Study of Al7075 Localized Corrosion Inhibition by a SiO2 Superhydrophobic Coating Employing an Electrochemical Noise Technique

Abstract

Aluminum presents localized corrosion when exposed to marine and industrial environments. In this research, we study the pitting susceptibility of Al7075 coated by a SiO₂ sol–gel coating as a corrosion inhibitor employing the electrochemical noise technique. The analysis methods were power spectral densities (PSDs) in noise impedance (Z_n), complaints by wavelet transform, recurrence plots, and Hilbert–Huang study due to the chaotic behavior of the EN signal. To perform the EN, the parameters employed were 2048 data, 1 datum per second in NaCl, and H₂SO₄ electrolytes at 3.5 wt.% simulating marine and industrial environments. EN tests were performed following ASTM G199. The results showed an increase in the localized corrosion process when SiO₂ coating was applied. This is due to the cracking morphology of the coating.

1. Introduction

Generating an anti-corrosion surface is one of the most important objectives in corrosion science [1,2]. Different ways of protecting a material against corrosion exist, such as anodization, vapor deposition, plasma spraying, chemical conversion, and organic coatings [3]. The inhibitors are part of organic and inorganic coatings; the primary function of inhibitors is to reduce the corrosion process of the material. The inhibitor efficiency depends directly on the adsorptive capacity of the inhibitor; however, factors such as electrolyte concentration, inhibitor characteristics, and change in surface charge affect the inhibitor’s adsorptive effects [4,5,6,7,8,9,10,11,12].

The efficiency of the inhibitor depends on the stability of the formed chelate and the inhibitor molecule. Most inhibitors are absorbed into the metal, displacing water molecules on the surface and forming a pressurized barrier. The availability of non-bonding and p electrons in the inhibitor facilitates the electron transference between metal and inhibitor. For that reason, the efficiency of the inhibitor depends on the chelate stability, which depends directly on the inhibitor [13,14,15].

Silicon dioxide (SiO₂), an inorganic nanoparticle, is used to enhance the properties of many organic base resins [16,17,18]. The mechanical and good thermal properties of SiO₂ make it a powerful tool as a barrier [19,20]. Furthermore, many researchers have confirmed that SiO₂ has hydrophobic properties, preventing the diffusion of corrosive agents from changing the surface and the penetration of corrosive ions [16]. The water repulsion of superhydrophobic coating has proven to be an excellent option for protecting a material against corrosion. Superhydrophobicity is defined as a water droplet with a static contact angle greater than 150° and a tilt angle lower than 10° [21,22].

In many applications, the surface-wetting behavior of nano-SiO₂ particles is crucial. For particle assembly, preserving the shape, consistency of the particle size distribution, and optimal wettability of nanoparticles is essential. These traits also control how picking emulsions, foams, and other dispersions develop and remain stable. In principle, a solid surface’s chemical makeup and geometric microstructure determine its wettability. Using physical or chemical alteration to customize the surface wettability is a successful tactic. Electrostatic adsorption, coating, and other physical interactions are the main methods used in physical modification to alter the surface [23].

The chemical modification uses the chemical reaction between silica hydroxyl and the modifier to change the molecular makeup on a particle surface and accomplish the goal of surface wettability transformation. Alcohols, silanes, amines, fatty acids, and organosilicon compounds are the primary modifiers utilized for the chemical alteration of silica nanoparticles; silane coupling agents and organosilicon compounds are most frequently used as treatment agents. Organic amines such as tetraethylenepentamine, triethylenetetramine, and diethylenetriamine are frequently used as modifiers [23,24,25].

Several authors have found that SiO₂ coatings reduced the corrosion rate of material due to changes in surface porosity. Also, the corrosion resistance increases when they mix SiO₂ with some epoxy.

Different corrosion inhibitors based on the hydrophobic properties of coating have also been developed. Chen et al. [26] commented that hydrophobic coating reduces the corrosion kinetic in NaCl at 3.5 wt.%. Factors like the dipping duration and hydrolysis pH impact how well silane coatings work as a corrosion barrier. Others examined how the protection provided by a silane coating made of a combination of tetraethoxysilane, methyltriethoxysilane, and glycidyloxypropyltrimethoxysilane on mild steel was affected by the length of time spent dipping (30, 60, and 120 s) in the silane solution and the pH of the solution (2.8–4.0) [27].

Other authors increased the corrosion resistance of silica by modifying the hydrolysis and polycondensation of TEOS and MTES employing 400 °C for 15 min at atmospheric pressure. The samples presented an effective coating against corrosion after 48 h of pre-immersion in corrosive media [28].

Alumina, one of the many oxides used in coating applications, is frequently added to coatings to increase their resistance to wear and corrosion since it is affordable, chemically inert, and thermally very stable [29]. Ruhi et al. [29,30] created sol–gel alumina coatings on zinc-phosphate mild steel, which were then sintered at 300, 400, and 500 °C.

TiO₂ is a great anti-corrosion substance because of its high chemical stability, low electron conductivity, and heat resistance. Mild steel’s resistance to corrosion in NaCl solution was shown to be enhanced by applying sol–gel TiO₂ or sol–gel SiO₂ coatings [31]. On the other hand, the sol–gel coating containing TiO₂-SiO₂ combined offers the least corrosion protection—even less than mild steel that is left exposed.

Superhydrophobic coating is a good option for anodization due to the porosity of anodized facilities and the penetration of Cl⁻ ions. The type of surface that generates the superhydrophobic coating is a solution for different corrosion problems generated by environmental contamination [32].

Li et al. [33] mentioned the importance of the hierarchical structures of superhydrophobic surfaces, indicating that the CA (contact angle) can be 160° and SA of 1°; this provokes a self-cleaning of the surface.

The military, aerospace, and other industries extensively use Al-7075 aluminum alloy as a structural material. Localized corrosion, such as intergranular corrosion, can considerably impact the material’s service life, which can occur in AA7075 aluminum alloy. The microstructure of the aluminum alloy AA7075, which is controlled by the heat treatments applied, affects the alloy’s localized corrosion behavior.

However, the literature has differing views, and the connection between microstructure and localized corrosion behavior is still unclear. For example, the aging process of T6 has been linked to a high risk of intergranular corrosion (IGC) [34,35,36,37,38].

Xiong et al. [39] demonstrated that Al-7075 alloy underwent several heat treatments, resulting in distinct localized corrosion behaviors. Additionally, the anodic nature of the corroded locations caused localized corrosion behaviors. The study’s findings also imply that the GL test, which is quicker, eliminates the need for complex test time selection, and produces specimens with a cleaner surface, all of which are advantageous for later characterization of corrosion morphology, and may be a more effective way to predict localized corrosion behaviors under natural corrosion conditions without applied current (i.e., the OCP test).

A material’s susceptibility to pitting corrosion is important for selecting the correct technique. The electrochemical noise (EN) technique helps study materials’ corrosion behavior.

One of the advantages of EN is its effectiveness as a non-perturbative method in localized processes, but it can also be used to monitor various corrosion processes; the type of analysis is correlated with the signal type in the system [40,41,42,43,44].

The various categories into which EN analysis falls are time-domain, frequency-domain, frequency–time, and chaotic systems. When the technique was first developed, a statistical analysis was used to examine the signal. Among others, Mansfeld, Cottis, Turgosse, Eden, and Bertocci worked to relate the type of corrosion with statistical parameters like the pitting index (based on the standard deviation of ECN and EPN) and localization index (LI). Additionally, a kinetic variable was related to the noise resistance (R_n) parameter, which was obtained and used as a homolog of Rp. Several scholars [26,27,28,29,30,31] also improved the LI to detect corrosion by employing Kurtosis and skewness.

A standard deviation from time series values (EPN and ECN) must be obtained to calculate noise resistance (R_n). The statistical values in Equation (1) provide details on the causes and dynamics of corrosion [45].

(1)$R_n={\displaystyle \frac{{\sigma }_v}{{\sigma }_I}}*A$

In this investigation, Kurtosis and skewness were employed to determine the type of corrosion. Because Mansfeld and Sun [46] determined in 1995 that the localization index (L.I.) had limitations and should be utilized cautiously, it was not considered. The third and fourth statistical moments, respectively, skewness and Kurtosis (Equations (2) and (3)), can be used to determine the kind of corrosion:

(2)$skewness={\displaystyle \frac{1}{N}}\sum _{i=1}^N{\displaystyle \frac{{{(x}_i-\overline{x})}^3}{{\sigma }^3}}$

(3)$kurtosis={\displaystyle \frac{1}{N}}\sum _{i=1}^N{\displaystyle \frac{{{(x}_i-\overline{x})}^4}{{\sigma }^4}}$

The time-domain EN must be converted to the frequency-domain using a fast Fourier transform (FFT) for PSD analysis. Equations (4) and (5) are used to determine the spectral density because of the connection with the EN signal (with a polynomial filter applied) [47,48,49].

(4)$R_{xx}\left(m\right)={\displaystyle \frac{1}{N}}\sum _{n=0}^{N-m-1}x\left(n\right)·x\left(n+m\right), when values are from 0<m<N$

(5)${\mathsf{\Psi }}_x\left(k\right)={\displaystyle \frac{\gamma ·t_m}{N}}·\sum _{n=1}^N\left(x_n-{\overline{x}}_n\right)·e^{{\displaystyle \frac{{-2\pi kn}^2}{N}}}$

Several authors separated the signal on the crystal, usually from 1 to 8, and used the wavelet transform to decompose the signal to obtain an energy map. The authors linked the initial crystal energy, D1 to D3, to metastable pitting. Crystals D4 through D6 are associated with localized corrosion, while crystals D7 and D8 are associated with diffusion or controlled processes (uniform corrosion). The S8 crystal is connected to the EN’s DC signal [50,51,52,53,54]. Wavelets break down a signal using a high–low filter; high frequencies are referred to as details, while low frequencies are considered approximations [40]. Equations (6)–(8) provide the total energy of a set of data points.

(6)$E=\sum _{n-1}^N{x}_n^2$

Additionally, Equation (10) provides energy fractions of details and approximation:

(7)${ED}_j^d={\displaystyle \frac{1}{E}}\sum _{n=1}^N{d}_{j,n}^2 {ED}_j^s={\displaystyle \frac{1}{E}}\sum _{n=1}^N{s}_{j,n}^2$

Equation (11) states that the total energy evaluated is equal to the energy of each wavelet transform component:

(8)$E={ED}_j^s\sum _{j=1}^j{ED}_j^d$

One helpful technique for analyzing nonlinear systems is a recurrence plot. Because corrosion is a chaotic and nonlinear system, several methodologies must be utilized to evaluate the kind and extent of corrosion on the surface. RP is a useful technique for achieving that analysis.

Recurrence plots are two-dimensional graphs that represent temporal functions. I and j are given as the route of x_i in Rm at a time interval of ε, where m is the spatial dimension. The RP shows the times ti and tj from two dimensions as a two-dimensional square matrix. Equation (3) [41,42,43,44,55,56] presents the matrix:

(9)$R_{ij}\left(\epsilon \right)=\mathsf{\Theta } \left(\epsilon -‖{\overrightarrow{x}}_i-{\overrightarrow{x}}_j‖\right), i,j=1,\dots , N$

where ||. || is the norm (Euclidean, maximum, or Manhattan), μ is the distance umbral, Θ(x) is the Heaviside function, and N is the number of data or points x_i. Equation (4) illustrates the recurrence rate, which determines the recurrence density:

(10)$RR\left(\epsilon \right)={\displaystyle \frac{1}{N^2}}\sum _{i, j=1}^N{R}_{i, j}$

In an umbral zone, the recurrence rate (RR) indicates the likelihood of reconstructing a single track.

The diagonal lines of RP show the system’s determinism and how the trajectory has changed over time in the phase space. The determinism (DET) is computed using the following equation:

(11)$DET={\displaystyle \frac{{\sum }_{l=l_{min}}^{N}l p\left(l\right)}{\sum _{l=1}^{N}l p\left(l\right)}}$

Periodicity requires long diagonal lines on the RP, and DET gets closer to unity; stochastic signals produce solitary dots, which limit DET to values close to zero. Equation (12) provides the average diagonal line length (L), another RQA measure related to diagonal lines:

(12)$L={\displaystyle \frac{{\sum }_{l=l_{max}}^{N}l p\left(l\right)}{{\sum }_{l=lmin}^{N}p\left(l\right)}}$

TT gives the vertical structures’ average length and the system’s average time.

(13)$TT={\displaystyle \frac{{\sum }_{v=v_{min}}^{N}v P\left(v\right)}{{\sum }_{v=vmin}^{N}P\left(v\right)}}$

The electrochemical noise (EN) approach describes the electrochemical phenomena throughout the corrosion process. One approach used to explain the EN method is the Hilbert–Huang analysis. This technology facilitates the frequency and time of energy exchanges; the energy, called instantaneous energy, is extracted from the intrinsic function of the signal and recovered by signal decomposition [41,57,58]. The empirical method of decomposition (EMD) is explained by Equation (8):

(14)$x\left(t\right)=\sum _{i=1}^N{h}^{\left(i\right)}\left(t\right)+d\left(t\right)$

where d(t) is the average of the trend at a low frequency of the time series x(t) and cannot be decomposed; h(i)(t) is the i-th term of IMF that is generated; these numbers must satisfy the conditions that the extreme and cross numbers are equal or differ by a maximum of 1 and that each point using the local maximum and minimum must be 0. The HHT is represented by Equation (9):

(15)$y_j\left(t\right)={\displaystyle \frac{1}{\pi }}p{\int }_{-\infty }^{\infty }{\displaystyle \frac{h_j\left(\tau \right)}{t-\tau }}d\tau$

where p is connected to the Cauchy principle and is associated with an average of IMF; y_j(t) is the Hilbert transform, and IMF is represented by h_j.

This work aimed to evaluate the electrochemical corrosion behavior of AHSS CP780 without treatment called substrate, phosphate, and CP780 with E-coat. The electrochemical noise technique was employed to study samples using time-domain (statistical), frequency-domain (PSD), time–frequency-domain (wavelet and Hilbert–Huang), and recurrence plots for chaotic analysis. The electrolyte used is NaCl at 3.5 wt.% simulating a marine environment.

2. Materials and Methods

2.1. Materials

Tetraethyl orthosilicate (TEOS, Si(OC₂H₅)₄, 98%), Sigma-Aldrich (Toluca, México) was used as the silica precursor to create SiO₂ nanoparticles (NPs). The catalyst was ammonium hydroxide (NH₄OH, 28%–30% NH₃), Sigma-Aldrich). For the hydrolysis process, deionized water (18 MΩ·cm) and isopropyl alcohol (C₃H₈O), CEDROSA (Naucalpan de Juárez, México) were used as solvents. Hexane (C₆H₁₄, 95%), J.T. Baker (Xalostoc, México) was utilized as the solvent in conjunction with 1H,1H,2H,2H-Perfluorodecyltriethoxysilane (PFDTES, C₁₆H₁₉F₁₇O₃Si, 97%), Matrix Scientific (Columbia, SC, USA) to modify the SiO₂ NPs and create a superhydrophobic coating.. Aluminium 7075-T6 cold-finished rectangle bar, OnlineMetals, (Portland, OR, USA) was used as the substrate. It was coated with an SiO₂ superhydrophobic coating to study its corrosion resistance.

2.2. Equipment

The following tools were used to synthesize and characterize the superhydrophobic coatings: a water purification system Water-Pro PS, Labconco^® (Kansas City, MO, USA) to acquire deionized water and a laboratory stirrer PC 410, Corning^® (Corning, NY, USA) to mix solutions. Lastly, a scanning electron microscope JSM-7401, JEOL (Tokyo, Japan) was used to conduct scanning electron microscopy (SEM).

2.3. Synthesis and Characterization

The Stöber-proposed approach was used to synthesize the SiO₂ nanoparticles (NPs). A 100 mL solution of SiO₂ NPs was made by mixing 95 mL of isopropyl alcohol at 300 rpm. Then, 350 μL of NH₄OH, 1 mL of TEOS, and 3.65 mL of deionized water were added. The mixture was kept at 40 °C for twenty-four hours while constantly stirring. After this time, 500 μL of PFDTES was added, the temperature was raised to 60 °C, and 25 mL of hexane was added to the solution. After 48 h of stirring the mixture at 300 rpm, 125 mL of superhydrophobic coating was produced.

The glass substrates were coated with 1, 3, 5, and 7 layers of the superhydrophobic dressing. The nozzle and substrate were kept 15 cm apart during this procedure, which was carried out at an air pressure of 30 psi. After that, the samples were left outside for 35 days to assess any changes in surface superhydrophobicity.

2.4. Electrochemical Characterization

In order to assess the corrosion behavior of Al 7075 and Al 7075 with the hydrophobic coating in 3.5% wt. NaCl and H₂SO₄ solutions, EN measurements were performed at room temperature using a standard three-electrode cell. Two ostensibly identical specimens served as the working electrodes (WE1 and WE2), and a saturated calomel electrode served as the reference electrode (RE), respectively. The ASTM G199-09 standard was followed for the EN measurements [59]. A scanning rate of 1 data/s was used to measure 2048 data points in each trial. The Princeton Applied Research Model 263A recorded the EN measurements simultaneously. Three duplicates of the tests were conducted.

An application developed in the MATLAB 2018a software (Math Works, Natick, MA, USA) was used for the data analysis obtained from EN measurements. Energy dispersion plots (EDP) were used in the frequency–time-domain analysis, where the original signal (with DC) was subjected to the orthogonal wavelet transform. In order to obtain the intrinsic functions (IMF) of the EN signal by an empirical decomposition method (EMD), EN analysis with the Hilbert–Huang transform (HHT) was required. Lastly, instantaneous frequencies were shown using a Hilbert spectrum. A program created by Potsdam University.

3. Results

3.1. SEM Morphology

Figure 1 shows the SEM morphology of Al-7075 before the corrosion test and the SiO₂ coating before the corrosion test. Al-7075 (Figure 1a,b) presented a porosity morphology with grind lines. On the other hand, SiO₂ presented a hierarchical morphology (Figure 1c,d) and a cracking structure, as shown in Figure 1d. The inhibitor is not distributed in a homogenous way.

3.2. Wettability Test

Figure 2 shows the wettability test, where the WCA was 157.5° ± 1.18° and the WSA was 3.2° ± 0.29°. The characteristics of the measurements indicate that the coating maintains its superhydrophobic properties.

3.3. Electrochemical Noise

Time Series

The time series showed a behavior related to localized processes, especially in Figure 3b, where ECN shows how localized corrosion occurs in both samples; however, the amplitude is higher in Al-7075 (4 × 10⁻³ V and 4 × 10⁻⁶A/cm²) without inhibitor in NaCl. This occurs because corrosion kinetics are higher for uncoated samples. However, both samples presented a trend of pitting (with 0.03 of LI and 2.8 of Kurtosis). The value of R_n is 973 Ω·cm² for the coated Al-7075 (see Table 1), indicating that corrosion resistance is higher.

Figure 4 shows the EPN and ECN of the samples in H₂SO₄. The behavior is similar concerning amplitudes; even in EPN and ECN, the higher amplitude (5 × 10⁻³ V and 5 × 10⁻⁵A/cm²) is for the uncoated Al-7075, indicating a faster corrosion kinetic. On the other hand, the presence of the transients indicates that the system is predominated by a localized corrosion process with 0.01 of LI and 1.4 of Kurtosis. The predisposition of the coated samples to localized corrosion can be related to a non-homogenous coating distribution. The value of R_n generally is higher for Al-7075 than for the inhibitor, which means that the inhibitor has a poor resistance to H₂SO₄.

Figure 5 shows the variability of R_n according to different time lapses according to Table 2. Figure 5 shows how Al-7075 presented a higher corrosion resistance than alloy coating with the inhibitor. That behavior can be related to different phenomena: The first is that the EN technique measures the resistance of SiO₂ inhibitor. Another explanation is that creating a passive layer in Al-7075 increases corrosion resistance. The final explanation is that inhibitors provoke a localized corrosion process because of the inhibitor’s cracking structure, which makes the corrosion kinetic of the material more aggressive. However, the corrosion resistance of Al-7075 increased at the final time-lapse. It occurs when a passive layer is generated, even in NaCl and H₂SO₄; however, in NaCl, the R_n value presented fluctuations for Al-7075, indicating an unstable passive layer generation due to the Cl ions’ penetration. On the other hand, the H₂SO₄ presented an increase in corrosion resistance for Al-7075, which occurs by generating a passive layer. In addition, the Al-7075 with the inhibitor presented a decrease in corrosion resistance, which can be attributed to the degradation of the coating.

Figure 6 shows the variation in LI at different times. When the samples are exposed to NaCl, in Figure 4a, the values are related to a mixed corrosion process. In contrast, Figure 4b shows how the Al-7075 presented a behavior of change in LI, taking values from mixed to uniform corrosion. It can be related to generating a passive layer or the diffusion of pitting in the surface.

Figure 7 and Figure 8 show the variation in Kurtosis and skew. The behavior is similar to LI, where the samples presented variation between the localized and uniform corrosion processes. That behavior is more evident in the sample with the inhibitor, which presented uniform values to localized corrosion. In addition, Al-7075 presented changes from localized to uniform corrosion. Again, that behavior can be related to the transition of a localized to a diffusion process.

3.4. Noise Impedance (Z_n)

The values of noise impedance (Z_n) in Figure 9 indicate that Al-7075 presented a higher corrosion resistance with 718 Ω·cm² (see Table 3); the difference is that in NaCl, the value was 351 Ω·cm², and in H₂SO_4, it presented a value difference. The resistance in H₂SO₄ decreased, meaning that the SiO₂ coating could be dissolved in that media.

That behavior matches the one obtained by the R_n method, indicating that inhibitor resistance is low. That behavior is related to the heterogeneity of coating.

3.5. Wavelets

Figure 10 indicates the energy accumulation in each crystal. Al-7075 in Figure 10a presented energy accumulation at crystals D5, D6, and D7, with a decrease in D8. That behavior is related to generating localized processes (D5 to D6) that convert to uniform processes (D7 and D8). Figure 10a shows the behavior of Al-7075 uncoated and with an inhibitor exposed to NaCl; for this analysis, the Al-7075 presented a higher energy fraction than the sample with an inhibitor, indicating that the corrosion process is more aggressive. The energy begins to present in crystal D5, with a significant energy accumulation in crystal D7, meaning that a diffusion process occurred on the surface. The energy at crystal D5 indicates that all begin with a localized process. The sample with the inhibitor shows a similar behavior but with lower energy, as it occurs at scale. That happens due to the protective behavior of the inhibitors, where the attacks begin as a localized process, but the diffusion cannot occur the same way.

Figure 10b shows the behavior of samples exposed to H₂SO₄; Al-7075 uncoated presented high energy accumulation in crystals D3 to D6, indicating that a localized process occurs. After that, at crystal D8, the energy increased; that behavior is related to a pitting diffusion. On the other hand, the energy accumulation of the coated samples is between crystals to D3 to D8. That could mean two things: the progression of a localized process that separates the inhibitor from the surface and the passivation of the surface after the inhibitor is removed. It occurs because energy accumulation is at the same level in almost every crystal (D3–D8).

3.6. Hilbert–Huang, Recurrence Plots, and SEM

Figure 11 shows the behavior of Al-7075 exposed to NaCl. The behavior of RP is associated with the localized process in the first seconds in zones where the lines are marked as interceptions.

That phenomenon occurs in the last seconds of the EN test, but the localized processes occurred at a lower frequency than in the first seconds. The HHT shows how localized processes occur at high frequencies and presents a trend to diffusion; it can be related to unstable passive layer generation. The SEM from Figure 11c,d shows how corrosion residuals accumulate in specific zones. However, the oxide layer presents imperfection and pitting-cracking zones, indicating that passivation is non-stable.

Figure 12 shows the behavior of Al-7075 with the inhibitor coating. This sample presented a more localized process due to the high number of attractors (lines). The behavior is higher at the first 1000 s; after that, the inhibitor presented the same conduct but with a lower intensity. The HHT shows how, at the first 1000 s, the energy accumulation occurs at high and middle frequencies, indicating that localized processes occur. In the first seconds, the behavior is associated with localized attacks due to a heterogenous inhibitor layer (cracking zones). The second behavior indicates that the energy of pitting decreased after 1000 s, which can be associated with an oxide layer generation in the zones where the coating was removed. Figure 12c,d shows the SEM results, and it can be observed that corrosion processes occur in cracking zones, where the ions Cl⁻ and OH⁻ penetrate the coating and begin to dissolve (Figure 12d). Therefore, the localized process is more evident in the coated sample.

Figure 13 shows that the Al-7075 in H₂SO₄ presents localized corrosion processes at high and middle frequencies in the HHT graphic. The behavior presented in Figure 13a is associated with a highly localized corrosion process that occurs with high intensity in the first seconds (before 1000 s). After that, the localized corrosion continued occurring with a lower intensity; however, the frequency increased due to the chart morphology. That behavior is also shown in Figure 12b, where energy is accumulated at the middle and high frequencies, indicating a localized corrosion process. It can be related to pitting corrosion because the SEM figures show how pitting corrosion occurs on material surfaces (Figure 13c,d). The localized corrosion is present and begins to dissolve the material.

Figure 14 shows the Al-7075 with the inhibitor, and the whole RP graphic presents localized corrosion behavior. Figure 13a shows the RP, where the behavior of high frequency of vertical and horizontal lines indicates that a localized corrosion process began to occur. It means that the inhibitor coating was attacked, but in this case, it was dissolved; for that reason, Figure 13b presents high energy accumulation at middle frequencies, indicating the dissolution of the coating occurred by a corrosion diffusion of the inhibitor until dissolved. Figure 14c,d shows how dissolution begins in the cracking zones as a localized process to be dissolved, a uniform process that generates a heterogeneous oxide layer. The oxide layer created presents cracking zones, indicating that the corrosion trend will be uniform after a time, so some processes are at low frequencies (Figure 14b).

4. Discussion

The water droplets are held in place by our coating’s hierarchical structure [60]. Creating hierarchical structures and altering the rough surface are crucial to creating an effective hydrophobic coating. For superhydrophobic coatings, the multiscale roughness improves superhydrophobicity, durability, and self-cleaning capabilities [61]. The coating’s hierarchical structure, with nano- and microscale particles contributing to its roughness and superhydrophobicity, was shown by morphological and structural analyses.

The anodic process in aluminum is about the reaction of Al³⁺ that is converted into an ion, as in the following equation:

(16)${Al}_{\left(s\right)}\to {Al}_{\left(aq\right)}^{3+}+{3e}^{-}$

In NaCl composite, the attack of Cl ions is crucial for the electrochemical corrosion of the material. The following equation shows the chemical reaction of Cl with aluminum:

(17)${{Al}^{3+}}_{\left(aq\right)}+{{3Cl}^{-}}_{\left(aq\right)}\to {AlCl}_3$

Reactions in those processes result in active oxidation; as oxygen permeates the surface, the corrosion products become porous and fail to passivate the surface. This results in a limited attack and hinders the passivation of pitting that develops on the surface of the ferrite zone by making it more straightforward for Cl-ions to attack the preference zone [62]. Cl-ions inhibit the formation of a stable oxide layer on the metal surface. Cl⁻ attacks the surface to create an unstable surface and expose the material to localized attacks, breaking the passive layer and introducing it as an interstitial ion.

The passive layer in aluminum is generated spontaneously; however, it can present several homogeneity problems. In addition, when Cl⁻ is added, it affects the generation of a passive layer [63]. According to Wang and these findings, specific ions are more harmful to different alloys, particularly Cl⁻ [64], because they cause the passive layer to dissolve. H₂SO₄ can cause more material dissolution, but Cl⁻ can cause more damage over time because it avoids the formed passive layer and exhibits a diffusion process. Therefore, the oxide layer created by NaCl in Al-7075 presented pitting corrosion due to the instability generated by Cl⁻ ions on the surface.

Aluminum’s susceptibility to Cl-ions supports the last paragraph’s assertion that Cl⁻ is responsible for the passive layer’s behavior in a NaCl electrolyte and prevents the layer from forming uniformly [65,66].

Moreover, the hydroxide reaction is very aggressive for diverse materials, where aluminum hydroxide is generated as the following equation presents:

(18)${{Al}^{3+}}_{\left(aq\right)}+{{3OH}^{-}}_{\left(aq\right)}\to {Al\left(OH\right)}_{3\left(s\right)}$

On the other hand, the chemical reaction of H₂SO₄ can generate a stable oxide layer; however, sometimes, the aggressivity of the reaction generates unstable oxide that can be dissolved by the acid media [67]. Aluminum will be dissolved for electrolyte aggressiveness, and an H₂ was developed, meaning a hydrogen evolution and a change in pH of the electrolyte [50,51,52]. The behavior of the corrosion system presented on Al-7075 with inhibitor exposed to H₂SO₄ was observed by Wicaksono et al. [53], which indicates a uniform corrosion process by exposition in the acid media. The specimen’s surface is nearly completely covered with corrosion, and the identical fractures and depressions show that the corrosion is uniform. The material’s erosion while submerged in H₂SO₄ results in a generally lower hardness value for the specimen submerged in H₂SO₄ than for the specimen submerged in NaCl solution [54,68,69,70].

The results obtained by EN showed that the uncoated Al-7075 presented increased R_n values when exposed to NaCl due to the generation of one oxide layer; however, that oxide layer is heterogeneous due to the role of Cl⁻. That behavior is observed even in wavelet, HHT, and RP’s graphics, where energy at high frequencies indicates that the passive layer generated is unstable and with cracking zones, as shown in the SEM figures. On the other hand, the increasing LI values and the Kurtosis and skew reflect the heterogeneity of the SiO₂ coating.

Figure 15 shows the diagram of the corrosion mechanism for each electrolyte. When the sample is exposed to NaCl, stage I shows how the ions begin to attack preference zones, in particular, Cl⁻ ions that provoke the pitting as is shown at the II stages, where the Cl⁻ ions penetrate in cracked zones, provoking the dissolution of inhibitor, generating an oxide layer as the corrosion product. The corrosion mechanism of H₂SO₄ is very similar to that in NaCl. The difference is the aggressivity of the corrosion process, which completely removes the inhibitor coating and generates oxides on the surface as corrosion products. This behavior is very similar to the one proposed by Wicaksono et al. [53]. The difference is that the H⁺ begins to fragilize the coating, causing the corrosion process to be provoked by SO₄⁻².

The corrosion mechanism in the coated Al-7075 is the same: corrosive ions attack the cracking zones of the material, dissolving those parts to expose the uncoated material. When the coated alloy is exposed to H₂SO₄, the corrosion mechanism is more notorious: the SiO₂ coating is removed from the surface, but a passive layer is created. Moreover, the difference in the corrosion resistance of Al-7075 and the inhibitor sample is due to EN measuring the inhibitor corrosion resistance; for that reason, it is lower. It is important to consider complementing this study with EIS and potentiodynamic polarization research soon. This is to determine the behavior of the whole system.

5. Conclusions

• The SiO₂ superhydrophobic sample presented a hierarchical structure; however, the distribution was not homogenous.

• NaCl electrolyte generates an oxide layer in Al-7075, but the oxide layer is weak due to the presence of Cl⁻ that avoids a homogenous passive layer barrier.

• The corrosion mechanism in SiO₂ superhydrophobic coating consists of attacks in cracking zones to dissolve the material.

• SiO₂ coating presents higher corrosion resistance in NaCl than in H₂SO₄ (351 to 40 Ω·cm²).

• H₂SO₄ was shown to be a more aggressive media than SiO₂ coatings, so the SiO₂ was dissolved. The role of H⁺ ions is important due to the fragilization of material being attacked by SO₄²⁻. The corrosion mechanism is very similar, but the coating was attacked due to the aggressive corrosion process in H₂SO₄.

• Corrosion-type coating concerns mixed corrosion; it occurs when the first attacks are on the weak zones (cracking areas), localized corrosion leads to the dissolving of the entire material, and uniform corrosion.

• It is important to study the behavior of R_n in different time lapses due to its relationship with generating one passive layer.

Author Contributions

Conceptualization, L.E.V.N., D.C.-M. and J.M.J.-M.; methodology L.E.V.N., D.C.-M., B.S.-S. and J.M.J.-M.; software: J.M.J.-M. formal analysis L.E.V.N. and J.M.J.-M.; investigation B.S.-S., J.S.A.-C. and A.V.-M.; resources, D.C.-M., B.S.-S. and A.V.-M.; data curation, L.E.V.N., D.C.-M., J.S.A.-C., A.A.L.-I. and J.M.J.-M.; writing—original draft preparation, L.E.V.N., D.C.-M., A.V.-M. and J.M.J.-M.; writing—review and editing, D.C.-M., J.S.A.-C., A.A.L.-I. and J.M.J.-M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. SEM analysis. (a) Al-7075 at 500×, (b) Al-7075 at 2000×, (c) SiO2 inhibitor at 500×, and (d) SiO2 inhibitor at 2000× before corrosion test.

Figure 2. Image of a water droplet on a Al-7075 substrate.

Figure 3. Electrochemical Potential Noise (EPN) (a) and Electrochemical Current Noise (ECN) (b) signals exposed to NaCl at 3.5 wt.%.

Figure 4. Electrochemical Potential Noise (EPN) (a) and Electrochemical Current Noise (ECN) (b) signal exposed to H2SO4 at 3.5 wt.%.

Figure 5. Rn variation in the time: (a) NaCl and (b) H2SO4 for both samples.

Figure 6. LI variation in time: (a) NaCl; (b) H2SO4.

Figure 7. Kurtosis variation in time: (a) NaCl; (b) H2SO4.

Figure 8. Skew variation in time-lapse: (a) NaCl; (b) H2SO4.

Figure 9. Noise impedance (Zn) when samples are exposed to (a) NaCl and (b) H2SO4.

Figure 10. Energy Display Energy (EDP) when samples are exposed to (a) NaCl and (b) H2SO4.

Figure 11. Recurrence plot (RP) (a), Hilbert specter (b), SEM at 500× (c), and 2000× (d) of Al-7075 exposed to NaCl 3.5 wt.%.

Figure 12. Recurrence plot (RP) (a), Hilbert specter (b), SEM at 500× (c), and 2000× (d) of Al-7075 SiO2 exposed to NaCl 3.5 wt.%.

Figure 13. Recurrence plot (RP) (a), Hilbert specter (b), SEM at 500× (c), and 2000× (d) of Al-7075 exposed to H2SO4 3.5 wt.%.

Figure 14. Recurrence plot (a), Hilbert specter (b), SEM at 500× (c), and 2000× (d) of Al-7075 SiO2 exposed to H2SO4 3.5 wt.%.

Figure 15. Diagram of the corrosion mechanism in each electrolyte for the Al7075 with the inhibitor. The corrosion mechanism is divided into three stages.

Table 1

Statistical parameters were obtained by EN time series to determine each system’s corrosion resistance and corrosion type.

Sample	Electrolyte	Rn (Ω·cm2)	LI	Corrosion Type	Kurtosis	Corrosion Type	Skew	Corrosion Type
Al 7075	NaCl	467	0.03	mixed	2.88	Pitting	0.16	Uniform
Inhibitor		973	0.01	mixed	3.7	Pitting	2.5	Pitting
Al 7075	H2SO4	141	0.01	mixed	1.3	Pitting	0.4	Uniform
Inhibitor		67	0.005	mixed	12	Pitting	2	Pitting

Table 2

Time segmentation for figures from 4 to 7.

Time	Lapse
0–256	1
256–512	2
512–768	3
768–1024	4
1024–1280	5
1280–1536	6
1536–1792	7
1792–2048	8

Table 3

Noise impedance (Z_n) parameters to determine the corrosion resistance.

Sample	Zn0 (Ω·cm2)
NaCl
Al 7075	718
Inihibitor	351
H2SO4
Al 7075	84
Inihibitor	40

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