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Electrochemistry Communications 9 (2007) 1394–1399

Analysis of deterioration process of organic protective coating usingEIS assisted by SOM network

Xia Zhao a, Jia Wang a,b,*, Yanhua Wang a, Tao Kong a, Lian Zhong a, Wei Zhang a

a College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, PR Chinab State Key Laboratory for Corrosion and Protection, Shenyang 110016, PR China

Received 6 December 2006; received in revised form 27 January 2007; accepted 29 January 2007Available online 3 February 2007

Abstract

Under cyclic wet–dry conditions, the deterioration process of the organic coating on carbon steel surface has been studied using elec-trochemical impedance spectroscopy (EIS) assisted by self-organizing feature map (SOM) network. According to the EIS characteristics,changing rate of impedance and the classification results by SOM network, the entire deterioration process can be divided into three mainstages shown as follows. Stage I is the medium penetration into coatings, which is a slow process. Stage II is the corrosion initiationunder coatings, which is a relatively fast transition period. Stage III is the corrosion extension which causes coating delaminationand makes the coatings lose its corrosion protection eventually. Besides, the results indicate that SOM network is a very simple and effec-tive technique for analyzing the deterioration process of organic coating.� 2007 Elsevier B.V. All rights reserved.

Keywords: Wet–dry; Organic coatings; EIS; SOM network; Classification

1. Introduction

EIS appears to be a widespread technique for the inves-tigation of the degradation of polymer coated metals,because of its good ability to high impedance system andto provide abundant information [1–5]. Even owing tothe sophisticated ways of interpretation, some of the con-clusions made in EIS study remain questionable. Becausethe deterioration of organic coating is always initiated bythe local defect, the average signals of the interface givenby EIS usually result in the ambiguity of the characteristicand the overlapping of time constants. Therefore, othermethods are in urgent need to assist EIS for further studyof coated metals.

With the development of complex data processing tech-niques, the artificial neural network (ANN) method hasbeen accepted as an effective method for analyzing the

1388-2481/$ - see front matter � 2007 Elsevier B.V. All rights reserved.

doi:10.1016/j.elecom.2007.01.049

* Corresponding author. Tel./fax: +86 532 667 825 10.E-mail addresses: zhxiakk@163.com (X. Zhao), jwang@mail.ouc.

edu.cn (J. Wang).

characteristics of coating systems more and more widely.Recently, Lee and Mansfield [6] used the traditionalANN method to classify coatings of different qualities.Gao et al. [7] analyzed the EIS data of 6 coated samplesin different coating deterioration levels under immersingstate, using Kohonen (SOM) network [8] by which theinformation in 5 stages of coating deterioration process isprovided.

However, it will be more valuable to obtain the contin-uous information of one coating sample during the entiredegradation process. The objectives of this paper are,firstly, to investigate the impedance characteristics of thepolymer-coated steel under periodic wet–dry conditions[9], secondly, to come up with a new method to assistEIS for the evaluation of coating deterioration process.

2. Experiments

The electrochemical cell was comprised of two carbonsteel electrodes (10 · 10 · 10 mm) embedded in epoxy0.02 mm apart, as shown in Fig. 1. The cell was pretreated

Fig. 1. Schematic diagram of the experimental cell used for EIS study.

-3 -2 -1 0 1 2 3 4 5 63

4

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7

8

9

10

-40

-50

-60

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-80

-90Ph

ase

shift

/deg

.

log(

|Z|/

cm2 )

log(f/Hz)

0.0 2.0x108 4.0x108 6.0x1080

1x108

2x108

3x108

4x108

5x108

6x108

Zim

/cm

2

Zre/Ω cm2

1 2 3 4 5 6 7

•

Ω•

Ω•

a

A

Fig. 2a. The EIS results for coating degradation at about 15 �C and 70%RH in 3.5% NaCl solution during 1–7 cycles.

X. Zhao et al. / Electrochemistry Communications 9 (2007) 1394–1399 1395

by abrasion with #800 SiC paper, degreasing in acetoneand rinsing with methyl alcohol. And then the samplewas coated with iron oxide red alkyd primer and dried sev-eral days for experiment.

The coating thickness was measured using a Fischerequipment with a precision, in the thickness range of thestudied materials, of ±2 lm.The thickness results are themean value of 20 measurements. And the coating with45 lm thickness was used in this paper.

The cell was exposed to alternate conditions of 8 himmersion and 16 h dryness at about 15 �C and 70% RHin 3.5% NaCl solution. All the impedance measurementswere carried out in a shield cage in order to reduce theexternal influence on studying system.

The electrochemical impedance measurements were per-formed in the 100 KHz–10 MHz frequency range using2263 testing system. The amplitude of the sinusoidal volt-age signal was 10–50 mV according to the correspondingcoating resistance measured. The measurement is taken atintervals of 1 or 2 h.

3. Results and discussion

3.1. EIS characteristics for coating/metal system

The EIS response was more complicated in wet–dry cyc-lic state than that in immersion state, but the followingcharacteristics can still be found:

(1) For single immersion process, the resistancedecreased gradually with time, and then kept stable5 h later.

(2) For single dry process, the resistance increased grad-ually with time, and also became stable 5 h later.

(3) EIS response changed successively between the twoadjacent cycles.

According to the above characteristics, the EIS datameasured 5 h later can be used for study because of theirstability. Therefore, in this paper, the 6th hour data ofthe immersion state in each cycle were selected for analysis.

Fig. 2 was the Nyquist plots and the Bode plots deter-mined at the 6th hour of immersion state in each cycle.

As shown in Fig. 2, the plots can be divided into threepatterns, which might represent three different stages. StageA (the first 7 cycles) was characterized by a single semi-cir-cle with high-impedance which may indicate that somewater penetrated into the coating, but did not reach thecoating/metal interface [10]. As shown in Fig. 3a, nochanges can be found on the coating surface in this stage.In stage C (from cycle11 to cycle 30), the resistance wasvery low. The Nyquist plots of electrochemical impedanceshowed a capacitive loop in the high frequency (HF) rangeand an oblique line in the low frequency (LF) range, whichprobably indicated that corrosive medium had diffused tothe coating/metal interface and the coating began todelaminate. As shown in Fig. 3b, one blister can beobserved on the coating surface with naked eyes in cycle

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log(

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)

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Pha

se s

hift/

deg.

0 1x108 2x108 3x108 4x108 5x1080

1x108

2x108

3x108

4x108

5x108

0.0 3.0x106 6.0x106 9.0x1060.0

3.0x106

6.0x106

9.0x106

10

Zre/

Zim

/

Zre/

8 9 10

Ωcm

2•

Zim

/Ωcm

2•

Ω cm2•

Ω cm2•

Ωcm

2•

b

B

Fig. 2b. The EIS results for coating degradation at about 15 �C and 70%RH in 3.5% NaCl solution during 8–10 cycles.

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log(

| | /

)

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Pha

se s

hift/

deg.

0.00 2.50x106 5.00x106 7.50x106 1.00x1070.00

2.50x106

5.00x106

7.50x106

1.00x107

11 12 14 16 18 22 26 30

Zim

/

Zre/Ω cm2•

Ωcm

2•

Ωcm

2•

Z

c

C

Fig. 2c. The EIS results for coating degradation at about 15 �C and 70%RH in 3.5% NaCl solution during 11–30 cycles.

1396 X. Zhao et al. / Electrochemistry Communications 9 (2007) 1394–1399

12. Stage B, including cycle 8 to cycle 10, was a transitionperiod from stage A to stage C, in which the resistancedecreased sharply and the EIS plots changed from a singlecapacitive loop in cycle 8, to double capacitive loops incycle 9, and then to a capacitive semi-circle at HF com-bined with an oblique line at LF in cycle 10.

But there were still some important problems which cannot be solved by EIS. For instance, whether the corrosivebehavior changed in cycle 9 or cycle 10 was unknown;the exact mechanism of the three stages left much to beinvestigated; and the time when a blister appeared on thecoating surface (Fig. 3b) cannot be clearly confirmed byEIS. Therefore, some other methods were needed for fur-ther study.

3.2. The changing rate of impedance

Compared with other EIS parameters, such as coatingresistance, coating capacitance, polarized resistance anddouble-layer capacitance, etc., the changing rate in imped-ance [7] which satisfies Eq. (1) can reflect the change ofimpedance more sensitively in the entire frequency rangeand can help to recognize the features of deterioration pro-cess more clearly.

kðf Þ0 ¼ dðjZjÞdðlog jf jÞ ð1Þ

The parameter (k(f) 0) expressed in differential form canprobably intensify the characteristics of each stage in thefrequency range. So with the help of k(f) 0, the deteriorationstages can be distinguished effectively without building ECand analyzing other parameters. All the k(f) 0 data were cal-culated by the EIS data at the sixth hour of immersion statein each cycle according to Eq. (1). As shown in Fig. 4, sim-ilar behavior can be observed in the first 7 cycles which wasperhaps related to the penetrating process. In addition, thesame change tendency is presented from cycle 11 to cycle30, which perhaps was corresponding to metal corrosionand coating delamination. The period from cycle 8 to cycle10 was a transition one with the occurrence of anode reac-tion and cathode reaction probably.

The parameter k(f) 0 played an important role in theexamination of the corrosion developing process and theinvestigation of the deterioration mechanism. But it wasstill not enough to characterize the process of cycle 8 tocycle 10, so all the k(f) 0 data were analyzed further bySOM network in the following part.

3.3. SOM network analysis of coating deterioration

The function of SOM network method is that it can clas-sify the training samples related to the study system just by

Fig. 3. The morphology of coating surface under cyclic wet–dry conditions: (a) 1–11 cycles, (b) 12cycle, (c) 22 cycle and (d) 30 cycle.

0 1x100 2x100 3x100 4x100-4.0x10 8

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-8.0x10 7

0.0

k f'

log(f/Hz)

1

A

a

0 1x100 2x100 3x100 4x100

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-2.5x107

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0.05.0x106

K f′

log(f/Hz)

9 15 10 16 11 17 12 18 13 19 14 20

a

b

Fig. 4. The changes of k(f) 0 at the 6th hour of immersion state withfrequency in the coating degradation process: (a) cycle1–cycle 30 and(b) the enlarged view of cycle 9–cycle 20.

X. Zhao et al. / Electrochemistry Communications 9 (2007) 1394–1399 1397

its self-adapting function, and then, combined with EIS, togive an objective evaluation of the entire deteriorationprocess.

3.3.1. SOM network method

The typical SOM network [11,12], one kind of ANN,consists of a single input layer and an output layer (com-petitive layer) in which neurons are arranged in 1D or2D pattern. The nodes in the output layer receive datafrom the input nodes and have links to their immediateneighbors and inspire one another. The link weights Wij(t)connecting the output nodes with the input nodes areadjusted through the self-adapting function of the network.As it is stable, all nodes in each region have the similar out-put value against the special input [13,14]. When an inputvector is presented to the network, the neurons in the out-put layer compete to come out as a winner. Different fromtraditional competitive networks, Kohonen networks donot need to be set the number of the input vector beforetraining. It can automatically classify the points with littledifference in the same group. The less difference the samplepoints have, the much closer the activation level which wasinspired by the corresponding points will be [15].

If an [n1] array is used which means n · 1 neurons arearranged on one line on the competitive plane, then, onthis line, the closer the activation level is, the less differentthe coating state will be, which avoided the principle con-fusion distributed by 2D pattern. Thus in this paper, the1D competitive SOM network was employed to classifythe coating state of 30 wet–dry cycles as well as toachieve the entire information of coating deteriorationprocess.

1398 X. Zhao et al. / Electrochemistry Communications 9 (2007) 1394–1399

3.3.2. SOM network analysis of k(f) 0

The parameter k(f) 0 was analyzed using Neural NetworkToolbox [16] function based on the Matlab. Thenet = newsom (minmax (p), [n1]) was employed to build aself-organizing feature map. [n1] was the dimension of theoutput layer, which means that n · 1 response neuronsare arranged on 1D output layer. Then, the functions oftrain ( ) and sim () were used to train, emulate and classify30 simples, which are the k(f) 0 data of 30 wet–dry cycles inthis experiment.

After 500 times training, the activation levels of 30training samples were obtained respectively as n = 6, 8,10, 12, 14, 16 (Fig. 5). The bigger n value was, the moreobvious the changing trend of activation level would be,and then the more deterioration information could beobtained. But, when n = 16, the trend of activation levelwas clear enough to describe the entire deterioration pro-cess. So other bigger n values (n > 16) were unnecessary inview of the relative simplicity of the data. As shown inFig. 5, the activation level slightly fluctuated before thefirst 7 cycles and changed rapidly from cycle 8 to cycle10, and then kept at a stable state after cycle 11. So thecoating degradation process can be probably divided intothree stages-I:cycle 1–7; II:cycle 8–10; III: cycle 11–30,which were in consistent with the results of the EISmethod. So, integrating the conclusions drawn by thesethree means, the three stages of the degradation processcan be described as follows:

Stage I of cycle 1–7 stood for medium permeating pro-cess through the coating. With the increase of the cyclenumber, the activation level of responsive neuron fluctu-ated while moving to the low region, which might be linkedto the micropores becoming more and bigger in the organiccoating. The permeation speed of the medium increasedwith increasing the cycle number. But in this stage, themedium had not reached the coating/metal interface andthe coating could still provide good protection [10].

In stage II of cycle 8–10, the activation level of respon-sive neuron decreased sharply. This dramatic change

0 5 10 15 20 25 30

0

5

10

15

20

Act

ivat

ion

leve

l

Cycle number

6 8 10 12 14 16

I II III

Fig. 5. Trendline of the activation levels with the cycle number.

might indicate that the medium had reached the coat-ing/metal interface, the micro-cell formed, and the corro-sion reaction occurred under the coating film. Theappearance of two time constants in EIS and the greatchange of k(f) 0 also proved that the deterioration processhad entered the corrosion stage. But the coating had notdelaminated as shown in Fig. 3a and was still partiallyprotective.

In stage III of cycle 11–30, the activation level of neu-ron had decreased to a low region. Thence, the coatingbegan to delaminate from the carbon steel and the protec-tive ability of the coating decreased quickly. The delami-nation can be speculated to occur in cycle 11, because theblister could be observed with the naked eyes in cycle 12as shown in Fig. 3b. Subsequently, the blisters developedquickly with increasing the cycle number as shown inFig. 3c and d.

Owing to the damage of the coating, an oblique line atLF of EIS appeared in Fig. 2c which possibly resulted fromthe oxygen diffusion. The k(f) 0 in Fig. 4 was more close tozero value and the levels of responsive neuron attained afairly steady state. These characteristics showed that plentyof medium had quickly diffused to metal surface and par-ticipated in the corrosion reaction. Consequently, the coat-ing lost its protective ability completely.

As mentioned above, the three stages corresponding tothe entire coating degradation processes probably can beexplained as follows:

Medium permeating in the coating! medium reachingmetal surface, the primary cell forming, corrosion occur-ring! corrosion extending, blister being observed andcoating being delaminated.

4. Conclusions

(1) Under cyclic wet–dry conditions, the entire deterio-ration process of organic coating could be dividedinto three main stages. I: medium penetratingthrough coating, II: medium reaching metal surfacecausing corrosion and III: corrosion expansionresulting in coating delamination. Furthermore, thefirst stage, including the first 7 cycles, was a slowprocess with the resistance decreasing gradually,the second stage, including the subsequent 3 cycles,was a fast transition period with EIS changing fromthe single capacitive loop to the double capacitiveloops, and the third stage, beginning from cycle11, was a relatively stable process controlled by oxy-gen diffusion.

(2) The successful application of SOM network was car-ried out in this paper. The classification results givenby SOM network were in general agreement with theconclusions drawn by EIS. SOM method is very sim-ple and convenient by avoiding some troubles of EISmethod. With the assistance of the SOM network,conclusions can be reached more accurately and reli-ably than done by EIS method alone.

X. Zhao et al. / Electrochemistry Communications 9 (2007) 1394–1399 1399

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