Origin of nonlinear force distributions in a composite system

Composite materials have been actively developed in recent years because they are highly functional such as lightweight, high yield strength, and superior load response. In spite of importance of the composite materials, mechanisms of the mechanical responses of composites have been unrevealed. Here, in order to understand the mechanical responses of composites, we investigated the origin and nature of the force distribution in heterogeneous materials using a soft particle model. We arranged particles with different softness in a lamellar structure and then we applied homogeneous pressure to the top surface of the system. It is found that the density in each region differently changes and then the density difference induces a nonlinear force distribution. In addition, it is found that the attractive interaction suppresses the density difference and then the force distribution is close to the theoretical prediction. Those findings may lead material designs for functional composite materials.

GENERIC-compliant simulations of Brownian multi-particle systems: modeling stochastic lubrication

A stochastic Lagrangian model for simulating the dynamics and rheology of a Brownian multi-particle system interacting with a simple liquid medium is presented. The discrete particle model is formulated within the GENERIC framework for Non-Equilibrium Thermodynamics and therefore it satisfies discretely the First/Second Laws of Thermodynamics and the Fluctuation Dissipation Theorem (FDT). Long-range fluctuating hydrodynamics interactions between suspended particles are described by an explicit solvent model. To this purpose, the Smoothed Dissipative Particle Dynamics method is adopted, which is a GENERIC-compliant Lagrangian meshless discretization of the fluctuating Navier–Stokes equations. In dense multi-particle systems, the average inter-particle distance is typically small compared to the particle size and short-range hydrodynamics interactions play a major role. In order to bypass an explicit—computationally costly—solution for these forces, a lubrication correction is introduced based on semi-analytical expressions for spheres under Stokes flow conditions. We generalize here the lubrication formalism to Brownian conditions, where an additional thermal-lubrication contribution needs to be taken into account in a way that discretely satisfies FDT. The coupled lubrication dynamics is integrated in time using a generalized semi-implicit splitting scheme for stochastic differential equations. The model is finally validated for a single particle diffusion as well as for a Brownian multi-particle system under homogeneous shear flow. Results for the diffusional properties as well as the rheological behavior of the whole suspension are presented and discussed.

RG203KR mutations in SARS-CoV-2 Nucleocapsid: Assessing the impact using Virus-like particle model system

The emergence and evolution of SARS-CoV-2 is characterized by the occurrence of diverse sets of mutations that affect virus characteristics, including transmissibility and antigenicity. Recent studies have focused mostly on Spike protein mutations; however, SARS-CoV-2 variants of interest (VoI) or concern (VoC) contain significant mutations in the nucleocapsid protein as well. To study the relevance of the mutations at the virion level, recombinant baculovirus expression system based VLPs were generated for the prototype Wuhan sequence along with Spike mutants like D614G, G1124V and the significant RG203KR mutation in Nucleocapsid. All the four structural proteins assembled in a particle wherein the morphology and size of the particle confirmed by TEM closely resembles the native virion. The VLP harbouring RG203KR mutations in nucleocapsid exhibited augmentation of humoral immune responses and enhanced neutralization by the immunized mice sera. Results demonstrate a non-infectious platform to quickly assess the implication of mutations in structural proteins of the emerging variant.

A Kinetic Study of Manganese Leaching from Low-Grade Psilomelane Ore by Acetic-Tannic Acid Lixiviant

Kinetic leaching of psilomelane using tannic acid as reductant and acetic acid as an acidic modifier is investigated in terms of tannic acid and acetic acid concentration, solid-liquid ratio, particle size and temperature. Kinetic modelling using three models: shrinking core, shrinking particle, and diffusion-interface transfer model revealed that at room temperature leaching (30 °C), experimental data are best modelled using diffusion-interface transfer model, indicating the dissolution of Mn is more affected by reaction rate among reactants and their concentration in bulk volume rather than by transfer across the boundary layer. At higher temperatures (≥ 50 °C), the shrinking particle model fits the experimental data best, suggesting the prominence of the diffusion process boundary layer. The apparent activation energy obtained at two temperatures were 13.1 and 52.7 kJ/mol for lower and higher temperatures. Plot between rate constant and concentration yields reaction order to be 1.28 for tannic acid and 0.73 for acetic acid. A semi-empirical model for each temperature range is proposed to describe the overall manganese leaching efficiency.

A multi-charged particle model with local $U(1)_{\mu - \tau}$ to explain muon $g-2$, flavor physics, and possible collider signature

We consider a model with multi-charged particles including vector-like fermions and a charged scalar under a local $U(1)_{\mu - \tau}$ symmetry. We search for allowed parameter region explaining muon anomalous magnetic moment (muon $g-2$) and $b \to s \ell^+ \ell^-$ anomalies, satisfying constraints from the lepton flavor violations, $Z$ boson decays, meson anti-meson mixing and collider experiments. Carrying out numerical analysis, we explore the typical size of the muon $g-2$ and Wilson coefficients to explain $b \to s \ell^+ \ell^-$ anomalies in our model when all other experimental constraints are satisfied. We then discuss the collider physics of the multicharged vectorlike fermions, considering some benchmark points in the allowed parameter space. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.

A comparison of particle and fluid models for positive streamer discharges in air

Both fluid and particle models are commonly used to simulate streamer discharges. In this paper, we quantitatively study the agreement between these approaches for axisymmetric and 3D simulations of positive streamers in air. We use a drift-diffusion-reaction fluid model with the local field approximation and a PIC-MCC (particle-in-cell, Monte Carlo collision) particle model. The simulations are performed at 300 K and 1 bar in a 10 mm plate-plate gap with a 2 mm needle electrode. Applied voltages between 11.7 and 15.6 kV are used, which correspond to background fields of about 15 to 20 kV/cm. Streamer properties like maximal electric field, head position and velocity are compared as a function of time or space. Our results show good agreement between the particle and fluid simulations, in contrast to some earlier comparisons that were carried out in 1D or for negative streamers. To quantify discrepancies between the models, we mainly look at streamer velocities as a function of streamer length. For the test cases considered here, the mean deviation in streamer velocity between the particle and fluid simulations is less than 4\%. We study the effect of different types of transport data for the fluid model, and find that flux coefficients lead to good agreement whereas bulk coefficients do not. Furthermore, we find that with a two-term Boltzmann solver, data should be computed using a temporal growth model for the best agreement. The numerical convergence of the particle and fluid models is also studied. In fluid simulations the streamer velocity increases somewhat using finer grids, whereas the particle simulations are less sensitive to the grid. Photoionization is the dominant source of stochastic fluctuations in our simulations. When the same stochastic photoionization model is used, particle and fluid simulations exhibit similar fluctuations.

Neuron particles capture network topology and behavior from single units

While networks of neurons, glia and vascular systems enable and support brain functions, to date, mathematical tools to decode network dynamics and structure from very scarce and partially observed neuronal spiking behavior remain underdeveloped. Large neuronal networks contribute to the intrinsic neuron transfer function and observed neuronal spike trains encoding complex causal information processing, yet how this emerging causal fractal memory in the spike trains relates to the network topology is not fully understood. Towards this end, we propose a novel statistical physics inspired neuron particle model that captures the causal information flow and processing features of neuronal spiking activity. Relying on synthetic comprehensive simulations and real-world neuronal spiking activity analysis, the proposed fractional order operators governing the neuronal spiking dynamics provide insights into the memory and scale of the spike trains as well as information about the topological properties of the underlying neuronal networks. Lastly, the proposed model exhibits superior predictions of animal behavior during multiple cognitive tasks.