RESUMO
Since the early work on Liesegang rings in gels, they have been a reference point for the study of pattern formation in chemical physics. Here we present a variant of the Liesegang experiment in gas phase, where ammonia and hydrochloric acid react within a glass tube producing a precipitate, which deposits along the tube wall producing a spatial pattern. With this apparently simple experiment a wide range of rich phenomenon can be observed due to the presence of convective flows and irregular dynamics reminiscent of turbulent behavior, for which precise measurements are scarce. In this first part of our work, we describe in detail the experimental setup, the method of data acquisition, the image processing, and the procedure used to obtain an intensity profile, which is representative of the amount of precipitate deposited at the tube walls. Special attention is devoted to the techniques rendering a data series reliable for statistical studies and model building, which may contribute to a characterization and understanding of the pattern formation phenomenon under consideration. As a first step in this direction, based on our data, we are able to show that the observed band pattern follows, with slight deviations, the spacing law encountered in common Liesegang rings, despite that the experimental conditions are very different. A further statistical correlation analysis of the data constitutes Paper II of this research.
RESUMO
The optimal noise amplitude for Stochastic Resonance (SR) is located employing an Artificial Neural Network (ANN) reference model with a nonlinear predictive capability. A modified Kalman Filter (KF) was coupled to this reference model in order to compensate for semi-quantitative forecast errors. Three manifestations of stochastic resonance, namely, Periodic Stochastic Resonance (PSR), Aperiodic Stochastic Resonance (ASR), and finally Coherence Resonance (CR) were considered. Using noise amplitude as the control parameter, for the case of PSR and ASR, the cross-correlation curve between the sub-threshold input signal and the system response is tracked. However, using the same parameter the Normalized Variance curve is tracked for the case of CR. The goal of the present work is to track these curves and converge to their respective extremal points. The ANN reference model strategy captures and subsequently predicts the nonlinear features of the model system while the KF compensates for the perturbations inherent to the superimposed noise. This technique, implemented in the FitzHugh-Nagumo model, enabled us to track the resonance curves and eventually locate their optimal (extremal) values. This would yield the optimal value of noise for the three manifestations of the SR phenomena.