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A variety of techniques are available for monitoring metal corrosion in electrolytes. However, only some of them can be applied in the atmosphere, in which case a thin discontinuous electrolyte film forms on a surface. Traditional and state-of-the-art real-time corrosion monitoring techniques include atmospheric corrosion monitor (ACM), electrochemical impedance spectroscopy (EIS), electrochemical noise (EN), electrical resistance (ER) probes, quartz crystal microbalance (QCM), radio-frequency identification sensors (RFID), fibre optic corrosion sensors (FOCS) and respirometry.
- atmospheric corrosion
- real-time corrosion monitoring
- atmospheric corrosion monitor
- electrochemical impedance spectroscopy
- electrochemical noise
- electrical resistance probes
- quartz crystal microbalance
- radio-frequency identification sensors
- fibre optic cor
1. Introduction
The atmospheric corrosion of metallic materials has huge financial, environmental and cultural implications. In 2016, the Association for Material Protection and Performance (AMPP) published the International Measures of Prevention, Application, and Economics of Corrosion Technologies (IMPACT) report, which estimated the global cost of corrosion to be equivalent to 3.4% of the global Gross Domestic Product. They calculated that by using the available corrosion control techniques, it would be possible to save between 15 and 35% of the total corrosion cost [1]. Alongside financial losses, undetected corrosion can cause sudden industrial and transport failures that may result in environmental catastrophes and hazards that endanger health and lives. Furthermore, corrosion is known to induce irreversible damage in, or even destruction of, unique cultural artefacts.
Atmospheric corrosion is a complex process of interaction between materials and the environment. Environmental corrosivity is dependent on various parameters, including relative humidity (RH), temperature (T) and air pollutant concentrations. Thus, understanding the corrosion of metallic materials requires detailed knowledge of these parameters and their effect on the underlying corrosion processes [2]. The main tools used to assess environmental corrosiveness, corrosion progression, material corrosion behaviour and the effects of coatings and inhibitors, involve cumulative or real-time monitoring of corrosion rates and environmental parameters, and frequent equipment inspections. This review is focused on real-time corrosion monitoring, which we define as a long-term instantaneous measuring of parameters directly linked to corrosion loss.
Many real-time corrosion monitoring techniques have been developed for monitoring of metal corrosion in electrolytes. However, their applicability is limited under atmospheric conditions, under which a thin electrolyte layer is formed on a metallic surface. This limitation particularly relates to electrochemical methods that require a conductive connection between the electrodes. Modified electrochemical and non-electrochemical real-time corrosion monitoring techniques have been developed for use in both indoor and outdoor atmospheres. Such techniques are designed to meet the requirements of easy measurement and data interpretation, direct corrosion rate determination, rapid responses to changes in corrosivity and wide applicability in environments with different influences on corrosivity [3]. In the last decade, these techniques have evolved, but no paper summarizing the developments has been published.
2. Comparison of Atmospheric Corrosion Monitoring Techniques
Studies applying the real-time atmospheric corrosion monitoring techniques described in the previous sections are summarised in Table 1 in terms of environments, sensing materials, ranges of detected corrosion rates and suitability for localised corrosion detection.
Table 1. Summary of studies applying real-time atmospheric corrosion monitoring.
| Technique | Environment * | Sensing Metal ** | Range of Measured Corrosion Rates ***, [µm·a−1] | References | Localised Corrosion Detection |
|---|---|---|---|---|---|
| ACM 1 | Outdoor exposures | Fe | 1 × 10−1–1 × 102 | [16,21,22,23,24,25,26,27,30] | – |
| Zn | Not calculated | [32] | |||
| ACTs | Fe | 1 × 102 | [20] | ||
| Laboratory tests | Fe | 1 × 101–1 × 103 | [19,31] | ||
| Zn | 1 × 101–1 × 103 | [19,31] | |||
| Cu | 1 × 101–1 × 103 | [19] | |||
| Al | 1 × 101–1 × 103 | [19] | |||
| ER | Outdoor exposures | Fe | 1 × 10−1–1 × 103 | [97,108,118] | [3,29,100] |
| Zn | 1 × 10−1–1 × 101 | [108] | |||
| Cu | 1 × 10−1–1 × 100 | [120] | |||
| ACTs | Fe | 1 × 101–1 × 103 | [2,93,101,108,113,114,119] | ||
| Zn | 1 × 100–1 × 103 | [2,93,101] | |||
| Cu | 1 × 103 | [2] | |||
| Al | 1 × 10−1–1 × 101 | [29] | |||
| Laboratory tests | Fe | 1 × 10−3–1 × 101 | [2,3,103] | ||
| Cu | 1 × 10−3–1 × 10−1 | [2,4,98,103] | |||
| Ag | 1 × 10−3–1 × 101 | [4,98,117] | |||
| Zn | 1 × 100–1 × 102 | [2] | |||
| Pb | 1 × 10−3–1 × 102 | [109,110,112] | |||
| Indoor exposures | Cu | 1 × 10−3–1 × 10−1 | [4,94,102,104,115,125] | ||
| Ag | 1 × 10−3–1 × 10−1 | [4,94,102,115,127] | |||
| Pb | 1 × 10−2–1 × 101 | [102,115,116] | |||
| EIS 2 | Outdoor exposures | Fe | 1 × 10−1–1 × 101 | [47,69] | [89,91] |
| Cu | 1 × 102–1 × 103 | [120] | |||
| ACTs | Fe | 1 × 102–1 × 103 | [70] | ||
| Laboratory tests | Fe | 1 × 10−1–1 × 104 | [45,48,50,52,71] | ||
| Zn-coated steel | 1 × 100–1 × 103 | [56,57,58,59] | |||
| Zn | 1 × 101 | [44] | |||
| Cu | 1 × 10−1–1 × 101 | [43,64,120] | |||
| EN 3 | Outdoor exposures | Fe | 1 × 10−1–1 × 101 | [89,90] | [89,90,91,209] |
| Cu | 1 × 10−2–1 × 102 | [86,209] | |||
| QCM 4 | Laboratory tests | Cu | 1 × 10−1–1 × 100 | [147] | – |
| Ag | 1 × 10−3–1 × 10−2 | [141,142,143,144,145,146] | |||
| Indoor exposures | Cu | 1 × 10−3–1 × 10−1 | [102,138] | ||
| Ag | 1 × 10−2–1 × 10−1 | [102,127,138] | |||
| Co | 1 × 10−2–1 × 10−1 | [138] | |||
| RFID | ACTs | Fe | 1 × 102–1 × 103 | [157,163] | [95,167] |
| Laboratory tests | Zn | 1 × 101 | [95,166,167] | ||
| FOCS | Fe | No data for atmospheric corrosion | – | ||
| Respirometry 5 | Laboratory tests | Fe | 1 × 10−1–1 × 102 | [200,205] | [195,197] |
| Cu | 1 × 10−2–1 × 10−1 | [200] | |||
| Al | 1 × 10−1–1 × 100 | [195] | |||
| Mg | 1 × 101–1 × 103 | [195,197] | |||
* Outdoor exposures–field exposures outdoors; ACTs–standardised accelerated corrosion tests; Laboratory tests–non-standardised tests in laboratories, e.g., wetting/drying cycles, thin electrolyte layers application, increased T and RH, pre-contamination; Indoor exposures–exposures in real indoor environments. ** Symbols Fe, Al, Zn, Cu and Mg refer to both pure metals and their alloys. Zn-coated Fe refers to zinc-coated steels. *** For easy orientation, and as exact values of minimum and maximum detected corrosion rates are often not given in the studies, they are summarised in orders of magnitude of µm·a−1 here. 1 In reference [22], the lowest detectable Fe corrosion rate of 7.7 µm a−1 was calculated. 2 Rp−1 (Rct−1) evolution in time is commonly used as a corrosion indicator instead of calculated corrosion rate as Tafel slopes are not usually known for systems under investigation, as, for example, for corrosion monitoring of Fe [49,51,53,54,55], Zn and Zn-coated steel [60,61], Cu [42,49,63,147], Al [65,68] and Sn [57]. 3 More frequently, Rn, ECN and/or EPN evolution in time is used as a corrosion indicator instead of corrosion rate, as, for example, for corrosion monitoring of Fe [88,210] and Al [76,91]. 4 Mass gain in time is frequently presented as a direct measurement output instead of thickness loss in time, as, for example, for corrosion monitoring of Ag [138,141,142,143,144,145,146], Cu [138,139,147,150,153], Pb [151,152] and Fe [153]. 5 For pre-corroded historical iron artefacts, when the corrosion mechanism is not known, ORR rate cannot be re-calculated to corrosion rate and oxygen consumption is used as a corrosion indicator [199,201,202,203,204].
A comparison of the techniques is given in Table 2. In the first column, current applications are described, whereas their potential applications are suggested in the second column based on the operation principle. The sensitivity is defined in general categories for a quick orientation. The number of suppliers of commercial products is an important indicator of the applicability in service. Finally, main advantages and drawbacks of the techniques are summarised in the last two columns.
Table 2. Comparison of atmospheric corrosion monitoring techniques.
| Technique | Current Applications | Potential Fields of Application | Sensitivity * | Commercial Suppliers | Main Advantages | Main Drawbacks |
|---|---|---|---|---|---|---|
| Coupons | Indoor and outdoor corrosivity classification according to standards Verification of other techniques |
Applicable in any environment | High at long exposure times, otherwise medium | Several | Standardised technique Easy data interpretation |
No real-time data Time-consuming |
| ACM | Outdoor monitoring TOW assessment |
Outdoor and indoor at higher RH | Medium | 1 | Not sensitive to temperature fluctuations Suitable for harsh outdoor environments |
Corrosion acceleration due to galvanic coupling Unclear data interpretation during rainfall Electrolyte presence required |
| EIS | Laboratory tests at higher RH and under thin electrolyte layers Assessment of protective coatings |
Outdoor and indoor at higher RH | Medium | 0 | Information about corrosion mechanism Non-destructive assessment of coatings |
Knowledge about investigated system needed for correct data interpretation Electrolyte presence required Unclear results under very thin electrolyte layers and in presence of thick corrosion products |
| EN | Outdoor corrosion monitoring | Outdoor and indoor at higher RH | Medium | 0 | Localised corrosion detection Corrosion mechanism determination |
Complex and unclear interpretation Electrolyte presence required |
| ER | Indoor and outdoor corrosion monitoring, laboratory studies Corrosivity classification |
Applicable in any environment | High | 4 | Universal technique High sensitivity Easy operation and data interpretation Optimal for uniform corrosion monitoring |
Sensitive to temperature fluctuations Limited possibilities in monitoring of non-uniform corrosion |
| QCM | Indoor corrosivity classification Laboratory tests |
Indoor at lower corrosivity | High | 2 | High sensitivity and short response time Electrolyte presence not required |
Sensitive to temperature fluctuations, moisture and pollutants presence Not suitable for harsh environments |
| RFID | Laboratory tests | Outdoor and indoor at higher corrosivity | Low | 0 | Compact and wireless Electrolyte presence not required |
Further development needed |
| FOCS | None for atmospheric corrosion | Not clear yet, as the technique is at the development stage | Not available | 0 | Not known for atmospheric corrosion yet | |
| Respirometry | Laboratory tests | Not clear yet, as the technique is at the development stage | High | 0 | High sensitivity Information about corrosion mechanism Electrolyte presence not required |
Sensitivity to RH, temperature and pressure fluctuations Further development needed |
* Low sensitivity–corrosion detection in high-corrosive outdoor environments. Medium sensitivity–detection of corrosion rate in an order of 10 µm·a−1 and higher corresponding to outdoor corrosivity. High sensitivity–detection of corrosion rate in an order of 1 × 10−3 µm·a−1 and higher corresponding to indoor corrosivity.
Metallic coupons are used for both indoor and outdoor corrosivity classification according to several standards. The technique is universal and provides easy-to-interpret results, but the corrosivity assessment using coupons is time-consuming and does not allow for real-time monitoring.
ACM is suitable for outdoor corrosion monitoring and assessment of time of wetness (TOW). The data have been used as an input for prediction models. The measurement is not sensitive to temperature fluctuations and showed a good correlation to mass loss of corrosion coupons. The main disadvantages of the technique are the necessity for the electrolyte presence to provide connection between electrodes, corrosion acceleration of the less noble metal caused by galvanic coupling and unclear data interpretation during rainfall and condensation, when the current output increases steeply. Future development of the technique should aim at the improvement of data interpretation algorithms, particularly for the rainfall effect correction.
Similarly to ACM, EIS and EN techniques require a continuous layer of electrolyte to connect the electrodes to be present on the surface. EIS proved to be able to generate useful laboratory or short-term outdoor data in atmospheric exposure conditions. However, it is not considered to be fit for long-term monitoring due to the complex signal interpretation and insufficient stability. EN has been tested outdoors. The main advantage of this method is its potential for localised corrosion detection and corrosion mechanism assessment, but the interpretation of its data is complex, and there is no generally valid data treatment method allowing for a direct corrosion rate calculation from the measured EN signal. The further development of the EN data processing procedure is thus of great importance.
ER is a universal technique that can be recommended for both indoor and outdoor measurements, as a compromise between sensitivity and lifetime can be found by a correct choice of sensor thickness. Sensors made of a wide range of metals and alloys are available. The method is suitable for long-term monitoring of uniform corrosion, providing direct and easy-to-interpret corrosion loss data. The measurement sensitivity to rapid temperature fluctuations which cannot be fully compensated by the reference part may, however, require additional data processing. Along with thermal noise elimination and filtering, the ability of the technique to detect localized corrosion should be considered as beneficial rather than disadvantageous and quantified in future studies.
QCM is an extremely sensitive technique used for corrosivity classification indoors and for laboratory studies of corrosion mechanisms. Due to its sensitivity to moisture, pollutants’ presence and temperature fluctuations, it is not suitable for exposures in harsh environments.
RFID is a state-of-the-art technique currently under development. It is probable to be applicable for corrosion monitoring in service in the future, especially under conditions which require compact wireless solutions. However, there is no ready-to-use solution available yet, as a number of technical obstacles need to be solved first.
FOCS is a technique potentially feasible for monitoring of atmospheric corrosion, but so far it has been developed and applied only for corrosion monitoring in concrete.
Respirometry is a highly-sensitive method which can provide information about corrosion rate and mechanisms. For these purposes, it has been used within laboratory investigations. The technique was tested as an indicator of historical artefacts’ degradation. It can be difficult to use it for real-time corrosion monitoring due to its sensitivity to RH, temperature and pressure variations, and the necessity of placing the monitored object into a sealed box, or to attach a sealed container to the surface.
5. Conclusions
Techniques used for real-time corrosion monitoring of metallic materials have been reviewed and compared focusing on their use in atmospheric conditions. Based on their key characteristics, such as sensitivity, lifetime, availability and data interpretation complexity, the following conclusions can be drawn.
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Electrochemical EIS, EN and ACM methods can be recommended for the use under outdoor conditions and in laboratory tests at higher RH when stable electric connection between electrodes in ensured.
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QCM is a powerful technique for extremely low corrosion rate detection in indoor environments.
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The ER technique is the most universal corrosion monitoring tool, which can be applied both in high and weakly corrosive environments, depending on the sensor’s thickness.
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Further development of the state-of-the art RFID, FOCS and respirometric techniques in the field of atmospheric corrosion is expected. At the current stage, it is too early to evaluate their application potential.
This entry is adapted from the peer-reviewed paper 10.3390/met12020171
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