Particle Interactions Research Articles

Ultra-High-Energy Cosmic Rays Accelerated by Magnetically Dominated Turbulence

Abstract Ultra-high-energy cosmic rays (UHECRs), particles characterized by energies exceeding 1018 eV, are generally believed to be accelerated electromagnetically in high-energy astrophysical sources. One promising mechanism of UHECR acceleration is magnetized turbulence. We demonstrate from first principles, using fully kinetic particle-in-cell simulations, that magnetically dominated turbulence accelerates particles on a short timescale, producing a power-law energy distribution with a rigidity-dependent, sharply defined cutoff well approximated by the form f cut E , E cut = sech ( E / E cut ) 2 . Particle escape from the turbulent accelerating region is energy dependent, with t esc ∝ E −δ and δ ∼ 1/3. The resulting particle flux from the accelerator follows dN / dEdt ∝ E − s sech ( E / E cut ) 2 , with s ∼ 2.1. We fit the Pierre Auger Observatory’s spectrum and composition measurements, taking into account particle interactions between acceleration and detection, and show that the turbulence-associated energy cutoff is well supported by the data, with the best-fitting spectral index being s = 2.1 − 0.13 + 0.06 . Our first-principles results indicate that particle acceleration by magnetically dominated turbulence may constitute the physical mechanism responsible for UHECR acceleration.

Effect of Froth on the Interaction Between Coal Particles and Cake Structures in the Dewatering Process of Clean Coal

Effective coal slurry water solid–liquid separation is indispensable for the recycling and sustainable development of coal resources. The interaction between bubble and coal particles plays a critical role in the process of dewatering for clean coal. In this study, we firstly conducted a comprehensive investigation of the impact of froth on the interactions between coal particles by rheological measurement and particle aggregation behavior. Furthermore, the macroscopic dewatering performance of coal slurry in the presence of froth and its microscopic cake structure were investigated using the filtration test and X-ray microtomography (CT). It was found that the interaction between coal particles in the presence of froth was enhanced as a result of the dynamic shear value, combined with the large floc size and compact structure, which led to a higher cake moisture and higher filtration velocity. The CT results indicated that the enhanced interaction of particles in the presence of froth also led to a dense microstructure of the filter cake. The porosity of the filter cake decreased to 2.05% when the aeration time increased from 0 s to 90 s, the throat radius in the filter cake was reduced to 1.32 μm, and the number of throat passages was reduced to one third. Multiple blind pores and low coordination numbers led to a poor connectivity of the pore network and high moisture content.

Investigation on the interfacial and emulsion stabilized behavior of dextran/ferritin/resveratrol composite nanoparticles

Glycation of ferritin provides a viable approach to enhance its physicochemical and functional properties. However, there is limited research on the interfacial adsorption properties of glycated ferritin-based colloidal particles. Therefore, this study selected recombinant human H-chain ferritin (rHuHF), rHuHF encapsulated with resveratrol (rHuHF–Res), and rHuHF–dextran glycoconjugates loaded with resveratrol (Dex–rHuHF–Res) as emulsifiers to investigate their interfacial adsorption properties. The results revealed that Dex–rHuHF–Res exhibited superior emulsifying properties and rheological behavior. It also increased the hydrophobicity of the microenvironment around Tyr and Trp residues, while hydrogen bonds, hydrophobic force, and salt bridge were identified as the most important intermolecular interactions. Dex–rHuHF–Res exhibited the biggest contact angle and lowest interface tension, which further reduced the diffusion (Kdiff) from the aqueous phase to the interface but promoted the penetration (Kp) and rearrangement (Kr) rates at the interface. Meanwhile, the emulsion stabilized by Dex–rHuHF–Res displayed excellent freeze-thaw stability, and Dex–rHuHF–Res can be more densely accumulated at the O/W interface to form an interface layer. These findings highlight the promising application of glycated ferritin in stabilizing Pickering emulsions, and deepen our understanding of the interplay among particle interaction, interface adsorption properties, and emulsion stability.

Construction of myofibrillar protein-based Pickering emulsion for grass carp (Ctenopharyngodon idellus) based on interfacial self-assembly of spice aldehyde molecules: Mechanism and application of imine reaction-stabilized emulsion.

The purpose of this research is to investigate multiple structural spice aldehydes (cinnamaldehyde (CA), citronellal (CN), and melonal (MA)) interact with myofibrillar protein in grass carp (GCMP) and the effects of interfacial interactions on stability properties of Pickering emulsion. It was demonstrated that the functional characteristics of interfacial protein might be greatly improved by the complexation of spice aldehydes with protein particles at the water-oil interface. The fluorescence quenching and molecular docking results revealed that the binding mode of spice aldehyde is primarily dominated by hydrophobic interactions, yet there are hydrogen bonding interactions exist in special structure. When comparing multiple structure aldehyde, CA has a greater binding affinity than CN and MA, as well as the most binding sites at different temperatures. Among them, the interaction of CA and GCMP is enhanced by forming hydrogen bonds through π-bonds owing to its special aromatic ring structure. The O/W structure was further stabilized by the formation of conjugated and unconjugated imines of interfacial protein with spice aldehydes. This work provides a theoretical basis for the interaction and development of protein particles with spice aldehyde in Pickering emulsion stabilization and novel ideas for the application of low value aquatic protein for high quality.

Low-latitude Auroras: Insights from 2023 April 23 Solar Storm

In 2023 April, a low-latitude aurora observed by the all-sky camera at Hanle, Ladakh, India (33°14’N geographic latitude), generated significant interest. This was the first such aurora recorded from the Indian region in the space era and occurred during a moderate solar storm. This study explores this low-latitude auroral sighting, which happened during the sheath-region passage of an interplanetary coronal mass ejection. We analyze in situ multispacecraft particle measurements and geomagnetic field observations from both ground-based and satellite-based magnetometers. The auroral observations at Hanle coincided with intense substorm activity. Our findings indicate that the aurora did not actually reach India; the equatorward boundary was beyond 50°N geographic latitude. Enhanced electron fluxes with energies below 100 eV were detected at 54°N geographic latitude at about 830 km altitude in the predawn sector (4–5 hr local time). In the midnight sector, the equatorward boundary is estimated to be around 52°N geographic latitude, based on Hanle observations and considering emission altitudes of 600–650 km due to low-energy electrons. Thus, the low-latitude red aurora observed from India resulted from the emissions at higher altitudes due to low-energy electron precipitation in the auroral oval and a slight equatorward expansion of the auroral oval. The low-energy electrons likely originated from the plasma sheet and were precipitated due to enhanced wave–particle interactions from strong magnetosphere compression during high solar wind pressure. This study is crucial in understanding low-latitude auroras in the modern space era.

Learning locally dominant force balances in active particle systems

We use a combination of unsupervised clustering and sparsity-promoting inference algorithms to learn locally dominant force balances that explain macroscopic pattern formation in self-organized active particle systems. The self-organized emergence of macroscopic patterns from microscopic interactions between self-propelled particles can be widely observed in nature. Although hydrodynamic theories help us better understand the physical basis of this phenomenon, identifying a sufficient set of local interactions that shape, regulate and sustain self-organized structures in active particle systems remains challenging. We investigate a classic hydrodynamic model of self-propelled particles that produces a wide variety of patterns, such as asters and moving density bands. Our data-driven analysis shows that propagating bands are formed by local alignment interactions driven by density gradients, while steady-state asters are shaped by a mechanism of splay-induced negative compressibility arising from strong particle interactions. Our method also reveals analogous physical principles of pattern formation in a system where the speed of the particle is influenced by the local density. This demonstrates the ability of our method to reveal physical commonalities across models. The physical mechanisms inferred from the data are in excellent agreement with analytical scaling arguments and experimental observations.

Hamiltonian for complex plasmas

The Hamiltonian for a complex plasma involving dust spinning motion in a magnetic field is proposed. The formulation is in a classical limit of a quantum mechanical approach based on the Dirac equation, a relativistic wave equation in which the spin of particles is considered. The quantum mechanical spin term –γs·B [γ=gq/2m, g=2(Lande´ g−factor),q is a charge, m is a mass of a particle, and s = spin operator] is replaced by –γs·B [γ with g=1, s = I(ω+γB), I is the moment of inertia of a dust particle, and ω is the spinning angular velocity of a dust particle] in a classical limit. The wave–particle interaction involving spin motion is described as an interaction between particles and quasiparticles by the second quantization. The equation of motion with a damping term shows the relaxation of spinning dust particles aligning with the magnetic field.

Self-floating Janus hydrovoltaics for sustainable electricity generation

Water is widely available in nature that attracts increasing interesting in simulating transpiration effect to induce the electricity. However, it faces great challenges in establishing continues moisture gradient rather than periodic wetting behaviors. Here, we propose a self-floating Janus generator containing the hydrophilic and hydrophobic regions to induce the continues electricity. The self-floating and hydrophilic behavior ensures a continuous water supply. The asymmetric Janus structure forms a distinct wet/dry interface to create a significant water gradient with evaporation equilibrium state. At wetting region, the electric double layer (EDL) is formed due to the interaction of water and carbon black particles. Attributed to the large water gradient and fast contact line evaporation, proton accumulates across the capillary front that induces a potential difference. As a result, the Janus generator achieves 0.46 V open circuit voltage lasting for 40 h. It also demonstrates the potential applications of Janus generator in power supply, moisture detection and sweat monitoring et al. The proposed Janus generators show great potential in ocean energy development and be of great significance to the sensing of rescue signals in the sea.

A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications

Deep learning (DL) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis of complex systems, from protein folding in biology to molecular discovery in chemistry and particle interactions in physics. However, the field of deep learning is constantly evolving, with recent innovations in both architectures and applications. Therefore, this paper provides a comprehensive review of recent DL advances, covering the evolution and applications of foundational models like convolutional neural networks (CNNs) and Recurrent Neural Networks (RNNs), as well as recent architectures such as transformers, generative adversarial networks (GANs), capsule networks, and graph neural networks (GNNs). Additionally, the paper discusses novel training techniques, including self-supervised learning, federated learning, and deep reinforcement learning, which further enhance the capabilities of deep learning models. By synthesizing recent developments and identifying current challenges, this paper provides insights into the state of the art and future directions of DL research, offering valuable guidance for both researchers and industry experts.

Equivariant neural networks for robust CP observables

We introduce the usage of equivariant neural networks in the search for violations of the charge-parity (CP) symmetry in particle interactions at the CERN Large Hadron Collider. We design neural networks that take as inputs kinematic information of recorded events and that transform equivariantly under a symmetry group related to the CP transformation. We show that this algorithm allows one to define observables reflecting the properties of the CP symmetry, showcasing its performance in several reference processes in top quark and electroweak physics. Imposing equivariance as an inductive bias in the algorithm improves the numerical convergence properties with respect to other methods that do not rely on equivariance and allows one to construct optimal observables that significantly improve the state-of-the-art methodology in the searches considered. Published by the American Physical Society 2024

To the theory of remagnetization kinetics of magnetic composites

Results of theoretical study of kinetics of the remagnetization of an ensemble of interacting ferromagnetic particles immobilized in a host non -magnetic medium are presented. The results show that the influence of interparticle interaction on the remagnetization is determined by the amplitude of the applied alternating field: it slows down this process in a weak field and accelerates it in a strong field. The interaction of particles increases both components of the complex magnetic susceptibility of the composite.

Research on the construction of novel nano‐modified clean fracturing fluid and the migration characteristics of proppants

AbstractClean fracturing fluid exhibits desirable characteristics, such as low residue, easy flowback, minimal reservoir damage, and favorable rheology, making it highly adaptable. However, challenges remain in terms of high cost, large consumption, and poor stability. Hence, this study focuses on incorporating SiO2 nanoparticles into clean fracturing fluid to develop a novel nano‐modified system. Experimental tests were conducted to analyze the impact of mass fractions of hexadecyltrimethylammonium chloride (CTAC), sodium salicylate (Nasal), and SiO2 nanoparticles on the viscosity of the solution. The results indicate a significant increase in viscosity with higher mass fractions of SiO2. A nano‐modified clean fracturing fluid composition of 1.5 wt% CTAC, 0.4 wt% Nasal, and 0.1 wt% SiO2 was selected as it meets the technical specifications for fracturing fluid while exhibiting excellent elastic modulus (2.279 Pa) and viscous modulus (0.818 Pa), thereby displaying superior viscoelastic properties. Furthermore, the system maintains a viscosity above 20 mPa.s under temperature (70°C) and shear stress (170 s−1), indicating robust temperature and shear resistance to meet the demands of downhole fracturing and proppant transport. Based on rheological experimental data, a Carreau rheological model was established for the nano‐modified clean fracturing fluid. Considering particle interaction, wall blockage, and fluid filtration, a settlement model was modified with a correction coefficient, leading to the construction of a mathematical model for correcting proppant particle settling in fractures. The error between measured settlement velocity and calculated values remained within 10%. Finally, the migration law of proppant is studied, and the settlement law of proppant in fracturing fluid and fracture is clarified.

A chronological catalog of methods and solutions in the Space–Time Computational Flow Analysis: I. Finite element analysis

AbstractThe Space–Time Computational Flow Analysis (STCFA) started in 1990 with the inception of the Deforming-Spatial-Domain/Stabilized Space–Time (DSD/SST) method. The DSD/SST was introduced as a moving-mesh method for flows with moving boundaries and interfaces, which is a wide class of problems that includes fluid–particle interactions, fluid–structure interactions (FSI), and free-surface and multi-fluid flows. The first 3D computations were reported in 1992. The original DSD/SST method is now called “ST-SUPS,” reflecting its stabilization components. As the STCFA evolved, advanced mesh moving methods, FSI coupling methods, and problem-class-specific methods were introduced to increase its scope and the ST Variational Multiscale was introduced to upgrade its stabilization components to the VMS. Complementary general-purpose methods developed in the evolution of the STCFA include the ST Isogeometric Analysis (ST-IGA) and the ST Slip Interface (ST-SI) and ST Topology Change (ST-TC) methods. The ST-IGA delivers superior accuracy through IGA basis functions not only in space but also in time. The ST-SI enables high-fidelity moving-mesh computations even over meshes made of patches with nonmatching meshes at the interfaces between those patches. The ST-TC enables high-fidelity moving-mesh computations even in the presence of topology changes in the fluid mechanics domain, such as an actual contact between moving solid surfaces. The STCFA brought first-of-its-kind solutions in many classes of problems, ranging from fluid–particle interactions in particle-laden flows to FSI in parachute aerodynamics, flapping-wing aerodynamics of an actual locust to ventricle-valve-aorta flow analysis to car and tire aerodynamics with near-actual geometries, road contact, and tire deformation. With the success we see in so many classes of problems, we can conclude that the STCFA has reached a level of remarkable sophistication, scope, and practical value. We present a chronological catalog of the methods and solutions in the STCFA. In Part I of this two-part article, we focus on the methods and solutions in finite element analysis.

Theoretical Study of Position-Dependent Mass Particle in Cosmic String Space-Time and External Magnetic Field

In this work, we discuss the case of a non-relativistic quantum particle in the cosmic string space-time under the influence of the presence of external magnetic and exponential potential. It’s shown with the help of the Laplace transform method the second-order differential equation of the Schrodinger equation is solvable where it is reduced to a first-order differential equation. We obtain the eigenfunction and the non-relativistic energy spectrum. In addition, these results may be useful in the future study of thermodynamic, optic, and magnetic properties of the atomic interaction of a system particle. Keywords: Schrodinger equation; Laplace transform method; Position dependent mass; External magnetic field; Cosmic string

Numerical Simulation of the Melting of Solid Particles in Thermal Convection with a Modified Immersed Boundary Method

A new immersed boundary method is proposed for the numerical simulation of the melting of solid particles in its own liquid at a high temperature. The main feature of the new method is the use of the modified direct-forcing immersed boundary method for the solution of the flow field and the sharp-interface immersed boundary method for the temperature field. The accuracy of the proposed method is validated via three problems: the sedimentation of a non-melting particle, the melting of a fixed particle under mixed thermal convection, and the sedimentation of a melting particle. The method is then applied to the investigation of the effects of various parameters, the particle interactions and the particle shape on the particle melting time. A correlation for the melting time of a circular particle in forced thermal convection is established as a function of the Reynolds, Prandtl, and Stefan numbers. The melting time of a particle in mixed thermal convection first increases and then decreases, as the Grashof number increases. The effects of the particle interactions on the melting time are complicated due to the natural convection between two particles. The sufficiently strong natural convection can even render the downstream particle melt faster than the single particle. For the same particle area, the elliptic particle with the aspect ratio being around 1.4 melts most slowly.

Quantum potential energy: Adaptive facades in architecture

This paper addressed the topic of creating quantum architecture through human awareness to transform the formal and spatial elements of architecture according to its environmental sustainability compatible with nature. The research emphasizes the creation of a built environment that uses natural resources to enhance the psychological and physical comfort of humans, relying on the quantum energy inherent in the interaction of particles, which is called “kinetic energy” in quantum mechanics, and it can be considered energy due to motion. The research aims to determine the recommended standards for use in designing the building facade according to the magnetic field lines. It focuses on the classification of smart interfaces in particular. The complexity of evaluating adaptive or dynamic facades is related to evaluating the performance of the facade elements and systems, the overall performance of the building, as well as the behavior and satisfaction of the occupants. The research seeks to present a conceptual and cognitive framework by linking the concept of potential kinetic energy and quantum theory with architecture and then presents a group of literary studies that will enrich and build the theoretical framework and then apply it to the groups of projects selected for practical study and then reach the final conclusions.

Interaction of complex particles: A framework for the rapid and accurate approximation of pair potentials using neural networks.

Motivated by the limitations of conventional coarse-grained molecular dynamics for simulation of large systems of nanoparticles and the challenges in efficiently representing general pair potentials for rigid bodies, we present a method for approximating general rigid body pair potentials based on a specialized type of deep neural network that maintains essential properties, such as conservation of energy and invariance to the chosen origins of the particles. The network uses a specialized geometric abstraction layer to convert the relative coordinates of the rigid bodies to input more suitable to a conventional artificial neural network, which is trained together with the specialized layer. This results in geometric representations of the particles optimized for the specific potential. The network can be trained directly on scalar values to fit a model without explicit gradient and then used to efficiently evaluate the force and torque on the particles resulting from the potential. The concept is demonstrated with an atomistic interaction model for carbon nanotubes and the resulting model is compared with a common type of coarse-grained model optimized for the same potential, with even very small networks comparing favourably and larger networks achieving up to two orders of magnitude lower cost. The sensitivity to noise in the training data is investigated and the model is found to strongly reject noise up to 12.5% given a dataset of 10^{7} samples. The performance of a proof-of-concept implementation is demonstrated on a variety of hardware, showing the models viability for large-scale simulations. Furthermore, generalization to soft bodies and potentials for polydisperse systems are discussed.

A study of nuclear radiation interaction parameters of some plant based radio-protective compounds

In radiation therapy, various radioprotective materials protect normal cells from the damage produced by ionizing radiation. Plant-based chemical compounds possess antioxidant properties and can scavenge free radicals produced by nuclear radiation. Determining radiation shielding parameters and understanding how nuclear radiation interacts with plant-based radioprotectors are crucial for choosing the optimal plant-based radioprotectors. In the present work, the interaction of gamma, neutron, electron, proton, and alpha particles with plant-based radioprotectives such as ferulic acid, genistein, hesperidin, lycopene, psoralidin, sesamol, troxerutin, vanillin, and zingerone has been studied. The interaction parameters such as MAC, Z eff , EBF, and EABF for gamma energy, MAF for thermal and fast neutrons, and MSP for electrons, protons, and alpha have been calculated for the plant-based radioprotectors using the software EpiXs, PyMLBUF, NGCal, ESTAR, PSTAR, and ASTAR. Results show that genistein and lycopene show high and low effective atomic numbers in the higher energy region of gamma photons, respectively. The neutron mass attenuation factor is found to be highest for lycopene and lowest for genistein for both thermal and fast neutrons. Among the selected plant-derived radioprotectives, troxerutin and lycopene are the most effective plant-based compounds for gamma and neutron radiation therapy applications. The shielding parameters of selected plant-based radioprotector materials are compared with those of some chemical radioprotectors. Besides, the shielding parameters of selected plant-based radioprotector materials are comparable to chemical radioprotectors except for lycopene.

Highly chiral exceptional point in coupled resonators perturbed by nanoscatterers

With employing strong chirality in an optical system, the direction of light propagation can be controlled and subwavelength particles can be detected. Here, we show that a different kind of chiral exceptional point (EP) with high (spatial) chirality can appear in a coupled resonator perturbed by nanoscatterers, in which both the distance and position of the scatterers can be tuned. We achieve strong chiral EP in two different distances between the resonators, with chirality around 0.99, in both cases. Besides, chiral EP associated with the higher harmonic whispering gallery mode is achieved, with chirality around 0.95. We also investigate the interaction of particles with same and different spin of light, which can mimic the spin-up and spin-down of electrons in quantum mechanics. The proposed device provides a tunable scheme to achieve high directionality of different cavity modes by incorporating one or more nanoscatterers. With incorporating more than one tunable mechanism such as nanoscatterers, nonlinearity, and time-modulation, simultaneously, the conventional limitations in chirality and sensitivity may be surpassed in the presence of unwanted imperfections of fabrication and noisy environment.

Global stability for McKean–Vlasov equations on large networks

Abstract We investigate the mean-field dynamics of stochastic McKean differential equations with heterogeneous particle interactions described by large network structures. To express a wide range of graphs, from dense to sparse structures, we incorporate the recently developed graph limit theory of graphops into the limiting McKean–Vlasov equations. Global stability of the splay steady state is proven via a generalised entropy method, leading to explicit graph structure-dependent decay rates. We highlight the robustness of the entropy approach by extending the results to the closely related Sakaguchi–Kuramoto model with intrinsic frequency distributions. We also present central examples of random graphs, such as power law graphs and the spherical graphop, and analyse the limitations of the applied methodology.