Density Profile Research Articles

Strain effects on electronic structure and optics of al-Doped monolayer GaS

This comprehensive study utilises rigorous first-principles calculations to meticulously investigate the influence of uniaxial tensile strain on the electronic configuration and optical behaviours of aluminium-doped gallium sulphide. The study highlights the modulation of electrical properties and optical responses in Al-doped monolayer GaS through the application of tensile strain. Notably, with increasing tensile strain, the bandgap value of the pristine GaS exhibits a pronounced decline, whereas the Al-doped variant maintains a more stable bandgap. A deeper exploration of the state density reveals the emergence of novel electronic states and energy levels in both the pristine and Al-doped monolayer GaS systems as tensile strain is applied. Crucially, the stabilising effect of the doped Al atoms minimises variations in the state density profiles of the Al-doped monolayer GaS systems, in contrast to their pristine counterparts. This result implies that fine control of the electrical structure and optical characteristics of Al-doped monolayer GaS may be achieved by the application of uniaxial tensile strain. The optoelectronic characteristics of GaS and its doped systems are explained in detail by these discoveries, which also offer a crucial theoretical foundation and direction for the use of these materials in optoelectronic devices.

Experimental and simulation study of reverse micelles formed by aerosol-OT and water in non-polar solvents.

The formation of reverse micelles by aerosol-OT [sodium bis(2-ethylhexyl) sulfosuccinate] in hydrocarbon solvents, and in the presence of water, is studied using a combination of atomistic molecular-dynamics simulations and small-angle neutron scattering (SANS). There have been many previous studies of aerosol-OT and its self-assembly in both water and non-aqueous solvents, but this work is focused on a combined experimental and simulation study of reverse-micelle formation. The effects of hydration (with water-to-surfactant molar ratios in the range 0-60) and solvent (cyclohexane and n-dodecane) are investigated. A force field is adapted that results in spontaneous formation of reverse micelles starting from completely randomized configurations. The computed dimensions of the reverse micelles compare very favourably with those determined in SANS experiments, providing validation of the simulation model. The kinetics of reverse-micelle formation are studied with a 50-ns, 1.7-million-atom system which contains, in the steady state, about 50 reverse micelles. The internal structures of reverse micelles are characterized with mass density profiles, and the effects of solvent, and the structural crossover from highly structured water to 'bulk' water in the core, are detailed. The corresponding changes in the molecular reorientation times of sequestered water are also determined. Overall, the combination of experiment and simulation gives a detailed picture of reverse-micelle self-assembly and structure.

SMARTINI3 parametrization of multi-scale membrane models via unsupervised learning methods

In this study, we utilize genetic algorithms to develop a realistic implicit solvent ultra-coarse-grained (ultra-CG) membrane model comprising only three interaction sites. The key philosophy of the ultra-CG membrane model SMARTINI3 is its compatibility with realistic membrane proteins, for example, modeled within the Martini coarse-grained (CG) model, as well as with the widely used GROMACS software for molecular simulations. Our objective is to parameterize this ultra-CG model to accurately reproduce the experimentally observed structural and thermodynamic properties of Phosphatidylcholine (PC) membranes in real units, including properties such as area per lipid, area compressibility, bending modulus, line tension, phase transition temperature, density profile, and radial distribution function. In our example, we specifically focus on the properties of a POPC membrane, although the developed membrane model could be perceived as a generic model of lipid membranes. To optimize the performance of the model (the fitness), we conduct a series of evolutionary runs with diverse random initial population sizes (ranging from 96 to 384). We demonstrate that the ultra-CG membrane model we developed exhibits authentic lipid membrane behaviors, including self-assembly into bilayers, vesicle formation, membrane fusion, and gel phase formation. Moreover, we demonstrate compatibility with the Martini coarse-grained model by successfully reproducing the behavior of a transmembrane domain embedded within a lipid bilayer. This facilitates the simulation of realistic membrane proteins within an ultra-CG bilayer membrane, enhancing the accuracy and applicability of our model in biophysical studies.

Capturing the mechanosensitivity of cell proliferation in models of epithelium

Despite the primary role of cell proliferation in tissue development and homeostatic maintenance, the interplay between cell density, cell mechanoresponse, and cell growth and division is not yet understood. In this article, we address this issue by reporting on an experimental investigation of cell proliferation on all time- and length-scales of the development of a model tissue, grown on collagen-coated glass or deformable substrates. Through extensive data analysis, we demonstrate the relation between mechanoresponse and probability for cell division, as a function of the local cell density. Motivated by these results, we construct a minimal model of cell division in tissue environment that can recover the data. By parameterizing the growth and the dividing phases of the cell cycle, and introducing such a proliferation model in dissipative particle dynamics simulations, we recover the mechanoresponsive, time-dependent density profiles in 2D tissues growing to macroscopic scales. The importance of separating the cell population into growing and dividing cells, each characterized by a particular time scale, is further emphasized by calculations of density profiles based on adapted Fisher-Kolmogorov equations. Together, these results show that the mechanoresponse on the level of a constitutive cell and its proliferation results in a matrix-sensitive active pressure. The latter evokes massive cooperative displacement of cells in the invading tissue and is a key factor for developing large-scale structures in the steady state.

Topological Approach to Void Finding Applied to the SDSS Galaxy Map

The structure of the low redshift Universe is dominated by a multiscale void distribution delineated by filaments and walls of galaxies. The characteristics of voids, such as morphology, average density profile, and correlation function, can be used as cosmological probes. However, their physical properties are difficult to infer due to shot noise and the general lack of tracer particles used to define them. In this work, we construct a robust, topology-based void-finding algorithm that utilizes Persistent Homology to detect persistent features in the data. We apply this approach to a volume-limited subsample of galaxies in the SDSS I/II Main Galaxy catalog with the r-band absolute magnitude brighter than M r = −20.19, and a set of mock catalogs constructed using the Horizon Run 4 cosmological N-body simulation. We measure the size distribution of voids, their averaged radial profile, sphericity, and the centroid nearest neighbor separation, using conservative values for the threshold and persistence. We find 32 topologically robust voids in the SDSS data over the redshift range 0.02 ≤ z ≤ 0.116, with effective radii in the range 21−56 h −1 Mpc. The median nearest neighbor void separation is found to be ∼57 h −1 Mpc, and the median radial void profile is consistent with the expected shape from the mock data.

Untangling stellar components of galaxies: Evaluation of dynamical decomposition methods in simulated galaxies with GalaxyChop

Galaxy formation is intrinsically connected to the distinct evolutionary processes of disk and spheroidal systems, which are the fundamental stellar components of galaxies. Understanding the mutual, dynamical interplay and co-evolution of these components requires a detailed dynamical analysis to allow for disentanglement of these systems. We introduce JEHistogram, a new method for the dynamical decomposition of simulated galaxies into disk and spheroidal stellar components that utilizes the angular momentum and energy of star particles. We evaluate its performance against five previously established methods using a sample of equilibrium galaxies with stellar masses in the range $10^ M_ gal /M_ $. Our assessment involves several metrics, including the completeness and purity of stellar particle classification, scale lengths, mass density profiles, velocity dispersion, and rotational velocity profiles. While all methods approximate the properties of the original components, such as mass fractions and density or velocity profiles, JEHistogram demonstrates a better accuracy, particularly in the inner regions of galaxies where component overlap complicates separation. Additionally, we apply JEHistogram to a Milky Way-like galaxy from the IllustrisTNG cosmological simulations, showcasing its capability to derive properties such as the size, mass, velocity, color, and age of dynamically defined disk and spheroidal components. All the dynamical decomposition methods we analyzed are publicly accessible through the Python package GalaxyChop

A Review of Perovskite-based Lithium-Ion Battery Materials

Lithium-ion batteries (Li-ion batteries or LIBs) have garnered significant interest as a promising technology in the energy industry and electronic devices for the past few decades owing to their superior energy and power density profiles, small size, long cycle life, low self-discharge rate, no memory effect, long-lasting power properties, and environmental friendly. The ongoing advancement of electrode and electrolyte materials has contributed significantly to enhancing and spreading the application of lithium-ion battery technology. Among the non-precious metal-based materials, perovskites have emerged as attention over the last decade, holding a prominent position in materials and energy. Due to their unique physical and chemical properties, these materials have garnered particular interest for their potential application in electrochemical energy devices. Perovskite oxides have piqued the interest of researchers as potential catalysts in Li-O₂ batteries due to their remarkable electrochemical stability, high electronic and ionic conductivity, and the ability to modify their properties through doping and element substitution. The purpose of this article is to provide an overview of recent developments in the application of perovskites as lithium-ion battery materials, including the exploration of novel compositions and structures, optimization of fabrication methods, and a deeper understanding of the fundamental mechanisms that can unveil the potential of perovskite materials.

X‐Raying Neutral Density Disturbances in the Mesosphere and Lower Thermosphere Induced by the 2022 Hunga‐Tonga Volcano Eruption‐Explosion

AbstractWe present X‐ray observations of the upper atmospheric density disturbance caused by the explosive eruption of the Hunga Tonga‐Hunga Ha'apai (HTHH) volcano on 15 January 2022. From 14 January to 16 January, the Chinese X‐ray astronomy satellite, Insight‐HXMT, was observing the supernova remnant Cassiopeia A. The X‐ray data obtained during Earth's atmospheric occultations allowed us to measure neutral densities in the altitude range of 90–150 km. The density profiles above 110 km altitude obtained before the major eruption are in reasonable agreement with expectations by both GAIA and NRLMSIS 2.0 models. In contrast, after the HTHH eruption, a severe density depletion was found up to 1,000 km away from the epicenter, and a relatively weak depletion extending up to km for over 8 hr after the eruption. In addition, density profiles showed wavy structures with a typical length scale of either 20 km (vertical) or 1,000 km (horizontal). This may be caused by Lamb waves or gravity waves triggered by the volcanic eruption.

The Power of High-precision Broadband Photometry: Tracing the Milky Way Density Profile with Blue Horizontal Branch Stars in the Dark Energy Survey

Blue horizontal branch stars (BHBs), excellent distant tracers for probing the Milky Way’s halo density profile, are distinguished in the g−r0 versus (i − z)0 color space from another class of stars, blue straggler stars. We develop a Bayesian mixture model to classify BHBs using high-precision photometry data from the Dark Energy Survey Data Release 2 (DES DR2). We select ∼2100 highly probable BHBs based on their griz photometry and the associated uncertainties, and we use these stars to map the stellar halo over the Galactocentric radial range 20 kpc ≲ R ≲ 70 kpc. After excluding known stellar overdensities, we find that the number density n ⋆ of BHBs can be represented by a power-law density profile n ⋆ ∝ R −α with an index of α=4.34−0.12+0.13±0.52 , consistent with existing literature values. In addition, we examine the impact of systematic errors and the spatial inhomogeneity on the fitted density profile. Our work demonstrates the effectiveness of high-precision griz photometry in selecting BHBs. The upcoming photometric survey from the Rubin Observatory, expected to reach depths 2–3 mag greater than DES during its 10 yr mission, will enable us to investigate the density profile of the Milky Way’s halo out to the virial radius, unraveling the complex processes of formation and evolution in our Galaxy.

Rescuing the primordial black holes all-dark matter hypothesis from the fast radio bursts tension

Primordial black holes (PBHs) as an all-dark matter (DM) hypothesis has recently been demotivated by the prediction that these objects would source an excessive rate of fast radio bursts (FRBs). However, these predictions were based on several simplifying assumptions to which this rate is highly sensitive. In this article, we improve previous estimates of this rate arising from the capture of PBHs by neutron stars (NSs), aiming to revitalise this theory. We more accurately compute the velocity distribution functions of PBHs and NSs and also consider an enhancement in the NS and DM density profiles at galactic centers due to the presence of a central supermassive black hole. We find that previous estimates of the rate of FRBs sourced by the capture of PBHs by NSs were 3 orders of magnitude too large, concluding that the PBHs as all DM hypothesis remains a viable theory and that the observed FRB rate can only be entirely explained when considering a central, sufficiently spiky PBH density profile. Published by the American Physical Society 2024

3DDA: A Novel Python Toolkit to Analyze 3D‐Dynamic Contact Angles from Molecular Dynamics Simulations

Obtaining fast and reliable contact angles in molecular dynamics simulations is crucial for screening surface wettability. Traditional methods rely on bidimensional density profile analysis to localize the liquid/vapor interface, which may not be optimal at the nanoscale, especially for nonsymmetrical droplets. Herein, 3DDA, a Python‐based code that uses the 3D droplet geometry to determine contact angles and analyze dynamic changes during spreading processes at the nanoscale, is presented. The 3D density profile of liquid molecules is enveloped using the Euclidean 3D convex hull algorithm to locate the contact line at the solid‐, liquid‐, and vapor‐phase boundaries. By fitting a general ellipsoidal function and optimizing it with the limited‐memory Broyden–Fletcher–Goldfarb–Shanno method, the best candidate function is analytically obtained, describing the interface. The angle derived from the ellipsoidal function and the solid boundary is used to gather a collection of contant angles, which are then fed into a kernel density estimator to determine the most probable angle along the contact line during the spreading process. This methodology is tested on systems such as liquid water and metals, demonstrating its efficacy and reliability. Herein, valuable insights are provided into the behavior of liquids, considering droplet asphericity induced by liquid/solid interactions.

Investigating the self-assembly of pH-sensitive switchable diamine surfactant using sum frequency generation spectroscopy and molecular dynamics simulations.

The responses of the N-alkyl diamine groups to variations in pH affect their conformations and surface activities, making them relevant to applications relying on interfacial interactions, such as controlled emulsification and mineral flotation. An in-depth understanding of interfacial self-assembly is crucial. Herein, a molecular-level study was performed to investigate the adsorption and self-assembly of N-dodecylpropane-1,3-diamine (DPDA) at the air-water (A/W) interface using sum frequency generation (SFG) spectroscopy and molecular dynamics (MD) simulations. The SFG spectra of DPDA, acquired under three pH conditions, suggest that the protonation of the DPDA diamine group influences the alkyl chain arrangement at a varying degree at the A/W interface. Analysis of the di-cationic DPDA SFG spectrum at a low pH showed fewer gauche defects at low concentration, as indicated by the relatively higher intensity ratio (ICH3SS/ICH2SS) of 18.1 ± 0.6. The density profiles from MD simulations at different surface areas per molecule and pH conditions, showing varying degrees of packing, support the observation of gauche defects in SFG. With MD simulation, the radial distribution factor for di-cationic species has the highest probability of forming hydrogen bonds compared to mono-cationic and non-ionic species. These g(r) probability results conform with observations obtained from SFG spectroscopy, where we observed a strong hydrogen bond interaction at low pH conditions with di-cationic species, forming tetrahedrally arranged water molecules at the A/W interface. Overall, comprehensive insights will facilitate the visualization of alkyl diamines and their potential derivatives at the A/W interface, enabling a better understanding of their behavior across various applications.

Simulating unsteady flows on a superconducting quantum processor

Recent advancements of quantum technologies have triggered tremendous interest in exploring practical quantum advantage. The simulation of fluid dynamics, a highly challenging problem in classical physics but vital for practical applications, emerges as a potential direction. Here, we report an experiment on the digital simulation of unsteady flows with a superconducting quantum processor. The quantum algorithm is based on the Hamiltonian simulation using the hydrodynamic formulation of the Schrödinger equation. With the median fidelities of 99.97% and 99.67% for parallel single- and two-qubit gates respectively, we simulate the dynamics of a two-dimensional (2D) compressible diverging flow and a 2D decaying vortex with ten qubits. Note that the former case is an inviscid potential flow, and the latter one is an artificial vortical flow with an external body force. The experimental results well capture the temporal evolution of averaged density and momentum profiles, and qualitatively reproduce spatial flow fields with moderate noises. This work demonstrates the potential of quantum computing in simulating more complex flows, such as turbulence, for practical applications.

Functional EWMA control chart for phase II profile monitoring

Due to rapid developments in technology, profile monitoring is becoming challenging, as the profile data are a series of observations on a specific quality characteristic that are taken across some continuum. In this paper, a functional exponentially weighted moving average (FEWMA) control chart is developed for phase-II monitoring of nonlinear profiles based on the functional data approach. In particular, the profiles are estimated using cubic B-splines to solve the problem of high dimensionality that hinders the implementation of classical multivariate control charts. An extensive simulation study has been conducted to validate the effectiveness of the proposed control scheme for detecting changes in the functional mean of the process to its magnitude and assuming scenarios of independence and dependence between curves. The properties of the run-length distribution of the proposed scheme are discussed. The FEWMA control chart is also applied to the real-time data of the vertical density profile of particleboard manufacturing.

Effect of local wall clearance on scrape-off layer electron density profiles in ASDEX Upgrade

Effect of local wall clearance on scrape-off layer electron density profiles in ASDEX Upgrade

Entropy generation in radiative motion of tangent hyperbolic nanofluid in the presence of gyrotactic microorganisms and activation energy

In this work, entropy generation is optimized through the application of the second law of thermodynamics. The slip mechanisms, Brownian diffusions, and thermophoresis are elaborated using the tangent hyperbolic nanomaterial model. Magnetohydrodynamic (MHD) fluid is taken into consideration. To characterize the impact of activation energy, a unique model involving the binary chemical reaction is deployed. The effects of mixed convection that is nonlinear in nature, bioconvection, and Joule effect are all taken into consideration. The key partial differential equations (PDEs) are reduced into ordinary differential equations (ODEs) by utilizing appropriate similarity transformations and then solved numerically with the help of a built-in ‘bvp4c’ technique of MATLAB software. Varied flow parameters’ impacts on the nanoparticle volume concentration, entropy number, microorganism concentration, temperature, and velocity fields are analyzed using graphs. Various flow variables are taken into consideration to calculate the total rate of entropy generation. The obtained results show that concentration irreversibility, Joule effect irreversibility, viscous dissipation, and heat irreversibility all influence the entropy. The numerical outcomes were observed by fixing the physical parameters as 0.1<α<4.0, 0.1<M<1.2, 0.1<Nr<2.2, 0.1<Le<2.2, 0.1<Nb<0.4, 0.1<Nt<1.0, 2.0<⁡Pr⁡<5.0, and 0.1<Lb<2.0, as well as their impact on the momentum, thermal, concentration, and microorganism density profiles. From results, an increasing estimate of the variable representing chemical reaction indicates a decline in the concentration. The higher the chemical reaction variable, Hartmann number, and Weissenberg number, the higher the entropy number, while the Bejan number has a contrary behavior. Subsequently, all the outcomes are plotted in graphs and discussed in detail, when subjected to the involving physical quantities.

The mass profiles of dwarf galaxies from Dark Energy Survey lensing

ABSTRACT We present a novel approach to extracting dwarf galaxies from photometric data to measure their average halo mass profile with weak lensing. We characterize their stellar mass and redshift distributions with a spectroscopic calibration sample. By combining the ${\sim} 5000\,\mathrm{deg}^2$ multiband photometry from the Dark Energy Survey and redshifts from the Satellites Around Galactic Analogs Survey with an unsupervised machine learning method, we select a low-mass galaxy sample spanning redshifts $z\lt 0.3$ and divide it into three mass bins. From low to high median mass, the bins contain [146 420, 330 146, 275 028] galaxies and have median stellar masses of $\log _{10}(M_*/\text{M}_\odot)=\left[8.52\substack{+0.57 -0.76},\, 9.02\substack{+0.50 -0.64},\, 9.49\substack{+0.50 -0.58}\right]$ . We measure the stacked excess surface mass density profiles, $\Delta \Sigma (R)$, of these galaxies using galaxy–galaxy lensing with a signal-to-noise ratio of [14, 23, 28]. Through a simulation-based forward-modelling approach, we fit the measurements to constrain the stellar-to-halo mass relation and find the median halo mass of these samples to be $\log _{10}(M_{\rm halo}/\text{M}_\odot)$ = [$10.67\substack{+0.2 -0.4}$, $11.01\substack{+0.14 -0.27}$, $11.40\substack{+0.08 -0.15}$]. The cold dark matter profiles are consistent with NFW (Navarro, Frenk, and White) profiles over scales ${\lesssim} 0.15 \, {h}^{-1}$ Mpc. We find that ${\sim} 20$ per cent of the dwarf galaxy sample are satellites. This is the first measurement of the halo profiles and masses of such a comprehensive, low-mass galaxy sample. The techniques presented here pave the way for extracting and analysing even lower mass dwarf galaxies and for more finely splitting galaxies by their properties with future photometric and spectroscopic survey data.

CLASSY. X. Highlighting Differences between Partial Covering and Semianalytic Modeling in the Estimation of Galactic Outflow Properties

Feedback-driven massive outflows play a crucial role in galaxy evolution by regulating star formation and influencing the dynamics of surrounding media. Extracting outflow properties from spectral lines is a notoriously difficult process for a number of reasons, including the possibility that a substantial fraction of the outflow is carried by dense gas in a very narrow range in velocity. This gas can hide in spectra with insufficient resolution. Empirically motivated analysis based on the apparent optical depth method, commonly used in the literature, neglects the contribution of this gas, and may therefore underestimate the true gas column density. More complex semianalytical line transfer (e.g., SALT) models, on the other hand, allow for the presence of this gas by modeling the radial density and velocity of the outflows as power laws. Here we compare the two approaches to quantify the uncertainties in the inferences of outflow properties based on 1D “down-the-barrel” spectra, using the UV spectra of the CLASSY galaxy sample. We find that empirical modeling may significantly underestimate the column densities relative to SALT analysis, particularly in the optically thick regime. We use simulations to show that the main reason for this discrepancy is the presence of a large amount of dense material at low velocities, which can be hidden by the finite spectral resolution of the data. The SALT models in turn could overestimate the column densities if the assumed power laws of the density profiles are not a property of actual outflows.

Data-driven discovery of chemotactic migration of bacteria via coordinate-invariant machine learning

BackgroundE. coli chemotactic motion in the presence of a chemonutrient field can be studied using wet laboratory experiments or macroscale-level partial differential equations (PDEs) (among others). Bridging experimental measurements and chemotactic Partial Differential Equations requires knowledge of the evolution of all underlying fields, initial and boundary conditions, and often necessitates strong assumptions. In this work, we propose machine learning approaches, along with ideas from the Whitney and Takens embedding theorems, to circumvent these challenges.ResultsMachine learning approaches for identifying underlying PDEs were (a) validated through the use of simulation data from established continuum models and (b) used to infer chemotactic PDEs from experimental data. Such data-driven models were surrogates either for the entire chemotactic PDE right-hand-side (black box models), or, in a more targeted fashion, just for the chemotactic term (gray box models). Furthermore, it was demonstrated that a short history of bacterial density may compensate for the missing measurements of the field of chemonutrient concentration. In fact, given reasonable conditions, such a short history of bacterial density measurements could even be used to infer chemonutrient concentration.ConclusionData-driven PDEs are an important modeling tool when studying Chemotaxis at the macroscale, as they can learn bacterial motility from various data sources, fidelities (here, computational models, experiments) or coordinate systems. The resulting data-driven PDEs can then be simulated to reproduce/predict computational or experimental bacterial density profile data independent of the coordinate system, approximate meaningful parameters or functional terms, and even possibly estimate the underlying (unmeasured) chemonutrient field evolution.

Fuzzy black hole models in f(G) Gravity

Abstract In this manuscript, we explore the concept of dark matter black holes, inspired by the Einasto density profile in the background of $f(\mathcal{G})$ gravity, where $\mathcal{G}$ is a Gauss-Bonnet invariant. This work extends the scope beyond the noncommutative tiny black hole structure which incorporates dark matter as a constituent forming the black hole. Our investigation demonstrates the feasibility of constructing black hole solutions for various Einasto index values, $f(\mathcal{G})$ gravity parameters, and parameters of mass function adopting anisotropic fluid configuration to characterize the matter sector. Specifically, by adopting an equation of state in which the radial pressure equals the negative of the energy density, we derive black hole solutions exhibiting a horizon structure with the central singularity replaced by a regular de Sitter core akin to the Reissner-Nordstr"{o}m black holes. Furthermore, we explore an alternative scenario where the equation of state is nonlocal, resulting in the construction of self-gravitating fuzzy dark matter droplets together with the dark source $f(\mathcal{G})$ corrections.