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Self-propelled particles

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to an external load they move with a constant velocity proportional to the applied force, just as objects in viscous media. Swirlons attract each other and coalesce forming a larger, joint swirlon. The coalescence is an extremely slow, decelerating process, resulting in a rarified state of immobile quasi-particles. In addition to the swirlonic state, gaseous, liquid and solid states were observed, depending on the inter-particle and self-driving forces. In contrast to molecular systems, liquid and gaseous states of self-propelled particles do not coexist.
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leader, yet the flock know exactly how to land in a unified way. The need for the group to land overrides deviating intentions by individual birds. The particle model found that the collective shift to landing depends on perturbations that apply to the individual birds, such as where the birds are in the flock. It is behaviour that can be compared with the way that sand avalanches, if it is piled up, before the point at which symmetric and carefully placed grains would avalanche, because the fluctuations become increasingly non-linear.
609:"Our main motivation was to better understand something which is puzzling and out there in nature, especially in cases involving the stopping or starting of a collective behavioural pattern in a group of people or animals ... We propose a simple model for a system whose members have the tendency to follow the others both in space and in their state of mind concerning a decision about stopping an activity. This is a very general model, which can be applied to similar situations." The model could also be applied to a swarm of unmanned 585:, the average density of marching bands is 50 locusts/m (50 million locusts/km), with a typical range from 20 to 120 locusts/m. The research findings discussed above demonstrate the dynamic instability that is present at the lower locust densities typical in the field, where marching groups randomly switch direction without any external perturbation. Understanding this phenomenon, together with the switch to fully coordinated marching at higher densities, is essential if the swarming of desert locusts is to be controlled. 3973: 22: 562: 451: 285: 2139: 549:
densities, they start falling into line and marching together, punctuated by abrupt but coordinated changes in direction. However, when densities reached a critical value at about 74 locusts/m, the locusts ceased making rapid and spontaneous changes in direction, and instead marched steadily in the same direction for the full eight hours of the experiment.
232:. Natural systems which have inspired the study and design of these particles include walking, swimming or flying animals. Other biological systems include bacteria, cells, algae and other micro-organisms. Generally, self-propelled particles often refer to artificial systems such as robots or specifically designed particles such as swimming 537:. If food is short they can gather together and start occupying neighbouring areas, recruiting more locusts. Eventually they can become a marching army extending over many kilometres. This can be the prelude to the development of the vast flying adult locust swarms which devastate vegetation on a continental scale. 466:
Simulations demonstrate that a suitable "nearest neighbour rule" eventually results in all the particles swarming together or moving in the same direction. This emerges, even though there is no centralised coordination, and even though the neighbours for each particle constantly change over time (see
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Symmetry breaking is a necessary condition for SPPs, as there must be a preferential direction for moving. However, the symmetry breaking may not come solely from the structure itself but from its interaction with electromagnetic fields, in particular when taken into account retardation effects. This
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To understand the ubiquity of such phenomena, physicists have developed a number of self-propelled particles models. These models predict that self-propelled particles share certain properties at the group level, regardless of the type of animals (or artificial particles) in the swarm. It has become
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reported a hitherto unrecognised state of self-propelled particles — which they called a "swirlonic state". The swirlonic state consists of "swirlons", formed by groups of self-propelled particles orbiting a common centre of mass. These quasi-particles demonstrate a surprising behaviour: In response
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allows, by properly tuning the environment (typically the surrounding solution), for the motion of the Janus particle. For instance, the two sides of the Janus particle can induce a local gradient of, temperature, electric field, or concentration of chemical species. This induces motion of the Janus
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In 2010, Bhattacharya and Vicsek used an SPP model to analyse what is happening here. As a paradigm, they considered how flying birds arrive at a collective decision to make a sudden and synchronised change to land. The birds, such as the starlings in the image on the right, have no decision-making
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Swarming animals, such as ants, bees, fish and birds, are often observed suddenly switching from one state to another. For example, birds abruptly switch from a flying state to a landing state. Or fish switch from schooling in one direction to schooling in another direction. Such state switches can
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transform ATP energy into directional motion. Recent work has shown that enzyme molecules will also propel themselves. Further, it has been shown that they will preferentially move towards a region of higher substrate concentration, a phenomenon that has been developed into a purification technique
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Most animals can be seen as SPP: they find energy in their food and exhibit various locomotion strategies, from flying to crawling. The most prominent examples of collective behaviours in these systems are fish schools, birds flocks, sheep herds, human crowds. At a smaller scale, cells and bacteria
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Walking grains are a typical realization of dry SPP: The grains are milli-metric disks sitting on a vertically vibrating plate, which serves as the source of energy and momentum. The disks have two different contacts ("feet") with the plate, a hard needle-like foot in the front and a large soft
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of a group increases, an abrupt transition occurs from individuals moving in relatively disordered and independent ways within the group to the group moving as a highly aligned whole. Thus, in the case of young desert locusts, a trigger point should occur which turns disorganised and dispersed
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In 2006, a group of researchers examined how this model held up in the laboratory. Locusts were placed in a circular arena, and their movements were tracked with computer software. At low densities, below 18 locusts per square metre, the locusts mill about in a disordered way. At intermediate
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imply directed motion is either the spontaneous formation of clusters or the separation in a gas-like and a liquid-like phase, an unexpected phenomenon when the SPP have purely repulsive interaction. This phase separation has been called Motility Induced Phase Separation (MIPS).
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Body interactions: the particles can be considered as points (no body interaction) like in the Vicsek model. Alternatively, one can include an interaction potential, either attractive or repulsive. This potential can be isotropic or not to describe spherical or elongated
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Self-propelled particles interact with each other, which can lead to the emergence of collective behaviours. These collective behaviours mimic the self-organization observed with the flocking of birds, the swarming of bugs, the formation of sheep herds, etc.
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and Mallouk. In a solution of hydrogen peroxide, this "nanomotor" would exhibit a catalytic oxidation-reduction reaction, thereby inducing a fluid flow along the surface through self-diffusiophoresis. A similar system used a copper-platinum rod in a bromine
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Another Janus SPP was developed by coating half of a polystyrene bead with platinum. These were used to direct the motion of catalytic motors when they were close to a solid surface. These systems were able to move the active colloids using geometric
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to isolate live enzymes. Additionally, microparticles or vesicles can become self-propelled when they are functionalized with enzymes. The catalytic reactions of the enzymes direct the particles or vesicles based on corresponding substrate gradients.
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Self-propelled particles can also be modeled using on-lattice models, which offer the advantage of being simple and efficient to simulate, and in some cases, may be easier to analyze mathematically. On-lattice models such as
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rubber foot in the back. When shaken, the disks move in a preferential direction defined by the polar (head-tail) symmetry of the contacts. This together with the vibrational noise result in a persistent random walk.
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Body orientation: for those particles with a body-fixed axis, one can include additional degrees of freedom to describe the orientation of the body. The coupling of this body axis with the velocity is an additional
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was tethered to the silica half of the particle and in solution of monomer would drive a catalytic polymerization. The resulting concentration gradient across the surface would propel the motor in solution.
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One can also include effective influences of the surrounding; for instance the nominal velocity of the SPP can be set to depend on the local density, in order to take into account crowding effects.
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Meredith, Caleb H.; Castonguay, Alexander C.; Chiu, Yu-Jen; Brooks, Allan M.; Moerman, Pepijn G.; Torab, Peter; Wong, Pak Kin; Sen, Ayusman; Velegol, Darrell; Zarzar, Lauren D. (2 February 2022).
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models have been used to study physical aspects of self-propelled particle systems (such as phase transitions and pattern-forming potential) as well as specific questions related to real
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occur with astonishing speed and synchronicity, as though all the members in the group made a unanimous decision at the same moment. Phenomena like these have long puzzled researchers.
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locusts into a coordinated marching army. When the critical population density is reached, the insects should start marching together in a stable way and in the same direction.
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There is a distinction between wet and dry systems. In the first case the particles "swim" in a surrounding fluid; in the second case the particles "walk" on a substrate.
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Somasundar A, Ghosh S, Mohajerani F, Massenburg LN, Yang T, Cremer PS, et al. (December 2019). "Positive and negative chemotaxis of enzyme-coated liposome motors".
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Tektonidis M, Hatzikirou H, Chauvière A, Simon M, Schaller K, Deutsch A (October 2011). "Identification of intrinsic in vitro cellular mechanisms for glioma invasion".
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Aligning interaction rules: in the spirit of the Vicsek model, neighboring particles align their velocities. Another possibility is that they align their orientations.
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can be used for the phototactic motion of even highly symmetrical nanoparticles. In 2021, it was experimentally shown that completely symmetric particles (spherical
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Baglietto G, Albano EV (November 2009). "Nature of the order-disorder transition in the Vicsek model for the collective motion of self-propelled particles".
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Paxton WF, Kistler KC, Olmeda CC, Sen A, St Angelo SK, Cao Y, et al. (October 2004). "Catalytic nanomotors: autonomous movement of striped nanorods".
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to detailed and specialized models aiming at describing specific systems and situations. Among the important ingredients in these models, one can list
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Dey KK, Zhao X, Tansi BM, Méndez-Ortiz WJ, Córdova-Figueroa UM, Golestanian R, Sen A (December 2015). "Micromotors Powered by Enzyme Catalysis".
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Zhang, Jianhua; Laskar, Abhrajit; Song, Jiaqi; Shklyaev, Oleg E.; Mou, Fangzhi; Guan, Jianguo; Balazs, Anna C.; Sen, Ayusman (10 January 2023).
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can also be treated as SPP. These biological systems can propel themselves based on the presence of chemoattractants. At even smaller scale,
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Another example of an artificial SPP are platinum spinner microparticles that have controllable rotations based on their shape and symmetry.
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Bertin E, Droz M, Grégoire G (2009). "Hydrodynamic equations for self-propelled particles: microscopic derivation and stability analysis".
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Bertin E, Droz M, Grégoire G (2009). "Hydrodynamic equations for self-propelled particles: microscopic derivation and stability analysis".
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Aditi Simha R, Ramaswamy S (July 2002). "Hydrodynamic fluctuations and instabilities in ordered suspensions of self-propelled particles".
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Vicsek T, Czirók A, Ben-Jacob E, Cohen I, Shochet O (August 1995). "Novel type of phase transition in a system of self-driven particles".
112: 107: 613:, to initiate the desired motion in a crowd of people, or to interpreting group patterns when stock market shares are bought or sold. 764: 3359:
Czirók A, Barabási AL, Vicsek T (1999). "Collective motion of self-propelled particles: Kinetic phase transition in one dimension".
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Flocks of birds can abruptly change their direction in unison, and then, just as suddenly, make a unanimous group decision to land
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Chowdhury D (2006). "Collective effects in intra-cellular molecular motor transport: coordination, cooperation and competition".
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Somfai E, Czirok A, Vicsek T (1994). "Power-law distribution of landslides in an experiment on the erosion of a granular pile".
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Sengupta S, Dey KK, Muddana HS, Tabouillot T, Ibele ME, Butler PJ, Sen A (January 2013). "Enzyme molecules as nanomotors".
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Che, Shengping; Zhang, Jianhua; Mou, Fangzhi; Guo, Xia; Kauffman, Joshua E.; Sen, Ayusman; Guan, Jianguo (January 2022).
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Jadbabaie A, Lin J, Morse AS (2003). "Coordination of groups of mobile autonomous agents using nearest neighbor rules".
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The first example of an artificial SPP on the nano or micron scale was a gold-platinum bimetallic nanorod developed by
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and walking grains. In the case of directed propulsion, which is driven by a chemical gradient, this is referred to as
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Ghosh S, Mohajerani F, Son S, Velegol D, Butler PJ, Sen A (September 2019). "Motility of Enzyme-Powered Vesicles".
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Bickel T, Majee A, Würger A (July 2013). "Flow pattern in the vicinity of self-propelling hot Janus particles".
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When the density of locusts reaches a critical point, they march steadily together without direction reversals.
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Zhang, Jianhua; Mou, Fangzhi; Tang, Shaowen; Kauffman, Joshua E.; Sen, Ayusman; Guan, Jianguo (1 March 2022).
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Ihle T (October 2013). "Invasion-wave-induced first-order phase transition in systems of active particles".
1999:"Photochemical micromotor of eccentric core in isotropic hollow shell exhibiting multimodal motion behavior" 403:
The prominent and most spectacular emergent large scale behaviour observed in assemblies of SPP is directed
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generally includes the formation of self-assembled structures, such as clusters and organized assemblies.
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Levine H, Rappel WJ, Cohen I (January 2001). "Self-organization in systems of self-propelled particles".
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are colloidal particles with two different sides, having different physical or chemical properties. This
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Pavlick RA, Sengupta S, McFadden T, Zhang H, Sen A (September 2011). "A polymerization-powered motor".
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Agudo-Canalejo, Jaime; Adeleke-Larodo, Tunrayo; Illien, Pierre; Golestanian, Ramin (16 October 2018).
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a challenge in theoretical physics to find minimal statistical models that capture these behaviours.
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Czirók A, Stanley HE, Vicsek T (1997). "Spontaneously ordered motion of self-propelled particles".
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systems (for example, identifying the underlying biological processes involved in tumor invasion).
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Helbing D, Farkas I, Vicsek T (September 2000). "Simulating dynamical features of escape panic".
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in this case) experience a net thermophoretic force when illuminated from a given direction.
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Another example of a Janus SPP is an organometallic motor using a gold-silica microsphere.
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Czirók A, Vicsek T (2006). "Collective behavior of interacting self-propelled particles".
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Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
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Zhao X, Gentile K, Mohajerani F, Sen A (October 2018). "Powering Motion with Enzymes".
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Das S, Garg A, Campbell AI, Howse J, Sen A, Velegol D, et al. (December 2015).
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Bhattacharya K, Vicsek T (2010). "Collective decision making in cohesive flocks".
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Reynolds CW (1987). "Flocks, herds and schools: A distributed behavioral model".
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Brooks AM, Tasinkevych M, Sabrina S, Velegol D, Sen A, Bishop KJ (January 2019).
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Another example is biphasic Janus oil droplets which shows self propelled motion.
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Unruh, Angus; Brooks, Allan M.; Aranson, Igor S.; Sen, Ayusman (28 April 2023).
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Golestanian, Ramin; Liverpool, Tanniemola B.; Ajdari, Armand (10 June 2005).
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Since then a number of models have been proposed, ranging from the simple
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Swirlonic Super Particles: Physicists Baffled by a Novel State of Matter
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occur in swarms independent of the type of animal that is in the swarm.
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Muddana HS, Sengupta S, Mallouk TE, Sen A, Butler PJ (February 2010).
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Brilliantov NV, Abutuqayqah H, Tyukin IY, Matveev SA (October 2020).
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Material was copied from this source, which is available under a
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On the dynamics and evolution of self-propelled particle models
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D'Orsogna MR, Chuang YL, Bertozzi AL, Chayes LS (March 2006).
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An example of a SPP: a gold-platinum nanorod which undergoes
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One of the key predictions of the SPP model is that as the
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Golestanian, R.; Liverpool, T. B.; Ajdari, A. (May 2007).
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SPP models have been applied in many other areas, such as
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This confirmed the behaviour predicted by the SPP models.
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in hydrogen peroxide due to self-electrophoretic forces.
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Pages displaying short descriptions of redirect targets
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Food and Agriculture Organization of the United Nations
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Creative Commons Attribution 4.0 International License
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Behaviour, ecology, biogeography, population dynamics
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the interactive simulation in the box on the right).
3751: 3608: 3358: 3026: 1996: 1870: 1619: 1197: 851:"Enhanced Diffusion and Chemotaxis at the Nanoscale" 511: 3309: 2965: 791: 410:Another class of large scale behaviour, which does 312:particle along the gradient through, respectively, 4316:Task allocation and partitioning of social insects 3205: 2761: 2690: 2512: 1826:"Chemical design of self-propelled Janus droplets" 964: 3471: 3087: 2623:"Desert locust guidelines: Biology and behaviour" 2395: 686: – Microscopic object able to traverse fluid 4328: 2819:Gautrais J, Jost C, Theraulaz G (October 2008). 2737: 2636: 2513:Bouré O, Fatès N, Chevrier V (1 December 2013). 905: 3081: 2867: 2686: 2684: 2682: 2620: 391: 2792: 2765:Journal of Physics A: Mathematical and General 2441:. University of Colorado. 2005. Archived from 1931: 423:The modeling of SPP was introduced in 1995 by 3814: 3416:. Oxford University Press. pp. 177–209. 2389: 2224: 2222: 1495:"Designing phoretic micro- and nano-swimmers" 1020:Li YX, Lukeman R, Edelstein-Keshet L (2007). 680: – Type of nanoparticle or microparticle 198: 3689:"Statistical physics: Closing in on evaders" 3526: 3407: 2679: 2340: 718:"From disorder to order in marching locusts" 711: 709: 707: 705: 353:Several other examples are described in the 2642: 1622:"Boundaries can steer active Janus spheres" 1383: 958: 303:, are the prototypical example of wet SPP. 3821: 3807: 2219: 2039: 205: 191: 3712: 3622: 3542: 3485: 3374: 3325: 3268: 3221: 3188: 3170: 3101: 3040: 2979: 2926: 2910: 2704: 2489: 2409: 2356: 2299: 2242: 2122: 2014: 1973: 1884: 1753: 1653: 1510: 1450: 1215: 1096: 980: 866: 809: 702: 224:are terms used by physicists to describe 2289: 1587:Journal of the American Chemical Society 1552:Journal of the American Chemical Society 1122:Journal of the American Chemical Society 1077:Journal of the American Chemical Society 592: 515: 418: 283: 20: 3663: 3575: 1013: 4329: 3769:Canadian Applied Mathematics Quarterly 3686: 2616: 2614: 2602: 2398:IEEE Transactions on Automatic Control 1584: 663:Clustering of self-propelled particles 3924:Patterns of self-organization in ants 3802: 3737: 3145: 2800:"Bird flock decision-making revealed" 2424:convergence proofs for the SPP model. 1488: 1486: 1414:from the original on 23 February 2024 279: 265: 3254: 3152: 908:"Hydrodynamics and phases of flocks" 787: 785: 365: 3414:Fluctuations and scaling in biology 2611: 1200:"Chemotactic separation of enzymes" 906:Toner J, Tu Y, Ramaswamy S (2005). 524: 299:Active colloidal particles, dubbed 153:Distributed artificial intelligence 63:Agent-based computational economics 13: 3934:symmetry breaking of escaping ants 3586:. Vol. 41. pp. 357–368. 2091:"Swirlonic state of active matter" 1483: 1398:10.1093/oso/9780192858313.003.0008 168:Multi-agent reinforcement learning 14: 4368: 3781: 1783:ACS Applied Engineering Materials 782: 616: 512:Some applications to real systems 3971: 2294:. Vol. 21. pp. 25–34. 2137: 671: – Type of bacterial motion 588: 560: 455:SPP model interactive simulation 449: 3583:Advances in Solid State Physics 3020: 2959: 2904: 2861: 2812: 2755: 2621:Symmons PM, Cressman K (2001). 2596: 2553: 2506: 2457: 2427: 2334: 2283: 2164: 2149: 2082: 1990: 1925: 1864: 1817: 1770: 1713: 1670: 1613: 1578: 1543: 1426: 1377: 1326: 1275: 1232: 1191: 581:In the field, according to the 3672:. Princeton University Press. 3240:10.1088/1751-8113/42/44/445001 2562:Journal of Theoretical Biology 1903:10.1103/PhysRevLett.105.098001 1148: 1113: 1064: 1030:Physica D: Nonlinear Phenomena 999:10.1088/1751-8113/42/44/445001 899: 842: 641:in urban green spaces. SPP in 383:In 2020, researchers from the 1: 3641:10.1103/PhysRevLett.89.058101 3457:10.1103/PhysRevLett.96.104302 2723:10.1088/1367-2630/12/9/093019 2665:10.1103/PhysRevLett.92.168701 2375:10.1016/S0378-4371(00)00013-3 1469:10.1103/PhysRevLett.94.220801 1157:Accounts of Chemical Research 855:Accounts of Chemical Research 828:10.1103/PhysRevLett.99.048102 696: 113:Platforms for software agents 78:Agent-based modeling software 73:Agent-based social simulation 3895:Mixed-species foraging flock 3846:Agent-based model in biology 3828: 3666:"Chapter 5: Moving together" 2435:"Self driven particle model" 1304:10.1021/acs.nanolett.9b01830 1261:10.1021/acs.nanolett.5b03935 1169:10.1021/acs.accounts.8b00286 877:10.1021/acs.accounts.8b00280 434:model introduced in 1986 by 392:Typical collective behaviour 68:Agent-based model in biology 7: 4142:Particle swarm optimization 3408:Czirók A, Vicsek T (2001). 2945:10.1016/j.physa.2006.05.005 2786:10.1088/0305-4470/27/20/001 2261:10.1103/PhysRevLett.75.1226 1384:Golestanian, Ramin (2022). 1051:10.1016/j.physd.2007.10.009 656: 649:, are often modeled by the 637:and the evolution of human 378: 260: 247: 220:(SPP), also referred to as 10: 4373: 3851:Collective animal behavior 3670:Collective Animal Behavior 3530:Journal of Fluid Mechanics 3504:10.1103/PhysRevE.63.017101 3393:10.1103/PhysRevLett.82.209 3344:10.1088/0305-4470/30/5/009 3287:10.1103/PhysRevE.88.040303 3190:10.1103/PhysRevE.83.030901 3120:10.1103/PhysRevE.88.012301 2890:10.1103/PhysRevE.80.050103 2582:10.1016/j.jtbi.2011.07.012 2115:10.1038/s41598-020-73824-4 2016:10.1016/j.apmt.2022.101371 1842:10.1016/j.matt.2021.12.014 1746:10.1038/s41467-019-08423-7 533:are solitary and wingless 88:Agent-oriented programming 25:SPP models predict robust 4243: 4205: 4160: 4112: 3980: 3969: 3836: 3561:10.1017/S0022112007009184 2828:Annales Zoologici Fennici 2531:10.1007/s11047-013-9389-2 1529:10.1088/1367-2630/9/5/126 1355:10.1038/s41565-019-0578-8 937:10.1016/j.aop.2005.04.011 559: 554: 448: 443: 430:as a special case of the 4180:Self-propelled particles 3592:10.1007/3-540-44946-9_29 1386:"Phoretic Active Matter" 472:active Brownian particle 218:Self-propelled particles 173:Self-propelled particles 16:Type of autonomous agent 4261:Collective intelligence 4127:Ant colony optimization 3788:Swarming desert locusts 3611:Physical Review Letters 3437:Physical Review Letters 3362:Physical Review Letters 2645:Physical Review Letters 2439:Interactive simulations 2420:10.1109/TAC.2003.812781 2231:Physical Review Letters 2189:10.1021/acsnano.2c07266 2060:10.1021/acsnano.0c10598 2003:Applied Materials Today 1873:Physical Review Letters 1795:10.1021/acsaenm.2c00249 1439:Physical Review Letters 798:Physical Review Letters 745:10.1126/science.1125142 669:Run-and-tumble particle 385:University of Leicester 236:, bimetallic nanorods, 158:Multi-agent pathfinding 4342:Complex systems theory 4281:Microbial intelligence 3941:Shoaling and schooling 3687:Vicsek T (July 2010). 2693:New Journal of Physics 2482:10.1098/rstb.2019.0378 1691:10.1002/anie.201103565 1499:New Journal of Physics 598: 521: 293: 230:persistent random walk 222:self-driven particles, 53:Multi-agent simulation 30: 3412:. In Vicsek T (ed.). 3153:Ihle T (March 2011). 1958:10.34133/2022/9816562 1726:Nature Communications 1626:Nature Communications 1335:Nature Nanotechnology 633:, the development of 596: 519: 419:Examples of modelling 287: 24: 4301:Spatial organization 4266:Decentralised system 4104:Sea turtle migration 3958:Swarming (honey bee) 3313:Journal of Physics A 3209:Journal of Physics A 2840:10.5735/086.045.0505 2808:. 14 September 2010. 2751:. 18 September 2010. 1392:. pp. 230–293. 968:Journal of Physics A 770:on 29 September 2011 163:Multi-agent planning 4352:Multi-agent systems 4276:Group size measures 3838:Biological swarming 3705:2010Natur.466...43V 3664:Sumpter DJ (2010). 3633:2002PhRvL..89e8101A 3553:2008JFM...595..239M 3496:2000PhRvE..63a7101L 3449:2006PhRvL..96j4302D 3385:1999PhRvL..82..209C 3336:1997JPhA...30.1375C 3279:2013PhRvE..88d0303I 3232:2009JPhA...42R5001B 3181:2011PhRvE..83c0901I 3112:2013PhRvE..88a2301B 3051:1997Natur.388...47H 2990:2000Natur.407..487H 2937:2006PhyA..372...84C 2882:2009PhRvE..80e0103B 2857:on 12 January 2011. 2778:1994JPhA...27L.757S 2715:2010NJPh...12i3019B 2657:2004PhRvL..92p8701H 2574:2011JThBi.287..131T 2367:2000PhyA..281...17C 2310:10.1145/37401.37406 2253:1995PhRvL..75.1226V 2161:, 11 February 2021. 2107:2020NatSR..1016783B 1950:2022Resea202216562C 1895:2010PhRvL.105i8001D 1738:2019NatCo..10..495B 1638:2015NatCo...6.8999D 1521:2007NJPh....9..126G 1461:2005PhRvL..94v0801G 1347:2019NatNa..14.1129S 1296:2019NanoL..19.6019G 1253:2015NanoL..15.8311D 1043:2008PhyD..237..699L 991:2009JPhA...42R5001B 929:2005AnPhy.318..170T 820:2007PhRvL..99d8102H 737:2006Sci...312.1402B 46:Multi-agent systems 27:emergent behaviours 4291:Predator satiation 4152:Swarm (simulation) 4147:Swarm intelligence 4122:Agent-based models 3953:Swarming behaviour 3790:– Video clip from 3576:Helbing D (2001). 3480:(1 Pt 2): 017101. 3165:(3 Pt 1): 030901. 3146:Further references 2876:(5 Pt 1): 050103. 2749:Medical News Today 2603:Uvarov BP (1977). 2476:(1807): 20190378. 2445:on 14 October 2012 2095:Scientific Reports 1646:10.1038/ncomms9999 1060:on 1 October 2011. 599: 542:population density 522: 294: 280:Artificial systems 266:Biological systems 31: 4324: 4323: 4311:Military swarming 4256:Animal navigation 4175:Collective motion 4162:Collective motion 4029:reverse migration 3963:Swarming motility 3738:Yates CA (2007). 3679:978-0-691-12963-1 3601:978-3-540-42000-2 3474:Physical Review E 3423:978-0-19-850790-1 3257:Physical Review E 3159:Physical Review E 3090:Physical Review E 2870:Physical Review E 2772:(20): L757–L763. 2747:(Press release). 2519:Natural Computing 1679:Angewandte Chemie 1599:10.1021/ja2082735 1564:10.1021/ja047697z 1407:978-0-19-285831-3 1341:(12): 1129–1134. 1217:10.1021/nn504418u 1163:(10): 2373–2381. 1134:10.1021/ja3091615 1089:10.1021/ja908773a 916:Annals of Physics 861:(10): 2365–2372. 576: 575: 464: 463: 405:collective motion 398:collective motion 366:Symmetry breaking 309:symmetry breaking 226:autonomous agents 215: 214: 4364: 4137:Crowd simulation 4114:Swarm algorithms 4085:Insect migration 3990:Animal migration 3982:Animal migration 3975: 3900:Mobbing behavior 3823: 3816: 3809: 3800: 3799: 3776: 3766: 3748: 3746: 3734: 3716: 3683: 3660: 3626: 3624:cond-mat/0108301 3605: 3572: 3546: 3523: 3489: 3487:cond-mat/0006477 3468: 3427: 3404: 3378: 3376:cond-mat/9712154 3355: 3329: 3327:cond-mat/0611741 3320:(5): 1375–1385. 3306: 3272: 3251: 3225: 3202: 3192: 3174: 3140: 3139: 3105: 3085: 3079: 3078: 3044: 3042:cond-mat/9805158 3024: 3018: 3017: 2998:10.1038/35035023 2983: 2981:cond-mat/0009448 2974:(6803): 487–90. 2963: 2957: 2956: 2930: 2908: 2902: 2901: 2865: 2859: 2858: 2856: 2850:. 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Index


emergent behaviours
a series
Multi-agent systems
Multi-agent simulation
Agent-based computational economics
Agent-based model in biology
Agent-based social simulation
Agent-based modeling software
Agent-oriented programming
Auto-GPT
Botnets
FIPA
Platforms for software agents
JADE
JACK
GORITE
Software agent
Distributed artificial intelligence
Multi-agent pathfinding
Multi-agent planning
Multi-agent reinforcement learning
Self-propelled particles
Swarm robotics
v
t
e
autonomous agents
persistent random walk
Janus colloids

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