Organic coatings are widely utilized to protect metals from corrosion and inevitably suffer degradation due to exposure to surroundings. Electrochemical technology is suitable for evaluating the protective performance of organic coatings since it has the advantages in rapidity and in-situ measurement. In this chapter, several electrochemical measurement technologies including open circuit potential (OCP), linear polarization resistance (LPR), electrochemical impedance spectroscopy (EIS) as well as electrochemical noise (EN) are introduced as ideal methods for acquiring mechanistic information about the failure behavior of the painted metal. The research status on measuring configurations, choosing data acquisition parameters and analytical methods are also discussed.
- organic coatings
- open circuit potential
- electrochemical impedance spectroscopy
- electrochemical noise
Organic coatings mostly have dual uses of protecting the substrate and being decorative. Concerning the engineering purposes, organic coating is presumably only for the function of preventing metal corrosion, which is an effective means for the corrosion protection of marine, pipeline, bridge and so on . However, coating degradation is always inevitable, due to the inherent nature and the preparation process of organic coatings. The protective function loses gradually when exposed to the corrosion environment, and it often cannot be detected in time, resulting in undetectable corrosion destruction of the metal beneath the coating. Therefore, developing an in-situ evaluation technique for the protective performance of coatings is an urgent demand presently.
The principle of corrosion of bare metal (active dissolution) in the electrolyte aqueous solution or in the humid environment is the balancing electrochemical reactions, i.e., the anodic reaction, e.g., metal dissolution (M → Mn+ +
Compared with the routine test methods for coating evaluation, electrochemical measurement technologies have many unique advantages . First, the measuring process is fast, and the instruments are relatively simple. Second, electrochemical methods achieve the quantitative or semi-quantitative evaluation for the protection level. The accurate results of the analysis are superior to other performance tests. More importantly, the in-situ examination of organic coatings makes it possible for continuous monitoring in the field. In this chapter, several electrochemical measurement technologies including open circuit potential (OCP), linear polarization resistance (LPR), electrochemical impedance spectroscopy (EIS) as well as electrochemical noise (EN), are introduced for assessing the performance and acquiring mechanistic information on the failure behavior of the painted metal. Although application of these technologies is relatively mature in the lab, lots of significant challenges still exist in the field evaluation, and the corresponding considerations are required. The aim of this review is to summarize the specific characteristics of electrochemical technologies, the data parameters and the analytical methods, which can assist the application for anti-corrosive evaluation of organic coatings.
2. Open circuit potential method
OCP is a simple but important parameter in the research of corrosion and protection, which is the potential of a working electrode (WE) when no external current is applied to the circuit . As a fundamental electrochemical method for assessing the anti-corrosion performance of coating/metal system, it is generally recognized that the OCP of coated metal is more positive than that of the bare metal . Gowri and Balakrishnan  confirmed that their greatest corrosion resistant specimen showed a more positive value in potential than other coated samples on general levels. There are several factors that affect the potential of coated metals, and the resistance of the film is the most significant one. Besides, there are corrosion products of local anode and cathode (for example, rising concentrations of Fe2+ and OH− due to their slow dispersion), the cathodic protection by some active pigments, etc. Deya et al.  showed that the OCP of alkyd coating varied jointly with ionic resistance and changed toward less negative values in cases of coatings with high ionic resistance. Liu et al.  measured the OCP of aluminum alloy specimens coated with 2 wt.% polyaniline (PANI) epoxy coating over 40 h (Figure 2). During the initial 20 h, the potential rapidly became negative. After this period, the potential gradually increased up to 48 h. It concluded that the protection mechanism of coating changed from barrier inhibition to ionic resistance by the formation of a complete oxide layer during immersion time.
A tracking measurement of OCP can reflect the corrosion process of metal substrate. Murray  reported the typical epoxy coated sample potential-time data through 3000 h of exposure to the ASTM-D-1141 substitute ocean water test solution. Figure 3 shows the OCP curve of epoxy mica coating/steel system in 3.5 wt.% NaCl solution under alternating hydrostatic pressure (AHP) condition . OCP of the coating as a whole has a negative relationship with immersion time. Three stages are found in the measurement. First, there is a large fluctuation appearing in the first 24 h, the OCP decreases to the minimum point from 0.52 V (vs. Ag/AgCl) to −0.41 V (vs. Ag/AgCl), and then rebounds to about 0.2 V (vs. Ag/AgCl) rapidly. This may be attributed to the water permeation at this time, and the conductive paths for electrolyte diffusion have been created. From 25 h to about 130 h, the OCP shows decrease tendency in fluctuations. It suggested that the coating starts to deteriorate, and the substrate under coating is corroding at a low rate. However, a significant decline in the value of OCP can be obtained after 130 h, and the OCP reaches to −0.53 V (vs. Ag/AgCl). This implied that the steel suffered from serious corrosion and the coating obviously degenerated. The rapid changes in OCP are much earlier than visible corrosion products on the surface of substrate.
Sometimes OCP is observed to fluctuate up and down in the initial time. This can attribute to a change in the ratio of local anodic area to cathodic area. Mayne described OCP fluctuations of coated steel by the concept of an
3. Linear polarization method
Linear polarization (LP) is one of the most commonly used electrochemical methods for the rapid test of metal corrosion rate. The characteristics of LP are sensitive and fast, which are suitable for the corrosion system in any electrolytes. The surface state of the sample would not be damaged due to a small polarization current, which is appropriate for the measurement of the anti-corrosion properties of the coated metals. The principle of LP technique is applying current polarization on the WE, the electrode potential of WE would change near the self-corrosion potential (about ±20 mV); thus, a linear relationship between Δ
4. Electrochemical impedance spectroscopy method
As early as 1980s, researchers have started using EIS to investigate the protective properties and deterioration of organic coatings. EIS can get the information of coating/metal systems in different frequency bands. According to the calculation of coating capacitance and coating resistance, information of coating body can be quantitatively acquired. The double-layer capacitance and charge-transfer resistance also reflect the corrosion process of metal substrate. Therefore, EIS becomes the main method to assess coating performance among the electrochemical techniques.
The electrochemical behavior of measured coating/metal system (i.e., the coated metal electrode) is different compared with that of bare metal. Due to a wider linear response region of coating/metal system, EIS tests were usually performed in the frequency range from 100 kHz to 10 mHz. In addition, organic coating is often a high impedance system, the impedance modulus of coating can reach 1011 Ω·cm2. Thus, a larger value in sinusoidal voltage is applied than that of bare metal, to avoid the errors caused by potential drift and improve the signal-noise ratio. Generally, 20 mV (rms) amplitude coupled with OCP is enough for coating system. When the coating has a higher impedance, a higher sinusoidal perturbance should be used. No more than 50 mV (rms) is accepted, otherwise the electrochemical process will be artificially changed. For the continued EIS tests, a special flat plate test cell was developed, which consists of a horizontally positioned coated-flat plate specimen, a clamped, O-ring seal and a glass tube . A Faraday cage is often utilized to effectively reduce the instrumentation and ambience interferences when EIS is applied in the lab and in the field .
4.1. Physical model of EIS and its evolution in the failure process of coating/metal system
There are two basic purposes of EIS measurement for organic coatings. One is the equivalent electrical circuit (EEC) model by fitting analysis. Furthermore, the evolution of failure process of coating/metal system can be reflected by different equivalent circuit models. Another purpose is to obtain some of the electrical parameters for evaluating on the protective performance of the coatings. The choice of physical model should follow the features of EIS plots and coating structure. Different kinds of organic coatings (involving the binder and even pigment) or the same coating in different service environments may have disparate EIS plots. Therefore, the physical models are impossible to have only a few fixed forms. Nonetheless, some typical stages in the coating failure process can be concluded by EIS analysis. It is generally accepted that the electrochemical behavior of coated metals during their exposure to aqueous solution at ambient temperature involves the penetration of water electrolyte, electrochemical reaction on the interface and further deterioration of the protectiveness (formation of under film corrosion, growth of blisters, delamination of paint film and so on), which finally culminates in the complete failure of the coatings . Liu et al.  measured the EIS of epoxy varnish coating at different immersion time. The result indicated that there were two basic stages of coating failure, i.e., the single capacitance arc stage and double capacitance arc stage, which implied that water was absorbed into the coating after initial immersion (the single capacitance arc stage), and then electrochemical corrosion started when water arrived at the interface between the coating and the steel (double capacitance arcs stage). During the second stage, the reaction rate determining step was controlled by corrosion.
Although the EEC models of coating systems are quite different, the failure process of coating can be determined according to some typical parameters, such as the time-constant, coating capacitance and coating resistance. At the initial period of immersion, the electrolyte permeates in the coating but has not reached the coating/metal interface yet. The EEC model with only one time-constant can be used due to the great barrier properties of coating. The coating capacitance increases meanwhile the coating resistance decreases with increasing immersion time. In the medium term, water diffuses to the coating/metal interface, and then the electrochemical reactions occur, resulting in the appearance of two time-constants in the EIS plots or EEC models. There is no macroscopic corrosion product on the coating surface at this stage. When the corrosion products can be observed by naked eyes, the EEC models often return to the characteristic of one time-constant, which defines as the final stage. The coating capacitance reaches a steady value, which indicates that the water absorption of the coating gets a saturated state, and the coating losses its barrier function against electrolyte permeation [13, 17].
According to the above analysis of coating failure, several typical equivalent circuit models for EIS results of organic coatings are summarized. As shown in Figure 4a, the R(CR) model is often used in the initial immersion or for an intact coating, which means that the coating can act as an isolation layer and provide a good protective performance. When the electrolyte reaches the coating/metal interface, EEC models in Figure 4b and c may be selected. The model R(C(R(CR))) in Figure 4b is suitable for the majority of organic coatings, because the blisters or corrosion at the coating/metal interface are often localized. Concerning the R(CR)(CR) model in Figure 4c, the water should uniformly permeate into the coating, such as the zinc-rich coating. Two time-constants represent the dielectric properties of polymer and the corrosion of zinc particles, respectively. Sometimes, the mass transfer of reactive particles is postponed due to the addition of pigments and fillers, resulting in diffusion characteristics of EIS, such as the Warburg impedance. Two typical EEC models with Warburg impedance are given. In Figure 4d, the R(C(RW(CR))) model is commonly used in the medium-term immersion, because the electrolyte diffusion occurs in the gaps among the fillers. When the diffusion region is next to the coating/metal interface, the R(C(R(C(RW)))) model in Figure 4e is appropriate. In later stage of immersion, the R(C(RW)) model in Figure 4f is often used, because macroscopic pores and blisters make the coating ineffective, and the diffusion process is mainly determined by the corrosion reactions of metal substrate.
When organic coating applies to a special environment, the fitting results may be different. Meng et al.  investigated the failure behavior of epoxy mica (EM) coating under AHP environment by EIS, which could be a typical example of organic coating with inert pigment. Four distinct stages of the coating deterioration were determined according to the evolution of EIS plots and the fitting results of EEC models (Figure 5a–d). At the first stage from 0 to 15 h (Figure 5a), the Nyquist plot reveals one capacitive characteristic, and the impedance modulus reaches 1011 Ω·cm2 during the initial periods of immersion. It demonstrated that the coating acted as a barrier layer with a parallel connection of a high-value coating resistance and a low-value coating capacitance. The corresponding equivalent circuit A (in Figure 5a) was used to fit the impedance data, which included the solution resistance
The second stage (16–95 h) was identified by the fitting results of EEC. As the immersion time increased to 16 h, the EIS data is no longer satisfactorily fitted by model A. Obvious deviation is visible in the plot (Figure 5e). Considering the water and oxygen molecules reached the substrate surface through micro-pores in the coating, model B (see Figure 5b) was applied which added the double-layer capacitance CPEdl and the charge-transfer resistance
After a period of immersion time under AHP, the diffusion character was added to the plot at low frequency from 96 to 150 h. The third stage shown in Figure 5c indicated that the corrosion behavior of the coated steel has been altered. At this moment, corrosion products were visible by the naked eyes at the surface of steel. It is probably that the corrosion of the steel substrate was accelerated at the interface area, a new diffusion field appeared around the substrate. As a result, model C (in Figure 5c) containing a diffusion component was applied to fit the experimental data.
At the final stage (until 240 h), a capacitive loop with a characteristic of Warburg impedance arc is observed (Figure 5d). Water was mainly responsible for the measured diffusion in the EIS response at the initial stage. Since water absorption approached or reached saturation, the ions or products involved in corrosion process of coating/metal interface were mainly responsible for the diffusion in the EIS response. The impedance modulus (|Z|) has dropped to 107 Ω·cm2. By this time the epoxy coating cannot prevent the corrosion of metals from happening under AHP. The electrolyte has reached the surface of metal, and obvious corrosion has been observed when epoxy coating fell below 106–107 Ω·cm2, which was chosen as an indicator of poor protective performance empirically.
4.2. EIS analysis on water diffusion process and coating/steel interfacial reaction
As a multi-interface corrosion system, the coating/metal system often has complex failure process, due to inhomogeneous physical and chemical properties of binder/pigment and coating/metal interfaces . Among several sub-processes of coating failure, there are two critical steps: the water diffusion process and the following electrochemical reactions happened at the coating/metal interface . In order to investigate how electrical parameters of EIS quantitatively evaluate the protective performance of organic coating, the physical meaning of parameters and their correlation to two critical steps of coating failure behavior have been discussed in details as follow.
The process of water diffusion, i.e., the water impermeability of coatings is the key performance indicators, which is closely related to
After a period of immersion, the interfacial reactions between coating and steel may occur. From the EIS data fitted by EEC, the interfacial reaction is mainly electrochemical reaction, while for that under AHP , the interfacial reaction includes water spread at the coating/steel interface and electrochemical reaction. The charge-transfer resistance,
5. Electrochemical noise method
Although EIS is a well-established method for corrosion monitoring, applying artificial disturbance to the measured system may affect the process of electrochemical reaction. Thus, researchers are hoping to utilize a nondestructive electrochemical measurement technique in the field. EN measurement is such an appropriate technique for the coating evaluation, which is expected to be comparable to EIS [22, 23]. The spontaneous fluctuations of electrode potential and current during electrochemical reactions are known as EN. High sensitivity to high resistance system and detection of localized corrosion are additional advantages of EN. Consequently, the application of EN has gained growing attention in corrosion research [24, 25]. In recent decades, studies on EN analysis of polymer coated metals also have been reported widely [3, 26, 27]. However, two issues restrict its application in the field. First, the on-site EN configuration and its reliability remain to be identified. Second, a quick and accurate EN analysis method is needed for the in-situ coating evaluation.
5.1. Measuring methods and configurations for EN
Theoretically, the measuring instruments for EN are very simple. While EN configurations are complex in practical applications. Since EN research began in 1968 , the measuring mode and system of EN have experienced several stages of evolution. Two-electrode (working electrode and reference electrode) system was first adopted to measure potential noise under OCP or a certain constant current . This simple configuration is mainly applied in the field of electro-deposition now. Then classical three-electrode system was applied so that the potential and current signals of WE can be separately measured through reference electrode (RE) and counter electrode (CE). Due to the respective measurement, however, some correlation analyses between potential and current noise cannot be made, such as noise resistance . In order to overcome this, the three-electrode system was improved by researchers. Zero resistance amperometer (ZRA) was connected between two working electrodes (WE 1 and WE 2), which were made of the same material to avoid polarization . Consequently, the current noise was measured as galvanic coupling current between two WEs, the potential noise between coupled WEs and RE was measured simultaneously. This electrode system has become the basic approach for EN measurement until now . As for organic coatings, the EN three-electrode configuration can also be utilized to evaluate the performance of organic coatings by measuring the corrosion reactions of substrate metal .
Based on the principle of EN measurement above, many attempts on the improvement of EN measuring methods for in-situ monitoring have been made in recent years. Mabbutt et al.  firstly designed a nonstandard EN configuration. Two saturated calomel electrodes (SCEs) were utilized as the WEs, and the substrate served as the RE. Jamali et al.  also proposed the so-called “single cell” configuration, since the SCE served as RE and CE consecutively but not simultaneously. It is noteworthy that this configuration belongs to asymmetric electrode configuration, and further investigation on the asymmetry of electrodes is still required to clarify. In addition, some in-situ test electrode techniques, such as microelectrodes or multiple electrodes, have been used for the application of EN in coating evaluation in the field or under some specific conditions. Bierwagen et al.  have used the microelectrodes to study EN measurement of the coatings in a cyclic salt fog test chamber. Simpson et al.  made a thin sheet of gold by electron-beam-deposition, which was deposited on the painted steel and served as the microelectrode. The coating degradation was tested by the electrochemical method in an atmospheric exposure chamber. Tan  measured the EN of various corrosion systems by the wire beam electrode, which not only detects noise signatures and noise resistance, but also provides unprecedented spatial and temporal information on localized corrosion. In a word, further improvement of the EN configuration is still needed, particularly in the case of asymmetric electrode and the electrode with complicated shapes, since two identical coating/metal WEs tend to impractical in the field.
In the actual measurement of EN, it is also very important to select an appropriate sampling frequency, which is directly related to the reliability of the results. An excessively high sampling frequency leads to the lower power spectral density of EN, which is close to the white noise and produces difficulty in the data analysis. If the frequency is excessively low, some useful information will be lost. The sampling frequencies at 0.5, 1 and 2 Hz are commonly used and suitable for the general corrosion system, the specific value should be determined according to the EN sources of the tested system. Regarding the coating/metal system, EN data are usually recorded with a data-sampling interval of 0.25 or 0.5 s.
For the original EN signals, data preprocessing before various theoretical analyzing is necessary. The original data is composed of the real EN signals and the direct current (DC) drift signals. The drift can significantly affect the analysis results from time domain and frequency domain . Considering that the generation of EN is a stable process, thus DC drift must be removed. At present, the DC drift removal method is still being explored. Tan et al. proposed the moving average removal (MAR) method to eliminate the DC drift of EN . However, improper selection of filter window will make an obvious erroneous result . In 2001, Mansfield proposed the linear fitting removal method and obtained satisfactory results in some corrosion systems . This method is only suitable for the condition of DC drift with a linear feature. Meanwhile, Bertocci et al. proposed the polynomial method , which had satisfactory results as well as a wide range of application, despite the physical meaning of choosing polynomial exponent is still not clear.
5.2. EN data analysis and parameter acquisition for coating/metal system
Presently, a variety of analysis methods have been developed for the processing of EN signals, which include statistical analysis , spectral analysis , wavelet analysis , fractal analysis  and so on. These methods have successfully been applied to analyze many corrosion or degradation mechanisms of coatings.
Meng et al.  measured EN of an epoxy coating/steel system under AHP condition. The characteristics of EN time records are related with different corrosion states of the substrate steel. Three distinct stages can be divided with different characteristics (Figure 7). In the first stage, the potential signal exhibited strong and stochastic fluctuations. The current signal displayed the characteristic of white noise with a narrow range of fluctuations, which always occurs in 0–36 h. In the second stage, the EN transients began to appear simultaneously in potential and current signals from about 37 to about 130 h. The transients may be caused by the localized corrosion which occurs on the surface of metal. Finally, a typical example of potential and current signals after 130 h is shown in the figure, which fluctuated in larger amplitude with quick ascending and slow recovery pattern.
Statistical analysis has several commonly used parameters , such as , , σE, σI and noise resistance
Wavelet analysis is the development and continuation of Fourier analytical method. For a large number of unsteady signals, the FFT transformation is not particularly appropriate. The wavelet transform is a time-scale analytical method of signal, which has the characteristics of multi-resolution analysis, and has the ability to characterize the local characteristics of the signal in time domain and frequency domain. Therefore, People tend to use wavelet transform to extract useful information of EN. In , wavelet analysis was applied and energy distribution plots (EDPs) of current noise at different immersion time were provided (Figure 9), to figure out the correlation between corrosion states of substrate and EN results. In Figure 9a (0–31 h immersion), the vast majority of energy distribution (ED) is in the crystal d7, d8. The scale range of time-constants of d7 and d8 is 32–128 s, it is considered that the diffusion process often occurs during this period. Therefore, the first stage should be the water permeation period, which is in agreement with the other analysis results. In the second stage (Figure 9b), ED of d8 decreased and ED of d1, d3 increased obviously. The crystal d1, d3 (scale range 0.25–0.5 s and 1–2 s) represent fast corrosion process, which could be attributed to pitting nucleation and metastable pitting process . It indicated that the corrosion of metal occurred at this time, the failure behavior was dominated by the mixed mechanisms of water transport and charge-transfer reaction. In the third stage, the energy was stored predominantly in the crystal d1 and d3 (Figure 9c), which might indicate the fast corrosion process in this period, and the charge-transfer mechanism was the dominant process. It could be found that the EDPs were not only in accordance with, but also reflect more information about the corrosion mechanisms.
To enable the rapid and automatic monitoring of the coatings by EN, the combination with artificial intelligence method and theory of nonlinear mathematics should be explored. For example, pattern recognition (PR) is an important method of information science that focuses on the recognition of regularities in data and the data classification . EN signals show different distribution characteristics in different stages of corrosion, thus the similarity and the difference of EN waveform features could be categorized by PR. A close correlation is expected to be established between the classification results and the corrosion states. Huang et al.  applied some PR procedures for identifying different pitting states for Q235 carbon steel in NaHCO3 + NaCl solutions. Meng et al.  applied PR method to the establishment of an evaluation model for EN statistical parameters. For the painted steel system, different failure stages can be conveniently identified. The unique role of new analytical method will be more pronounced in the future.
Since water penetrates to the coating/steel interface, the delamination of coating from steel and the corrosion of steel substrate lead to obvious changes in electrochemical signals. Therefore, for the state of coating/steel interface, electrochemical evaluation technique can get the first-hand information. In addition, defects in the coating body, such as pores and cracks of pigment/binder interfaces, are gradually increasing by the erosion environment. Enlarged conductive paths for electrolyte cause a decrease in permeability resistance of coating, which can be detected by electrochemical methods consequently. In short, coating adhesion and compactness are two critical parts in failure process of organic coating, which can be used to achieve in-situ evaluation by electrochemical measurement methods. As to the further investigation of electrochemical evaluation, the application of artificial intelligence analysis [49, 50] may be an essential trend.
The investigation is supported by the National Natural Science Fund of China under the Contract No. 51622106, and the Fundamental Research Funds for the Central Universities under the Contract No. N170203005 and No. N170212021.
Conflict of interest
The authors declare no conflict of interest.
Akbarinezhad E, Bahremandi M, Faridi HR, Rezaei F. Another approach for ranking and evaluating organic paint coatings via electrochemical impedance spectroscopy. Corrosion Science. 2009; 51:356-363. DOI: 10.1016/j.corsci.2008.10.029
Funke W. Problems and progress in organic coatings science and technology. Progress in Organic Coating. 1997; 31:5-9. DOI: 10.1016/S0300-9440(97)00013-1
Jamali SS, Mills DJ. A critical review of electrochemical noise measurement as a tool for evaluation of organic coatings. Progress in Organic Coating. 2016; 95:26-37. DOI: 10.1016/j.porgcoat.2016.02.016
Mansfeld F, Han LT, Lee CC, Chen C, Zhang G, Xiao H. Analysis of electrochemical impedance and noise data for polymer coated metals. Corrosion Science. 1997; 39:255-279. DOI: 10.1016/S0010-938x(97)83346-X
Ahadi MM, Attar MM. OCP measurement: A method to determine CPVC. Scientia Iranica. 2007; 14:369-372
Murray JN. Electrochemical test methods for evaluating organic coatings on metals: An update. Part II: Single test parameter measurements. Progress in Organic Coating. 1997; 31:255-264. DOI: 10.1016/S0300-9440(97)00084-2
Gowri S, Balakrishnan K. The effect of the PVC/CPVC ratio on the corrosion resistance properties of organic coatings. Progress in Organic Coating. 1994; 23:363-377. DOI: 10.1016/0033-0655(94)87005-5
Deya MC, Blustein G, Romagnoli R, del Amo B. The influence of the anion type on the anticorrosive behaviour of inorganic phosphates. Surface & Coatings Technology. 2002; 150:133-142. DOI: 10.1016/S0257-8972(01)01522-5
Liu S, Liu L, Meng F, Li Y, Wang F. Protective performance of polyaniline-sulfosalicylic acid/epoxy coating for 5083 aluminum. Materials (Basel). 2018; 11:19. DOI: 10.3390/ma11020292
Meng F, Liu L, Tian W, Wu H, Li Y, Zhang T, Wang F. The influence of the chemically bonded interface between fillers and binder on the failure behaviour of an epoxy coating under marine alternating hydrostatic pressure. Corrosion Science. 2015; 101:139-154. DOI: 10.1016/j.corsci.2015.09.011
Stern M, Geaby AL. Electrochemical polarization. Journal of the Electrochemical Society. 1957; 104:56-63. DOI: 10.1149/1.2428496
Marchebois H, Touzain S, Joiret S, Bernard J, Savall C. Zinc-rich powder coatings corrosion in sea water: Influence of conductive pigments. Progress in Organic Coating. 2002; 45:415-421. DOI: 10.1016/S0300-9440(02)00145-5. Pii: S0300-9440(02)00145-5
Murray JN. Electrochemical test methods for evaluating organic coatings on metals: An update. 1. Introduction and generalities regarding electrochemical testing of organic coatings. Progress in Organic Coating. 1997; 30:225-233. DOI: 10.1016/S0300-9440(96)00677-7
Mansfeld F, Han LT, Lee CC, Zhang G. Evaluation of corrosion protection by polymer coatings using electrochemical impedance spectroscopy and noise analysis. Electrochimica Acta. 1998; 43:2933-2945. DOI: 10.1016/S0013-4686(98)00034-6
Liu XW, Xiong JP, Lv YW, Zuo Y. Study on corrosion electrochemical behavior of several different coating systems by EIS. Progress in Organic Coating. 2009; 64:497-503. DOI: 10.1016/j.porgcoat.2008.08.012
Liu Y, Wang JW, Liu L, Li Y, Wang FH. Study of the failure mechanism of an epoxy coating system under high hydrostatic pressure. Corrosion Science. 2013; 74:59-70. DOI: 10.1016/j.corsci.2013.04.012
Mansfeld F. Use of electrochemical impedance spectroscopy for the study of corrosion protection by polymer-coatings. Journal of Applied Electrochemistry. 1995; 25:187-202
Zhang JT, Hu JM, Zhang JQ, Cao CN. Studies of impedance models and water transport behaviors of polypropylene coated metals in NaCl solution. Progress in Organic Coating. 2004; 49:293-301. DOI: 10.1016/s0300-9440(03)00115-2
Tian W, Meng F, Liu L, Li Y, Wang F. The failure behaviour of a commercial highly pigmented epoxy coating under marine alternating hydrostatic pressure. Progress in Organic Coating. 2015; 82:101-112. DOI: 10.1016/j.porgcoat.2015.01.009
Stratmann M, Feser R, Leng A. Corrosion protection by organic films. Electrochimica Acta. 1994; 39:1207-1214. DOI: 10.1016/0013-4686(94)E0038-2
Tian WL, Liu L, Meng FD, Liu Y, Li Y, Wang FH. The failure behaviour of an epoxy glass flake coating/steel system under marine alternating hydrostatic pressure. Corrosion Science. 2014; 86:81-92. DOI: 10.1016/j.corsci.2014.04.038
Le Thu Q, Bierwagen GP, Touzain S. EIS and ENM measurements for three different organic coatings on aluminum. Progress in Organic Coating. 2001; 42:179-187. DOI: 10.1016/S0300-9440(01)00171-0
Valentini C, Fiora J, Ybarra G. A comparison between electrochemical noise and electrochemical impedance measurements performed on a coal tar epoxy coated steel in 3% NaCl. Progress in Organic Coating. 2012; 73:173-177. DOI: 10.1016/j.porgcoat.2011.10.012
Bertocci U, Huet F. Noise-analysis applied to electrochemical systems. Corrosion. 1995; 51:131-144
Deya MC, del Amo B, Spinelli E, Romagnoli R. The assessment of a smart anticorrosive coating by the electrochemical noise technique. Progress in Organic Coating. 2013; 76:525-532. DOI: 10.1016/j.porgcoat.2012.09.014
Allahar KN, Su Q, Bierwagen GP. Electrochemical noise monitoring of the cathodic protection of Mg-rich primers. Corrosion. 2010; 66:12
Mansfeld F, Sun Z, Hsu CH. Electrochemical noise analysis (ENA) for active and passive systems in chloride media. Electrochimica Acta. 2001; 46:3651-3664. DOI: 10.1016/S0013-4686(01)00643-0
Iverson WP. Transient voltage changes produced in corroding metals and alloys. Journal of the Electrochemical Society. 1968; 115:617-625. DOI: 10.1149/1.2411362
Blanc G, Gabrielli C, Keddam M. Measurement of electrochemical noise by a cross-correlation method. Electrochimica Acta. 1975; 20:687-689. DOI: 10.1016/0013-4686(75)90069-9
Bosch RW, Cottis RA, Csecs K, Dorsch T, Dunbar L, Heyn A, Huet F, Hyokyvirta O, Kerner Z, Kobzova A, Macak J, Novotny R, Oijerholm J, Piippo J, Richner R, Ritter S, Sanchez-Amaya JM, Somogyi A, Vaisanen S, Zhang WZ. Reliability of electrochemical noise measurements: Results of round-robin testing on electrochemical noise. Electrochimica Acta. 2014; 120:379-389. DOI: 10.1016/j.electacta.2013.12.093
Mills DJ, Mabbutt S. Investigation of defects in organic anti-corrosive coatings using electrochemical noise measurement. Progress in Organic Coating. 2000; 39:41-48. DOI: 10.1016/s0300-9440(00)00098-9
Jamali SS, Mills DJ, Sykes JM. Measuring electrochemical noise of a single working electrode for assessing corrosion resistance of polymer coated metals. Progress in Organic Coating. 2014; 77:733-741
Bierwagen GP, Allahar KN, Su Q, Gelling VJ. Electrochemically characterizing the AC-DC-AC accelerated test method using embedded electrodes. Corrosion Science. 2009; 51:95-101. DOI: 10.1016/j.corsci.2008.09.023
Simpson TC, Moran PJ, Hampel H, Davis GD, Shaw BA, Arah CO, Fritz TL, Zankel K. Electrochemical monitoring of organic coating degradation during atmospheric or vapor-phase exposure. Corrosion. 1990; 46:331-336
Zhang Y, Yu B, Lu S, Meng X, Zhao X, Ji Y, Wang Y, Fu C, Liu X, Li X, Sui Y, Lang J, Yang J. Effect of Cu doping on YBaCo2O5+delta as cathode for intermediate-temperature solid oxide fuel cells. Electrochimica Acta. 2014; 134:107-115. DOI: 10.1016/j.electacta.2014.04.126
Cottis RA. Interpretation of electrochemical noise data. Corrosion. 2001; 57:265-285
Tan YJ, Bailey S, Kinsella B. The monitoring of the formation and destruction of corrosion inhibitor films using electrochemical noise analysis (ENA). Corrosion Science. 1996; 38:1681-1695. DOI: 10.1016/S0010-938x(96)00061-3
Bertocci U, Huet F, Nogueira RP, Rousseau P. Drift removal procedures in the analysis of electrochemical noise. Corrosion. 2002; 58:337-347
Mansfeld F, Sun Z, Hsu CH, Nagiub A. Concerning trend removal in electrochemical noise measurements. Corrosion Science. 2001; 43:341-352. DOI: 10.1016/s0010-938x(00)00064-0
Bertocci U, Gabrielli C, Huet F, Keddam M. Noise resistance applied to corrosion measurements. 1. Theoretical analysis. Journal of the Electrochemical Society. 1997; 144:31-37. DOI: 10.1149/1.1837361
Cheng YF, Luo JL, Wilmott M. Spectral analysis of electrochemical noise with different transient shapes. Electrochimica Acta. 2000; 45:1763-1771. DOI: 10.1016/S0013-4686(99)00406-5
Aballe A, Bethencourt M, Botana FJ, Marcos M. Using wavelets transform in the analysis of electrochemical noise data. Electrochimica Acta. 1999; 44:4805-4816. DOI: 10.1016/S0013-4686(99)00222-4
Garcia-Ochoa E, Corvo F. Copper patina corrosion evaluation by means of fractal geometry using electrochemical noise (EN) and image analysis. Electrochemistry Communications. 2010; 12:826-830. DOI: 10.1016/j.elecom.2010.03.044
Meng FD, Liu L, Li Y, Wang FH. Studies on electrochemical noise analysis of an epoxy coating/metal system under marine alternating hydrostatic pressure by pattern recognition method. Progress in Organic Coating. 2017; 105:81-91. DOI: 10.1016/j.porgcoat.2016.11.025
Liu JG, Gong GP, Yan CW. EIS study of corrosion behaviour of organic coating/Dacromet composite systems. Electrochimica Acta. 2005; 50:3320-3332. DOI: 10.1016/j.electacta.2004.12.010
Shi YY, Zhang Z, Su JX, Cao FH, Zhang JQ. Electrochemical noise study on 2024-T3 aluminum alloy corrosion in simulated acid rain under cyclic wet-dry condition. Electrochimica Acta. 2006; 51:4977-4986. DOI: DOI 10.1016/j.electacta.2006.01.050
Luciano G, Traverso P, Letardi P. Applications of chemometric tools in corrosion studies. Corrosion Science. 2010; 52:2750-2757. DOI: 10.1016/j.corsci.2010.05.016
Huang JY, Qiu YB, Guo XP. Cluster and discriminant analysis of electrochemical noise statistical parameters. Electrochimica Acta. 2009; 54:2218-2223. DOI: 10.1016/j.electacta.2008.10.039
Tian W, Meng F, Liu L, Li Y, Wang F. Lifetime prediction for organic coating under alternating hydrostatic pressure by artificial neural network. Scientific Reports. 2017; 7:40827. DOI: 10.1038/srep40827
Meng F, Liu Y, Liu L, Li Y, Wang F. Studies on mathematical models of wet adhesion and lifetime prediction of organic coating/steel by grey system theory. Materials (Basel). 2017; 10:715. DOI: 10.3390/ma10070715