Electrochemical noise spectral analysis

Analysis methods for electrochemical noise data can be separated into three categories, (1) deterministic, (2) statistical, and (3) spectral. Deterministic methods involve the use of mixed potential theory to explain the oscillations that occur. For example, if the ZRA current increases suddenly while the potential difference between the two current electrodes and the potential electrode increases, localized corrosion has likely initiated on one of the current electrodes. A common pitfall in such a measurement is that if a nominally identical reference electrode is used, it could pit as well, leading to no change in potential versus the coupled electrodes. Due to the need for careful interpretation, deterministic methods are not widely used. 

Noise analysis has been particularly fruitfiil in characterizing various aspects of hydrodynamics, as noted above for the specific case of corrosion processes. First of all, multiphase flows were investigated, either gas/water [78], solid/liquid [79, 80], oil/water [81] or oil/brine [82]. In these flows, fluctuations are due primarily either to fluctuations in transport rates to an electrode or to fluctuations in electrolyte resistance. If one phase preferentially wets the electrode, then there may be fluctuations due to variation in the effective electrode area. Each of these phenomena has a characteristic spectral signature. Turbulent flows close to a wall have been investigated by means of electrochemical noise by using electrochemical probes of various shapes, by measuring the power spectral density of the limiting diffusion current fluctuations [83-86],... 

Sliding conditions affect the amplitude of the electrochemical noise, namely fluctuations of the open circuit potential as shown in Figure 2 (Ponthiaux et al., 1997). It is usually impossible to isolate the elementary transients. However, the spectral analysis of such a noise allows characteristic quantities to be derived such as the mean amplitude or duration and the average frequency at which transients occur. These characteristics are essential for getting a better understanding of the nature of the contact and the dissolution and passivation kinetics on the bare surface. 

While the measurement of electrochemical noise is straightforward, the data analysis can be complex. Such analysis is usually directed at distinguishing among different forms of corrosion, quantifying the noise signals, and processing the vast number of accumulated data points into a summarized format. Data processing approaches have included frequency spectral analysis [fast Fourier transforms (FFT) and the maximum entropy method (MEM)] and chaotic dynamics. 

Flicker-noise spectroscopy — The spectral density of - flicker noise (also known as 1// noise, excess noise, semiconductor noise, low-frequency noise, contact noise, and pink noise) increases with frequency. Flicker noise spectroscopy (FNS) is a relatively new method based on the representation of a nonstationary chaotic signal as a sequence of irregularities (such as spikes, jumps, and discontinuities of derivatives of various orders) that conveys information about the time dynamics of the signal [i—iii]. This is accomplished by analysis of the power spectra and the moments of different orders of the signal. The FNS approach is based on the ideas of deterministic chaos and maybe used to identify any chaotic nonstationary signal. Thus, FNS has application to electrochemical systems (-> noise analysis). 

---