Brownian Motion Research Articles

Accelerating and breaking adaptive nano-colloids (

Unlike conventional colloids showing random mobility because of Brownian motion, active colloids contain nanomotors that translate chemical or physical triggers into directed movement. Whereas the acceleration of such particles works well, it is difficult to decelerate them by request. Compared to the existing literature on microscaled swimmers/robots, the main question of the current paper is whether nanoscaled colloids (<100 nm) can also be actively controlled despite the stronger relevance of rotational diffusion at such dimensions. We developed nanoparticles comprising two independent mechanisms for propulsion: a chemical engine associated with a Janus-type modification of organosilica nanoparticles and physical locomotion because of a superparamagnetic core inside these particles. Both triggers can be used independently to initiate the particles' directed and anisotropic movement. The magnetic forces can be tuned, most importantly concerning the angle defining the chemical acceleration. Superposition and a boost state are adopted for a parallel alignment. However, when the magnetic field acting on the particles is turned to an antiparallel orientation, a rapid deceleration can be observed, and the colloids halt.

Random-guided optimizer: a metaheuristic that shifts random search to guided search through iteration

This study offers a new swarm-based metaheuristic: random-guided optimizer (RGO). RGO has novel mechanics in shifting the random motion into a guided motion strategy during the iteration. In RGO, the iteration is divided into three equal size phases. In the first phase, the unit walks randomly inside the search space to tackle the local optimal problem earlier. In the second phase, each unit uses a unit selected randomly among the population as a reference in conducting the guided motion. In the third phase, each unit conducts guided motion toward or surpasses the best unit. Through simulation, RGO successfully finds the acceptable solution for 23 benchmark functions. Moreover, RGO successfully finds the global optimal solution for four functions: Branin, Goldstein-Price, Six Hump Camel, and Schwefel 2.22. RGO also outperforms slime mold algorithm (SMA), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO) in solving 12, 20, 12, and 1 function consecutively. In the future, improvement can be made by transforming RGO into solid multiple-phase strategy without losing its identity as a metaheuristic with multiple strategy in every iteration.

Impact of ageing on structure of random sequential adsorption packings of discorectangles

The ageing processes in the systems of elongated particles (discorectangles) adsorbed on a plane was studied numerically. An off-lattice model with continuous positional and orientational degrees of freedom was tested. The initial jammed state was produced using the random sequential adsorption (RSA) model. The transition from the jammed to equilibrium states were performed via translational and rotational Brownian motions. An aspect ratio of the particles (length-to-width ratio) was varied within the range ε=1−12 . At ɛ < 10 the relaxation into the isotropic state was always observed. In the intermediate range, 10⩽ε⩽11 , the relaxation transition into the states with ordered domains was observed. Particularly, for ɛ = 10 this transition was related with significant finite size scaling effects. For particles with ɛ = 12 formation of the large scale quasi-nematic domains and unlimited domain growth was observed. The impact of the relaxation on the domain structure, porosity, percolation connectivity and tracer particle diffusion in such barrier systems was also discussed.

Motor domain of condensin and step formation in extruding loop of DNA.

During the asymmetric loop extrusion of DNA by a condensin complex, one domain of the complex stably anchors to the DNA molecule, and another domain reels in the DNA strand into a loop. The DNA strand in the loop is fully relaxed, or there is no tension in the loop. Just outside of the loop, there is a tension that resists the extrusion of DNA. To maintain the extrusion of the DNA loop, the condensin complex must have a domain capable of generating a force to overcome the tension outside of the loop. This study proposes that the groove-shaped HEAT repeat domain Ycg1 plays the role of a molecular motor. A DNA molecule may bind to the groove electrostatically, and the weak binding force facilitates the random thermal motion of DNA molecules. A mechanical model that random collisions between DNA and the nonparallel inner surfaces of the groove may generate a directional force which is required for the loop extrusion to sustain. The hinge domain binds to the DNA molecule and acts as an anchor during asymmetric DNA loop extrusion. When the effects of ATP hydrolysis and the viscous drag of the fluid environment are considered, the motor-anchor model for the condensin complex and the mechanical model might explain the asymmetric loop extrusion, the formation of steps, the step size distribution in the loop extrusion, the tension-dependent extrusion speed, the interaction between coexisting loops on the DNA strand, and untying the knots during extrusion. This model can also explain the observed formation of the Z-loop.

Unveiling Hidden Hyperuniformity: Radial Turing Pattern Formation of Marangoni-Driven SiO2 Nanoparticles on Liquid Metal Surface.

Mastering the self-organization of nanoparticle morphologies is pivotal in soft matter physics and film growth. Silicon dioxide (SiO2) nanoparticles are an archetypical model of nanomotor in soft matter. Here, the emphasis is on the self-organizing behavior of SiO2 nanoparticles under extreme conditions. It is unveiled that manipulating the states of the metal substrate profoundly dictates the motion characteristics of SiO2 nanoparticles. This manipulation triggers the emergence of intricate morphologies and distinctive patterns. Employing a reaction-diffusion model, the fundamental roles played by Brownian motion and Marangoni-driven motion in shaping fractal structures and radial Turing patterns are demonstrated, respectively. Notably, these radial Turing patterns showcase hyperuniform order, challenging conventional notions of film morphology. These discoveries pave the way for crafting non-equilibrium morphological materials, poised with the potential for self-healing, adaptability, and innovative applications.

Spectral Indices and Evidence of Wave–Wave Modulation in Observations of the Interplanetary Magnetic Field

We present wave and turbulence observations from the DSCOVR spacecraft during the 2017 September solar flare and coronal mass ejection (CME) events. On September 4–12, the spectral index within the magnetic field power spectral density inertial range was consistent with Kolmogorov −5/3. This is despite the 9 days being composed of a complex mix of different features, including solar flares, solar energetic particle events, and CMEs. When analyzing shorter time periods, the spectral index varies. For two days where there were consecutive CMEs, the index follows Kraichnan–Iroshinikov −3/2, while on two quiet days, it was a mixture of −1, −3/2, and −2. The inertial range spectral index taken over the entire 9 days hides or averages out spectral features that occur over shorter time periods. We use a more realistic estimate of the amount of Doppler shifting into the spacecraft frame a show that the break frequencies on most days were located close to the H+ cyclotron frequency. We present evidence of wave–wave modulation and suggest that lower-frequency waves in the solar wind can modulate the growth rates/propagation of ion cyclotron waves, providing a method to transfer energy in the solar wind to smaller scales. Furthermore, we suggest that the indices in the inertial range can be explained by combining containment due to wave generation/propagation and stochastic Brownian motion in the solar wind. When these two phenomena are equal, they combine to create a −3/2 index.

Valuation of Vulnerable Barrier Options in a Mixed Fractional Brownian Motion Environment

Valuation of Vulnerable Barrier Options in a Mixed Fractional Brownian Motion Environment

Prcing Maximum Value Options under the Mixed Fractional Brownian Motion with Jumps

Prcing Maximum Value Options under the Mixed Fractional Brownian Motion with Jumps

Valuation of Reset Option with Multiple Reset Features under Mixed Fractional Brownian Motion Model

Valuation of Reset Option with Multiple Reset Features under Mixed Fractional Brownian Motion Model

Reciprocal effect on lateral diffusion of receptor for advanced glycation endproducts and toll-like receptor 4 in the HEK293 cell membrane.

Receptor for advanced glycation endproducts (RAGE) and toll-like receptor 4 (TLR4) are pattern-recognition receptors that bind to molecular patterns associated with pathogens, stress, and cellular damage. Diffusion plays an important role in receptor functionality in the cell membrane. However, there has been no prior investigation of the reciprocal effect of RAGE and TLR4 diffusion properties in the presence and absence of each receptor. This study reports how RAGE and TLR4 affect the mobility of each other in the human embryonic kidney (HEK) 293 cell membrane. Diffusion properties were measured using single-particle tracking (SPT) with quantum dots (QDs) that are selectively attached to RAGE or TLR4. The Brownian diffusion coefficients of RAGE and TLR4 are affected by the presence of the other receptor, leading to similar diffusion coefficients when both receptors coexist in the cell. When TLR4 is present, the average Brownian diffusion coefficient of RAGE increases by 40%, while the presence of RAGE decreases the average Brownian diffusion coefficient of TLR4 by 32%. Diffusion in confined membrane domains is not altered by the presence of the other receptor. The mobility of the cell membrane lipid remains constant whether one or both receptors are present. Overall, this work shows that the presence of each receptor can affect a subset of diffusion properties of the other receptor without affecting the mobility of the membrane.

Leaf functional trait evolution and its putative climatic drivers in African Coffea species.

Leaf traits are known to be strong predictors of plant performance and can be expected to (co)vary along environmental gradients. We investigated the variation, integration, environmental relationships, and evolutionary history of leaf functional traits in the genus Coffea L., typically a rainforest understory shrub, across Africa. A better understanding of the adaptive processes involved in leaf trait evolution can inform the use and conservation of coffee genetic resources in a changing climate. We used phylogenetic comparative methods to investigate the evolution of six leaf traits measured from herbarium specimens of 58 African Coffea species. We added environmental data and data on maximum plant height for each species to test trait-environment correlations in various (sub)clades, and we compared continuous trait evolution models to identify variables driving trait diversification. A substantial leaf trait variation was detected across the genus Coffea in Africa, which was mostly interspecific. Of these traits, stomatal size and stomatal density exhibited a clear trade-off. We observed low densities of large stomata in early branching lineages and higher densities of smaller stomata in more recent taxa, which we hypothesise to be related to declining CO2 levels since the mid-Miocene. Brownian Motion evolution was rejected in favour of White Noise or Ornstein-Uhlenbeck models for all traits, implying these traits are adaptively significant rather than driven by pure drift. The evolution of leaf area was likely driven by precipitation, with smaller leaves in dryer climates across the genus. Generally, Coffea leaf traits appear to be evolutionarily labile and governed by stabilising selection, though evolutionary patterns and correlations differ depending on the traits and clades considered. Our study highlights the importance of a phylogenetic perspective when studying trait relationships across related taxa, as well as the consideration of various taxonomic ranges.

A discrete-time model that weakly converges to a continuous-time geometric Brownian motion with Markov switching drift rate

A discrete-time model that weakly converges to a continuous-time geometric Brownian motion with Markov switching drift rate

Exploring the Macroevolutionary Signature of Asymmetric Inheritance at Speciation.

Popular comparative phylogenetic models such as Brownian Motion, Ornstein-Ulhenbeck, and their extensions, assume that, at speciation, a trait value is inherited identically by two descendant species. This assumption contrasts with models of speciation at a micro-evolutionary scale where descendants' phenotypic distributions are sub-samples of the ancestral distribution. Different speciation mechanisms can lead to a displacement of the ancestral phenotypic mean among descendants and an asymmetric inheritance of the ancestral phenotypic variance. In contrast, even macro-evolutionary models that account for intraspecific variance assume symmetrically conserved inheritance of ancestral phenotypic distribution at speciation. Here we develop an Asymmetric Brownian Motion model (ABM) that relaxes the assumption of symmetric and conserved inheritance of the ancestral distribution at the time of speciation. The ABM jointly models the evolution of both intra- and inter-specific phenotypic variation. It also infers the mode of phenotypic inheritance at speciation, which can range from a symmetric and conserved inheritance, where descendants inherit the ancestral distribution, to an asymmetric and displaced inheritance, where descendants inherit divergent phenotypic means and variances. To demonstrate this model, we analyze the evolution of beak morphology in Darwin finches, finding evidence of displacement at speciation. The ABM model helps to bridge micro- and macro-evolutionary models of trait evolution by providing a more robust framework for testing the effects of ecological speciation, character displacement, and niche partitioning on trait evolution at the macro-evolutionary scale.

Nonlinear convective transport of nanofluid over an inclined plate situated in a Darcy–Forchheimer porous medium with viscous dissipation and varying thermo-physical features

This article presents a theoretical analysis of the effects of varying thermo-physical properties, chemical reactions, and activation energy on the quadratic convective transport of nanofluids. The flow is created through an inclined plate in a Darcy–Forchheimer porous medium. The nanofluid model includes thermophoresis and Brownian motion to account for nanoparticle movement. The original flow equations are transformed using similarity transformations, and the resulting dimensionless equations are solved with a multi-domain spectral collocation approach. The effects of physical parameters on flow variables, heat and mass transfer coefficients are discussed. Comparisons between vertical and inclined plates reveal that inclination improves thermal performance but decreases velocity and engineering-related quantities. Fluctuating viscosity and nonlinear convection speed up fluid flow, whereas temperature rises with variable thermal conductivity and Biot number. Variable fluid properties, nonlinear convection, and convective heat conditions enhance heat transport coefficients. Activation energy boosts nanoparticle volume fraction profiles but decreases mass transport rates.

A Performance Analysis of Stochastic Processes and Machine Learning Algorithms in Stock Market Prediction

In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox–Ingersoll–Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for predicting the trends of stock indices XLF (financial sector), XLK (technology sector), and XLV (healthcare sector). The results showed that stochastic processes achieved remarkable prediction performance, especially the CIR model. Additionally, this study demonstrated that the metrics of machine learning algorithms are relatively lower. However, it is important to note that stochastic processes use the actual current index value to predict tomorrow’s value, which may overestimate their performance. In contrast, machine learning algorithms offer a more flexible approach and are not as dependent on the current index value. Therefore, optimizing the hyperparameters of machine learning algorithms is crucial for further improving their performance.

Programmable Crowding and Tunable Phases in a Binary Mixture of Colloidal Particles under Light-Driven Thermal Convection.

We employ photothermally driven self-assembly of colloidal particles to design microscopic structures with programmable size and tunable order. The experimental system is based on a binary mixture of "plasmonic heater" gold nanoparticles and "assembly building block" microparticles. Photothermal heating of the gold nanoparticles under visible light causes a natural convection flow that efficiently assembles the microscale building block particles (diameter 1-10 μm) into a monolayer. We identify the onset of active Brownian motion of colloidal particles under this convective flow by varying the conditions of light intensity, gold nanoparticle concentration, and sample height. We realize a crowded assembly of microparticles around the center of illumination and show that the size of the particle crowd can be programmed using patterned light illumination. In a binary mixture of gold nanoparticles and polystyrene microparticles, we demonstrate the formation of rapid and large-scale crystalline monolayers, covering an area of 0.88 mm2 within 10 min. We find that the structural order of the assembly can be tuned by varying the surface charge of the nanoparticles and the size of the microparticles, giving rise to the formation of different phases-colloidal crystals, crowds, and gels. Using Monte Carlo simulations, we explain how the phases emerge from the interplay between hydrodynamic and electrostatic interactions, as well as the assembly kinetics. Our study demonstrates the promise of self-assembly with programmable shapes and structural order under nonequilibrium conditions using an accessible setup comprising only binary mixtures and LED light.

Intriguing Brownian Diffusivity Characteristics of Complex Nanoparticles

Abstract This article reveals how apparently similar looking nanoparticles with same size, shape, and mass may exhibit widely varying Brownian diffusivity due to inherent features of nanoscale dynamics. Such variabilities may, in certain cases, reach order of magnitude fluctuations depending on the interfacial and bulk properties of the Brownian body. Accordingly, the theory explains several unanswered questions in connection to submicron systems including anomalous thermal properties of nanofluids and strangely varying transmittivities of biologically originated particulate droplets.

Implementing the predictor-corrector approach to examine the thermo-solutal convection for buongiorno eyring-powell nanofluid model with squeezed microcantilivier surface

The Buongiorno nanofluid model serves as a foundational model for theoretical and experimental research in the field of nanofluids, and this model can be applied to various engineering problems, including heat exchangers, biomedical applications, solar collectors, nuclear reactors, and different cooling systems in the automotive and electronics industries. The use of nanofluids in the Eyring-Powell fluid over a squeezed sensing surfaces is a vital area of research for the design and improvement of microfluidic devices and sensors, which find widespread usage in industrial and medical applications. The main purpose of this work is to note the augmentation in thermal conductivity by using the Buongiorno nanofluid model along with the natural convective flow of non-Newtonian Eyring-Powell fluid that is moving on the squeezed sensory surface along with slip velocity. The viscosity of a fluid is assumed to be dependent on temperature. The Cattaneo-Christov heat and mass flux and chemical reaction are assumed to determine the combined effect of heat and mass transport. The governing model of equations is in the form of dimensional partial differential equations, and we have to convert these equations into ordinary differential equations; therefore, a set of similarity transformations is adopted for making the equations into dimensionless form. The numerical results were obtained by adopting the predictor and corrector multistep method, namely the ‘Adams-Bashforth’ technique, in Matlab software. The use of natural convective flow enhances the velocity region. The velocity slip parameter increases the velocity of the fluid, whereas the viscosity parameter drops the velocity profile. The thermophoresis and Brownian motion parameters are the sources of the increment in the temperature region. The thermal relaxation and solutal relaxation parameters are the sources of decline in the temperature region. The squeezing parameter is the source of reduction in the skin friction, whereas the result indicates that the fluid parameter, Grashof number, and viscosity coefficients increase the skin friction.

Anomalous scalings of fluctuations of the area swept by a Brownian particle trapped in a [formula omitted] potential

We study the fluctuations of the area A=∫0Tx(t)dt under a one-dimensional Brownian motion x(t) in a trapping potential ∼|x|, at long times T→∞. We find that typical fluctuations of A follow a Gaussian distribution with a variance that grows linearly in time (at large T), as do all higher cumulants of the distribution. However, large deviations of A are not described by the “usual” scaling (i.e., the large deviations principle), and are instead described by two different anomalous scaling behaviors: Moderately-large deviations of A, obey the anomalous scaling PA;T∼e−T1/3fA/T2/3 while very large deviations behave as PA;T∼e−TΨA/T2. We find the associated rate functions f and Ψ exactly. Each of the two functions contains a singularity, which we interpret as dynamical phase transitions of the first and third order, respectively. We uncover the origin of these striking behaviors by characterizing the most likely scenario(s) for the system to reach a given atypical value of A. We extend our analysis by studying the absolute area B=∫0T|x(t)|dt and also by generalizing to higher spatial dimension, focusing on the particular case of three dimensions.

Significance of variable thermal conductivity and suction/injection in unsteady MHD mixed convection flow of Casson Williamson nanofluid through heat and mass transport with gyrotactic microorganisms

AbstractThe present paper explores a two‐dimensional mixed bio‐convective unsteady viscous hydro‐magnetic Casson Williamson nanofluid flow model with heat and mass transport incorporating motile microorganisms towards a stretchy spinning disc. The flow concept is accomplished by rotating a stretched disc with a time‐varying angular velocity. By applying a magnetic field normal to the axial direction, a magnetic interaction is taken into consideration. The Casson Williamson nanofluid contains nanosized particles suspended with swimming motile microorganisms and the rotation of the disc is exhibited by buoyancy forces, thermophoresis, suction/injection, zero mass flux conditions, variable thermal conductivity, Joule heating and so forth. The obtained flow narrating differential equations of the model are transformed into ordinary differential system. This is accomplished by simulating boundary value problems using the shooting technique using the ‘ND‐Solve’ approach included in the Mathematica software (Mathematica 12). The implications of the engaged parameters such as Williamson fluid parameter, Casson fluid (CF) parameter, thermophoresis parameter, Brownian motion parameter and so forth, on both axial and radial velocities, temperature, concentration of nanoparticles and microorganisms are explained by means of graphical and tabular constructions. This paper's validity has been confirmed and its findings align with those of other previously published papers. Furthermore, it is found that both the axial and radial velocity profiles are seen to be diminishing functions of the CF parameter. The identified observation may have theoretical implications for a number of engineering procedures, solar energy systems, biofuel cells and extrusion system improvement. Moreover, this work finds application in micro‐fabrication techniques and the chemical industry.