Flicker-noise Spectroscopy Research Articles

REVIEW OF FLICKER NOISE SPECTROSCOPY IN ELECTROCHEMISTRY

This review presents the fundamentals of Flicker-Noise Spectroscopy (FNS), a general phenomenological methodology in which the dynamics and structure of complex systems, characterized by non-linear interactions, dissipation, and inertia, are analyzed by extracting information from various signals with stochastically varying components generated by the systems. The basic idea of FNS is to treat the correlation links present in sequences of different irregularities, such as spikes, "jumps", and discontinuities in derivatives of different orders, on all levels of the spatiotemporal hierarchy of the system under study as main information carriers. The tools to extract and analyze the information are power spectra and difference moments (structural functions) of various orders. Presently, FNS can be applied to three types of problems: (1) determination of parameters or patterns that characterize the dynamics or structural features of complex systems; (2) finding precursors of abrupt changes in the state of various complex systems based on a priori information about the dynamics of the systems; and (3) determination of flow dynamics in distributed systems based on the analysis of dynamic correlations in stochastic signals that are simultaneously measured at different points in space. Examples of FNS applications to such problems as parameterization of the images produced with atomic force microscopy (AFM), determination of precursors for electric breakdowns and major earthquakes, and analysis of electric potential fluctuations in electromembrane systems, as well as to some other problems in electrochemistry and medicine are discussed.

Investigations of Structural Changes in Low-Density Polyethylene Film by Flicker Noise Spectroscopy

Investigations of Structural Changes in Low-Density Polyethylene Film by Flicker Noise Spectroscopy

On the parameterization of surface structures

The procedure for the parameterization of complex surface structures is proposed on the basis of the analysis of spatial “series” formed by the projections on the coordinate axes of roughness profiles recorded during the surface scanning with the sensitive element of atomic force microscope. To extract parameters of thus obtained “spatial series,” flicker-noise spectroscopy is used that allows retrieval of information relevant to specific (“resonance”) and nonspecific (chaotic) components of analyzed complex signals.

Flicker noise spectroscopy and its application: Information hidden in chaotic signals (review)

Fundamentals of flicker noise spectroscopy (FNS), a general phenomenological approach to analyzing dynamics and structure of complex nonlinear systems by extracting information contained in chaotic signals of diverse nature generated by such systems, are presented. The primary idea of FNS is to disclose information hidden in correlation links which are present in a sequence of various irregularities (spikes, jumps, discontinuities in derivatives of different orders) that occur in the measured dynamic variables at all levels of spatiotemporal hierarchy of systems under study. The information is derived from power spectra S(f) (f, frequency) and transient difference moments Φ(p)(τ)(τ, time delay parameter) of different orders p. The procedures of averaging over time interval T, which are introduced in FNS when computing S(f) and Φ(p)(τ), differ from the procedures of averaging in the Gibbs approach. In the latter case, due to the adoption of the ergodic hypothesis, the average values of dynamic quantities over time are replaced by the average values of the same quantities over a statistical ensemble. It came to pass that the Φ(p)(τ) functions are formed exclusively by jumps of a dynamic variable on different spatiotemporal levels of the system’s hierarchy, whereas the formation of S(f) is contributed to by spikes and jumps. The informative parameters extracted from S(f) and Φ(p)(τ) describe correlation times and characterize loss of “memory” (correlation links) in these correlation time intervals for the “spike” and “jump” irregularities. These parameters can be determined using the expressions derived for the case of steady-state evolution. Here the “steady state” implies an evolution state that is characterized by the same values of informative parameters on every level of the system’s hierarchy. In contrast to the scaling self-similarity in theory of fractals and renormgroup, FNS introduces a multiparametric self-similarity for the S(f) and Φ(p)(τ) functions, which is generally characterized by a set of parameters rather than one scaling factor. The S(f) and Φ(2)(τ) functions which are related to different types of information may be viewed, in the steady-state case, as fluctuation-dissipation relations that complement each other informatively. Examples of such generalized relations are presented for fluctuations of electric voltage under open-circuit conditions (the Nyquist theorem), the Levy diffusion, the hydrodynamic fluctuations at fully developed turbulence, and the flicker noise fluctuations of the electric current density in electron-conducting systems. To analyze the dynamics of non-steady processes, formulas are presented for calculating nonstationarity indicators (factors) and estimating time instants when most noticeable changes occur in systems under study, including those preceding catastrophic evolution changes. The studies carried out to date show that the FNS method can be used in solving problems of three types. The first is the determination of parameters or patterns that characterize the dynamics or specific features of structural organization of open complex systems. The second involves search for precursors of sharpest changes in the states of various open dissipative systems on the basis of available information conceming the dynamics of such systems. And the third problem concerns the dynamics of redistribution of perturbations in distributed systems. It is solved by analyzing dynamic correlations in chaotic signals that are measured simultaneously at different points in space. The review demonstrates some applications of the FNS methodology. In particular, it considers some physicochemical and natural processes the data for which were obtained from electrochemical and biological measurements.

Regular and stochastic behavior of Parkinsonian pathological tremor signals

Regular and stochastic behavior in the time series of Parkinsonian pathological tremor velocity is studied on the basis of the statistical theory of discrete non-Markov stochastic processes and flicker-noise spectroscopy. We have developed a new method of analyzing and diagnosing Parkinson's disease (PD) by taking into consideration discreteness, fluctuations, long- and short-range correlations, regular and stochastic behavior, Markov and non-Markov effects and dynamic alternation of relaxation modes in the initial time signals. The spectrum of the statistical non-Markovity parameter reflects Markovity and non-Markovity in the initial time series of tremor. The relaxation and kinetic parameters used in the method allow us to estimate the relaxation scales of diverse scenarios of the time signals produced by the patient in various dynamic states. The local time behavior of the initial time correlation function and the first point of the non-Markovity parameter give detailed information about the variation of pathological tremor in the local regions of the time series. The obtained results can be used to find the most effective method of reducing or suppressing pathological tremor in each individual case of a PD patient. Generally, the method allows one to assess the efficacy of the medical treatment for a group of PD patients.

Electrical conduction mechanism in conjugated polymers studied using Flicker noise spectroscopy

We have performed detailed analysis of the fluctuations of the electrical current in electrochemically deposited conductive polymers (CP) using as example polyaniline and poly(3-methylthiophene). These heterogeneous and disordered materials cannot be analyzed in terms of classical conduction mechanisms (like Schottky or Poole–Frenkel emission). Instead, the electrical transport in CPs is to be considered as a stochastic process with large component of noise. We have been able to distinguish several modes of the conduction process in CPs by applying Flicker noise spectroscopy. Thus, we have established that the transport of charge carriers in highly doped CPs is much less correlated than in non-doped ones at the same electric field strength. While applied electric field increases, correlations become lower in a sequence of elementary events contributing to the conductivity of CP. Apparently, the change in the correlation length corresponds to changing mechanism of the electrical conduction. The lower correlation in highly doped sample can be attributed to various factors including change in CP conformation, enhancement in interchain charge transfer and generation of polaron lattice.

The effect of porphyrin supramolecular structure on singlet oxygen photogeneration

This work is to show that activity of porphyrins in singlet oxygen generation (SOG) is determined not only by their molecular structure but also by supramolecular structure of porphyrin containing systems. This is demonstrated by results of studies of SOG in the gas phase by vacuum deposited tetraphenylporphyrin (TPP) layers under photoexcitation conditions. The structure of the layers was studied by atomic force microscopy (AFM), TEM in diffraction regime (TED) and by the flicker-noise spectroscopy (FNS) method. It was shown that substrates affect the deposited layers structure only at earlier stages of the layer formation. AFM and TED data, together with FNS quantitative parameters of TPP layers, show directly that the chemical activity of solid-phase systems can be determined not only by their chemical nature but also by their supramolecular structure.

Cluster Structure of Liquid Alcohols, Water, and n-Hexane

The processes of cluster formation in liquid alcohols, water, methanol, n-hexanol, and n-hexane have been investigated by the method of flicker-noise spectroscopy. Two types of clusters — clusters with a close-packed structure and clusters with a loose structure — have been detected. The energy of formation of different clusters in methanol and n-hexane ranges, respectively, from −250 to +250 J/mole and from −50 to +50 J/mole. The smallest clusters of methanol, n-hexanol, water, and n-hexane consist, respectively, of six, two, eleven, and two molecules, and their largest clusters represent oscillators consisting, respectively, of 50,400, 17,200, 93,500, and 33,150 molecules at 274 K. In methanol at 271 K, more than 44 types of clusters consisting of 6, 97, 152, 219, 297, 492, 1029, 1368, 1560, etc. molecules were detected. In n-hexanol at 273 K, 57 types of clusters were detected. Models of small clusters are proposed. In water, the content of close-packed clusters is maximum at 277 K. The energy of formation/decomposition of small clusters in water ranges from −0.4 to +0.4 kJ/mole and increases with increase in the water temperature. The hysteresis of transformation of the (H2O)280 cluster in the process of heating and cooling of water in the temperature range 273–280 K was detected. Series of energy spectra of clusters in liquids at different temperatures are presented and discussed.

Flicker-noise spectroscopy: a new approach to investigate the time dynamics of geoelectrical signals measured in seismic areas

The Flicker-Noise Spectroscopy (FNS) is an innovative method of extracting quantitative dynamical information from time series. The method represents the signal as sequences of irregularities (spikes, jumps, discontinuities of derivatives), derived on the basis of power spectra and structural functions of different orders. By means of this method we investigated one-year hourly geoelectrical data measured during 2002 in the station Giuliano, southern Italy, one of the most seismically active areas of Mediterranean region. The analysis performed seems to reveal the potential of the FNS to identify seismic precursors.

Low-frequency noise properties of GaN Schottky barriers deposited on intermediate temperature buffer layers

Metal–semiconductor–metal (MSM) structures were fabricated by RF-plasma-assisted MBE using different buffer layer structures. One type of buffer structure consists of an A1N high-temperature buffer layer (HTBL) and a GaN intermediate temperature buffer layer (ITBL), another buffer structure consists of just a single A1N HTBL. Systematic measurements in the flicker noise and deep level transient Fourier spectroscopy (DLTFS) measurements were used to characterize the defect properties in the films. Both the noise and DLTFS measurements indicate improved properties for devices fabricated with the use of ITBL and is attributed to the relaxation of residue strain in the epitaxial layer during growth process.

Flicker-noise spectroscopy in earthquake prediction research

Abstract. The problem of earthquake prediction and the methods of identification of geophysical precursory signals are discussed. To get information on the dynamics of earthquake preparation processes, fluctuations in geophysical time series are analyzed with the method of flicker-noise spectroscopy. Integral indices – power spectra and various moments ("structural functions") – are used as information relations. We demonstrate that the method allows us to reveal earthquake precursors.

Analysis of morphology of porous silicon layers using Flicker noise spectroscopy

In this work we analyze the surface roughness of porous silicon films and show that it is possible to quantify some fine differences in the morphology of different samples and even in different points of the surface of one sample using a novel method of Flicker Noise Spectroscopy (FNS). High informative potential of the method is now illustrated by applying it to the analysis of laterally non-uniform porous silicon films produced by the local anodization of silicon using a point cathode placed in the vicinity of the silicon wafer. The possibilities for future explorations are shown.

Forecasting of electrical breakdown in porous silicon using Flicker noise spectroscopy

Owing to the influence of environment and impact of energy fluxes (electric polarisation, heating, photon irradiation, etc.) physical and chemical properties of porous semiconductors (and other materials as well) experience changes which have to be monitored in order to distinguish the moment when the properties become out of a working range. There is a necessity of forecasting the development of catastrophic events in the porous film. In the present work we study the possibility of predicting the occurrence of the electrical breakdown in thin porous films through analysis of the fluctuations of the electric current in the vicinity of this event using a novel method called Dynamic Flicker Noise Spectroscopy – DFNS.

Flicker-Noise Spectroscopy for Analyzing Chaotic Time Series of Dynamic Variables: Problem of Signal-to-Noise Relation

The underlying principles of flicker-noise spectroscopy (FNS), which is a new method for obtaining information contained in chaotic time signals, are presented. The method's essence is the imparting of an information value to sequences of distinguishable irregularities (spikes, jumps, discontinuities of derivatives of various orders) in dynamic variables that characterize systems under test. The information distinguishability of the types of irregularities introduced here follows from the fact that the “passport data” that characterize the aggregate properties of the irregularities are distinguishably extracted when analyzing power spectra and difference momenta (“structural functions”) of different orders. Shown are potentialities of the FNS method associated with the introduction of quantitative criteria for the nonstationarity of time series produced by real systems. Revealing such criteria is of interest in connection with the all-pervading character of the nonstationarity phenomenon and with the search for “harbingers” of most dramatic changes in the state of evolving systems. The FNS method may be used for extracting phenomenological properties when analyzing time series that characterize evolution of complex systems. As an example, the dynamics of the electric potential fluctuation in an electromembrane system with a cation-exchange membrane in the region of the overlimiting current is considered. The introduced techniques of parametrization of a chaotic signal may effectively be used for the signal isolation against a “noisy” background even in the case where in fact one has to solve the problem of the isolation of two informatively-valuable signals if the information concerning parameters of the target signal is prespecified.

Jumpwise Development of the Processes with the Participation of Colloidal Systems

Concepts of jumpwise evolution with the participation of colloidal systems were formulated using thermodynamics of hierarchic and nonlinear open systems. Using various complex irreversible processes as examples, it was shown that most of these processes obeys the principle of jumpwise development related to the principles of continuity, correspondence, and maximum entropy production. A significant role of colloidal or biocolloidal states of substance was noted in the jumpwise processes beginning with the geological transformation in Earth's core, the evolution of the transition of dead nature to self-organizing and self-reproducing systems and ending with the traditional domains of colloid chemistry. The aforementioned considerations were confirmed by examples from lithology, hydration dispersion of coal-bearing and carbon silicate materials, hydraulic hardening of binder compositions, as well as by the changes in silicon porous structure at various stages of anodization based on the notions of flicker-noise spectroscopy.

Analysis of electrical and optical properties of silicon nanoclusters using flicker‐noise spectroscopy

AbstractThis paper presents the results of a study on the electrical properties, chemical composition and photoluminescence from silicon nanocrystals produced by electrochemical etching of silicon wafers. A new phenomenological tool, known as flicker‐noise spectroscopy (FNS), was used to analyze the experimental data. The electrical and optical signals from silicon nanoclusters are quite noisy. We show that FNS can be applied efficiently in analysis of these signals, allowing one to obtain significant information on the nature of dynamic microscopic processes assisting the flow of the electric current and emission of light from silicon clusters. The FNS method is also suitable for resolving the problem of the irreproducibility of the properties of nanostructured materials and devices. Deviations in size, local chemical impurities, structural defects and other micro‐inhomogeneities strongly influence the output signals from nanostructures and make their characteristics poorly reproducible. The FNS approach makes it possible to scale the output information from nanostructured devices to a level where these deviations and irreproducibilities are no longer important. Copyright © 2001 John Wiley & Sons, Ltd.

Informative essence of noise: New findings in the electrochemistry of silicon

The paper outlines recent advances in studies on the electrochemical dissolution of silicon made with flicker noise spectroscopy (FNS), a novel phenomenological method of studying the evolution of nonlinear dissipative systems in time, space, and energy. We analyze stochastic components of the anodic current (voltage) during the electrochemical dissolution of silicon and show that FNS provides microscopic-level information on the dissolution kinetics and mechanism. We also study the giant oscillations of voltage during the anodic polarization of silicon and show that it is a collective effect where all local sites at the corroding Si surface are working synchronously to yield an integral oscillatory response of the system. Finally, we discuss the electrical and optical properties of nanocrystalline porous silicon films and show that the FNS method gives the possibility to specify the mechanisms contributing to the electroconduction and luminescent properties of silicon nanostructures.

Principles of flicker noise spectroscopy and its application to disordered semiconductors: IonImplanted silicon

Flicker noise spectroscopy in disordered semiconductors is gaining wide acceptance both in physical investigation and in analysis of various microelectronics processes. Its successful use as a diagnostic tool in microtechnology is a result of advances in fundamental studies of flicker noise in semiconductors with disordered structure. In this work, we systematize experimental data for flicker noise phenomena in disordered semiconductors, specifically, in ion-implanted silicon

Science of complexity: Phenomenological basis and possibility of application to problems of chemical engineering

The basic principles of flicker-noise spectroscopy, a new method of analysis of chaotic time and space series, are presented. This method can be considered the phenomenological basis of the currently developing science of complexity, which deals with open nonlinear dissipative systems of various essences-real natural objects. The method is based on the postulate that irregularities in measured dynamic variables are of crucial informational significance in accepting a new hypothesis of scale invariance. In this approach, the power spectra and the structure functions of different orders are governed by irregularities of various types, namely, dynamic spikes and jumps of measured variables. For processes under investigation, the expressions for both the power spectra and the structure functions prove to be identical (invariant) at each of the spatiotemporal levels of the system. The introduced phenomenological parameters completely and unambiguously characterize the state of the evolving system, thus serving as the “passport data” of the system. The possibility of applying the proposed methodology to solving topical problems of chemical engineering is discussed.

Kinetics of porous silicon growth studied using flicker-noise spectroscopy

The mechanism of the formation of porous silicon (PS) is studied using flicker noise spectroscopy (FNS), a new phenomenological method that allows us to analyze the evolution of nonlinear dissipative systems in time, space and energy. FNS is based on the ideas of deterministic chaos in complex macro- and microsystems. It allows us to obtain a set of empiric parameters (“passport data”) which characterize the state of the system and change of its properties due to the evolution in time, energy, and space. The FNS method permits us to get new information about the kinetics of growth of PS and its properties. Thus, the PS formation mechanisms at n-Si and p-Si, as revealed using the FNS, seem to be essentially different. p-Si shows larger “memory” in the sequence of individual events involved in PS growth than n-Si (if anodized without light illumination). The influence of the anodization variables (such as current density, HF concentration, duration of the process, light illumination) onto the “passport data” of PS is envisaged. The increase of the current density increases memory of the PS formation process, when each forthcoming individual event is more correlated with the preceding one. Increasing current density triggers electrochemical reactions that are negligible at lower currents. Light illumination also produces a positive effect onto the “memory” of the system. The FNS makes it possible to distinguish different stages of the continuous anodization process which are apparently associated with increasing pore length. Thus, FNS is a very sensitive tool in analysis of the PS formation and other complex electrochemical systems as well.