Chaos in batch Belousov-Zhabotinskii systems

Ruoff, Peter

doi:10.1021/j100202a006

Cited by 14 publications

(12 citation statements)

References 4 publications

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“…These results (E,F) agree quite well with the experimental ®ndings reported by Ruoff [8] for the BZ reaction in a batch reactor. In agreement with Ruoff we see that the chaotic behaviour occurs when the system approaches an excitable steady-state.…”

Section: Results and Outlooksupporting

confidence: 91%

“…This drastic reduction, on the one hand, incorporates the full richness of the pioneering work of Field et al [5] (FKN model), and the subsequent improvements suggested by Turanyi et al [7] and, on the other hand, it is enough to account, in particular, for the experimental results with a batch reactor reported by Ruoff [8]. An interesting ®nding is chaotic evolution in a batch system and hence clearly chaos not related to hydrodynamic mixing between in¯ow reagents and the bulk reaction mixture of the CSTR.…”

Section: Introductionmentioning

confidence: 92%

“…The time series were analysed by The Professional Version of the``Chaos Data Analyzer'' [10]. For the initial conditions of reactants we have taken those used by Ruoff [8]. For the initial conditions of reactants we have taken those used by Ruoff [8].…”

Section: Methologymentioning

confidence: 99%

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Chaos and Hyperchaos in a Model of the Belousov-Zhabotinsky Reaction in a Batch Reactor

Nieto-Villar¹,

Velárde²

2001

Journal of Non-Equilibrium Thermodynamics

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A simpli®cation down to twenty six reaction steps of twenty species from the Gyorgyi, Turanyi and Field model (GTF) with eighty steps and twenty six species of the Belousosv-Zhabotinsky reaction is shown to be enough to account for recently available experimental results obtained by Rouff in a batch reactor. The bromate concentration was used as the control parameter for the system. Upon variation of this parameter we see the evolution of the system from an excitable steady-state to a limit cycle, then to a subsequent hyperchaotic regime through crisis, and back to a steadystate.

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Section: Results and Outlooksupporting

confidence: 91%

Section: Introductionmentioning

confidence: 92%

See 1 more Smart Citation

Chaos and Hyperchaos in a Model of the Belousov-Zhabotinsky Reaction in a Batch Reactor

Nieto-Villar¹,

Velárde²

2001

Journal of Non-Equilibrium Thermodynamics

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“…In this assignment the intensities are consistent with the thermal population, 26 and the shifts are close to expectation according to the anharmonicity constants x 16 ϭ0.31 cm Ϫ1 and x 13 ϭ0.98 cm Ϫ1 . 12,26,[29][30][31] A striking feature of the N j ϭ4 1 overtone of chloroform is the splitting of the Q branch of CH 35 Cl 3 into two components of nearly equal intensity separated by 4 cm Ϫ1 indicating a close, almost perfect local resonance of 4 1 with a second state ͑see Fig. 4͒ Cl.…”

Section: Resultsmentioning

confidence: 99%

Isotope selective overtone spectroscopy of CHCl3 by vibrationally assisted dissociation and photofragment ionization

Hippler

Qüack

1996

The Journal of Chemical Physics

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“…Though the effect of mixing on autocatalytic systems has been studied, its effect on systems exhibiting threshold kinetics is less well defined (18)(19)(20). Many common mechanisms produce a threshold response to the concentration of an activator, [C] (nM) (21).…”

Section: Introductionmentioning

confidence: 99%

Rate of Mixing Controls Rate and Outcome of Autocatalytic Processes: Theory and Microfluidic Experiments with Chemical Reactions and Blood Coagulation

Pompano

Ismagilov

2008

Biophysical Journal

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This article demonstrates that the rate of mixing can regulate the rate and outcome of both biological and nonbiological autocatalytic reaction systems that display a threshold response to the concentration of an activator. Plug-based microfluidics was used to control the timing of reactions, the rate of mixing, and surface chemistry in blood clotting and its chemical model. Initiation of clotting of human blood plasma required addition of a critical concentration of thrombin. Clotting could be prevented by rapid mixing when thrombin was added near the critical concentration, and mixing also affected the rate of clotting when thrombin was added at concentrations far above the critical concentration in two clinical clotting assays for human plasma. This phenomenon was modeled by a simple mechanism--local and global competition between the clotting reaction, which autocatalytically produces an activator, and mixing, which removes the activator. Numerical simulations showed that the Damköhler number, which describes this competition, predicts the effects of mixing. Many biological systems are controlled by thresholds, and these results shed light on the dynamics of these systems in the presence of spatial heterogeneities and provide simple guidelines for designing and interpreting experiments with such systems.

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Chaos in batch Belousov-Zhabotinskii systems

Cited by 14 publications

References 4 publications

Chaos and Hyperchaos in a Model of the Belousov-Zhabotinsky Reaction in a Batch Reactor

Chaos and Hyperchaos in a Model of the Belousov-Zhabotinsky Reaction in a Batch Reactor

Isotope selective overtone spectroscopy of CHCl3 by vibrationally assisted dissociation and photofragment ionization

Rate of Mixing Controls Rate and Outcome of Autocatalytic Processes: Theory and Microfluidic Experiments with Chemical Reactions and Blood Coagulation

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