical uncertainties to derived quantities: the pressure and incompressibility of symmetric nuclear matter, the nuclear symmetry energy, and its derivative. Our results, which are validated by statistical diagnostics, demonstrate that an understanding of truncation-error correlations between different densities and different observables is crucial for reliable UQ. The methods developed here are publicly available as annotated Jupyter notebooks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Halted-pendulum Relaxation: Application to White Dwarf Binary Initial Data

Studying compact-star binaries and their mergers is integral to determining progenitors for observable transients. Today, compact-star mergers are typically studied via state-of-the-art computational fluid dynamics codes. One such numerical technique, smoothed particle hydrodynamics (SPH), is frequently chosen for its excellent mass, energy, and momentum conservation. The natural treatment of vacuum and the ability to represent highly irregular morphologies make SPH an excellent tool for the study of compact-star binaries and mergers. For many scenarios, including binary systems, the outcome of simulations is only as accurate as the initial conditions. For SPH, it is essential to ensure that the particles are distributed regularly, representing the initial density profile but without long-range correlations. Particle noise in the form of high-frequency local motion and low-frequency global dynamics must be damped out. Damping the latter can be as computationally intensive as the actual simulation. We discuss a new and straightforward relaxation method, halted-pendulum relaxation (HPR), to remove global oscillation modes of SPH particle configurations. In combination with effective external potentials representing gravitational and orbital forces, we show that HPR has an excellent performance in efficiently relaxing SPH particles to the desired density distribution and removing global oscillation modes. We compare the method to frequently used relaxation approaches and test it on a white dwarf binary model at its Roche-lobe overflow limit. We highlight the importance of our method in achieving accurate initial conditions and its effect on achieving circular orbits and realistic accretion rates when compared with other general relaxation methods.

79 ASTRONOMY AND ASTROPHYSICS↗

Laser-interference pulse number dependence of surface chemistry and sub-surface microstructure of AA2024-T3 alloy

The laser-based treatment of a metal surface is an intrinsically non-chemical technique that can alter both the topology and chemistry of surfaces. A laser-based treatment for coating and joining applications can offer alternative surface preparations to the chemically-based surface preparation techniques, which are subject to severe environmental protection and hazardous-waste management considerations. In this study, the surface chemistry and sub-surface microstructural changes are investigated for a novel surface processing method using laser interferometry produced by two beams of a pulsed Nd:YAG laser. The two-beam laser-interference allowed the structuring of the surface at length scales much less than that of the laser beam spot. Surface chemistry changes in the oxide layer of AA 2024-T3 aluminum alloy rolled sheet due to laser processing were investigated using x-ray photoelectron spectroscopy (XPS). Near surface microstructural changes have been investigated with scanning electron microscopy and energy dispersive x-ray spectroscopy (SEM/EDS), and scanning transmission electron microscopy (STEM) as a function of number of interfering laser shots. SEM microstructure pictures of the top surface shows the minimization of surface defects, as all of the sharp features from a rolled sheet surface were smoothed by laser-structuring. STEM images indicate that the laser-interference processing reduced the formation of CuMn-rich precipitates over a 500–800 nm depth from the top surface. XPS data indicated that the Al oxide layer is modified compared to that of the baseline specimen and that the oxide thickness increases with the number of shots per spot. Finally, the additional thicker oxide on Al alloys is expected to increase the corrosion resistance of the coated Al 2024.

36 MATERIALS SCIENCE↗

BeyondPlanck X. Bandpass and beam leakage corrections

We discuss the treatment of bandpass and beam leakage corrections in the Bayesian BeyondPlanck CMB analysis pipeline as applied to the Planck LFI measurements. As a preparatory step, we first apply three corrections to the nominal LFI bandpass profiles including removal of a known systematic effect in the ground measuring equipment at 61 GHz; smoothing of standing wave ripples; and edge regularization. The main net impact of these modifications is an overall shift in the 70 GHz bandpass of +0.6 GHz; we argue that any analysis of LFI data products, either from Planck or BeyondPlanck, should use these new bandpasses. In addition, we fit a single free bandpass parameter for each radiometer of the form Δ i = Δ 0 + $δ$ i , where Δ 0 represents an absolute frequency shift per frequency band and $δ$i is a relative shift per detector. The absolute correction is only fitted at 30 GHz with a full $\chi$ 2 -based likelihood, resulting in a correction of Δ 30 = 0.24 ± 0.03 GHz. The relative corrections are fitted using a spurious map approach, fundamentally similar to the method pioneered by the WMAP team, but without introducing many additional degrees of freedom. All bandpass parameters are sampled using a standard Metropolis sampler within the main BeyondPlanck Gibbs chain, and bandpass uncertainties are thus propagated to all other data products in the analysis. In total, we find that our bandpass model significantly reduces leakage effects. For beam leakage corrections, we adopt the official Planck LFI beam estimates without additional degrees of freedom, and only marginalize over the underlying sky model. We note that this is the first-time leakage from beam mismatch has been included for Planck LFI maps.

79 ASTRONOMY AND ASTROPHYSICS↗

Flexosensitive polarization vortices in thin ferroelectric films

In this work, we present a theoretical study on the influence of the flexoelectric coupling on the spatial distribution and temperature behavior of the spontaneous polarization for several types of stable domain structure in thin ferroelectric films, such as stripe domains and vortices. Finite-element modeling (FEM) for BaTiO 3 films and analytical calculations within the Landau-Ginzburg-Devonshire approach reveals that an out-of-plane polarization component can be very sensitive to the flexoelectric coupling for periodic quasi-2D stripe domains and 3D vortex-antivortex structures. However, the influence is rather different for these structures. The flexoelectric coupling increases significantly the amplitude of a small out-of-plane polarization component in the stripe domains, and the “up” or “down” direction of the component is defined by the sign of the flexoelectric coefficients. Concerning the vortex-antivortex pairs, their antivortices with in-plane antiсirculation have smooth wide dipolar cores through the entire film, whose shape and other features are almost insensitive to the coupling. The vortices with in-plane vorticity have spikelike cores with an out-of-plane quadrupolar moment induced by the flexoelectric coupling. The cores are located near the film–dead-layer interfaces. FEM results corroborated by analytical calculations prove that a change of the flexoelectric coefficient sign leads to a reorientation of the core axial polarization, making the flexosensitive 3D vortices similar to the recently introduced “flexons” in cylindrical nanoparticles. The relatively wide temperature range (from 200 to 400 K) of the flexosensitive vortices' existence gives us confidence that they can be observed experimentally in thin ferroelectric films by scanning-probe and nonlinear optical microscopy methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Symmetry Breaking in an Extended-O(2) Model

Motivated by attempts to quantum simulate lattice models with continuous Abelian symmetries using discrete approximations, we consider an extended-O(2) model that differs from the ordinary O(2) model by an explicit symmetry breaking term. Its coupling allows to smoothly interpolate between the O(2) model (zero coupling) and a $q$-state clock model (infinite coupling). In the latter case, a $q$-state clock model can also be defined for non-integer values of $q$. Thus, such a limit can also be considered as an analytic continuation of an ordinary $q$-state clock model to non-integer $q$. The phase diagram of the extended-O(2) model in the infinite coupling limit was established in our previous work, where it was shown that for non-integer $q$, there is a second-order phase transition at low temperature and a crossover at high temperature. In this work, we investigate the model at finite values of the coupling using Monte Carlo and tensor methods. The results may be relevant for configurable Rydberg-atom arrays.

Hostetler, Leon↗

An end-to-end deep learning method for solving nonlocal Allen–Cahn and Cahn–Hilliard phase-field models

Here, we propose an efficient end-to-end deep learning method for solving nonlocal Allen–Cahn (AC) and Cahn–Hilliard (CH) phase-field models. One motivation for this effort emanates from the fact that discretized partial differential equation-based AC or CH phase-field models result in diffuse interfaces between phases, with the only recourse for remediation is to severely refine the spatial grids in the vicinity of the true moving sharp interface whose width is determined by a grid-independent parameter that is substantially larger than the local grid size. In this work, we introduce non-mass conserving nonlocal AC or CH phase-field models with regular, logarithmic, or obstacle double-well potentials. Because of non-locality, some of these models feature totally sharp interfaces separating phases. The discretization of such models can lead to a transition between phases whose width is only a single grid cell wide. Another motivation is to use deep learning approaches to ameliorate the otherwise high cost of solving discretized nonlocal phase-field models. To this end, loss functions of the customized neural networks are defined using the residual of the fully discrete approximations of the AC or CH models, which results from applying a Fourier collocation method and a temporal semi-implicit approximation. To address the long-range interactions in the models, we tailor the architecture of the neural network by incorporating a nonlocal kernel as an input channel to the neural network model. We then provide the results of extensive computational experiments to illustrate the accuracy, predictive capabilities, and cost reductions of the proposed method.

42 ENGINEERING↗

Carbonation of Alkaline Earth Metal Hydroxides: Structure across Nano- to Mesoscales

The conversion of gaseous CO 2 into a solid constitution through mineralization is an active area of carbon capture, and alkaline earth metal hydroxides (M(OH) 2 , M = Ca 2+ , Mg 2+ ) are frontrunners in this area. As model systems, nanolime samples are excellent templates for the study of this reaction. Here, we have examined these under ambient pressure conditions with controlled humidity and CO 2 . Utilizing a broad range of analytical methods, we first established the purity and structures of the selected materials. We then examined the structural changes due to carbonation, using infrared spectroscopy, X-ray scattering, and neutron scattering. The resulting structural changes are resolved from nanoscale to mesoscale and from early-stage to late-stage carbonation. Ca(OH) 2 and Mg(OH) 2 are found to behave quite differently. As expected from prior work, the carbonation of Ca(OH) 2 is kinetically favored. Further, our structure studies suggest this is due to a facile reaction at the fractal interface of the particles. From early- to late-stage carbonation, there is a consistent increase in the fractal roughness. This is in contrast to Mg(OH) 2 where the same surface evolves into a smooth conformal coating. For this material the major reacting component is at the mesoscale, suggesting globular particle growth or evolving macro-porosity. Because neutron scattering is sensitive to hydrogen content, we expected a significant change as M(OH) 2 evolves to MCO 3 . Such a change is found for Ca(OH) 2 but not for Mg(OH) 2 , providing evidence for the formation of hydrated carbonates for the later material. The formation of a conformal layer along with water-rich carbonate formation is an impediment to the use of Mg(OH) 2 for carbon capture. For energy-efficient carbon capture, it would be desirable to enhance carbonation rates for Mg(OH) 2 , and one possible route would be the use of anhydrous fluids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders *

Abstract Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization, and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we explore the shift-invariant variational autoencoders (shift-VAEs) that allow disentangling characteristic repeating features in the images, their variations, and shifts that inevitably occur when randomly sampling the image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data. We further introduce an approach for training shift-VAEs that allows finding the latent variables that comport to known physical behavior. In this specific case, the condition is that the latent variable maps should be smooth on the length scale of the atomic lattice (as expected for physical order parameters), but other conditions can be imposed. The opportunities and limitations of the shift VAE analysis for pattern discovery are elucidated.

97 MATHEMATICS AND COMPUTING↗

Finding MIDDLE Ground: Scalable and Secure Distributed Learning

Edge computing methods allow devices to efficiently train a high-performing, robust, and personalized model for predictive tasks. However, these methods succumb to privacy and scalability concerns such as adversarial data recovery and expensive model communication. Furthermore, edge computing methods unrealistically assume that all devices train an identical model. In practice, edge devices have varying computational and memory constraints which may not allow certain devices to have the space or speed to train a specific model. To overcome these issues, we propose MIDDLE: a model independent distributed learning algorithm which allows heterogeneous edge devices to assist each other’s training while communicating only non-sensitive information. MIDDLE unlocks the ability for edge devices, regardless of computational or memory constraints, to assist each other even with completely different model architectures. Furthermore, MIDDLE does not require model or gradient communication which greatly reduces communication size and time. We prove that MIDDLE attains the optimal convergence rate O(1/sqrt(TM)) of stochastic gradient descent for convex and non-convex smooth optimization (for total iterations T and batch size M). Finally, our experimental results demonstrate that MIDDLE (even in non-IID data settings) attains robust and high-performing models without model or gradient communication.

Bornstein, Marc I.↗

Grid Resilient, Self-Powered, Fuel Flexible, High Efficiency Heating System (Final Report)

The Grid Independent High Efficiency System (GIHES) delivers a heating system that operates free from the power grid and provides ultra-low emissions and high efficiency heating for residential and commercial buildings. The project demonstrated two weeks of continuous grid independent operation of a storage water heater equipped with a powered damper. The Thermoelectric Generator (TEG) integrated GIHES technology revolutionizes heating systems to provide grid resilient hot water production, while delivering significant value to the end user by eliminating impacts of power interruption and enhancing comfort, convenience and productivity. The system maximizes the thermal-to-electric conversion efficiency by optimizing location and orientation of the TEGs and durability while lowering system costs. The TEGs generate enough power to charge a battery and run the storage water heater unit with a powered damper uninterrupted by providing the required parasitic electrical power for start-up, shutdown and during idling. Several TEGs were analytically and experimentally evaluated based on the size, surface area and the power being generated before designing and fabricating a complete multi-TEG assembly for integration with the water heater. The GIHES technology can be extended to boilers, furnaces and tankless water heaters and integrated with heat pump to further increase the overall equipment efficiency. The technology has the potential to reduce the amount of dispatch power and peak power plant operation. An advanced burner was designed, developed and 3D printed for fuel flexible operation. The burner was designed to enable tighter integration with TEGs for potentially higher TEG output, should this be necessary. A bench-scale test rig to perform testing of a 200,000 Btu/h burner and capable of handling up to 10% H2 (by volume) was setup at GTI’s laboratory with appropriate safety and controls to ensure smooth and safe operation. Testing of the 3D burner showed < 5 ppm NOx emissions can be achieved for the entire firing range and with different levels of hydrogen blends with natural gas. The burner will enable a more integrated TEG-burner module design for improved system performance, should this be required in the future. Techno-economic analysis (TEA) of the TEG integrated with the water heaters was performed. The testing data and the power output were used to develop the TEA. The analysis for the storage unit with damper showed that the TEG integrated system costs $95. This is much lower than a one-time charge needed to wire and install dedicated power. Discussions with TEG manufacturers and OEM’s will provide more detailed information and methods to further lower these costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Binning is Sinning (Supernova Version): The Impact of Self-calibration in Cosmological Analyses with Type Ia Supernovae

Recent cosmological analyses (e.g., JLA, Pantheon) of Type Ia supernovae (SNe Ia) have propagated systematic uncertainties into a covariance matrix and either binned or smoothed the systematic uncertainty vectors in redshift space. We demonstrate that systematic error budgets of these analyses can be improved by a factor of ~ 1.5 × with the use of unbinned and unsmoothed covariance matrices. To understand this, we employ a separate approach that simultaneously fits for cosmological parameters and additional self-calibrating scale parameters that constrain the size of each systematic. We show that the covariance-matrix approach and scale-parameter approach indeed yield equivalent results, implying that in both cases the data can self-calibrate certain systematic uncertainties, but that this ability is hindered when information is binned or smoothed in redshift space. We review the top systematic uncertainties in current analyses and find that the reduction of systematic uncertainties in the unbinned case depends on whether a systematic is solely degenerate with the cosmological model in redshift space or whether it can be described by additional correlations between supernova properties and luminosity. Furthermore, we show that the power of self-calibration increases with the size of the data set, which presents a tremendous opportunity for upcoming analyses of photometrically classified samples, like those of Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Telescope (NGRST). However, to take advantage of self-calibration in large, photometrically classified samples, we must first address the issue that binning is required in currently used photometric analysis methods.

79 ASTRONOMY AND ASTROPHYSICS↗

Framework for phase transitions between the Maxwell and Gibbs constructions

By taking the nucleon-to-quark phase transition within a neutron star as an example, we present a thermodynamically consistent method to calculate the equation of state of ambient matter so that transitions that are intermediate to those of the familiar Maxwell and Gibbs constructions can be described. This method does not address the poorly known surface tension between the two phases microscopically (as, for example, in the calculation of the core pasta phases via the Wigner-Seitz approximation) but instead combines the local and global charge neutrality conditions characteristic of the Maxwell and Gibbs constructions, respectively. Overall charge neutrality is achieved by dividing the leptons to those that obey local charge neutrality (Maxwell) and those that maintain global charge neutrality (Gibbs). The equation of state is obtained by using equilibrium constraints derived from minimizing the total energy density. The results of this minimization are then used to calculate neutron star mass-radius curves, tidal deformabilities, equilibrium and adiabatic sound speeds, and nonradial g-mode oscillation frequencies for several intermediate constructions. Various quantities of interest transform smoothly from their Gibbs structures to those of Maxwell as the local-to-total electron ratio η, introduced to mimic the hadron-to-quark interface tension from 0 (Gibbs) to ∞ (Maxwell), is raised from 0 to 1. As a result, a notable exception is the g-mode frequency for the specific case of η = 1 for which a gap appears between the quark and hadronic branches.

79 ASTRONOMY AND ASTROPHYSICS↗

Robustness of Optical Response for Self-Assembled Plasmonic Metamaterials with Morphological Disorder and Surface Roughness

Bottom-up fabrication of metallized biotemplated nanostructures to form specific plasmonic nanoresonators holds promise as a means of achieving large-scale optical metamaterials. However, in contrast to top-down methods, the stochastic growth of self-assembled nanoresonators is prone to significant disorder and surface roughness, which naturally raise an important question about the robustness of their resonant properties in terms of structural imperfections. An aggregated-random-sphere model is developed to mimic the nucleated growth of metallized DNA origami assembly, leading to meta-atoms with realistic, experimentally observed morphological disorder and surface roughness. Using the well-known split-ring-resonator (SRR) motif as an example, the resonant properties of meta-atoms under different levels of roughness are investigated and a strong tolerance of optical response against morphological disorder is revealed. It is found that in SRRs, even with dramatic roughness introduced, the expected resonances are still observed, despite broadening line shapes compared to ideal smooth structure. Only for extreme disorder, which causes drastic segmentation of SRRs, does the resonant response disappear. Finally, the demonstrations are very encouraging for the prospects of bottom-up fabrication toward versatile functional metamaterials and metadevices.

36 MATERIALS SCIENCE↗