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Evaluation and Analyses of Rhizophora mangle L. Leaf-Extract Corrosion-Mechanism on Reinforcing Steel in Concrete Immersed in Industrial/Microbial Simulating-Environment



Joshua Olusegun Okeniyi, Cleophas Akintoye Loto and Abimbola Patricia Idowu Popoola
 
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ABSTRACT

Test-data from electrochemical monitoring methods were obtained from Rhizophora mangle L. leaf-extract admixed steel-reinforced concretes for detailing mechanism of the extract on steel-rebar corrosion in 0.5 M H2SO4 (simulating industrial/microbial environment). These electrochemical test-measurements, including corrosion potential, corrosion current and corrosion rate, were subjected to the analyses of probability distributions as per ASTM G16-95 R05 through the Kolmogorov-Smirnov goodness-of-fit test-statistics. Results showed that corrosion rate exhibited correlations with function of the natural plant-extract concentration and compact series of the inverse electrochemical noise resistance; the ratio of standard deviations of corrosion potential and corrosion current. Both the experimental and the correlated-prediction model identified Rhizophora mangle L. leaf-extract admixture concentrations that exhibited inhibition efficiency performance of η>70% on steel-rebar corrosion in the acidic test-medium. The adsorption isotherm modelling of the experimental and the predicted electrochemical test-results exhibited good agreements by following the Langmuir and Flory-Huggins isotherm fittings. In addition, the study identified physisorption as the prevalent corrosion-protection mechanism of the steel-rebar by the plant extract through both of the experimental and the correlated-prediction models of adsorption isotherm analyses.

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Joshua Olusegun Okeniyi, Cleophas Akintoye Loto and Abimbola Patricia Idowu Popoola, 2015. Evaluation and Analyses of Rhizophora mangle L. Leaf-Extract Corrosion-Mechanism on Reinforcing Steel in Concrete Immersed in Industrial/Microbial Simulating-Environment. Journal of Applied Sciences, 15: 1083-1092.

DOI: 10.3923/jas.2015.1083.1092

URL: https://scialert.net/abstract/?doi=jas.2015.1083.1092
 
Received: May 25, 2015; Accepted: July 30, 2015; Published: September 14, 2015



INTRODUCTION

Steel-reinforcement corrosion in concrete due to attacks of aggressive agents in the service-environments of the steel-reinforced concrete is a global problem to building structures and infrastructure stakeholders (Jiang and Jin, 2013; Okeniyi et al., 2013a; Tang et al., 2012). Such attacks, among other possible sources, could ensue from acid rain due to the combination of SO2 with atmospheric water (Tang et al., 2012; Tommaselli et al., 2009) in industrial environments or from the activities of sulphur-reducing bacteria, e.g., Thiobacilli spp. (Shing et al., 2012; De Muynck et al., 2009; Hewayde et al., 2007), in microbial/sewage environments. Both of these sources are potent at producing sulphuric acid that could both attack concrete and render the steel-rebar embedment susceptible to corrosion degradation. For these reasons, studies deliberate on improving resistance of concrete (De Muynck et al., 2009; Hewayde et al., 2007) as well as of the embedded reinforcing steel in the concrete to sulphuric acid attack (Okeniyi et al., 2014a; Gerengi et al., 2013). For both of these protection methods, many of the cited studies have identified the use of admixtures in concrete as an effective and economical protection system against corrosion degradation of steel-reinforced concrete.

In spite of these, problems ensue from the use of admixtures for inhibiting concrete steel-reinforcement corrosion in aggressive environment. One of these include the fact that traditional inhibitors that are well known for inhibiting steel-rebar corrosion in acidic environment suffer the drawbacks that they could be toxic and hazardous to the environmental ecosystem (Okeniyi et al., 2014b; Yadav et al., 2013). For these reasons, restriction against their use is increasing in many countries thus necessitating research for alternative environmentally-friendly replacement (Okeniyi et al., 2013b; Patel et al., 2013; Yadav et al., 2013; Fu et al., 2010). However, this lead to the other problem that adequate monitoring and requisite interpretations would be required to ascertain the performance effectiveness of alternative substances for the hazardous but highly effective traditional inhibitors. This is problematic from the view, posited in studies, that while known electrochemical methods exhibiting good relations to the technical and financial scope of non-destructive electrochemical testing provide information on what is ongoing in concrete, none gives the whole story (Birbilis and Cherry, 2005). Suggested approach for tackling these include combining different electrochemical test-techniques for complimenting one another (Gulikers, 2010; Song and Saraswathy, 2007) even as test-data from them could also be subjected to further analyses for obtaining more meaningful conclusion on effectiveness performance (Birbilis and Cherry, 2005).

These constitute motivations by which this study deliberates on the analyses of test-data from three different electrochemical techniques, obtained from Rhizophora mangle L. leaf-extract admixed steel-reinforced concrete in H2SO4 medium, for evaluating corrosion-mechanism. The use of Rhizophora mangle L. leaf-extract was considered in this study due to its identification in literature that extract from this natural plant exhibited no sign of toxicity to living organisms (Perera et al., 2010). This is therefore, potent as a green inhibitor with additional advantages of renewability and cost effectiveness (Mangai and Ravi, 2013). The modelling of electrochemical corrosion and inhibition by this natural plant will be based on the analyses of the corrosion potential, corrosion current and corrosion rate for detailing performance of the extract on steel-rebar corrosion in the industrial/microbial simulating-environment.

MATERIALS AND METHODS

Preparation of plant leaf-extract: Leaves of Rhizophora mangle L. (Rhizophoraceae) Euphorbiaceae, collected fresh from Ehin-more, Nigeria and identified at Forestry Herbarium Ibadan, Nigeria (FHI No. 109501), were dried under shade and blended into powder. Plant extract solution was then obtained from the blended powder using CH3OH (methanol), from Sigma Aldrich®, as solvent in a condenser equipped soxhlet extractor (Okeniyi et al., 2014c; Hameurlaine et al., 2010). The plant extract solution was then concentrated into paste over water bath, which was then used as admixture in mixing water for concrete casting, as per ASTM C192/192M-02 (ASTM., 2005c), from 0 g dm–3 (or g L–1) for the blank samples in increment of 1.6667 g dm–3 up to 8.3333 g dm–3. These total six variations of admixture designs.

Steel reinforced concrete samples: The steel-reinforced concrete samples were cast in duplicates (Dup) having similar admixtures such that 12 steel-reinforced samples were studied in the experimental work. The deformed steel used as rebar specimen in each concrete is of 12 mm diameter. This steel-rebar has elemental composition: C = 0.273%, Mn = 0.780%, Si = 0.403%, Cu = 0.240%, Cr = 0.142%, Ni = 0.109%, P = 0.039%, S = 0.037%, Mo = 0.016%, Co = 0.0086%, Nb = 0.0083%, Sn = 0.0063%, Ce = 0.0037%, V = 0.0032%, while Fe = the % balance. Specimens of 190 mm steel-rods cut from this were subjected to similar surface preparations according to standard procedures prescribed in ASTM G109-99a (ASTM., 2005d) and described by Okeniyi et al. (2014b, c) and Muralidharan et al. (2004). From each rod of specimens, 150 mm was centrally embedded in 100×100×200 mm concrete casting such that 40 mm of each steel-rod protruded out of the concrete. These protrusions find usefulness as connectors for the electrochemical corrosion monitoring techniques that were employed in the study.

Setup of electrochemical monitoring experiment: Each sample of steel-reinforced concretes was partially immersed in bowls containing test-solution of 0.5 M H2SO4 (Sigma Aldrich®) for simulating industrial/microbial service-environments of concretes (Okeniyi et al., 2013b; Gerengi et al., 2013; Shing et al., 2012). Electrochemical corrosion test-monitoring were then obtained from each concrete sample for 89 days experimental period. The electrochemical test-monitoring techniques employed in the study are (Okeniyi et al., 2013c, 2014d; Song and Saraswathy, 2007; Broomfield, 2002).

Half-Cell Potential (HCP) versus Cu/CuSO4 electrode (CSE), Model 8-A, obtained from Tinker and Rasor®, that was measured through high impedance digital multimeter as per ASTM C876-91 R99 (ASTM., 2005a; Okeniyi et al., 2013c, 2014a, e; Omotosho et al., 2014).

Electrochemical Cell Current (ECC) versus CSE that was measured through Zero Resistance Ammeter (ZRA), Model ZM3P obtained from Corrosion Service® (McCarter and Vennesland, 2004; Jaggi et al., 2001).

Corrosion Rate (CR) by linear polarization resistance that was measured through three-electrode LPR Data Logger, Model MS1500L obtained from Metal Samples® (Sastri, 2011).

Statistical analyses of measured corrosion test-data: According to standard procedure prescribed in ASTM G16-95 R04 (ASTM., 2005b) and in Roberge (2003), measured corrosion test-data was subjected to the Normal and the Weibull probability distribution functions (pdf’s). Also, the compatibility of the scatter of each variable of electrochemical corrosion test-data, to each of the statistical distribution function models was studied using the Kolmogorov-Smirnov goodness-of-fit (K-S GoF) test-statistics (Okeniyi et al., 2014d; Ajayi et al., 2013; Okeniyi and Okeniyi, 2012; Roberge, 2003). This was done to heed warning from ASTM G16-95 R04 (ASTM., 2005b) on the need to avoid grossly erroneous conclusion that could be accrued from describing corrosion test-data by a distribution that the test-data were not statistically distributed. The parameters, e.g., mean and standard deviation, for each statistical distribution model were estimated, from the dataset of electrochemical test-variables obtained per specimen of steel-reinforced concrete sample using formulas detailed in (Okeniyi et al., 2014c; Haynie, 2005).

Model-estimation of noise resistance (Rn): The statistical distribution of better-fit for the HCP and ECC find usefulness for the estimation of the noise resistance that was obtained through the ratio of the standard deviation of the HCP to the standard deviation of the ECC test-data. This could be expressed by the equation (Okeniyi et al., 2014b, c; Eden, 2000; Kelly et al., 1996):

(1)

Model-estimations of surface coverage and inhibition efficiency: The statistical distribution of better-fit of the CR test-data also find usefulness for the estimations of surface coverage (θ) and inhibition efficiency (η) performance of R. mangle L. leaf-extract admixture on the reinforcing steel corrosion in the concrete samples using the relationships (Okeniyi, 2014; Alagbe et al., 2006):

(2)

(3)

RESULTS AND DISCUSSION

Results from the distribution modelling of electrochemical test-variables: The mean values of electrochemical corrosion test-data, HCP, ECC and CR, obtained from the probability distribution fittings, by the Normal and by the Weibull distributions, are plotted in Fig. 1. The HCP and ECC plots, Fig. 1a and b, also include plots of standard deviations from the mean values of these electrochemical corrosion test-variables. In addition, linear plots where included in Fig. 1a for corrosion risk interpretations as per ASTM C876-91 R99 (ASTM., 2005a) and (Zamora et al., 2009) and in Fig. 1c for corrosion rate criteria according to literature (Soylev et al., 2007; Bungey et al., 2006).

Thus, the Kolmogorov-Smirnov goodness-of-fit test-statistics of the scatter of the electrochemical test-data like the Normal and Weibull pdf’s are plotted in Fig. 2, in which α = 0.05 linear plot for directly interpreting dataset not following the pdf’s was also included. This shows that while only the HCP and ECC datasets of the 8.3333 g dm–3 R. mangle L. admixture were not distributed like the Normal pdf, only the CR dataset of the 0 (blank)_Dup sample comes from the Normal pdf, according to the K-S GoF test-criteria at α = 0.05. This indicates that HCP and ECC datasets of 11 out of the 12 steel-reinforced concrete samples studied distributed like the Normal pdf, but CR datasets of 11 out of the 12 steel-reinforced concrete samples used for the experiments were not distributed like the Normal pdf. In comparison, all the datasets of electrochemical test-variables, i.e., HCP, ECC and CR, from all the 12 steel-reinforced concrete samples followed the Weibull pdf models as per the K-S GoF test-statistics at α = 0.05 level of significance. This support use of the Weibull probability distribution function as the descriptive statistics for detailing the prevailing corrosion condition in the steel-reinforced concrete samples immersed in the industrial/microbial simulating-environment being studied.

Correlation modelling analyses for corrosion rate and noise resistance: The distribution of the electrochemical test-variables, obtained from the steel-reinforced concrete specimens, like the Weibull pdf model facilitates application of Eq. 1 for evaluating the noise resistance Rn from the Weibull standard deviation models of HCP and ECC. The plot of this model of noise resistance is plotted with the corrosion rate, in ranking order of corrosion rate for the H2SO4-immersed steel-reinforced concrete specimens in Fig. 3. The expectation from the plotting from this figure was that the samples with the higher-valued Rn would be attended with low corrosion rate while sample with the lower-valued Rn would exhibit high corrosion rate. This would have found agreement with Kelly et al. (1996) where Rn values tracked linear polarization resistance just as it had been established in other reported works (Okeniyi et al., 2014b, c).

Fig. 1(a-c):
Results of the distribution models of electrochemical corrosion test-variables (a) Mean and standard deviations of HCP with linear plots of corrosion risks as per ASTM C876-91 R99 (ASTM., 2005a), (b) Mean and standard deviations of ECC and (c) Corrosion rate with corrosion criteria classification as prescribed by Soylev et al. (2007) and Bungey et al. (2006)

However, this form of tracking is not very obvious from the Rn and CR plots in Fig. 3, where the noise resistance plots generally undulates about the ranked corrosion rate.

This form of undulating model of noise resistance about the ranking of corrosion rate exemplified the position upheld in ASTM G16-95 R04 (ASTM., 2005b). In that ASTM standard, it had been stated that corrosion test-results are potent at exhibiting values that deviate in a more or less random way from expected values for the condition that are present in the corrosive system. The prescription proffered by that standard for attaining better approximations to the expected values include the necessity of statistical analyses for determining associations that could exist between variables and developing quantitative expressions relating variables.

Fig. 2:Kolmogorov-Smirnov goodness-of-fit test-statistics for the distribution of electrochemical corrosion test-variables like the Normal and the Weibull pdf’s

Table 1:Numerical values of the constant coefficients aj in the correlation Eq. 4

Table 2: ANOVA for the correlation fitting model in Eq. 4

Based, on this, several correlation fitting models were applied to the corrosion rate CR as dependent variable and the noise resistance Rn as well as the R. mangle L. admixture concentration ρ as the independent variable. From the analyses, it was observed that the CR exhibited a relationship with the independent variables that can be written in the compact form:

(4)

where, V is the volume of concrete mixing water which is a constant = 1.2 dm3 ≡ 1.2 L. The constant coefficients aj in j = 0, 1, 2, …, 7 have the numerical values given in Table 1. For the correlation fitting in Eq. 4, correlation coefficient, r = 93.66% and Nash-Sutcliffe efficiency, NSE = 87.72%. By these modelling criteria, the correlation fitting model in Eq. 4 classifies to the "very good" model efficiency, according to the model efficiency interpretations from literature (Okeniyi et al., 2013c, 2014d; Coffey et al., 2013). Also, as specified by ASTM G16-95 R04 (ASTM., 2005b), the correlation fitting model facilitates estimation of confidence interval from the relationships of the measured variables and this was obtained from analysis of variance (ANOVA) for the fitting model, which is presented in Table 2. From the table, the ANOVA p-value = 0.0960 which bear indication that it cannot be rejected that there is statistically significant relationship between the correlated dependent variable CR and the independent variables ρ and Rn within 90.40% confidence interval.

Inhibition efficiency performance and modelling of adsorption mechanisms: The application of Eq. 3 to the experimental and correlation predicted models of CR facilitates estimation of inhibition efficiency performance that was averaged over each of the duplicated samples of steel-reinforced concrete admixed with R. mangle L. leaf-extract.

Fig. 3:
Plots of noise resistance and corrosion rate in ranking order of corrosion rate performance of P. muellerianus admixtures in concrete samples

Fig. 4:
Experimental and correlation prediction model of R. mangle L. leaf-extract effectiveness at inhibiting steel-rebar corrosion in H2SO4-immersed concrete

The results of the averaged inhibition efficiency models estimations are plotted in ranking order of R. mangle L. leaf-extract effectiveness at inhibiting concrete steel-rebar corrosion in Fig. 4, for the experimental and correlation prediction model.

From the figure, 6.6667 g dm–3 R. mangle L. leaf-extract admixture exhibited optimal inhibition effectiveness, η = 72.87±5.49% by the experimental model. By the correlation prediction model the inhibition effectiveness of the 6.6667 g dm–3 R. mangle L. leaf-extract admixture at η = 70.55±5.34% falls short that of the 3.3333 g dm–3 R. mangle L. leaf-extract admixture that exhibited η = 78.42±18.32%. By the experimental model, three R. mangle L. leaf-extract admixtures, the 6.6667, the 3.3333 and 8.3333 g dm–3 concentrations, exhibited η>70% inhibition effectiveness. In comparison, only two R. mangle L. leaf-extract admixtures, the 6.3333 and the 3.3333 g dm–3 concentrations, exhibited η>70% inhibition effectiveness by the correlation prediction model; the inhibition efficiency by the 8.3333 g dm–3 admixture just fall short with η = 69.64±1.58%. These inhibition efficiency models find comparisons with the range of inhibition efficiency performance that were reported in literature (Tommaselli et al., 2009) for steel-reinforcement corrosion in acidic medium in the presence of inorganic chemical inhibitors. These bare suggestions of the suitability of R. mangle L. leaf-extract admixture for inhibiting steel-rebar corrosion in concrete designed for the industrial/microbial environments that were simulated by the corrosive medium used in this study.

Applications of the surface coverage Eq. 2 to the experimental and correlation predicted data facilitate modelling of the electrochemical test-results to different models of adsorption isotherms of the Langmuir, Flory-Huggins, Frumkin and Freundlich. Among these, the experimental and predicted result followed the Langmuir and the Flory-Huggins adsorption isotherm models given, respectively as (Okeniyi, 2014; Okeniyi et al., 2014c; Foo and Hameed, 2010; Eddy and Mamza, 2009).

Fig. 5:
Fittings of experimental and predicted electrochemical corrosion performance of R. mangle L. leaf-extract to adsorption isotherm models for H2SO4-immersed concrete steel-reinforcement (a) Langmuir isotherm model and (b) Flory-Huggins isotherm model

Table 3:Estimated parameters from the adsorption isotherm fittings of experimental and predicted data

(5)

(6)

where, KLang and KFH are the equilibrium constants (Kads) of the Langmuir and Flory-Huggins desorption-adsorption process, respectively; nFH is the Flory-Huggins model exponent; while μ0 ≡ CRblank. The plots of these adsorption isotherm models followed by the test-results in this study are presented in Fig. 5, in which plots of the Langmuir model are in Fig. 5a and plots of the Flory-Huggins models are in Fig. 5b. The estimated requisite parameters from the isotherm fitting models, which are useful for interpreting the fitting performance, are presented in Table 3. These include estimation of the free energy of adsorption using the Gibbs-Helmotz equation and the separation factor, RL, a dimensionless constant for indicating adsorption nature of R. mangle L. leaf-extract on steel-rebar surface. These parameters are, respectively given by Okeniyi et al. (2014b, c), Foo and Hameed (2010) and Eddy and Mamza (2009) as:

(7)

(8)

The parameters estimated from the fitting performances of the Langmuir and the Flory-Huggins isotherm models of electrochemical corrosion test-results showed that the predicted models followed both isotherm models than the experimental models. This was well indicated by the Kads and correlation coefficients of the predicted model that were higher than those of the experimental model by the fitting isotherm functions. In spite of these, however, both experimental and correlated fitting models bare agreements on the basis that the adsorption of R. mangle L. leaf-extract on the rebar surface is favourable from the separation factor models RL values from Table 3 that satisfy the condition 0<RL<1. Also, the experimental and the correlation prediction models exhibited agreements in the negative values of which suggest spontaneity of the adsorption process and stability of the adsorbed layer on the reinforcing steel surface. In addition, further agreements between the experimental and the correlated models was fostered by the values of which is around -20 kJ mol–1 and that suggest prevalent physical adsorption (physisorption) as the mechanism of R. mangle L. adsorption on the rebar.

By these results, it is established in this study that the performance of R. mangle L. leaf-extract admixture indicates the natural plant extract as an effective inhibitor of steel-rebar corrosion in concrete designed for the industrial/microbial, environments. That the plant-extract has been identified in biochemical studies as non-toxic also bare indication that its effectiveness at inhibiting concrete steel-rebar corrosion in the acidic medium is potent with the additional advantage that the plant extract is an environmentally-friendly inhibitor.

CONCLUSION

Corrosion mechanism of R. mangle L. leaf-extract admixture on reinforcing steel in concrete has been evaluated through analyses of experimental and correlation predictions from the steel-reinforced samples immersed in industrial/microbial simulating environment. The conclusions from the study include:

•  The datasets of electrochemical corrosion monitoring come from the Weibull distribution function according to the Kolmogorov-Smirnov goodness-of-fit statistics at α = 0.05 level of significance, thus supporting preference of the Weibull pdf for detailing prevailing corrosion condition in the experimental steel-reinforced concrete specimens
Although the noise resistance undulates about the performance ranking of corrosion rate, correlation analyses portrayed that the corrosion rate model from the R. mangle L. admixed steel-reinforced concrete samples bear relationship with the R. mangle L. admixture concentration and the inverse function of noise resistance model with correlation coefficient, R = 93.66% and Nash-Sutcliffe efficiency, NSE = 87.72%, which indicated "very good" model efficiency and ANOVA p-value = 0.0960 that suggest statistically significant relationship at 90.40% confidence interval
By the experimental model of electrochemical test-results, the 6.6667 g dm–3 R. mangle L. leaf-extract exhibited optimal inhibition efficiency, η = 72.87±5.49%, on concrete steel-rebar corrosion in the corrosive medium tested, while both the experimental and correlation prediction models identified, in agreements, admixtures with inhibition efficiency performance of η>70% that is within the range of effectiveness performance by inorganic inhibitors that where reported in literature
Both experimental and correlated prediction models of electrochemical corrosion test-results from the steel-reinforced concrete samples exhibited agreements by following adsorption isotherm modelling of the Langmuir and of the Flory-Huggins isotherms, with correlation coefficients, r>90%, even as both isotherm models bare suggestions of physical adsorption (physisorption), for the experimental and predicted test-results, as the prevalent mechanism of the R. mangle L. leaf-extract adsorption on steel-rebar surface
By these results, it is established in this study that the performance of R. mangle L. leaf-extract admixture indicates the natural plant extract as an effective inhibitor of steel-rebar corrosion in concrete designed for the industrial/microbial, environments that is also potent with the additional advantage that the plant extract is an environmentally-friendly inhibitor of steel-reinforcement in the acidic medium.
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