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This paper presents a mathematical theory of behavioral swarms, where the state of interacting entities, which are called active particles, includes in addition to position and velocity, an internal variable, called activity, which has the ability to interact with mechanical variables thus affecting the interaction rules. In turn, the mechanical variables can modify the dynamics of activity variable. This approach is useful for describing the dynamics of living systems with a finite number of interacting entities. This paper provides a general conceptual framework that extends the pioneering work [N. Bellomo, S.-Y. Ha and N. Outada, Towards a mathematical theory of behavioral swarms, ESAIM : Control Theory Var. Calculus 26 (2020) 125, https:/ /doi.org/10.1051/cocv/2020071]. The theory is firstly developed for the constant number of active particles. Then, it is considered for the case of particles tending to infinity. This theory is useful for describing the dynamics of living systems. Therefore, it provides the conceptual basis for developing a mathematical theory of behavioral swarms, which naturally lead to the study of a theory of swarm intelligence. A study of the dynamics of swarms shows how the theory can be applied to real world collective dynamics. [ABSTRACT FROM AUTHOR]

This paper presents a critical analysis of the literature and perspective research ideas for modeling the epidemics caused by the SARS-CoV-2 virus. It goes beyond deterministic population dynamics to consider several key complexity features of the system under consideration. In particular, the multiscale features of the dynamics from contagion to the subsequent dynamics of competition between the immune system and the proliferating virus. Other topics addressed in this work include the propagation of epidemics in a territory, taking into account local transportation networks, the heterogeneity of the population and the study of social and economic problems in populations involved in the spread of epidemics. The overall content aims to show how new mathematical tools can be developed to address the above topics and how mathematical models and simulations can contribute to the decision making of crisis managers. [ABSTRACT FROM AUTHOR]

In the last decade, the thermostatted kinetic theory has been proposed as a general paradigm for the modeling of complex systems of the active matter and, in particular, in biology. Homogeneous and inhomogeneous frameworks of the thermostatted kinetic theory have been employed for modeling phenomena that are the result of interactions among the elements, called active particles, composing the system. Functional subsystems contain heterogeneous active particles that are able to perform the same task, called activity. Active matter living systems usually operate out-of-equilibrium; accordingly, a mathematical thermostat is introduced in order to regulate the fluctuations of the activity of particles. The time evolution of the functional subsystems is obtained by introducing the conservative and the nonconservative interactions which represent activity-transition, natural birth/death, induced proliferation/destruction, and mutation of the active particles. This review paper is divided in two parts: In the first part the review deals with the mathematical frameworks of the thermostatted kinetic theory that can be found in the literature of the last decade and a unified approach is proposed; the second part of the review is devoted to the specific mathematical models derived within the thermostatted kinetic theory presented in the last decade for complex biological systems, such as wound healing diseases, the recognition process and the learning dynamics of the human immune system, the hiding-learning dynamics and the immunoediting process occurring during the cancer-immune system competition. Future research perspectives are discussed from the theoretical and application viewpoints, which suggest the important interplay among the different scholars of the applied sciences and the desire of a multidisciplinary approach or rather a theory for the modeling of every active matter system. • Kinetic equations with Gaussian-type thermostat. • Nonlinear partial-integro differential equations. • Complex living systems. • Immune system modeling. • Fibrosis disease modeling. [ABSTRACT FROM AUTHOR]

Most coal-fired industrial flue gases contained low concentration CO. How to deal with it effectively was a research hotspot in recent years. Catalytic oxidation was considered as the most promising method in the 21st century for the removement of CO with the high efficiency, environmentally friendly, easy to operate and low cost. In this review, the reaction mechanisms of CO oxidation were described, which could provide ideas for the development of new catalysts. The effects of supports and preparation methods on catalysts activity was also reviewed systematically. In addition, some suggestions and outlooks were provided for future development of CO catalytic oxidation. [ABSTRACT FROM AUTHOR]

In this paper we study Nash equilibria in auctions from the kinetic theory of active particles point of view. We propose a simple learning rule for agents to update their bidding strategies based on their previous successes and failures, in first-price auctions with two bidders. Then, we formally derive the corresponding kinetic equations which describe the evolution over time of the distribution of agents on the bidding strategies. We show that the stationary solution of the equation corresponds to the symmetric Nash equilibrium of the auction, and we prove the convergence to this stationary solution when time goes to infinity. We also introduce a more general learning rule that only depends on the income of agents, and we apply to both first- and second-price auctions. We show that agents learn the Nash equilibrium in first- and second-price auctions with these rules. We present agent-based simulations of the models, and we discuss several open problems. [ABSTRACT FROM AUTHOR]

This editorial paper reviews the articles published in a special issue devoted to the application of active particle methods applied to the study of the collective dynamics of large systems of interacting entities in science and society. The applications presented in this special issue focus on the study of financial markets, cell dynamics in the context of cancer modeling, vehicle and crowd vehicle and crowd dynamics, and classical problems in the kinetic theory of active particles and swarm theory. A critical analysis is proposed to look forward to research. Perspectives with emphasis on the interaction of multiple social dynamics. [ABSTRACT FROM AUTHOR]

A systematic investigation of the dynamic clustering behavior of active particles under confinement, including the effects of both particle density and active driving force, is presented based on a hybrid coarse-grained molecular dynamics simulation. First, a series of scaling laws are derived with power relationships for the dynamic clustering time as a function of both particle density and active driving force. Notably, the average number of clusters N ¯ assembled from active particles in the simulation system exhibits a scaling relationship with clustering time t described by N ¯ ∝ t − m . Simultaneously, the scaling behavior of the average cluster size S ¯ is characterized by S ¯ ∝ t m . Our findings reveal the presence of up to four distinct dynamic regions concerning clustering over time, with transitions contingent upon the particle density within the system. Furthermore, as the active driving force increases, the aggregation behavior also accelerates, while an increase in density of active particles induces alterations in the dynamic procession of the system. [ABSTRACT FROM AUTHOR]

This paper focuses on Herbert A. Simon's visionary theory of the Artificial World. The artificial world evolves over time as a result of various actions, including interactions with the external world as well as interactions among its internal components. This paper proposes a mathematical theory of the conceptual framework of the artificial world. This goal requires the development of new mathematical tools, inspired in some way by statistical physics and stochastic game theory. The mathematical theory is applied in particular to the study of the dynamics of organizational learning, where cooperation and competition evolve through decomposition and recombination of organizational structures; the effectiveness of the evolutionary changes depends on the dynamic prevalence of cooperative over selfish behaviors, showing features common to the evolution of all living systems. [ABSTRACT FROM AUTHOR]

This editorial is dedicated to presenting the papers published in a special issue focused on modeling, qualitative analysis and simulation of the collective dynamics of systems in engineering and life sciences. All papers have a minor or major reference to living, i.e. complex systems, and a critical analysis of the overall content of the issue is proposed, leading to a forward look at research perspectives. This paper first defines the goals of the issue. Then, a brief description of the scientific contribution of the papers published in this issue is given. Finally, a look into the future is proposed. [ABSTRACT FROM AUTHOR]

Artificial active matters on a macroscopic scale, including vibrating particles, robots, and camphor boats, have attracted increasing attentions due to their uniform properties, rich and easily controllable parameters, convenient observation, and the independence of biochemical processes from physical processes, especially providing these unique advantages for researching the collective behaviors under strong confinement and crowded surroundings. In this review, we present an overview of motion models, mechanisms, and dynamic characteristics of various active particles, both in free and complex media. Additionally, we delve into the collective behaviors of "dry'' active matter, covering structural and dynamic properties observed in experiments and theoretical models. We summarize the impact of hydrodynamic interactions on the dynamics and structures of these active particles within hydrodynamic environments. Lastly, we discuss emerging opportunities and challenges for future advancement of macroscopic artificial active matter.

This article deals with reactions occurring under the action of active particles formed during the flash of a corona electric discharge between a solid electrode and the surface of a liquid—electrochemical reactions without an electrode. Reactions take place in a thin surface layer of water, where there are no electrodes, so the reactions are called electrodeless. The electrodes are used only to excite the reaction zone and do not directly participate in the reaction. The oxidation of organic compounds to carbon dioxide and water occurs in an electrodeless reaction. [ABSTRACT FROM AUTHOR]

This paper deals with the micro–macro derivation of models from the underlying description provided by methods of the kinetic theory for active particles. We consider the so-called exotic models according to the definition proposed in [ N. Bellomo, N. Outada, J. Soler, Y. Tao and M. Winkler, Chemotaxis and cross diffusion models in complex environments: Modeling towards a multiscale vision, Math. Models Methods Appl. Sci. 32 (2022) 713–792]. The first part of the presentation focuses on a survey and a critical analysis of some phenomenological models known in the literature. We refer to a selection of case studies, in detail, the transport of virus models, social dynamics, and Keller–Segel in a fluid. The second part shows how a Hilbert-type approach can be developed to derive models at the macroscale from the underlying description provided by the kinetic theory of active particles. The third part deals with the derivation of macroscopic models corresponding to the selected case studies. Finally, a forward look into the future research perspectives is proposed. [ABSTRACT FROM AUTHOR]

Self‐propelling active particles are an exciting and interdisciplinary emerging area of research with projected biomedical and environmental applications. Due to their autonomous motion, control over these active particles that are free to travel along individual trajectories, is challenging. This work uses optically patterned electrodes on a photoconductive substrate using a digital micromirror device (DMD) to dynamically control the region of movement of self‐propelling particles (i.e., metallo‐dielectric Janus particles (JPs)). This extends previous studies where only a passive micromotor is optoelectronically manipulated with a translocating optical pattern that illuminates the particle. In contrast, the current system uses the optically patterned electrode merely to define the region within which the JPs moved autonomously. Interestingly, the JPs avoid crossing the optical region's edge, which enables constraint of the area of motion and to dynamically shape the JP trajectory. Using the DMD system to simultaneously manipulate several JPs enables to self‐assemble the JPs into stable active structures (JPs ring) with precise control over the number of participating JPs and passive particles. Since the optoelectronic system is amenable to closed‐loop operation using real‐time image analysis, it enables exploitation of these active particles as active microrobots that can be operated in a programmable and parallelized manner. [ABSTRACT FROM AUTHOR]

We numerically study structure and dynamics of single files composed of active particles, as well as, active-passive binary mixtures. Our simulation results show that when the persistent length of self-propelled particles is much larger than the average inter-particle separation and the self-propulsion velocity is larger than the thermal velocity, particles in the file exist as clusters of various sizes. Average cluster size and structures of the file are very sensitive to self-propulsion properties, thermal fluctuations and composition of the mixture. In addition to the variation of file composition, our study considers two sorts of mixture configurations. One corresponds to the uniform distribution of active passive throughout the mixture in the single file. In the other configuration, active particles are on one side of the file. For the both configurations, even a little fraction of active particles produces a large impact on the structure and dynamics of the file. When persistent length of active species is much larger than average inter-particle separation, active-species in a single-file exist as clusters of various sizes. Due to the motility transfer mechanism, little fractions of active particles have great impacts on the structure and dynamics of single-files composed of active-passive binary mixtures. [ABSTRACT FROM AUTHOR]

This editorial paper reviews the articles published in a special issue devoted to the application of active particle methods to the study of the collective dynamics of large systems of interacting entities in science and society. The applications presented in this special issue focus on the study of financial markets, cell dynamics in the context of cancer modeling, crowd vehicle and crowd dynamics, and classical problems in the kinetic theory of active particles and swarm theory. A critical analysis is proposed to look forward at new perspectives, with emphasis on the interaction of multiple social dynamics. [ABSTRACT FROM AUTHOR]

This paper proposes a kinetic theory approach coupling together the modeling of crowd evacuation from a bounded domain with exit doors and infectious disease contagion. The spatial movement of individuals in the crowd is modeled by a proper description of the interactions with people in the crowd and the environment, including walls and exits. At the same time, interactions among healthy and infectious individuals may generate disease spreading if exposure time is long enough. Immunization of the population and individual awareness to contagion is considered as well. Interactions are modeled by tools of game theory, that let us propose the so-called tables of games that are introduced in the general kinetic equations. The proposed model is qualitatively studied and, through a series of case studies, we explore different scenarios related to crowding and gathering formation within indoor venues under the spread of a respiratory infectious disease, obtaining insights on specific policies to reduce contagion that may be implemented. [ABSTRACT FROM AUTHOR]

The parameters of the gas phase and the kinetics of reactive ion etching of SiO2 and Si3N4 under conditions of an induction RF (13.56 MHz) discharge with a varying HBr/Cl2 ratio is studied. The study includes plasma diagnostics using Langmuir probes, plasma modeling to find stationary concentrations of active particles, measuring velocities, and analyzing etching mechanisms in the effective interaction probability approximation. It is found that the substitution of HBr by Cl2 at a constant argon content (a) is accompanied by a noticeable change in the electrical parameters of the plasma; (b) leads to a weak increase in the intensity of ion bombardment of the treated surface; and (c) causes a significant increase in the total concentration and flux density of reactive particles. It is shown that the etching rates of SiO2 and Si3N4 increase monotonically as the proportion of Cl2 increases in a mixture, while the main etching mechanism is an ion-stimulated chemical reaction. The model description of the kinetics of such a reaction in the first approximation assumes (a) the additive contribution of bromine and chlorine atoms and (b) the direct proportional dependence of their effective interaction probabilities on the intensity of ion bombardment. The existence of an additional channel of heterogeneous interaction with the participation of HCl molecules is proposed. [ABSTRACT FROM AUTHOR]

To explore the coupling between a growing population of microorganisms such as E. coli and a nonuniform nutrient distribution, we formulate a minimalistic model. It consists of active Brownian particles that divide and grow at a nutrient-dependent rate following the Monod equation. The nutrient concentration obeys a diffusion equation with a consumption term and a point source. In this setting the heterogeneity in the nutrient distribution can be tuned by the diffusion coefficient. In particle-based simulations, we demonstrate that passive and weakly active particles form proliferation-induced clusters when the nutrient is localized, without relying on further mechanisms such as chemotaxis or adhesion. In contrast, strongly active particles disperse in the whole system during their lifetime and no clustering is present. The steady population is unaffected by activity or nonuniform nutrient distribution and only determined by the ratio of nutrient influx and bacterial death. However, the transient dynamics strongly depends on the nutrient distribution and activity. Passive particles in almost uniform nutrient profiles display a strong population overshoot, with clusters forming all over the system. In contrast, when slowly diffusing nutrients remain centred around the source, the bacterial population quickly approaches the steady state due to its strong coupling to the nutrient. Conversely, the population overshoot of highly active particles becomes stronger when the nutrient localisation increases. We successfully map the transient population dynamics onto a uniform model where the effect of the nonuniform nutrient and bacterial distributions are rationalized by two effective areas.

A systematic investigation of the dynamic clustering behavior of active particles under confinement, including the effects of both particle density and active driving force, is presented based on a hybrid coarse-grained molecular dynamics simulation. First, a series of scaling laws are derived with power relationships for the dynamic clustering time as a function of both particle density and active driving force. Notably, the average number of clusters N¯ assembled from active particles in the simulation system exhibits a scaling relationship with clustering time t described by N¯∝t−m. Simultaneously, the scaling behavior of the average cluster size S¯ is characterized by S¯∝tm. Our findings reveal the presence of up to four distinct dynamic regions concerning clustering over time, with transitions contingent upon the particle density within the system. Furthermore, as the active driving force increases, the aggregation behavior also accelerates, while an increase in density of active particles induces alterations in the dynamic procession of the system.

Bacteria and other self-propelling microorganisms produce and respond to signaling molecules to communicate with each other (quorum sensing) and to direct their collective behavior. Here, we explore agents (active particles) which communicate with each other to coordinate their collective dynamics for maximizing nutrient consumption. Using reinforcement learning and neural networks, we identify three different strategies: a ‘clustering strategy’, where the agents accumulate in regions of high nutrient concentration; a ‘spreading strategy’, where particles stay away from each other to avoid competing for sparse resources; and an ‘adaptive strategy’, where the agents adaptively decide to either follow or stay away from others. Our work exemplifies the idea that machine learning can be used to determine parameters that are evolutionarily optimized in biological systems but often occur as unknown parameters in mathematical models describing their dynamics.

Abstract Self‐propelling active particles are an exciting and interdisciplinary emerging area of research with projected biomedical and environmental applications. Due to their autonomous motion, control over these active particles that are free to travel along individual trajectories, is challenging. This work uses optically patterned electrodes on a photoconductive substrate using a digital micromirror device (DMD) to dynamically control the region of movement of self‐propelling particles (i.e., metallo‐dielectric Janus particles (JPs)). This extends previous studies where only a passive micromotor is optoelectronically manipulated with a translocating optical pattern that illuminates the particle. In contrast, the current system uses the optically patterned electrode merely to define the region within which the JPs moved autonomously. Interestingly, the JPs avoid crossing the optical region's edge, which enables constraint of the area of motion and to dynamically shape the JP trajectory. Using the DMD system to simultaneously manipulate several JPs enables to self‐assemble the JPs into stable active structures (JPs ring) with precise control over the number of participating JPs and passive particles. Since the optoelectronic system is amenable to closed‐loop operation using real‐time image analysis, it enables exploitation of these active particles as active microrobots that can be operated in a programmable and parallelized manner.

The active Brownian motion of single dust particles of various types in the 3D electrostatic DC discharge trap under the action of laser radiation is studied experimentally. Spherical dust particles with a homogeneous surface, as well as Janus particles, are used in the experiment. The properties of the active Brownian motion of all types of dust particles are studied. In particular, the 3D analysis of trajectories of microparticles is carried out, well as an analysis of their root mean square displacement. The mean kinetic energy of motion of the dust particle of various types in a 3D trap is determined for different laser powers. Differences in the character of active Brownian motion in electrostatic traps with different spatial dimensions are found. [ABSTRACT FROM AUTHOR]

The object of research is a new, potentially effective and practical process for the decontamination of radioactive soil, based on combination of plasma hydroseparation and plasma activation. The cleaning effect is ensured by the destruction of the bonds of radionuclides with soil particles due to a series of electrophysical discharges at which active particles and shock waves appear. In a designed setup, the process of plasma-chemical treatment is implemented in a plasma cell with a self-sustaining pulsating mode of burning an electric discharge, which occurs in an aqueous solution. The setup realizes a resonant increase in the intensity of shock waves, turbulence and multiple expansion of the core, such that the expansion of the plasma-liquid interface becomes a real basis for scaling up the setup. Regardless of the material of the electrodes and in a wide range of electrical conductivity (measured from 100 to 5,000 μS/cm), the restructuring of the combustion regime is accompanied by an increase in the size and stabilization of the luminous zone, fragmentation of bubbles, and an increase in the rate of their evacuation from the discharge zone. The main factors of such a restructuring are the channel dimensions and temperature of the solution. Various materials of the walls of plasma-chemical reactor have been tested: plexiglass, ceramics and stainless steel with the thickness of 2 mm. The maximum increase in the amplitude of resonance oscillations depends on the cell radius. A dynamic pressure, which in an individual discharge is about 5–15 mm of the water column at the mouth of the discharge, increases to 150–200 mm of the water column at the bottom of the plasma cell at resonance. An increase in efficiency is achieved by an optimal choice of the duration of the current phase and the distance between the electrodes, which is 15–30 mm. The voltage drop is 70–80 % across the spark discharge, the rest falls across the solution. The transition of the discharge to a periodic pulsating current mode with an increase in the temperature of the solution has been found. Tests on a mobile plasma-chemical facility for the process of plasma co-precipitation of radionuclides 137Cs, 134Cs and 90Sr with ferrocyanide sorbents under real conditions of hydroseparation of contaminated soil from fields around the Fukushima Daiichi have shown a decrease in organic substances in water by 40 times, and of radioactivity by 75 times. [ABSTRACT FROM AUTHOR]

This paper presents a survey of advanced concepts and research perspectives, of a philosophical-mathematical approach to describe the dynamics of systems of many interacting living entities. The first part introduces the general conceptual framework. Then, a critical analysis of the existing literature is developed and referred to a multiscale view of a mathematics of living organisms. This paper attempts to understand how far the present state-of-the-art is far from the achievement of such challenging objective. The overall study leads to identify research perspectives and possible hints to deal with them. [ABSTRACT FROM AUTHOR]

The processes of plasma-chemical and reactive-ion etching of silicon in trifluoromethane (CHF3) are studied using optical emission spectroscopy. The dependences of the radiation intensities of atoms and molecules on the etching time, input power, and pressure of the plasma-forming gas are obtained and analyzed. [ABSTRACT FROM AUTHOR]

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This paper considers a system consisting of a number of interacting living entities whose state at the microscopic scale is heterogeneously distributed among the said entities. This state includes, in addition, the classical mechanical variables, such as position and velocity, also a behavioral variable which is modified by interactions. It is shown how the pioneering ideas proposed in Bellomo et al. [Towards a mathematical theory of behavioral swarms, ESAIM Control Optim. Calc. Var. 26 (2020) 125] can be developed towards modeling behavioral swarms within a quest towards a mathematical theory of living systems. The first part of the paper presents a qualitative analysis of the emerging behaviors predicted by the model in aforementioned work. Some simulations follow to depict the said emerging behaviors. The last part of the paper is devoted to derive a new, more general theory in view of applications to model living systems. [ABSTRACT FROM AUTHOR]

The radiation spectra of the RF discharge plasma of dichlorodifluoromethane during copper etching are obtained and analyzed. It is shown that radiation of the RF discharge plasma is represented by atomic and molecular components and it is assumed that the dependences of the intensities of the lines and bands on the external discharge conditions are determined by the excitation of the emissive states on direct electron impact. Moreover, their behavior agrees with the character of the dependences of the etching rate under the same conditions. [ABSTRACT FROM AUTHOR]

This review paper is devoted to study the conceptual difficulties that mathematics meets when attempting to describe the complexity of living matter focusing on the challenging perspective of developing a mathematical theory for living systems including mutations and selection. The quest starts with the identification of a number of common complexity features of living systems. Then, mathematical structures are derived to include these features, while mathematical models are derived by inserting in the structures models of individual based interactions. Three applications are examined by active particles methods, i.e., models of SARS2-CoV-2 pandemics, models of idiosyncratic learning in open markets and of the dynamics of prices accounting for human behaviors. A critical study, which pervades the whole paper, shows that also economics can be viewed as a behavioral science thus accounting for specific aspects typical of living systems. • Presents a critical analysis of theoretical tools to model living systems. • Provides a survey of active particles methods by theoretical tools of statistical physics. • Provides a survey of behavioral swarms theory for evolutionary systems. • Presents applications in specifically SARS-CoV-2 epidemics, and price dynamics within the framework of behavioral economics. • Further research perspectives are brought to the attention of the interested reader. [ABSTRACT FROM AUTHOR]

Optimal navigation in complex environments is a problem with multiple applications ranging from designing efficient search strategies to engineering microscopic cargo delivery. When motion happens in presence of strong external forces, route optimization is particularly important as active particles may encounter trapping regions that would substantially slow down their progress. Here, considering a self-propelled agent moving at a constant speed, we study the efficiency of Zermelo’s classical solution for navigation in a sinusoidal potential landscape. Investigating both cases of motion on the plane and on curved surfaces, we focus on the regime where the external force exceeds self-propulsion in finite regions. There, we show that, despite the fact that most trajectories following the trivial policy of going straight get arrested, the Zermelo policy allows for a comprehensive exploration of the environment. However, our results also indicate an increased sensitivity of the Zermelo strategy to initial conditions, which limits its robustness and long-time efficiency, particularly in presence of fluctuations. These results suggest an interesting trade-off between exploration efficiency and stability for the design of control strategies to be implemented in real systems.

The contents of this editorial is proposed in three parts devoted to the modeling and simulation of mutating virus pandemics in a globally connected world. The presentation is as follows: first, a general framework is presented according to the idea that mathematical models should go beyond deterministic population dynamics by considering the multiscale, heterogeneous features of the complex system under consideration; second, the contents of the papers in this issue are presented; finally, there is a critical analysis looking ahead to research perspectives. [ABSTRACT FROM AUTHOR]

This paper deals with the micro–macro-derivation of virus models coupled with a reaction–diffusion system that generates the dynamics in space of the virus particles. The first part of the presentation focuses, starting from [N. Bellomo, K. Painter, Y. Tao and M. Winkler, Occurrence versus absence of taxis-driven instabilities in a May–Nowak model for virus infection, SIAM J. Appl. Math. 79 (2019) 1990–2010; N. Bellomo and Y. Tao, Stabilization in a chemotaxis model for virus infection, Discrete Contin. Dyn. Syst. S 13 (2020) 105–117], on a survey and a critical analysis of some phenomenological models known in the literature. The second part shows how a Hilbert type can be developed to derive models at the macro-scale from the underlying description delivered by the kinetic theory of active particles. The third part deals with the derivation of macroscopic models of various virus models coupled with the reaction–diffusion systems. Then, a forward look to research perspectives is proposed. [ABSTRACT FROM AUTHOR]

The characteristics of the gas phase and the kinetics of reactive-ion etching of silicon in a 50% C6F12O + 50% Ar plasma are studied. The study scheme includes plasma diagnostics using Langmuir probes and optical emission spectroscopy, as well as the measurement of etching rates with varying input power (200–600 W) and gas pressure (4–12 mTorr). It is shown that (a) the nature of the change in the parameters of the electron and ion components of the plasma generally corresponds to the regularities known for other fluorocarbon gases; and (b) the kinetics of the formation of fluorine atoms is significantly affected by bulk processes of the form CFx + O → COFx–1 + F. It is established that the change in the silicon etching rate is determined by the kinetics of the heterogeneous reaction Si + xF → SiFx flowing in the mode of limitation by the flow of fluorine atoms. It is assumed that the effective probability of this reaction under constant temperature conditions is determined by the processes of competitive adsorption of oxygen atoms and/or surface oxidation. [ABSTRACT FROM AUTHOR]

Summary: A mesoscale‐continuum framework is presented for modelling the charging and discharging of a Li‐ion battery electrode microstructure through a semi‐concurrent finite element method (FEM)—discrete element method (DEM) framework. The framework accounts for (a) size changes due to stress‐diffusion interactions within each active particle and (b) the inter‐particle interactions resulting due to these size changes. The size alterations of individual active particles are captured through coupled chemo‐mechanical equations using the FEM. The resulting size changes lead to the inter‐particle interactions which are modelled using the DEM. A staggered approach composed of multiple cycles is implemented. Each cycle consists of sequential sub‐steps of the FEM and DEM. This framework is employed to study the mono‐ and poly‐sized assembly of the microstructures consisting of active particles. The packing fraction evolution agrees reasonably well with the theoretical and experimental predictions. From the simulations, it is observed that the polydispersity does not influence the final macroscopic packing fraction of the assembly upon charging or discharging. However, in assemblies with a lower packing fraction, the relative size changes among all the particles are almost the same, contrary to assemblies with a higher packing fraction, where the extent of the relative radii change is different across the particles. The framework can also handle the multiple cycles of charge and discharge, which can help studies concerning capacity fade and other associated phenomena in the future. [ABSTRACT FROM AUTHOR]

It is well known that the performance and durability of lithium-ion batteries (LIBs) can be severely impaired by fracture events that originate in stresses due to Li ion diffusion in fast charge–discharge cycles. Existing models of battery damage overlook either the role of particle shape in stress concentration, the effect of material disorder and preexisting defects in crack initiation and propagation, or both. In this work we present a novel, three-dimensional, and coupled diffusive-mechanical numerical model that simultaneously accounts for all these phenomena by means of (i) a random particle generator and (ii) a stochastic description of material properties implemented within the lattice method framework. Our model displays the same complex fracture patterns that are found experimentally, including crack nucleation, growth, and branching. Interestingly, we show that irregularly shaped active particles can suffer mechanical damage up to 60% higher than that of otherwise equivalent spherical particles, while material defects can lead to damage increments of up to 110%. An evaluation of fracture effects in local Li-ion diffusivity shows that effective diffusion can be reduced up to 25% at the particle core due to lithiation, while it remains at ca. 5% below the undamaged value at the particle surface during delithiation. Using a simple estimate of capacity loss, we also show that the C-rate has a nonlinear effect on battery degradation, and the estimated capacity loss can surpass 10% at a 2C charging rate. [ABSTRACT FROM AUTHOR]

This paper focuses on the multiscale modeling of the COVID-19 pandemic and presents further developments of the model [ 7 ] with the aim of showing how relaxations of the confinement rules can generate sequential waves. Subsequently, the dynamics of mutations into new variants can be modeled. Simulations are developed also to support the decision making of crisis managers. [ABSTRACT FROM AUTHOR]

[Display omitted] Adsorbate molecules present in a reaction mixture may bind to and block catalytic sites. Measurement of the surface coverage of these molecules via adsorption isotherms is critical for modeling and design of catalytic reactions on surfaces. However, it is challenging to measure isotherms in solution in a way that is directly relevant to catalytic activity under reaction conditions, particularly since adsorbates may bind with an enormous range of surface affinity parameters. Here we used the motion of self-propelled catalytic Janus particles, which employ the decomposition of hydrogen peroxide fuel as a propulsion mechanism, to determine the effective surface coverage of thioglycerol, furfural, and ethanol on a platinum surface as a function of concentration in aqueous solution by measuring the decrease in active motion due to the blocking of active sites. For strongly adsorbing thioglycerol, this effective coverage was compared and contrasted to the total adsorbed amount measured using inductively-coupled plasma analysis. Demonstrating the broad applicability of this approach, the surface affinity of the three adsorbates spanned more than four orders of magnitude. For each species, the adsorbate-mediated attenuation of active motion occurred over a wide concentration range and was well-described by a Langmuir isotherm. The strongly interacting thioglycerol had the highest affinity towards the surface (K a = 15.5 ± 4.3 mM−1) and fully deactivated the active particle motion at surface saturation. Furfural had an intermediate affinity (K a = 0.42 ± 0.07 mM−1) but did not fully block H 2 O 2 access to the surface at apparent saturation, consistent with a maximum fractional surface coverage of θ max = 0.67. Ethanol exhibited even lower affinity (K a = 0.0025 ± 2x10-4 mM−1) and its coverage saturated at only θ max = 0.38. Analysis of isotherms at elevated temperatures enabled direct extraction of the enthalpies of adsorption. The degree of surface coverage at adsorbate saturation appeared to correlate with the relative energies of adsorption for the different adsorbate species and was consistent with adsorbate saturation of one of multiple active site populations towards H 2 O 2 decomposition. Moreover, computational investigations into solvent effects on furfural adsorption showed good quantitative agreement with the experimental results. This work leverages unique properties of active particles to explore fundamental catalysis questions and demonstrates a novel paradigm for significant and experimentally accessible multidisciplinary research. [ABSTRACT FROM AUTHOR]

In this paper, an exact description of the statistical motion of active particles in three dimensions is presented in the framework of a generalized diffusion equation. Such a generalization contemplates a nonlocal, in time and space, connecting (memory) function. This couples the rate of change of the probability density of finding the particle at position x at time t with the Laplacian of the probability density at all previous times and to all points in space. Starting from the standard Fokker–Planck-like equation for the probability density of finding an active particle at position x swimming along the direction v ̂ at time t , we derive in this paper in an exact manner the connecting function that allows a description of active motion in terms of this diffusion equation. [ABSTRACT FROM AUTHOR]

The active Brownian motion of single dust particles of various types in the 3D electrostatic DC discharge trap under the action of laser radiation is studied experimentally. Spherical dust particles with a homogeneous surface, as well as Janus particles, are used in the experiment. The properties of the active Brownian motion of all types of dust particles are studied. In particular, the 3D analysis of trajectories of microparticles is carried out, well as an analysis of their root mean square displacement. The mean kinetic energy of motion of the dust particle of various types in a 3D trap is determined for different laser powers. Differences in the character of active Brownian motion in electrostatic traps with different spatial dimensions are found.

We conduct a numerical study exploring the rotation of a symmetric gear driven by chiral particles in a two-dimensional box with periodic boundary conditions. The symmetric gear is submerged in a sea of chiral active particles. Surprisingly, even though the gear is perfectly symmetric, the microscopic random motion of chiral active particles can be converted into macroscopic directional rotation of the gear. (i) In the case of zero alignment interaction, the direction of rotation of the gear is determined by the chirality of active particles. Optimal parameters (the chirality, self-propelled speed, and packing traction) exist, at which the rotational speed reaches its maximum value. (ii) When considering a finite alignment interaction, alignment interactions between particles play an important role in driving the gear to rotate. The direction of rotation is dictated by the competition between the chirality of active particles and the alignment interactions between them. By tuning the system parameters, we can observe multiple rotation reversals. Our findings are relevant to understanding how the macroscopic rotation of a gear connects to the microscopic random motion of active particles.

We study the induced dynamics of an inertial tracer particle elastically coupled to passive or active Brownian particles. We integrate out the environment degrees of freedom to obtain the exact effective equation of the tracer—a generalized Langevin equation in both cases. In particular, we find the exact form of the dissipation kernel and effective noise experienced by the tracer and compare it with the phenomenological modeling of active baths used in previous studies. We show that the second fluctuation-dissipation relation (FDR) does not hold at early times for both cases. However, at finite times, the tracer dynamics violate (obeys) the FDR for the active (passive) environment. We calculate the linear response formulas in this regime for both cases and show that the passive medium satisfies an equilibrium fluctuation response relation, while the active medium does not—we quantify the extent of this violation explicitly. We show that though the active medium generally renders a nonequilibrium description of the tracer, an effective equilibrium picture emerges asymptotically in the small activity limit of the medium. We also calculate the mean squared velocity and mean squared displacement of the tracer and report how they vary with time.

This paper presents a survey of advanced concepts and research perspectives, of a philosophical-mathematical approach to describe the dynamics of systems of many interacting living entities. The first part introduces the general conceptual framework. Then, a critical analysis of the existing literature is developed and referred to a multiscale view of a mathematics of living organisms. This paper attempts to understand how far the present state-of-the-art is far from the achievement of such challenging objective. The overall study leads to identify research perspectives and possible hints to deal with them.

A long-standing goal in colloidal active matter is to understand how gradients in fuel concentration influence the motion of phoretic Janus particles. Here, we present a theoretical description of the motion of a spherical phoretic Janus particle in the presence of a radial gradient of the chemical solute driving self-propulsion. Radial gradients are a geometry relevant to many scenarios in active matter systems and naturally arise due to the presence of a point source or sink of fuel. We derive an analytical solution for the Janus particle's velocity and quantify the influence of the radial concentration gradient on the particle's trajectory. Compared to a phoretic Janus particle in a linear gradient in fuel concentration, we uncover a much richer set of dynamic behaviors including circular orbits and trapped stationary states. We identify the ratio of the phoretic mobilities between the two domains of the Janus particle as a central quantity in tuning their dynamics. Our results provide a path for developing optimum protocols for tuning the dynamics of phoretic Janus particles and mixing fluid at the microscale. In addition, this work suggests a method for quantifying the surface properties of phoretic Janus particles, which have proven to be challenging to probe experimentally.

Lockdown and vaccination policies have been the major concern in the last year in order to contain the SARS-CoV-2 infection during the COVID-19 pandemic. In this paper, we present a model able to evaluate alternative lockdown policies and vaccination strategies. Our approach integrates and refines the multiscale model proposed by Bellomo et al., 2020, analyzing alternative network structures and bridging two perspectives to study complexity of living systems. Inside different matrices of contacts we explore the impact of closures of distinct nodes upon the overall contagion dynamics. Social distancing is shown to be more effective when targeting the reduction of contacts among and inside the most vulnerable nodes, namely hospitals/nursing homes. Moreover, our results suggest that school closures alone would not significantly affect the infection dynamics and the number of deaths in the population. Finally, we investigate a scenario with immunization in order to understand the effectiveness of targeted vaccination policies towards the most vulnerable individuals. Our model agrees with the current proposed vaccination strategy prioritizing the most vulnerable segment of the population to reduce severe cases and deaths. [ABSTRACT FROM AUTHOR]