Application of electrochemical noise (EN) technology to evaluate the passivation performances of adsorption and film-forming type corrosion inhibitors

Journal Pre-proof Application of electrochemical noise (EN) technology to evaluate the passivation performances of adsorption and film-forming type corrosion inhibitors Jun Cui, Dayang Yu, Ziwei Long, Beidou Xi, Xiaosong He, Yuansheng Pei PII:

S1572-6657(19)30852-5

DOI:

https://doi.org/10.1016/j.jelechem.2019.113584

Reference:

JEAC 113584

To appear in:

Journal of Electroanalytical Chemistry

Received Date: 10 June 2019 Revised Date:

12 October 2019

Accepted Date: 17 October 2019

Please cite this article as: J. Cui, D. Yu, Z. Long, B. Xi, X. He, Y. Pei, Application of electrochemical noise (EN) technology to evaluate the passivation performances of adsorption and film-forming type corrosion inhibitors, Journal of Electroanalytical Chemistry (2019), doi: https://doi.org/10.1016/ j.jelechem.2019.113584. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

Graphical Abstract

1

Application of electrochemical noise (EN) technology to

2

evaluate the passivation performances of adsorption and

3

film-forming type corrosion inhibitors

4 5

Jun Cuia,b,c, Dayang Yua, Ziwei Longa, Beidou Xib,c, Xiaosong Heb,c, Yuansheng Peia

6

a

7

Environment, Beijing Normal University, Beijing 100875, China

8

b

9

Academy of Environmental Sciences, Beijing 100012, China

The Key Laboratory of Water and Sediment Sciences, Ministry of Education, School of

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research

10

c

11

Pollution, Chinese Research Academy of Environmental Sciences, Beijing 100012, China

State Environmental Protection Key Laboratory of Simulation and Control of Groundwater

12 13

ABSTRACT: Electrochemical noise (EN) technology is an accurate, non-destructive,

14

and rapid method for evaluating corrosion protection performance that has been

15

undergoing development for the last decade. In this work, EN was applied to detect

16

the inhibitor type and evaluate the short-term (12 h) and long-term (30 d) passivation

17

performances. The results indicated that the EN signal amplitude and the noise

18

resistance (Rn) value significantly increased after adding inhibitors, which could be

19

utilized to identify the existence of the inhibitors. The shot noise and the energy

20

distribution results exhibited different characteristics with the variation of the

21

corrosion types in the presence of the different types of inhibitors. Rn showed a

22

positive relationship with the polarization resistance and was inversely proportional to

23

the corrosion current during a long-term passivation process. The EN results were

24

verified by electrochemical impedance spectroscopy, potentiodynamic polarization

25

curves, and surface morphology analysis. These results provided a theoretical

26

foundation for the application of the EN technology to evaluate passivation

27

performances of corrosion inhibitors.

28

Keywords: Electrochemical noise, Corrosion inhibitor, Inhibitor type, Passivation

29

performance

30

1 Introduction

31

The addition of corrosion inhibitors is one of the most effective methods to protect

32

carbon steel from corrosion, and this approach has been widely applied in anti-

33

corrosion engineering [1]. According to the passivation mechanism, the inhibitors can

34

be categorized into adsorption type inhibitors (ATI) and film-forming type inhibitors

35

(FFTI) [2,3]. Generally, the adsorption process and the chemical reaction process

36

dominate the formation of the passive film in the presence of the ATI and the FFTI,

37

respectively [4]. However, the passive film response is sensitive to the variation of the

38

external environment conditions, including the pH value, temperature, and the

39

coexisting ions, etc., resulting in the fluctuation of the passivation efficiency and the

40

reduction of the passivation period [5].

41

It is well recognized that the most direct method to detect the inhibitor type is to

42

analyse the film ingredients [6,7]. However, prior to the component analysis, an intact,

43

homogeneous, and stable passive film should be formed on the surface. Otherwise,

44

the component analysis results are not reliable. In addition, other critical concerns

45

exist regarding how to monitor the stability of the passive film and evaluate the

46

passivation performance during a long-term corrosion process. Weight loss

47

experiments, electrochemical impedance spectroscopy (EIS) measurements, and

48

potentiodynamic polarization curves (PPC) have been well recognized as accurate

49

methods to evaluate inhibitor passivation performance [8-11], although each of these

50

technologies presents its own weakness. Compared to the electrochemistry tests,

51

weight loss experimentation exhibits a relatively long test period (72 h); EIS requires

52

strict test conditions (a stable open circuit potential, and causal, linear, stability, and

53

finiteness conditions) to ensure the accuracy of the results; and PPC destroys the

54

passive film structure by applied bias voltage, thus affecting the further detection of

55

the passive film. Hence, it is meaningful to construct a rapid, convenient, and non-

56

destructive method to estimate the inhibitor type and evaluate the passivation

57

performance.

58

Electrochemical noise (EN) technology, as an in situ and non-destructive

59

technology, has attracted substantial attention in the field of in situ monitoring [12,13].

60

The primary purpose of the EN measurement is to record the spontaneous fluctuations

61

of the current and potential signals, which are produced from the charge transfer

62

process during the corrosion event [14]. EN can be measured without external

63

perturbation, ensuring the stability of the measurement system and keeping the

64

variation of the passive film to the minimum [15]. However, corrosion is a non-

65

stationary and nonlinear process contributing to the existence of the direct current

66

(DC) signal component in the EN signal [16]. Therefore, prior to the EN signal

67

analysis, the DC signal drift should be removed [17]. Then, the processed EN

68

transient signal can be used as a straightforward parameter to reflect the transient

69

corrosion status [14]. To acquire more information to describe the corrosion

70

characteristics from the EN data, various EN signal analysis technologies are applied,

71

including power spectral density (PSD) [18], the Hilbert-Huang transfer [19], and

72

wavelet analysis [15], etc. On the basis of these advantages, EN technology has been

73

widely used to evaluate the anti-corrosion performance of the coating [20,21].

74

However, there are few studies devoted to estimating the inhibitor passivation

75

performance using EN technology due to the instability of the passive film [18,22].

76

This work presents a pioneering study on the application of the EN technology to

77

detect the inhibitor type and to evaluate passivation performances of the ATI and

78

FFTI. Then, the EN results were further verified by the EIS, PPC, and the surface

79

morphology results. The results advanced the application of EN technology in the

80

field of in situ corrosion monitoring in the presence of inhibitors.

81

2 Experimental

82

2.1 Selection of the inhibitors

83

Two kinds of inhibitors, the ATI and the FFTI, were selected. Sodium

84

hexametaphosphate and a boron-based controlled-release inhibitor were selected on

85

behalf of the ATI and the FFTI, respectively. The descriptions of the boron-based

86

controlled-release inhibitor are shown in our previous literatures [23-27]. Fourier

87

transform infrared spectroscopy (FTIR, Nexus 670) was conducted in the range of

88

400-4000 cm−1. The crystalline nature of the FFTI was analysed using X-ray

89

diffraction (XRD, Rigaku D/Max-B) at a scan rate of 4°/min. The elemental

90

composition was detected using X-ray photoelectron spectroscopy (XPS; Axis Ultra

91

Dld, Shimadzu). The concentration of sodium hexametaphosphate was set as 200 mg

92

L-1, an appropriate concentration to form an adsorption layer [28]. To characterize the

93

controlled-release behaviour, ultrapure water (Milli-Q, USA) was added daily to

94

compensate for the evaporation volume. FFTI (2.4 g) was added into 500 mL of

95

corrosive medium (CM), and an aliquot (1 mL) of the solution sample was collected

96

to measure the total boron concentration using inductively coupled plasma atomic

97

emission spectrometry (ICP-AES, Leeman, Profile). Prior to the ICP-AES

98

measurement, all of the solution samples were diluted to 5 mL and passed through a

99

0.22 µm membrane to ensure an appropriate concentration range for the ICP-AES

100

detection. In addition, the metal substrate that was immersed in CM without adding

101

any inhibitors was regarded as the control group.

102

2.2 Preparations of the CM and the metal substrate

103

According to the recirculating water quality (Kaifeng power plant, China), the CM

104

was prepared. The chemical compositions and the corresponding concentrations are

105

listed in Table 1.

106 107 108 109

Carbon steel electrodes and coupons, composed of 0.15 wt % C, 0.46 wt % Mn,

110

0.28 wt % Si, < 0.042 wt % P, < 0.049 wt % S, and the remainder of the Fe were cut

111

from the same plate. The carbon steel electrode used as the working electrode (WE)

112

was in the shape of a cylinder. A copper wire was soldered to each WE. To facilely

113

measure the corrosion parameters, only one surface was exposed to the CM as the

114

working surface (diameter: 1 cm, area: 0.785 cm2). Other surfaces were sealed with

115

epoxy resin (epoxy value: 44%). Similarly, all of the couples were sealed with the

116

epoxy resin, except for the working surface (1×2 cm2). All of the working surfaces

117

were wet-abraded with successive grades of emery paper ranging from 200 to 2000

118

grade, and diamond polishing fluid using a polisher (Truer, GP-2DE), until a mirror-

119

like surface was obtained. After abrasion, all of the working surfaces were washed

120

thoroughly with ultrapure water to remove any remaining powder and were degreased

121

with acetone. Subsequently, the pretreated carbon steel material was dried in a

122

vacuum drier, stored in a drying cabinet at 20 °C, and used within 48 h.

123

2.3 Electrochemical measurements

Table 1 goes here

124

All EN measurements were conducted in a three-electrode system (Fig. S1) using

125

an electrochemical workstation (PGSTAT302N, Metrohm) with an ECN module.

126

Carbon steel electrodes were used as working electrode 1 (WE1) and working

127

electrode 2 (WE2), and the saturated calomel electrode (SCE) was used as reference

128

electrode (RE). The electrochemical current noise (ECN) and electrochemical

129

potential noise (EPN) signals were continuously and simultaneously recorded during

130

the first 12 h of immersion for all groups. Then, the ECN and EPN signals were

131

simultaneously recorded each day for 3600 s during a 30-day experiment. All EN

132

measurements were carried out at 25 °C and with the frequency of 4 Hz. Prior to the

133

statistical analysis, the DC drift was removed from the original EN data using a five-

134

order fitting method [29]. The EN signals were analysed using MATLAB software

135

(MathWorks, USA). Continuous wavelets analysis using Daubechies wavelets “db4”

136

orthogonal function was applied at eight levels of decomposition.

137

Moreover, in order to verify the EN results, EIS and PPC were conducted in a

138

traditional three-electrode system. A Pt slice (3×3 cm2) and an SCE were used as the

139

auxiliary electrode (CE) and RE, respectively. Prior to the measurements, a steady

140

open circuit potential (EOCP) was obtained to ensure the stability of the test condition.

141

EIS tests were measured daily, and only one WE was used for all EIS testing during

142

30 days of experimentation. In contrast, 30 WEs were used to conduct PPC testing

143

during 30 days of experimentation. The EIS data were measured with EOCP in the

144

frequency range from 10 mHz to 100 kHz (10 measurement points per decade) with

145

an A.C. amplitude of ±5 mV (rms). The PPC was measured in the potential range

146

from –0.3 V to 1.8 V in relation to the EOCP at a sweep rate of 0.005 V/s. All

147

electrochemical measurements were conducted in a Faraday cage to avoid

148

interference from external electromagnetic fields.

149

2.4 Surface morphology analysis

150

The surface morphologies of the different samples after 12 h, 5 d, 16 d, and 30

151

days of immersion in CM were observed using a field emission scanning electron

152

microscope (FESEM, Hitachi S-4800). The surface roughnesses of all samples after

153

30 days of immersion were characterized using an atomic force microscope (AFM,

154

Bruker Multimode 8) with an 800×800 nm2 area. Prior to electron microscope

155

observation, all of the samples were gently washed three times using ultrapure water

156

to remove the loose particles on the surface.

157

3 Results and discussion

158

3.1 Characterization of the FFTI

159

A boron-based controlled-release inhibitor that was reported in our previous

160

literature was applied as FFTI [25]. No obvious diffraction peaks were observed from

161

the XRD pattern (Fig. S2), indicating the amorphous nature of the FFTI. According to

162

the FTIR results (Fig. S3), a H-O stretching vibration peak (3427 cm-1), a [BO3]

163

antisymmetric stretching vibration peak (1417 cm-1), a [BO4] antisymmetric stretching

164

vibration peaks (1075 cm-1 and 923 cm-1), and Si-O-Si flexural vibration peaks (696

165

cm−1 and 451 cm−1) were observed. Furthermore, the chemical elements of the

166

inhibitor were analysed by XPS before and after 30 days of release (Fig. S4). It should

167

be noted that the boron peak disappeared after 30 days of dissolution, confirming that

168

boron was the primary released element.

169

Previous publications have reported that the passivation behaviour of the FFTI is

170

closely related to the boron concentration [23]. Hence, the variation of the total boron

171

concentration with increasing dissolution time was analysed (Fig. 1). The cumulative

172

boron concentration and the dissolution-release rate were gradually increased and

173

decreased with increasing dissolution time, respectively. The cumulative boron

174

concentration reached 106.21 mg L-1 after 5 days of dissolution. We have indicated

175

that an acceptable passivation performance can be obtained until the cumulative boron

176

concentration exceeds 100 mg L-1 [23]. Therefore, for the FFTI group, the carbon

177

steel materials were added into the CM after 5 days of dissolution, which was

178

regarded as the 1st day of the FFTI group, and the 30th day of the FFTI group was the

179

35th day of dissolution, when the cumulative boron concentration reached 276.84 mg

180

L-1.

181 182 183

Figure 1 goes here

3.2 Detection of the corrosion inhibitor type

184

Fig. 2 shows the variations of the ECN and EPN signals during the first 12 h. A

185

higher EN signal amplitude represented a stronger interface reaction intensity [30].

186

For the control group, the signal amplitude might be derived from the adsorption and

187

migration of the corrosive ions on the carbon steel surface [31]. In contrast, for the

188

ATI and FFTI groups, the formation of the passive film contributed to the strong

189

signal amplitude during the early stage (0-7200 s) [32]. In addition, both ECN and

190

EPN signal amplitudes gradually decreased with increasing immersion time for all

191

groups, revealing the gradual decrease in the interface reaction intensity. The decrease

192

in the EN signal amplitude was derived from the formation of a corrosion product

193

layer (the control group), a deposition/adsorption layer (the ATI group), or a passive

194

film (the FFTI group) on the carbon steel surface, revealing that the WE surface

195

became stable.

196 197 198 199 200 201

Figure 2 goes here

The noise resistance (Rn), derived from the EN data, was calculated using the following equation [33]: ܴ௡ =

ఙೇ ఙ಺

(1)

202

where σV and σI refer to the standard deviations in the fluctuations of EPN and ECN

203

signals, respectively. The variations of σV and σI during the first 12 h immersion can

204

be observed in Fig. S5, and the corresponding Rn calculation results are illustrated in

205

Fig. 3. Both σV and σI exhibited a gradually decreasing trend due to the formation of

206

incomplete barrier layers for all samples [34]. It is well known that Rn shows an

207

inverse relationship with the corrosion current (icorr) [35]. As shown in Fig. 3, for the

208

control group, the Rn value gradually increased from 24 Ω cm2 to 132 Ω cm2. In

209

contrast, Rn values reached 416 Ω cm2 and 474 Ω cm2 for the ATI and FFTI groups,

210

respectively. On the basis of these results, we concluded that the Rn could be

211

significantly increased after adding ATI and FFTI, representing the decrease in the

212

icorr due to the passivation performance of the inhibitors. Therefore, we believed that

213

the Rn variation trend could be utilized to detect the absence or presence of inhibitor.

214 215 216

Figure 3 goes here

217

Shot noise theory was employed to thoroughly investigate the EN signals by

218

considering them to be packets of charge in the frequency domain. The charge of each

219

corrosion event (Qn) and the occurrence frequency of the corrosion event (fn) can be

220

calculated using the following equations [33]:

221

ܳ௡ =

(ඥఅಶ ×ඥఅ಺ ) ஻

(2)

݂௡ = ‫ ܤ‬ଶ /(ߖா × ‫)ܣ‬

222

(3)

223

where ΨE and ΨI are the low-frequency PSD values of the EPN and ECN signals,

224

respectively, B is the Stern-Geary coefficient (the average B value is 0.026 V), Q is

225

calculated based on the values of ΨE and ΨI at 0.001 Hz, and A defines the WE area.

226

The variations of Qn and fn for all groups are shown in Figs. 4a and 4b, respectively.

227

For the control group, both Qn and fn values were gradually decreased with increasing

228

immersion time. High Qn and fn values represented the occurrence of severe general

229

corrosion [36]. Both Qn and fn values then gradually decreased due to the formation of

230

the porous corrosion product layer [37], representing the weakening of corrosion

231

intensity. For the ATI group, the Qn value fluctuated near 2×10-3 C and the fn value

232

exhibited a gradually decreasing trend. The results indicated that the ATI group

233

suffered general corrosion during the early stage (0-5 h), and then the corrosion type

234

changed to localized corrosion during the late stage (5-12 h). Compared to the control

235

and ATI groups, the FFTI group exhibited relatively low Qn and fn values during the

236

first 12 h immersion. The Qn value was significantly decreased from 1.6×10-3 C to

237

0.08×10-3 C and the corresponding fn value fluctuated near 0.1 Hz cm-2 during the first

238

12 h, which was regarded as a passivation state [3]. 8The results indicated that the

239

FFTI exhibited a rapid passivation performance for carbon steel.

240

According to the shot noise analysis results, the general corrosion intensity

241

gradually decreased, which could be regarded as the sample without any added

242

inhibitors; the corrosion type was changed from general corrosion to localized

243

corrosion, which could be defined as adding ATI due to the slow passivation process.

244

Moreover, the sample exhibited passivation status during the first 12 h, which could

245

be recognized as adding FFTI due to the rapid film forming process. Therefore, shot

246

noise theory could be utilized to detect the inhibitor type with respect to the variations

247

of the corrosion states.

248 249 250

Figure 4 goes here

251

To uncover more detailed information and eliminate the effect of the current

252

contribution caused by capacitance charging, the energy distribution plot (EDP) was

253

calculated from the EPN data using Daubechies wavelet “db4” orthogonal function at

254

eight levels of decomposition (Fig. 5). The vertical coordinate was the percentage of

255

each part considering the total energy. Previous publications have reported that the

256

D1-D3 region mainly characterizes the small time scale process, reflecting the

257

activation process; region D3-D6 strongly associates with the medium time scale

258

process controlling the diffusion and activation processes; and region D6-D8 closely

259

reflects the large time scale process information relating to the diffusion process [39-

260

41].

261

It can be observed from Fig. 5a that a relatively high percentage of region D6-D8

262

was observed during the first 12 h for the control group, indicating that the corrosion

263

process was dominated by the large time scale process, such as in the case of general

264

corrosion. For the ATI group (Fig. 5b), a high percentage of region D6-D8 was

265

obtained during 0-4 h, indicating that the corrosion process was controlled by the

266

diffusion process. Then, the corrosion process was controlled by the activation

267

process, revealing that the corrosion event was changed from general corrosion to

268

localized corrosion. For the FFTI group (Fig. 5c), D1-D8 regions exhibited similar

269

percentages, revealing a relatively stable passivation state.

270

According to the above analysis results, we believed that EDP analysis could be

271

utilized as a potential technology to identify inhibitor type. The reasons were as

272

follows: (a) The EDP was continuously dominated by the D6-D8 region,

273

demonstrating the absence of the inhibitor. This is because any kind of passive film

274

could exhibit a suppressive performance on the diffusion process. However, the

275

corrosion product layer could not suppress the diffusion process because of the multi-

276

porosity structure. (b) If the dominated region were significantly changed from the

277

large time scale process (region D6-D8) to the small time scale process (region D1-

278

D3), we believed that the inhibitor could be regarded as the ATI. The substrate

279

surface primarily suffered from general corrosion during the early stage. Then, an

280

adsorption layer was formed on the substrate surface, and the dominant process was

281

shifted from the large time scale process to the small time scale process. (c) No

282

obvious region was found, which was supposed to indicate FFTI. Normally, a

283

dissolution process existed on the substrate surface before the formation of a

284

passivation layer in the presence of FFTI due to the oxidation layer. Then, a passive

285

film was rapidly formed on the substrate surface, resulting in the suppression of the

286

diffusion process. No significant time scale process dominated during the early and

287

late stages. Therefore, EDP was a reliable parameter to identify the inhibitor type.

288 289

Figure 5 goes here

290 291

To verify the reliability of the EN analysis results, EIS was applied as a

292

conventional method. Prior to the EIS measurement, stable EOCP values were achieved

293

for all samples (Fig. S6), and the last EOCP value was held potentiostatically for EIS

294

measurements. The typical Nyquist curves are shown in Fig. 6, and the corresponding

295

equivalent circuits are shown in Fig. 7. The EIS results of the control group were well

296

fitted using the [R([R(QR)]Q)] model (Fig. 7a), and the EIS results of the ATI and

297

FFTI groups were well fitted using the [R(RQ)] model (Fig. 7b). Rs is the solution

298

resistance, RP represents the corrosion product layer resistance in Fig. 7a and the

299

resistance of the passive film in Fig. 7b, and Rct can be regarded as the charge transfer

300

impedance. In this work, constant phase element (CPE) rather than capacitance (Q)

301

was applied in the equivalent circuit because the solid electrode interface often reveals

302

a frequency dispersion as a result of the capacitive dispersion [42]. According to the

303

Nyquist curves, all samples exhibited an intact semicircle, revealing that the

304

electrochemical process was under the control of charge transfer [43]. However, the

305

arc was slightly depressed in the ATI group, which was related to the roughness and

306

inhomogeneity of the carbon steel surface [44]. The capacitance arc radii followed the

307

order of control (93 Ω cm2) < ATI (436 Ω cm2) ≤ FFTI (456 Ω cm2). Moreover, the

308

Bode plots are shown in Fig. S7, and it was well recognized that a high impedance

309

modulus at low frequency represents high corrosion resistance [45]. The Bode

310

impedance magnitude results indicated that the surface resistance followed the order

311

of control < ATI ≤ FFTI. In the phase angle plots, the maximum phase angle peak was

312

gradually increased in the order of control < ATI ≤ FFTI. The maximum phase angle

313

peak was close to 90° in the FFTI group, exhibiting good passivation performance.

314

The EIS results indicated that the passivation performance increased after adding the

315

ATI or FFTI, which coincided with the EN results.

316 317 318 319 320

Figure 6 goes here Figure 7 goes here

321

The PPC of all samples are illustrated in Fig. 8. It can be observed that the anodic

322

process was strongly affected by the ATI and the FFTI, and the anodic branch was

323

changed from the typical active dissolution characteristic (the control) to slight (ATI)

324

and clear (FFTI) passivating characteristics [46,47]. icorr was calculated using the

325

Tafel extrapolation method. Compared to the control group, the icorr value was

326

decreased by 1 and 2 orders of magnitude in the presence of ATI and FFTI,

327

respectively. This variation can be applied to verify the higher Rn values of the ATI

328

and FFTI groups versus the control group (Fig. 3). The appearance of an evident

329

passivating region indicated that a passive film was formed on the carbon steel

330

surface in the presence of the FFTI [48].

331 332 333

Figure 8 goes here

334

The optical images of all groups after 0, 4, 8, and 12 h immersion in CM can be

335

observed in Fig. S8. For the control group, the amount of the corrosion products

336

gradually increased with lengthening immersion time. However, a light-white layer

337

was observed on the sample surface in the presence of the ATI after 4 h immersion,

338

which supported the decrease of fn (Fig. 4b) after 5 h of immersion for the ATI group.

339

For the FFTI group, no significant changes could be found on the sample surface,

340

representing good passivation performance. To gain more insights, the surface

341

morphology results of all groups after 12 h immersion are shown in Fig. 9. The

342

control group exhibited a loose and multi-porous layer, which was mainly composed

343

of Fe and O, confirming the mapping results. The ATI group exhibited a fractured

344

surface due to the formation of an adsorption layer composed of phosphate. Further,

345

elemental Fe was only detected in the crevice regions. For the FFTI group, a relatively

346

smooth surface was observed. Combined with our previous results [23], we believed

347

that a passive film with Fe-O-B structure was formed on the substrate surface,

348

confirming the elemental detection results. The FESEM results further confirmed that

349

different kinds of barrier layers were formed in the presence of the different inhibitors.

350

According to the EIS, PPC, and FESEM results, we believed that the EN signals and

351

the corresponding parameters could be effectively used to identify the inhibitor type

352

and detect the absence or existence of inhibitor.

353 354 355

Figure 9 goes here

3.3 Evaluation of long-term passivation performance

356

To evaluate the long-term passivation performance of the inhibitors, EN was

357

measured daily during a long-term experiment (30 days). In addition, EIS and PPC

358

were conducted daily to verify the accuracy of the EN results.

359

The variation trend of Rn with increasing immersion time is shown in Fig. 10. For

360

the control group, the Rn value fluctuated between 150 Ω cm2 and 300 Ω cm2. For the

361

ATI group, the Rn value gradually increased (0-10 d) and stabilized (10-30 d) near

362

1000 Ω cm2. In contrast, the Rn value exhibited an increase-stabilization-decrease

363

variation trend in the FFTI group. However, the Rn value followed the order of control

364

< ATI < FFTI during 30 days of experimentation. A higher Rn value represents a

365

better passivation performance [49]. Therefore, the FFTI group exhibited the best

366

passivation performance among all groups. The gradual decrease in Rn value from the

367

24th day of the experiment was due to the breakdown of the passive film. However,

368

the Rn value remained higher than those of other groups. According to the variation of

369

the Rn value, we believed that the passivation performance significantly enhanced

370

after adding the inhibitors during a long-term period.

371 372 373

Figure 10 goes here

374

The variations of Qn and fn during 30 days of immersion are shown in Figs. 11a

375

and 11b, respectively. For the control group, the Qn value gradually increased (0-10 d)

376

and then stabilized (10-30 d), and the corresponding fn value gradually decreased (0-9

377

d) and then stabilized (9-30 d). The Qn-fn variation represented that the intensity of the

378

general corrosion slightly decreased (0-10 d) and then stabilized (10-30 d) due to the

379

formation of a porous corrosion product layer. For the ATI and the FFTI groups, the

380

Qn values gradually decreased and then stabilized during the early and middle stages

381

(near 0-20 d) due to the formation of the passive film, and then the Qn values

382

gradually increased during the late stage due to the occurrence of crevices on the

383

passive film surface. Additionally, the fn values were relatively stable during 30 days

384

of experimentation for ATI and FFTI groups. The results indicated that the corrosion

385

types of the ATI and FFTI groups changed from passivation to localized corrosion as

386

a result of the failure of the passive film.

387 388 389

Figure 11 goes here

390

According to the variations of the Rn, fn, and Qn, we divided the experimental

391

period into two parts: part I was 0-5 d, 0-14 d, and 0-22 d for the control, ATI, and

392

FFTI groups, respectively; part II was 5-30 d, 14-30 d, and 22-30 d for the control,

393

ATI, and FFTI groups, respectively. To obtain the characteristics of each part, two

394

days were selected to represent characteristics. The EDP results of the selected days

395

are shown in Fig. 12. The EDP plots of the control group are dominated by the D6-D8

396

region on days 2 and 4 (part I, Fig. 12a), relating to the large time scale process.

397

However, the EDP was predominated by the D1-D3 region on days 12 and 24 (part II,

398

Fig. 12d), which was attributed to the small time scale process. For the ATI group, an

399

opposite characteristic was obtained. Regions D1-D3 and D6-D8 mainly contributed

400

to parts I and II, respectively (Figs. 12b and 12e). Similarly, the FFTI group showed

401

the same variation trend with the ATI group (Figs. 12c and 12f). The variations of the

402

EDP for the ATI and FFTI groups could be attributed to the formation and the

403

breakdown of the passive film during parts I and II, respectively. The EDP results

404

were consistent with the Qn-fn and Rn analysis results, which could be used to reveal

405

the corrosion control process and verify the long-term passivation performance of the

406

inhibitors.

407 408 409

Figure 12 goes here

410

Since it was difficult to distinguish the corrosion type in the time-domain EN

411

curves, a wavelet transformation method was applied to deconvolute the time-domain

412

EN curves, and the deconvolution results with two-dimensional diagrams are shown

413

in Fig. 13. In the two-dimensional diagram, higher energy generated from the

414

corrosion event corresponds to a lighter colour [15]. For the control group, the

415

rectangle colours for the 1-4 levels became gradually darker after 20 days of

416

immersion during the early stage (0-12 min). The results demonstrated that the

417

general corrosion event intensity was decreased, because the carbon steel was directly

418

exposed to the CM during the early stage and a multi-porous barrier layer was formed

419

on the surface to protect carbon steel from corrosion during the middle and later

420

stages. In contrast, the ATI and the FFTI groups showed patterns of intersecting light

421

and

422

formation/breakdown/re-passivation process. Moreover, the rectangle colours were

423

lighter in levels 5-8 than levels 1-4, revealing the domination of the localized

424

corrosion process [23]. Furthermore, the area of the dark section was broader in the

dark

colours,

indicating

that

the

passive

film

experienced

a

425

FFTI group than the ATI group, indicating a better passivation performance of the

426

FFTI. These results were consistent with the analysis results of the Qn-fn.

427 428 429

Figure 13 goes here

430

4,096 data points acquired from the EPN signals were transferred into the PSD

431

using maximum entropy method (Fig. 14). It could be observed that the white noise

432

region and the frequency dependent region were higher in the control group than in

433

the ATI and FFTI groups, revealing that both ATI and FFTI could suppress carbon

434

steel corrosion in CM. Moreover, the white noise and the frequency dependent

435

regions gradually decreased with lengthening immersion time, demonstrating that

436

both the corrosion product layer and the passivation film could protect carbon steel

437

from corrosion.

438 439 440

Figure 14 goes here

441

EIS and PPC were measured to confirm the accuracy of the EN results, and the

442

corresponding Rp and icorr values were calculated. The EOCP and Nyquist plots are

443

shown in Figs. S9 and S10, respectively, and the variation of the Rp during 30 days of

444

experimentation is shown in Fig. 15. The control group samples exhibited an intact

445

capacitance arc with the increasing immersion time. In contrast, for the ATI group,

446

the low frequency arc gradually evolved into a straight line during the late stage of

447

experimentation, revealing the typical characteristic of the Warburg impedance. The

448

appearance of Warburg impedance indicates the increase in the permeability of the

449

passive film [50]. The results indicated that the passivation performance of ATI was

450

decreased. In addition, all of the Rp values were similar in the control group (100 Ω

451

cm2). In contrast, the Rp values of the ATI and the FFTI groups were gradually

452

increased to 1500 Ω cm2 (23 d) and 2600 Ω cm2 (22 d), respectively, following by a

453

decreasing trend until the end of experimentation. According to Fig. 15, the Rp values

454

followed the order of control < ATI < FFTI, which was consistent with the variation

455

of the Rn results.

456 457 458

Figure 15 goes here

459

Similarly, the PPC and the corresponding variation trends of icorr are illustrated in

460

Figs. S11 and 16, respectively. The icorr value was decreased after 5 days of

461

immersion and stabilized at 10 µA cm-2 for the control group. In contrast, the icorr

462

values of ATI and FFTI groups fluctuated near 3.5 µA cm-2 and 2 µA cm-2,

463

respectively. The results confirmed that an inverse relationship existed between the Rn

464

and the icorr in the long-term passivation period. Namely, a higher Rn value

465

represented a lower icorr value and a better passivation performance in the presence of

466

the inhibitor. Therefore, we believed that the Rn value could be utilized as a reliable

467

parameter to evaluate long-term passivation performance of the inhibitors.

468 469 470

Figure 16 goes here

471

The FESEM images are shown in Fig. 17. It can be observed that a barrier layer

472

with multi-porous structure was formed on the control group sample surface (Figs.

473

17a-17c), coinciding with the optical photographs. The observation results could

474

further confirm that the corrosion was controlled by the dissolution-diffusion process

475

in the control group. For the ATI group, a barrier layer with multiple crevices was

476

observed due to the formation of a P-based adsorption layer, as confirmed by Fig. 9.

477

Further, some particles were observed on the sample surface. Combined with the

478

optical photographs, these particles might be determined to consist of the deposition

479

substances and the corrosion products, and the amounts of these particles increased

480

with lengthening immersion time due to the breakdown of the adsorption layer (Figs.

481

17e and 17f). This result could be used to explain the gradual decreases in Rn and Rp

482

and the increase in icorr during 30 days of immersion. In contrast, a relatively smooth

483

sample surface was observed in the presence of the FFTI during 30 days of immersion.

484

Although both Rn and Rp exhibited gradually decreasing trends during the late stage,

485

the surface resistance still exhibited an acceptable passivation performance for carbon

486

steel. In addition, AFM was conducted to describe the surface morphology after 30

487

days of immersion (Fig. 18). The heights of the control, ATI, and FFTI groups were

488

69.6, 23.4, and 7.6 nm, respectively. The control and the FFTI groups exhibited the

489

highest and the lowest roughness among all groups, which was consistent with the

490

FESEM results. The observation results of the optical photographs, FESEM, and

491

AFM images could be utilized to support the EN analysis results.

492

493

Figure 17 goes here

494 495 496 497

Figure 18 goes here

4 Conclusions

498

The results of this work demonstrated that inhibitor type and long-term

499

passivation performance could be effectively monitored by the time-domain and

500

frequency–domain EN curves. The results were verified by EIS, PPC, and surface

501

morphology analysis. The following results were obtained:

502

●

The amplitudes of the EN signals and the Rn exhibited significant decreases after

503

the formation of a passive film on the substrate surface. This decrease could be

504

used to identify the absence or presence of the corrosion inhibitor.

505

●

Shot noise theory (Qn-fn) and EDP exhibited different variation trends in response

506

to the variation of the corrosion process resulting from different inhibitor types.

507

These trends could be used to detect the inhibitor type.

508

●

The variation of Rn showed a positive relationship with the Rp and exhibited a

509

negative relationship with icorr values during a long-term passivation process,

510

indicating that Rn coupled with Qn-fn and EDP could be used as a reliable

511

evaluation system to evaluate the long-term passivation performance of the

512

inhibitors.

513 514

Acknowledgements This work is supported in part by National Natural Science Foundation of China

515

(51579009) and China Postdoctoral Science Foundation (Pre-station) (2019TQ0292).

516

References

517 518 519 520 521 522 523 524 525 526

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TABLE 1 Chemical Compositions of the CM (500 mL) Ions

Ca2+

Mg2+

NO3-

HCO3-

Na+

Cl-

Analytic grade reagent Concentration / (mg L-1)

CaCl2 200

MgCl2•6H2O 120

NaNO3 100

NaHCO3 100

-200

-640

Note: Na+ and Cl- were provided by the CaCl2, MgCl2•6H2O, NaNO3, and NaHCO3.

Fig. 1. Variation of the total boron concentration during 40 days of dissolution in CM.

Fig. 2. Variations of the (a) ECN and (b) EPN time-domain signals during the first 12 h.

Fig. 3. Variation of Rn extracted from the EN time-domain signal during the first 12 h.

Fig. 4. Variations of (a) Qn and (b) fn extracted from the EN frequency-domain signals during the first 12 h.

Fig. 5. EDP variations of (a) the control group, (b) the ATI group, and (c) the FFTI group during the first 12 h.

Fig. 6. Nyquist plots of all samples after 12 h immersion.

Fig. 7. Equivalent circuit models used to fit the experimental impedance data. A, B, C, D, and E represent the carbon steel, double electrode layer, corrosion product layer, solution, and passive film, respectively.

Fig. 8. PPC of all samples after 12 h immersion.

Fig. 9. Surface morphology observation results and the corresponding elemental analysis results of (a) the control group, (b) the ATI group, and (c) the FFTI group after 12 h immersion.

Fig. 10. Variation of Rn extracted from the EN time-domain signal during 30 days of immersion.

Fig. 11. Variations of (a) Qn and (b) fn extracted from the EN frequency-domain signals during 30 days of immersion.

Fig. 12. EDP results of the selected days: part I of (a) the control group, (b) the ATI group, and (c) the FFTI group; part II of (d) the control group, (e) the ATI group, and (f) the FFTI group I during 30 days of immersion.

Fig. 13. Two-dimensional visual representation of the discrete time wavelet transformation of the EPN signals for the control, ATI, and FFTI groups.

Fig. 14. PSD of all samples after 1, 10, 20, and 30 days of immersion for (a) the control group, (b) the ATI group, and (c) the FFTI group.

Fig. 15. Variation of RP values extracted from the EIS during 30 days of immersion.

Fig. 16. Variation of icorr values extracted from the PPC during 30 days of immersion.

Fig. 17. FESEM images of the control group after (a) 5, (b) 16, and (c) 30 days of immersion, the ATI group after (d) 5, (e) 16, and (f) 30 days of immersion, and the FFTI group after (g) 5, (h) 16, and (i) 30 days of immersion.

Fig. 18. AFM images of (a) the control, (b) ATI, and (c) FFTI surface morphology after 30 days of immersion.

Highlights


The variations of the EN amplitude and noise resistance value can be used to identify the existence of the corrosion inhibitor or not.




The shot noise theory results and energy distribution plots exhibit different characteristics in the present of the different types of inhibitors.




The noise resistance shows a positive relationship with the polarization resistance and was inversely proportional to the corrosion current.

Declaration of Interest statement The authors declared that they have no any actual or potential conflict of interest to this work, including any financial, personal or other relationships with other people or organizations.