Using Recurrence Plot to Study the Dynamics of Reinforcement Steel ...

ISSN 20702051, Protection of Metals and Physical Chemistry of Surfaces, 2015, Vol. 51, No. 4, pp. 716–724. © Pleiades Publishing, Ltd., 2015.

INVESTIGATION METHODS FOR PHYSICOCHEMICAL SYSTEMS

Using Recurrence Plot to Study the Dynamics of Reinforcement Steel Corrosion1 E. GarcíaOchoa and F. Corvo Centro de Investigación en Corrosión (CICORR), Universidad Autónoma de Campeche, Av. Juan de la Barrera s/n, Col. Buenavista, C.P. 24039 Campeche, Cam, Mexico email: [email protected], [email protected] Received May 27, 2014

Abstract—The corrosion initiation process dynamics of reinforcement steel coated with mortar is studied using a nonlinear signal analysis technique named “recurrence plot”. Electrochemical noise currenttime series with an open circuit are analyzed to determine the magnitude of the interaction of corrosion micro cells in presence and in absence of a corrosion inhibitor. The corrosion dynamic shows a complex nature. The interaction among corrosion microcells changes depends on a localization index (LI). A higher LI implies a more discrete distribution of corrosion microcells along with a significant interaction, and a corrosion sys tem autoorganization. DOI: 10.1134/S2070205115040115 1

1. INTRODUCTION

Concrete is the most widely used construction material. It is classified as a composite material. Depending on the requirements, it can be reinforced using steel bars. The main components of concrete are Portland cement, sand, stone and water. It is a low cost material and offers excellent properties. Steel rein forced bars used for concrete are placed into an alka line environment with a pH of about 12.6. In these conditions, steel becomes passive, its corrosion rate decreases and the durability of reinforced bars increases. Pollutants like carbon dioxide (CO2) and chlorides in the environment usually affect the durability of reinforced concrete. These pollutants are dissolved in the water present in the pores of concrete. When the water reaches the surface of the steel bar the pollutants destroy the passive conditions of the steel bar and then, corrosion starts to be significant [1, 2]. Chloride and CO2induced corrosion in carbon steel reinforce ment is the most significant cause of premature failure in reinforced concrete structures. Economic and material losses and the increment of accidents are linked to the changes in the passive conditions of the reinforced steel bars; that is why, it is necessary to eval uate and monitor the corrosion rate of reinforced con crete bars. A simple methodology used to evaluate the corro sion of reinforced concrete structures is established in the ASTM C 87691[3] standard. It consists in mea suring the corrosion potential of steel bars. If the potential values are lower than –250 mV versus a satu 1 The article is published in the original.

rated copper/copper sulphate electrode, the probabil ity of corrosion is higher than the 90% expected. If the corrosion potential is higher than –100 mV expected, the corrosion probability decreases to 10%. Several researches concerning steel bars embedded in con crete have been made using this qualitative criterion [4–6], but some criticisms to this approach have risen [7, 8]. The methodology described above offers infor mation about the corrosion activity, but it is based on a thermodynamic parameter (corrosion potential) which is not directly linked to the corrosion rate. More reliable information about the kinetics of the corrosion process can be obtained using electrochem ical techniques. Polarization curves and electrochem ical impedance spectroscopy (EIS) offer very signifi cant information about the mechanisms of the corro sion processes in concrete [9–11, 12]. Reinforcement steel is usually coated with con crete or mortar. When steel is immersed in a chloride environment, the chloride ions flow through the con crete or the mortar coat and reach the steel surface. The corrosion process starts as soon as the first small amount of chloride ions is present on the steel surface. Steel is originally in passive conditions and when chlo ride ions appear on its surface, a dynamic process of corrosion starts, which results in unstable conditions of the steel. Non linear analysis (recurrence plot) can be used to determine the different corrosive events happening in the rebar surface, as well as the corrosion inhibitor influence in this process. Thus, dangerous events for the material integrity can be observed. This is an advantage regarding the probabilistic estimation of the corrosion potential.

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USING RECURRENCE PLOT TO STUDY THE DYNAMICS

Electrochemical noise (EN) consists in measuring small changes in current and/or potential when an electrochemical process like corrosion is happening). EN is classified as: Potential EN which is based on measuring changes or fluctuations in potential and Current EN which is based on measuring changes or fluctuations in the electric current [13]. One of the most important advantages offered by this electro chemical technique is its lack of intrusiveness, i.e. its application does not involve artificial disturbances in the system. Based on these results, it is possible to characterize different types of corrosion [14]. When an experimental variable is measured, the register is composed by two elements: the signal gener ated by the system studied and the noise, usually asso ciated to errors and defects in the measuring process. In optimal conditions, the ratio between the signal and the noise is high. In regards to other types of signals, electrochemical noise contains information about the system studied, and should not be associated to defects in the measuring process. The term “noise” is used due to historical factors because in the beginning when the technique was developed, it was considered that the system information in the signal of electrochemi cal noise did not exist. The origins of the electrochemical noise are the corrosion processes, where two types of reactions take place: anodic and cathodic. In anodic reactions, metallic atoms get inside the solution in the form of cathions followed by an increase of electrons in the metal. This causes a decrease of the electrochemical potential. In contrast, cathodic reactions cause an increase in the electrochemical potential, because they use metal oxidation free electrons. When free and used electrons are in activity, no potential change is expected and no potential electrochemical noise exists. Nevertheless, the interface characteristics may change with time due to the corrosion process or external actions. As a consequence, the potential value can change continuously which in turn is the origin of the potential electrochemical noise. On the other hand, the interface characteristics may change not only in time, but in space. This means that cathodic and anodic reactions on a metallic surface may occur in different areas which are not necessarily distributed uniformly on the surface. If the metallic surface is divided into two parts, it is possible that the number of free and used electrons could not be on both parts. In these conditions, the potential of each part may be dif ferent; however, if both parts are connected through a ZRA, they will have the same potential. A current flow should then exist with a different magnitude or direc tion depending on the timing and the free and used electrons produced on each side. This is called “cur rent electrochemical noise” [15, 16]. EN potential and EN current can be determined at the same time if the electrical measurement system is open. Noise resistance can be calculated with the data obtained (Rn). The noise resistance is correlated to the

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polarization resistance. This suggests that a possible correlation exists between the corrosion rate and the noise resistance. This parameter is inversely propor tional to the corrosion rate. Rn is the ratio existing between the standard deviation of the potential values time series (σV) and the standard deviation of the cur rent time series (σi), according to the following equa tion [15, 16]: σ (1) R n = V . σi EN is an electrochemical technique that can be used for determining localized corrosion. It is widely used for studying localized corrosion due to the use of the localization index (LI) proposed by Eden [16]. This index is based on statistical information obtained from the current signal of EN and through the calcu lation of the ratio existing between the standard devia tion and the root mean square (RMS) of the current signal. RMS is calculated according to the following expression where 〈I〉 is the average of the current fluc tuations. σi (2) L I = , RMS 2

2

(3) RMS = σ i + 〈 I〉 . LI can oscillate between 0 and 1. Values around 0.001 correspond to uniform corrosion phenomena. Values around 1 indicate localized corrosion. Among several corrosion protective methods, the corrosion inhibitors offer a simple and cost effective prevention technique, primarily used to prevent and stop the chlorideinduced corrosion. The use of corro sion inhibitors is one of the main methods for control ling corrosion. Corrosion inhibitors are chemical compounds that, added in small amounts to the envi ronment, produce an important decrease of the corro sion rate. An excellent review about concrete corro sion inhibitors was made by Soylev and Richardson [17]. They report a wide versatility in corrosion inhib itors used in concrete and the pressing necessity to continue researching in this line. Nowadays, the per formance of commercial inhibitors is only partially satisfactory [18]. Benzotriazole is a chemical compound used as a corrosion inhibitor. Its chemical structure makes pos sible a link among ironbenzotriazole orbitals. A pro tective adsorbed film is formed on the metallic surface. Inhibitor properties of Benzotriazole have been evalu ated in alkaline solutions [19, 20] simulating an elec trolyte that exists in the concrete porous system. How ever, there are no reports about its corrosive behaviour in concrete mortars. In alkaline solutions, the behav iour of pitting potential is evaluated as a key parameter to clarify the inhibitor efficiency. With the use of the EN the Localization Index can also be used as a key parameter to select the most efficient type of inhibitor or the optimal concentration of it. Localized corrosion

PROTECTION OF METALS AND PHYSICAL CHEMISTRY OF SURFACES Vol. 51 No. 4 2015

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GARCÍAOCHOA, CORVO (a)

(b)

2 cm

3.03 cm

17.5 cm 15 cm

2.5 cm

7.5 cm

(c) ACM

Fig. 1. Set up for the electrochemical noise measurements. a—Lateral schema of reinforced mortar sample. b—Reinforced mor tar sample schema (upper vision). c—Reinforced mortar sample connected to an ACM Instrument and a laptop computer.

may be caused by irregularities on the concrete or mortar surface due to the differences in the composi tion of mortar or concrete, taking into account their inhomogeneous nature. In this situation, the localiza tion index constitutes a key parameter to evaluate cor rosion in mortars and concrete. The evaluation carried out in concrete mortar is closer to the corrosion behaviour in concrete than the evaluations carried out in aqueous alkaline solutions. In these conditions the results will be closer to reality and will contribute to improve the performance of inhibitors in practice. In alkaline Ca(OH)2 solutions and also in concrete, the main factor causing the destruction of passive properties is the presence of chloride ions [21]. Visual recurrence analysis is another approach used to study the behaviour of nonlinear dynamic systems. This procedure has been used to differentiate stochas tic and chaotic variabilities. The principal instruments of recurrence analyses are the recurrence plots (RPs) which are especially useful for the graphical represen tation of multidimensional dynamic systems [22]. RPs is a valuable tool for assessing the geometry of the dynamics exploiting nonlinear dependencies even in nonstationary systems.

This is a graphical tool for the diagnosis of drifted and hidden periodicities in the time evolution of dynamic systems, which otherwise would be unnotice able. Recurrence plots (RP) are graphical tools elabo rated by Eckmann et al. based on phase space recon struction [22]. The method of RPs was introduced to visualize the time dependent behaviour of the systems dynamics, which can be pictured as a trajectory in the phase space [23, 24]. It represents the recurrence of the mdimensional phase space trajectory x i . The fol lowing is a graphical representation of the N ⋅ N matrix: R ij = θ ( ε – x i – x j ), i, j = 1, 2, 3, …, N,

(4)

where x i ∈ R d (set of real numbers in ndimensional space containing vectors) stands for the point in phase space at which the system is situated at a given time i, ε is a state dependent cutoff distance (a predefined threshold), ||.|| is the norm of vectors, θ(.) is the Heavi side function, and N is the number of states. One assigns a ‘‘black’’ dot to the value one and a ‘‘white’’ dot to the value zero. The twodimensional graphical representation of Ri, j is called then RP. There are two different types of RPs: unthresholded recurrence plots (UTRP) and thresholded recurrence plots (TRPs).


USING RECURRENCE PLOT TO STUDY THE DYNAMICS 0.0 mM 0.01 mM 0.05 mM 0.1 mM 0.5 mM

Rn (Ω cm2)

1011

Rn (Ω cm2)

2.4 × 109 2.2 × 109 2.0 × 109 1.8 × 109 1.6 × 109 1.4 × 109 1.2 × 109 1.0 × 109 8.0 × 108 6.0 × 108 4.0 × 108 2.0 × 108 0 –2.0 × 108

0

2

4

5 8 t, week

10

12

0 mM 1.0 mM 10.0 mM

1011 1010 109 Rn (Ω cm2)

1010

109

Fig. 2. Rn (Ω cm2) values versus time elapsed (weeks) with different benzotriazole concentrations: 0, 0.01, 0.05, 0.1 and 0.5 mM.

108 107 106 105 104 103 0

719

2

4

6 8 t, week

10

12

Fig. 3. Values of Rn (Ω cm2) versus time elapsed (weeks) with benzotriazole concentrations 0, 1 and 10 mM.

An unthresholded RP is not binary but its matrix is given by the (real valued) distances of the vec

u R i, j

tors X i and X j . The matrix then is usually represented in a two dimensional coloured plot. It has been shown that from an unthresholded RP it is possible to recon struct time series [24]. However, unthresholded RPs are more difficult to quantify than binary RPs. For this reason, in data analysis binary RPs is frequently used. The basic idea to keep in mind when studying RPs is simple: If the underlying signal is truly random and has no structure, the distribution of colours over the RP will be uniform, and there will not be any identifi able patterns. On the other hand, if there is some determinism in the signal generator, it can be detected

0

0.01 0.05

0.1 0.5 C mM

1.0

10.0

Fig. 4. Summa Rn (Ω cm2) values at different concentra tions of benzotriazole vs. time elapsed (weeks).

by a characteristic distinct distribution of colours. Considering this, the length of diagonal line segments of the same colour on the UTRP provides an idea about the signal predictability. Thus, it is possible to visualize and study (qualitatively) the motion of the system trajectories and infer some characteristics of the dynamic system that generated the time series. Recurrence plots contain subtle patterns that are not easily ascertained by a qualitative visual inspec tion. Zbilut and Webber have presented the recurrence quantification analysis (RQA) to quantify an RPs [23, 25, and 26]. Several RQA variables are usually exam ined. Percent recurrence (%R) quantifies a percentage of the plot occupied by recurrent points. It quantifies the number of instants characterized by a recurrence in the interaction of signals: the more periodic the sig nal dynamics, the higher the %R value. Percent deter minism (%D), is a quantified percentage among the recurrent points that form upward diagonal line seg ments and the entire set of recurrence points. The diagonal line consists of two or more points that are diagonally adjacent without intervening the white space. This parameter contains the information about the duration of a stable interaction: the longer the interactions are, the higher the %D value is. Recurrence plot analysis has been successfully used in different scientific disciplines such as medicine [27, 28], chemistry [29], physics[30] and electrochemical corrosion researches[31–33]. The aim of the present paper is to use a nonlinear signal analysis (recurrence plot) to study corrosion process dynamics in reinforcement corrosion in mor tar. Electrochemical noise current time series at open potential are analyzed to determine the interaction between corrosion microcells in presence and absence of a corrosion inhibitor. Benzotriazole was used as a corrosion inhibitor with different concentrations. A complex corrosion dynamic was determined. The


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Values of Noise Resistance (Rn) and Localization Inde × (LI) obtained for different inhibitor concentrations at differents weeks of exposure Inhibitor concentration, (mM L–1)

Number of weeks 0 1 2 3 4 5 6 7 8 9 10 11 12

LI Rn LI Rn LI Rn LI Rn LI Rn LI Rn LI Rn LI Rn LI Rn LI Rn LI Rn LI Rn LI Rn

0.00

0.01

0.05

0.10

0.50

1.0

10.0

0.094 2.85 × 104 0.918 8.02 × 105 0.988 6.76 × 105 0.187 9.28 × 107 0.934 1.55 × 108 0.999H 3.69 × 106 0.425 1.26 × 106 0.916 1.27 × 107 0.190L 5.40 × 108 0.343 1.21 × 107 0.149 4.00 × 107 0.397 3.75 × 103 0.415 2.94 × 103

0.987 1.08 × 107 0.841 2.27 × 107 0.259 1.22 × 107 0.026 2.71 × 109 0.898 3.11 × 108 0.987 1.30 × 108 0.607 9.08 × 108 0.971 7.12 × 107 0.739 1.18 × 109 0.202L 1.56 × 105 0.999H 1.57 × 108 0.335 4.94 × 105 0.203 2.58 × 103

0.998H 1.17 × 107 0.981 4.99 × 105 0.963 8.09 × 105 0.505 1.23 × 108 0.986 4.06 × 105 0.298 8.40 × 106 0.948 2.62 × 108 0.982 9.80 × 108 0.164L 1.47 × 107 0.922 4.86 × 107 0.506 5.33 × 107 0.484 4.46 × 103 0.359 1.21 × 106

0.810 1.1 × 108 0.854 5.41 × 105 0.540 1.15 × 108 0.995 4.70 × 108 0.999H 1.49 × 108 1.000 1.37 × 108 0.986 9.78 × 107 0.996 6.74 × 106 0.203L 1.96 × 108 0.763 4.31 × 106 0.383 3.87 × 107 0.984 1.56 × 108 0.916 9.68 × 104

0.998 4.75 × 106 0.407 2.57 × 108 0.589 2.55 × 106 0.999H 5.25 × 108 0.640 4.18 × 108 0.310 7.01 × 108 0.101 6.15 × 108 0.122 7.17 × 108 0.197L 4.50 × 108 0.798 1.13 × 108 0.798 1.13 × 108 0.211 4.53 × 107 0.610 3.43 × 107

0.065 2.37 × 1011 0.210L 9.60 × 108 0.982 6.23 × 107 0.555 4.77 × 108 0.999H 2.39 × 108 0.265 4.14 × 105 0.071 4.86 × 106 0.259 2.40 × 109 0.180 3.15 × 108 0.949 7.48 × 106 0.161 6.44 × 107 0.923 5.31 × 105 0.307 7.01 × 106

0.134 4.50 × 1011 0.165L 1.74 × 107 0.523 5.16 × 108 0.293 2.65 × 109 0.888 1.24 × 108 0.987 4.93 × 106 0.937 3.35 × 107 0.999H 1.97 × 108 0.978 2.97 × 108 0.560 5.25 × 108 0.579 8.22 × 108 0.872 3.07 × 105 0.492 5.16 × 103

LI = Localization Index; Rn = Noise resistance, Ω cm2.

use of non linear signal analysis tools permit to carry out a systematic study of corrosion dynamics. 2. MATERIALS AND METHODS Concrete mortar samples were elaborated with a water/cement (w/c) ratio of 0.5. The proportion of 1 part of cement/3 parts of sand was maintained. The relatively high (w/c) ratio was selected to facilitate chloride ions reach and ingress the rebar surface. Dis tilled water was added according to the w/c ratio established. This procedure was carried out in a clean wood surface in order to avoid contamination, and a soft mix was obtained. Different benzotriazole quanti ties were added to this soft mix to obtain the following concentrations: 0.01, 0.05, 0.1, 0.5, 1, 0, 10 mM. Next, the mix was placed in moulds and three carbon steel bars (diameter = 0.7 cm) were introduced at a distance of 2 cm. Previously, the carbon steel bars were introduced in an acid bath in order to eliminate oxides from the surface, rinsed with distilled water, and

washed with acetone. An anticorrosive paint was applied leaving a free surface of 1 cm2. The painted area was covered with an isolating tape. The samples were left in the moulds for 24 h. After this period, they were submitted to a curing process of 28 days by intro ducing them in distilled water. After 28 days, the sam ples were exposed to the laboratory atmosphere for 48 h. The carbon steel composition was the following: wt%: 0.18 C, 0.35 Mn, 0.17 Si, 0.0025 S, 0.03 P and the remainder was Fe. To perform these experiments, three identical steel electrodes were used; two of them were connected as working electrodes and the third one was connected as the reference electrode. The current noise was mea sured between the two working electrodes. Simulta neously, the potential noise was measured between one of the working electrodes and the reference electrode. Changes in corrosion potential of the coupled elec trode assembly are measured using a third electrode, either a reference electrode, in our case a pseudorefer ence and even a piece of metal of similar composition



721

(a) 0

IL = 0.2

2000

mA/cm2

1.0 × 10–5 5.0 × 10–8

1500

0

1000

–5.0 × 10–6

500

–1.0 × 10–5 0

200 400 600 t (seg)

800 1000 1200

500

1000

1500 2000 0.33

week 9 (b) IL = 0.99

0

1.0 × 10–6

2000

8.0 × 10–7 6.0 × 10–7 mA/cm2

4.0 ×

1500

10–7

2.0 × 10–7 0

1000

–2.0 × 10–7

500

–4.0 × 10–7 –6.0 × 10–7 0

200 400

600 800 1000 1200 t (seg) week 10

500

1000

1500 20001.24

Fig. 5. EN signal currenttime series and recurrence plot for a mortar probe at benzotriazole concentration 0.01 mM. Two expo sure times: a—Low IL (0.2) and b—High IL (0.99).

as the electrode assembly. Since in this later case there are two uncorrelated potential sources, the Vn (mea 2 2 sured) = √( V 1 + V 2 ), where V1 and V2 are the noise signals from the potential noise by √2 to correct the signal. The three electrode arrangement enables changes in current noise that produce changes in cor rosion potential to be followed as the environmental conditions change. For testing, the samples were immersed in a 3% NaCl solution. To maintain the water saturated in oxy gen, air flow was constantly passed through the solu tion. Each sample was introduced in an individual ves sel (see Fig. 1). The EN measurement was made using a GILL DC instrument. This instrument is sensitive enough to measure current fluctuations of the system studied. Potential and current were measured simulta neously every 0.5 s. A series of 2048 values was col lected. The testing time period was of 12 weeks.

3. RESULTS AND DISCUSSION The behaviour of noise resistance (Rn) versus the time elapsed can be observed in Figs. 2 and 3. The pas sivity breakdown and the regeneration process are very dynamic. The passivity regeneration process reaches higher Rn values in the presence of benzotriazole, which indicates a decrease in the corrosion rate as benzotriazole concentration increases. The process in the benzotriazole concentrations of 1 and 10 mM is very intense and it is necessary to plot the data in a logarithmic scale to be able to distinguish the effect (see Fig. 3). The total amount of the weekly Rn values (accumu lative Rn vs. time values) is determined in a 12 weeks period with different benzotriazole concentrations. It is presented on Fig. 4. A decrease in the corrosion rate can be clearly observed when the inhibitor concentra tion increases. The higher corrosion protection is obtained at concentrations of 1 and 10 mM. It is in agreement with the results shown in Fig. 3, where the


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GARCÍAOCHOA, CORVO

use of a logarithmic scale was necessary for these two concentrations. This is a consequence of the complex 1800 nature of the corrosion process. 1600 1500 1400 Localization index (LI) and noise resistance (Rn) 1200 values at different times (during the measurement 1000 1000 800 period = 12 weeks) with different inhibitor concentra 600 tions are presented in Table 1. Significant fluctuations 500 400 were observed. Low values (0.2) and high values 200 (around 0.99) were determined. Current time series 200 60010001400 18001.03 500 1000 1500 1.24 for low and high L values were selected and processed. I 400 8001200 1600 2000 An estimation of the corrosion microcells interaction week 5 week 10 was made for high and low LI values in order to deter 0.0 mM 0.01 mM 0 0 mine if some periodicity might be an index of the sys 2000 2000 tem autoorganization. With this approach, it is neces sary to use recurrence plots to obtain evidences of the 1500 1500 system organization. 1000 1000 Current time series and corresponding recurrence plot for a mortar probe were selected. At a given 500 500 moment a localization index of 0.20 was determined and in another moment the same probe presented LI = 500 1000 1500 3.49 500 10001500 0.73 0.99 for a 0.01 mM inhibitor concentration. The week 3 2000 week 4 2000 results are presented in Fig. 5. A very significant change is observed. In LI = 0.2 (more uniform corro 0.5 mM 1.0 mM sion microcells distribution) the recurrence is very low. The most abundant colour was green, indicating a noncyclic dynamic showing random fluctuations; Fig. 6. Recurrence plot obtained using current EN signal at however, this does not occur all the time, because in a four different concentrations and a common IL = 0.99. 0

0

2000

LI = 0.20

20 %R

f

g d

15 20 5 0

%R

30 25

e a

c

R = 0.46 b 0 4.0 × 108 8.0 × 108 2.0 × 108 6.0 × 108 1.0 × 109 Rn, Ω cm2

50

g

LI = 0.20

30

%D

%D

40

a = 0.0 mM b = 0.01 mM c = 0.05 mM d = 0.1 mM e = 0.5 mM f = 1.0 mM g = 10.0 mM

20 10

c

0

b 0

d

e

a

R = 0.45 f

8.0 × 108 4.0 × 8 2.0 × 10 6.0 × 108 1.0 × 109 Rn, Ω cm2 108

100 95 90 85 80 75 70 65 60

105 100 95 90 85 80 75 70 65

LI = 0.99

e

f g c

b d R = 0.84

a 0

2 × 108 4 × 108 6 × 108 1 × 108 3 × 108 2 5 × 108 Rn, Ω cm

LI = 0.99

e

f g

c

b d

R = 0.90

a 0

2 × 108 4 × 108 6 × 108 1 × 1080 3 × 108 5 × 108 Rn, Ω cm2

Fig. 7. Plot of recurrence (%) and determinism (%) vs. Rn (Ω cm2) in IL = 0.2 and IL = 0.99. PROTECTION OF METALS AND PHYSICAL CHEMISTRY OF SURFACES Vol. 51 No. 4 2015


CONCLUSIONS 1. Corrosion process initiation of steel bars inside concrete mortar is very dynamic. Changes in intensity and localization index were continuously recorded. The presence of benzotriazole influences significantly this process. Corrosion intensity decreases with the increase of benzotriazole concentration.

Rn, Ω cm2

10

8

100 95 90 85 80

107

%R

IL = 0.99

75 70 65 60

106 0.01 0.05 0.10 0.50 1.00 10.0 C, mM

IL = 0.99 108

107

106 0

105 100 95 90 85 80 75 70 65

%D

0

Rn, Ω cm2

different period of exposure, due to changes in the sur face, a value of LI = 0.99 in the same probe is deter mined. A significant change in the EN signal is observed. The recurrence increases significantly which indicates cyclic processes. Many white and yellow dots appear. This fact shows that an autoorganization pro cess is occurring and it can only be determined using recurrences plot. Recurrence plots at different inhibitors concentra tions and a common LI = 0.99 value are presented in Fig. 6. A high recurrence is observed because many white and yellow dots appear. The corrosion process is very cyclic and with high autoorganization. When the inhibitor concentration increases, recurrence also increases up to a concentration of 0.5 mM. After this value, in higher concentrations, recurrence decreases. This behaviour demonstrates that when the metallic surface adsorbs the inhibitor it causes significant changes in the corrosion dynamic. The interaction among the active areas increases and the discrete dis tribution of the corrosion microcells is constant because LI remains the same. The relation among Rn and Recurrence per cent (%R) and Determinism per cent (%D) at low (0.2) and high (0.99) LI is presented in Fig. 7. An important effect of LI value in periodic and stable interaction of corrosion microcells is observed. High LI values, cor responding to a more discrete corrosion microcell distribution, cause the presence of an autoorganiza tion phenomenon. This could be explained based on a given synchronization of microcells reflected in higher values of %R and %D, as an information exchange among microcells. A more homogeneous distribution at lower LI values shows almost no inter action among microcells. Random oscillations are observed. Microcells increase their interaction at LI = 0.99 when the inhibitor concentration also increases (see Fig. 8). The higher value is obtained at an inhibitor concentration of 0.5 mM. It is important to note that the presence of the inhibitor affects significantly the microcells interaction when they have a discrete dis tribution, synchrony increases and its auto organiza tion gives a higher Rn value (inversely proportional to the corrosion rate). It is presumed that a more ordered and synchronized system shows a lower corrosion rate under these conditions. In the present time, other experiments are in pro cess to corroborate results obtained in this first test.

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0.01 0.05 0.10 0.50 1.00 10.0 C, mM

Fig. 8. Rn (Ω cm2), Recurrence (%) and Determinism (%) at IL = 0.99 for different inhibitor concentrations.

2. Recurrence plot is a powerful tool to determine the corrosion system periodicity and the stability of corrosion microcells interaction of reinforcement in mortar. 3. Low values of %R and %D where obtained at low IL values showing that there is no significant interac tion among corrosion microcells. When corrosion microcells distribution is very discrete (high values of LI) a high value of %R is obtained. %D also increases showing more stable interactions. 4. The presence of benzotriazole as a corrosion inhibitor is important in corrosion microcells inter action. The corrosion microcells show a discrete dis tribution (LI = 0.99, increase in %R and %D values) and a higher synchronizing level. REFERENCES 1. Aperador, W., Mejía de Gutiérrez, R., and Bastidas, D., Corros. Sci., 2009, vol. 51, p. 2027. 2. Yong, A.K. and Song, H.W., Corros. Sci., 2007, vol. 49, p. 4113. 3. ASTM Standard C87691: Standard test method for half cell potentials of uncoated reinforcing steel in concrete, 1999. 4. González, J., Miranda, J., and Feliu, S., Corros. Sci., 2004, vol. 46, p. 2467.


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5. Zamora, M.A.B., Mendoza, D.N., Zamora, H.H., and Calderón, F.A., Port. Electrochim. Acta, 2009, vol. 27, p. 237. 6. AlMehthel, M., AlDulaijan, S., AlIdi, S., et al., Constr. Build. Mater., 2009, vol. 23, p. 1768. 7. Videm, K. and Myrdal, R., Corros. Sci., 1997, vol. 53, p. 734. 8. Castañeda, A., Corvo, F., and O’Reilly, V., Mater. Con str. (Madrid, Spain), 2003, vol. 53, p. 155. 9. González, J.A., Miranda, J.M., and Feliu, S., Corros. Sci., 2004, vol. 46, p. 2467. 10. Tommaselli, M.A.G., Mariano, N.A., and Kuri, S.E., Constr. Build. Mater., 2009, vol. 23, p. 328. 11. Etteyeb, N., Dhouibi, L., Takenouti, H., et al., Electro chim. Acta, 2007, vol. 52, p. 7506. 12. Valcarce, M. and Vázquez, M., Electrochim. Acta, 2008, vol. 53, p. 5007. 13. Kearns, J.R., Scully, J.R., Roberge, P.R., et al., Electro chemical Noise Measurement for Corrosion Applications, West Conshohocken, PA: ASTM, 1996. 14. Legat, A., Leban, M., and Bajt, Z., Electrochim. Acta, 2004, vol. 49, p. 2741. 15. Cottis, R. and Turgoose, S., Electrochemical Imped ance, Noise, Houston, TX: NACE Int., 1999. 16. Botana Pedemonte, F., Aballe Villero, A., and Bár cena, M., Ruido Electroquímico Métodos de Análisis, España: Oviedo, 2002. 17. Soylev, T.A. and Richardson, M.G., Constr. Build. Mater., 2008, vol. 22, p. 609. 18. Ormellese, M., Lazzari, L., Goidanich, S., et al., Cor ros. Sci., 2009, vol. 51, p. 2959. 19. Monticelli, C., Frignani, A., and Trabanelli, G., Cement Concrete Res., 2000, vol. 30, p. 635.

20. Mennucci, M.M., Banczek, E.P., Rodrigue, P.R.P., and Costa, I., Cement Concrete Comp., 2009, vol. 31, p. 418. 21. Guofu, Q. and Jinping, O., Electrochim. Acta, 2007, vol. 52, p. 8008. 22. Eckmann, J., Oliffson, S., and Kamphost, D., Euro phys. Lett., 1987, vol. 4, p. 973. 23. Zbilut, J.P. and Webber, C.L., Plot. Phys. Lett., 1992, vol. 171, p. 199. 24. McGuire, G., Azar, N., and Shelhamer, M., Phys. Lett. A, 1997, vol. 237, p. 43. 25. Webber, C.L. and Zbilut, J.P., J. Appl. Physiol., 1994, vol. 76, p. 965. 26. Trulla, L., Giuliani, A., Zbilut, J., and Webber, C.L., Phys. Lett. A, 1996, vol. 223, p. 255. 27. Morana, C., Ramdani, S., Perrey, S., and Varray, A., J. Neurosci. Methods, 2009, vol. 177, p. 73. 28. Censi, F., Barbaro, V., Bartolini, P., et al., Ann. Biomed. Eng., 2000, vol. 28, p. 61. 29. Colafranceschi, M., Colosimo, A., Zbilut, J., et al., J. Chem. Inf. Model., 2005, vol. 45, p. 183. 30. Marwan, N., Romano, M. C., Thiel, M., and Kurths, J., Phys. Rep., 2007, vol. 438, p. 237. 31. CazaresIbáñez, E., VázquezCoutiño, G., and Gar ciaOchoa, E., J. Electroanal. Chem., 2005, vol. 583, p. 17. 32. GarciaOchoa, E., GonzalezSanchez, J., Acuña, N., and Euan, J., J. Appl .Electrochem., 2009, vol. 39, p. 637. 33. Yang, Y., Zhang, T., Shao, Y., et al., Corros. Sci., 2010, vol. 52, p. 2697.

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