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Journal articles on the topic 'Pattern formation'

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Published: 4 June 2021
Last updated: 6 September 2023

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1

Short, Nicholas. "Patterns of pattern formation." Nature 378, no. 6555 (November 1995): 331. http://dx.doi.org/10.1038/378331a0.

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2

Reinitz, John. "Pattern formation." Nature 482, no. 7386 (February 2012): 464. http://dx.doi.org/10.1038/482464a.

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3

Woychik, R. "Pattern formation." Reproductive Toxicology 11, no. 2-3 (June 1997): 339–44. http://dx.doi.org/10.1016/s0890-6238(96)00217-1.

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4

Saito, Yoshiyuki, G. Goldbeck-Wood, and H. Müller-Krumbhaar. "Dentritic Pattern Formation." Solid State Phenomena 3-4 (January 1991): 139–42. http://dx.doi.org/10.4028/www.scientific.net/ssp.3-4.139.

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5

Saito, Y., G. Goldbeck-Wood, and H. Müller-Krumbhaar. "Dendritic Pattern Formation." Physica Scripta T19B (January 1, 1987): 327–29. http://dx.doi.org/10.1088/0031-8949/1987/t19b/001.

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6

Chuong, Cheng-Ming, and Michael K. Richardson. "Pattern formation today." International Journal of Developmental Biology 53, no. 5-6 (2009): 653–58. http://dx.doi.org/10.1387/ijdb.082594cc.

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7

Benka, Stephen G. "Spontaneous pattern formation." Physics Today 57, no. 12 (December 2004): 9. http://dx.doi.org/10.1063/1.4796357.

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8

Falkovitz, Meira S., and Joseph B. Keller. "Precipitation pattern formation." Journal of Chemical Physics 88, no. 1 (January 1988): 416–21. http://dx.doi.org/10.1063/1.454617.

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9

Luo, Nan, Shangying Wang, and Lingchong You. "Synthetic Pattern Formation." Biochemistry 58, no. 11 (January 22, 2019): 1478–83. http://dx.doi.org/10.1021/acs.biochem.8b01242.

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10

Or-Guil, Michal, Markus Bär, and Mathias Bode. "Hierarchical pattern formation." Physica A: Statistical Mechanics and its Applications 257, no. 1-4 (August 1998): 470–76. http://dx.doi.org/10.1016/s0378-4371(98)00179-4.

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11

Hohm, T., and E. Zitzler. "Multicellular pattern formation." IEEE Engineering in Medicine and Biology Magazine 28, no. 4 (July 2009): 52–57. http://dx.doi.org/10.1109/memb.2009.932905.

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12

Vicsek, Tamás, and János Kertész. "Laplacian Pattern Formation." Europhysics News 19, no. 2 (1988): 24–27. http://dx.doi.org/10.1051/epn/19881902024.

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13

Perrimon, Norbert, and Claudio Sternt. "Pattern formation and developmental mechanisms unresolved issues of pattern formation." Current Opinion in Genetics & Development 9, no. 4 (August 1999): 387–89. http://dx.doi.org/10.1016/s0959-437x(99)80058-6.

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14

Yamaguchi, Tetsuo. "Rapid Swelling and Pattern Formation in Hydrogel Particles." Nihon Reoroji Gakkaishi 42, no. 2 (2014): 129–33. http://dx.doi.org/10.1678/rheology.42.129.

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15

Kotov, M. M. "Speckle pattern formation in spatially limited optical systems." Semiconductor Physics Quantum Electronics and Optoelectronics 19, no. 1 (April 8, 2016): 47–51. http://dx.doi.org/10.15407/spqeo19.01.047.

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16

NEUFELD, M., and R. FRIEDRICH. "PATTERN FORMATION IN ROTATING BÉNARD CONVECTION." International Journal of Bifurcation and Chaos 04, no. 05 (October 1994): 1155–63. http://dx.doi.org/10.1142/s021812749400085x.

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Using a model equation we study pattern formation in rotating, high Prandtl-number Bénard convection in circular geometry and in a rectangular vessel with periodic boundary conditions. We report on drifting pattern, defect motion, Küppers-Lortz instability and domain wall turbulence. In circular geometry we observed spiral patterns which disappear and reappear in the Küppers-Lortz unstable regime. We define a pattern entropy for the patterns and show that this quantity is related to the Nusselt number.
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17

Thalmeier, Dominik, Jacob Halatek, and Erwin Frey. "Geometry-induced protein pattern formation." Proceedings of the National Academy of Sciences 113, no. 3 (January 6, 2016): 548–53. http://dx.doi.org/10.1073/pnas.1515191113.

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Protein patterns are known to adapt to cell shape and serve as spatial templates that choreograph downstream processes like cell polarity or cell division. However, how can pattern-forming proteins sense and respond to the geometry of a cell, and what mechanistic principles underlie pattern formation? Current models invoke mechanisms based on dynamic instabilities arising from nonlinear interactions between proteins but neglect the influence of the spatial geometry itself. Here, we show that patterns can emerge as a direct result of adaptation to cell geometry, in the absence of dynamical instability. We present a generic reaction module that allows protein densities robustly to adapt to the symmetry of the spatial geometry. The key component is an NTPase protein that cycles between nucleotide-dependent membrane-bound and cytosolic states. For elongated cells, we find that the protein dynamics generically leads to a bipolar pattern, which vanishes as the geometry becomes spherically symmetrical. We show that such a reaction module facilitates universal adaptation to cell geometry by sensing the local ratio of membrane area to cytosolic volume. This sensing mechanism is controlled by the membrane affinities of the different states. We apply the theory to explain AtMinD bipolar patterns in Δ EcMinDE Escherichia coli. Due to its generic nature, the mechanism could also serve as a hitherto-unrecognized spatial template in many other bacterial systems. Moreover, the robustness of the mechanism enables self-organized optimization of protein patterns by evolutionary processes. Finally, the proposed module can be used to establish geometry-sensitive protein gradients in synthetic biological systems.
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18

FRIEDRICH, R., M. BESTEHORN, and H. HAKEN. "PATTERN FORMATION IN CONVECTIVE INSTABILITIES." International Journal of Modern Physics B 04, no. 03 (March 10, 1990): 365–400. http://dx.doi.org/10.1142/s0217979290000188.

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The present article reviews recent progress in the study of pattern formation in convective instabilities. After a brief discussion of the relevant basic hydrodynamic equations as well as a short outline of the mathematical treatment of pattern formation in complex systems the self-organization of spatial and spatio-temporal structures due to convective instabilities is considered. The formation of various forms of convective patterns arising in the Bénard experiment, i.e. in a horizontal fluid layer heated from below, is discussed. Then the review considers pattern formation in the Bénard instability in spherical geometries. In that case it can be demonstrated how the interaction among several convective cells may lead to time dependent as well as chaotic evolution of the spatial structures. Finally, the convective instability in a binary fluid mixture is discussed. In contrast to the instability in a single component fluid the instability may be oscillatory. In that case convection sets in in the form of travelling wave patterns which in addition to a complicated and chaotic temporal behaviour exhibit more or less spatial irregularity already close to threshold.
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19

Ackermann, J., and T. Kirner. "Parasites and Pattern Formation." Zeitschrift für Naturforschung A 54, no. 2 (February 1, 1999): 146–52. http://dx.doi.org/10.1515/zna-1999-0209.

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Abstract Biological information is coded in replicating molecules. To maintain a given amount of in-formation a cooperative interaction between these molecules is essential. The main problem for the stability of a system of prebiotic replicators are emerging parasites. Stabilization against such parasites is possible if space is introduced in the model. Complex patterns like spiral waves and self-replicating spot patterns have been shown to stabilize such systems. Stability of replicating systems, however, occurs only in parameter regions were such complex patterns occur. We show that parasites are able to push such systems into a parameter region were life is possible. To demonstrate this influence of parasites on such systems, we introduce a parasitic species in the Gray-Scott model. The growing concentration of parasites will kill the system, and the cooperative Gray-Scott system will be diluted out in a well mixed flow reactor. While considering space, in the model stabilizing pattern formation in a narrow parameter region is possible. We demonstrate that the concentration of the parasitic species is able to push the system into a region were stabilizing patterns emerge.
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20

Jeong, Seong-Ok, Hie-Tae Moon, and Tae-Wook Ko. "Nearest pattern interaction and global pattern formation." Physical Review E 62, no. 6 (December 1, 2000): 7778–80. http://dx.doi.org/10.1103/physreve.62.7778.

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21

Bentil, D. E., and J. D. Murray. "Pattern selection in biological pattern formation mechanisms." Applied Mathematics Letters 4, no. 3 (1991): 1–5. http://dx.doi.org/10.1016/0893-9659(91)90022-n.

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22

Baxtiyorovna, Nizamova Barno, and Karimova Xusnida Djuma qizi. "THE FEATURES OF PATTERN FORMATION ON FLAT KNITTING MACHINES." International Journal of Advance Scientific Research 02, no. 02 (February 1, 2022): 1–11. http://dx.doi.org/10.37547/ijasr-02-02-01.

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The development of the production of knitwear will lead to the further application of new technologies and the expansion of the range of knitwear. In the fields of trade industry, as well as in the service sector, the main requirement is the production of knitwear, which is combined with high manufacturability and wide distribution, which will lead to low cost, with relatively acceptable consumer characteristics and parameters. In this regard, the solution to the above problems in the technological part of the production of knitwear is of particular importance and is necessary. The article explores the features of improving the technology of production of knitted fabrics using knitted elements, the development of practical methods for obtaining knitted knitwear based on scientific generalization, the formation of patterns on flat knitting machines.
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23

Ghadiri, M., and R. Krechetnikov. "Pattern formation on time-dependent domains." Journal of Fluid Mechanics 880 (October 7, 2019): 136–79. http://dx.doi.org/10.1017/jfm.2019.659.

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In the quest to understand the dynamics of distributed systems on time-dependent spatial domains, we study experimentally the response to domain deformations by Faraday wave patterns – standing waves formed on the free surface of a liquid layer due to its vertical vibration – chosen as a paradigm owing to their historical use in testing new theories and ideas. In our experimental set-up of a vibrating water container with controlled positions of lateral walls and liquid layer depth, the characteristics of the patterns are measured using the Fourier transform profilometry technique, which allows us to reconstruct an accurate time history of the pattern three-dimensional landscape and reveal how it reacts to the domain dynamics on various length and time scales. Analysis of Faraday waves on growing, shrinking and oscillating domains leads to a number of intriguing results. First, the observation of a transverse instability – namely, when a two-dimensional pattern experiences an instability in the direction orthogonal to the direction of the domain deformation – provides a new facet to the stability picture compared to the one-dimensional systems in which the longitudinal (Eckhaus) instability accounts for pattern transformation on time-varying domains. Second, the domain evolution rate is found to be a key factor dictating the patterns observed on the path between the initial and final domain aspect ratios. Its effects range from allowing the formation of complex sequences of patterns to impeding the appearance of any new pattern on the path. Third, the shrinkage–growth process turns out to be generally irreversible on a horizontally evolving domain, but becomes reversible in the case of a time-dependent liquid layer depth, i.e. when the dilution and convective effects of the domain flow are absent. These experimentally observed enigmatic effects of the domain size variations in time are complemented here with appropriate theoretical insights elucidating the dynamics of two-dimensional pattern evolution, which proves to be more intricate compared to one-dimensional systems.
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24

SAWADA, Yasuji. "Crystals and Pattern Formation." Nihon Kessho Gakkaishi 33, no. 6 (1991): 319–25. http://dx.doi.org/10.5940/jcrsj.33.319.

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25

Hunt, J. D. "Pattern formation in solidification." Materials Science and Technology 15, no. 1 (January 1999): 9–14. http://dx.doi.org/10.1179/026708399773002755.

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26

Gyorgy, Andras, and Murat Arcak. "Pattern Formation over Multigraphs." IEEE Transactions on Network Science and Engineering 5, no. 1 (January 1, 2018): 55–64. http://dx.doi.org/10.1109/tnse.2017.2730261.

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27

Sang, James H. "Pattern Formation during Development." Quarterly Review of Biology 74, no. 1 (March 1999): 75. http://dx.doi.org/10.1086/392985.

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28

Bel'kov, V. V., J. Hirschinger, V. Novák, F. J. Niedernostheide, S. D. Ganichev, and W. Prettl. "Pattern formation in semiconductors." Nature 397, no. 6718 (February 1999): 398. http://dx.doi.org/10.1038/17040.

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29

Hunt, J. D. "Pattern formation in solidification." Science and Technology of Advanced Materials 2, no. 1 (January 2001): 147–55. http://dx.doi.org/10.1016/s1468-6996(01)00040-7.

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30

Trimper, Steffen, and Knud Zabrocki. "Memory driven pattern formation." Physics Letters A 331, no. 6 (November 2004): 423–31. http://dx.doi.org/10.1016/j.physleta.2004.09.018.

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31

Nepomnyashchy, Alexander A. "Coarsening versus pattern formation." Comptes Rendus Physique 16, no. 3 (April 2015): 267–79. http://dx.doi.org/10.1016/j.crhy.2015.03.004.

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32

Bajaj, Renu, and S. K. Malik. "Pattern formation in ferrofluids." Journal of Magnetism and Magnetic Materials 149, no. 1-2 (August 1995): 158–61. http://dx.doi.org/10.1016/0304-8853(95)00361-4.

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33

Herrmann, Hans-J. "Pattern Formation of Dunes." Nonlinear Dynamics 44, no. 1-4 (June 2006): 315–17. http://dx.doi.org/10.1007/s11071-006-2016-3.

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34

Prati, F., M. Brambilla, and L. A. Lugiato. "Pattern formation in lasers." La Rivista Del Nuovo Cimento Series 3 17, no. 3 (March 1994): 1–85. http://dx.doi.org/10.1007/bf02724484.

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35

Erlebacher, J., and K. Sieradzki. "Pattern formation during dealloying." Scripta Materialia 49, no. 10 (November 2003): 991–96. http://dx.doi.org/10.1016/s1359-6462(03)00471-8.

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36

GREEN, P. B. "Developmental Biology: Pattern Formation." Science 229, no. 4709 (July 12, 1985): 156. http://dx.doi.org/10.1126/science.229.4709.156.

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37

Onuki, Akira. "Pattern Formation in Gels." Journal of the Physical Society of Japan 57, no. 3 (March 15, 1988): 703–6. http://dx.doi.org/10.1143/jpsj.57.703.

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38

Armbruster, Dieter, Marguerite George, and Iuliana Oprea. "Parametrically forced pattern formation." Chaos: An Interdisciplinary Journal of Nonlinear Science 11, no. 1 (2001): 52. http://dx.doi.org/10.1063/1.1350454.

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39

Krischer, K. "Spatio-Temporal Pattern Formation." Zeitschrift für Physikalische Chemie 208, Part_1_2 (January 1999): 280–81. http://dx.doi.org/10.1524/zpch.1999.208.part_1_2.280.

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40

Yuzhakov, Vadim V., Hsueh-Chia Chang, and Albert E. Miller. "Pattern formation during electropolishing." Physical Review B 56, no. 19 (November 15, 1997): 12608–24. http://dx.doi.org/10.1103/physrevb.56.12608.

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41

Czirok, Andras, and Charles D. Little. "Pattern formation during vasculogenesis." Birth Defects Research Part C: Embryo Today: Reviews 96, no. 2 (June 2012): 153–62. http://dx.doi.org/10.1002/bdrc.21010.

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42

Berking, Stefan. "Pattern Formation in Hydrozoa." Naturwissenschaften 84, no. 9 (September 24, 1997): 381–88. http://dx.doi.org/10.1007/s001140050414.

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43

Bonner, J. T., and Edward C. Cox. "Pattern formation in dictyostelids." Seminars in Developmental Biology 6, no. 5 (January 1995): 359–68. http://dx.doi.org/10.1016/s1044-5781(06)80077-0.

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44

Aifantis, E. C. "Pattern formation in plasticity." International Journal of Engineering Science 33, no. 15 (December 1995): 2161–78. http://dx.doi.org/10.1016/0020-7225(95)00086-d.

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45

MASELKO, JERZY. "PATTERN FORMATIONS IN CHEMICAL SYSTEMS." Advances in Complex Systems 06, no. 01 (March 2003): 3–14. http://dx.doi.org/10.1142/s0219525903000712.

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The formation of complex patterns in chemical systems is discussed in the following cases: relations of pattern formation to thermodynamics theories; unusually complex pattern formation in very simple experimental chemical systems; and numerical simulation of patterns that develop in multicellular chemical systems. The paper concludes with a discussion on future technological applications.
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46

Halatek, J., F. Brauns, and E. Frey. "Self-organization principles of intracellular pattern formation." Philosophical Transactions of the Royal Society B: Biological Sciences 373, no. 1747 (April 9, 2018): 20170107. http://dx.doi.org/10.1098/rstb.2017.0107.

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Dynamic patterning of specific proteins is essential for the spatio-temporal regulation of many important intracellular processes in prokaryotes, eukaryotes and multicellular organisms. The emergence of patterns generated by interactions of diffusing proteins is a paradigmatic example for self-organization. In this article, we review quantitative models for intracellular Min protein patterns in Escherichia coli , Cdc42 polarization in Saccharomyces cerevisiae and the bipolar PAR protein patterns found in Caenorhabditis elegans . By analysing the molecular processes driving these systems we derive a theoretical perspective on general principles underlying self-organized pattern formation. We argue that intracellular pattern formation is not captured by concepts such as ‘activators’, ‘inhibitors’ or ‘substrate depletion’. Instead, intracellular pattern formation is based on the redistribution of proteins by cytosolic diffusion, and the cycling of proteins between distinct conformational states. Therefore, mass-conserving reaction–diffusion equations provide the most appropriate framework to study intracellular pattern formation. We conclude that directed transport, e.g. cytosolic diffusion along an actively maintained cytosolic gradient, is the key process underlying pattern formation. Thus the basic principle of self-organization is the establishment and maintenance of directed transport by intracellular protein dynamics. This article is part of the theme issue ‘Self-organization in cell biology’.
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47

HAKEN, HERMANN. "SYNERGETICS: FROM PATTERN FORMATION TO PATTERN ANALYSIS AND PATTERN RECOGNITION." International Journal of Bifurcation and Chaos 04, no. 05 (October 1994): 1069–83. http://dx.doi.org/10.1142/s0218127494000782.

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It is by now well known that numerous open systems in physics (fluids, plasmas, lasers, nonlinear optical devices, semiconductors), chemistry and biology (morphogenesis) may spontaneously develop spatial, temporal or spatiotemporal structures by self-organization. Quite often, striking analogies between the corresponding patterns can be observed in spite of the fact that the underlying systems are of quite a different nature. In this paper I shall first give an outline of general concepts that allow us to deal with the spontaneous formation of structures from a unifying point of view that is based on concepts of instability, order parameters and enslavement. We shall discuss a number of generalized Ginzburg-Landau equations. In most cases treated so far, theory started from microscopic or mesoscopic equations of motion from which the evolving structures were derived. In my paper I shall address two further problems that are in a way the reverse, namely (1) Can we derive order parameters and the basic modes from observed experimental data? (2) Can we construct systems by means of an underlying dynamics that are capable of producing patterns or structures that we prescribe? In order to address (1), a new variational principle that may be derived from path intergrals is introduced and illustrated by examples. An approach to the problem (2) is illustrated by the device of a computer that recognizes patterns and that may be realized by various kinds of spontaneous pattern formations in semiconductors and lasers.
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48

Van Gorder, Robert A. "Influence of temperature on Turing pattern formation." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, no. 2240 (August 2020): 20200356. http://dx.doi.org/10.1098/rspa.2020.0356.

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The Turing instability is one of the most commonly studied mechanisms leading to pattern formation in reaction–diffusion systems, yet there are still many open questions on the applicability of the Turing mechanism. Although experiments on pattern formation using chemical systems have shown that temperature differences play a role in pattern formation, there is far less theoretical work concerning the interplay between temperature and spatial instabilities. We consider a thermodynamically extended reaction–diffusion system, consisting of a pair of reaction–diffusion equations coupled to an energy equation for temperature, and use this to obtain a natural extension of the Turing instability accounting for temperature. We show that thermal contributions can restrict or enlarge the set of unstable modes possible under the instability, and in some cases may be used to completely shift the set of unstable modes, strongly modifying emergent Turing patterns. Spatial heterogeneity plays a role under several experimentally feasible configurations, and we give particular consideration to scenarios involving thermal gradients, thermodynamics of chemicals transported within a flow, and thermodiffusion. Control of Turing patterns is also an area of active interest, and we also demonstrate how patterns can be modified using time-dependent control of the boundary temperature.
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49

MOGI, IWAO, and SUSUMU OKUBO. "PATTERN FORMATION IN ELECTROLESS DEPOSITION." Fractals 03, no. 02 (June 1995): 371–75. http://dx.doi.org/10.1142/s0218348x9500028x.

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Metal-forest patterns of electroless deposits of silver and gold in thin-layer aqueous solutions were studied in connection with their chemical reactions. While the silver metal-forest with a simple redox reaction of Cu and Ag+ showed a DLA (diffusion limited aggregation)-like pattern, the gold metal-forest with a redox reaction of Pb and [Formula: see text] showed a DBM (dense branching morphology). The reaction mechanism of the latter was investigated by means of cyclic voltammetry, and the growth condition of the DBM is discussed.
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50

VON BRECHT, JAMES H., DAVID UMINSKY, THEODORE KOLOKOLNIKOV, and ANDREA L. BERTOZZI. "PREDICTING PATTERN FORMATION IN PARTICLE INTERACTIONS." Mathematical Models and Methods in Applied Sciences 22, supp01 (April 2012): 1140002. http://dx.doi.org/10.1142/s0218202511400021.

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Large systems of particles interacting pairwise in d dimensions give rise to extraordinarily rich patterns. These patterns generally occur in two types. On one hand, the particles may concentrate on a co-dimension one manifold such as a sphere (in 3D) or a ring (in 2D). Localized, space-filling, co-dimension zero patterns can occur as well. In this paper, we utilize a dynamical systems approach to predict such behaviors in a given system of particles. More specifically, we develop a nonlocal linear stability analysis for particles uniformly distributed on a d - 1 sphere. Remarkably, the linear theory accurately characterizes the patterns in the ground states from the instabilities in the pairwise potential. This aspect of the theory then allows us to address the issue of inverse statistical mechanics in self-assembly: given a ground state exhibiting certain instabilities, we construct a potential that corresponds to such a pattern.
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