Modeling of Chloride Binding Capacity in Cementitious Matrices Including Supplementary Cementitious Materials

: The improvement in the chloride binding capacity of concrete has been shown to increase corrosion resistance. The addition of supplementary cementitious materials (SCMs) to Portland cement has been proven to increase the binding capacity, except for silica fume, whereas the impact of chemical additives is not extensively addressed in the literature. This work studies the inﬂuence of SCMs and chemical additives, i.e., calcium nitrite inhibitor (CNI), migrating corrosion inhibitor (MCI), and Caltite as a hydrophobic material, on binding capacity. The addition of both corrosion inhibitors (MCI and CNI) has minimal effect on the binding capacity, while the addition of Caltite reduces the binding capacity by limiting the contact of the samples with the salt in water due to its hydrophobic nature. In addition, the study compares the performance of the available ﬁtting–binding models against the available experimental work in the literature, and shows that the Freundlich isotherm is the best ﬁtting model for describing the relationship between the binding capacity and the free chloride. The study further relates the binding capacity to different compositions in cement and SCMs, and shows, by conducting quantitative analysis, that the Al 2 O 3 content is the dominant factor affecting the binding capacity. Finally, this work proposes a new model, which uses Al 2 O 3 content and free salt concentration to predict the bound chloride. The model shows adequate correlations to the experimental work and, further, can be used in service-life modeling of concrete. A.M.A.E.F.; acquisition, All


Introduction
Reinforced concrete is one of the major fabricated construction materials that is widely used throughout the globe. Chloride-induced corrosion is the leading cause of deterioration in reinforced concrete and has been the major focus of numerous studies in the literature. Some addressed the time needed for starting corrosion [1,2]. Others showed the influence of supplementary cementitious materials (SCMs) on chloride diffusion [3][4][5][6], whereas other studies focused on the impact of marine harsh environments on chloride diffusion [7][8][9][10]. Chloride migration through concrete has been explained by multiple literary sources [11][12][13] as the process in which chloride travels under a concentration differential from higher concentration at the surface of the concrete towards lower concentrations deeper below the surface. This diffusion process is assumed to follow Fick's laws of diffusion [14,15]. When the concentration of chloride ions reaches a certain threshold [11,16], the iron atoms in the reinforcement are activated and react with the chloride ions to form ferrous chloride, which then reacts with water to form ferrous hydroxide, thus regenerating the chloride ions, which become available and are able to react with more iron, leading to further corrosion.
Chloride exists in concrete in two main forms, namely, free chloride and bound chloride. Bound chlorides are chloride ions fixed on cement hydrates and do not move in trations. The paper further uses the results of binding, alongside the experimental data available in the literature, and quantitatively studies the influence of different parameters, mainly the oxide composition ratios, on the binding capacity and the performance of different available models from the literature. Finally, the study proposes a new model to predict the bound chloride, which can be used in the modeling of concrete service life.

Materials and Mix Proportioning
Three mixtures used silica fume (SF), slag cement (SC), and fly ash (FA) as SCMs. A standard mixture of OPC and a type V (high sulfate resistance) mixture was used as well. Table 1 provides the chemical composition and physical properties of the SCMs, OPC, and Type V cement. The remaining mixtures contained OPC and chemical admixtures only, which were a calcium nitrite-based admixture (CNI), migration corrosion inhibitor (MCI), and a hydrophilic chemical (Caltite). This study used a water-to-binder ratio (w/b) of 0.4, which represents the recommended ratio for durability, and a reduction in the w/b leads to a lower binding capacity [9]. Table 2 shows the replacement ratios for the SCMs and dosages of the chemical admixtures according to the manufacturers' recommendation. CNI was obtained from Sika, MCI from Cortec Corp, and Caltite from Cementaid. Slag cement replaced 70% of cement by weight in Mix 5, similar to the Mix of the Bahrain causeway. In Mix 4, fly ash was 25% of the total binder, similar to the optimal value reported in the literature to improve the pore structure [31,32]. Silica fume had a replacement percentage of 6% by weight of cement in Mix 3 because studies have reported best performance to be in the range of 6% to 8% [7,27]. CNI and MCI were added to the mixing water at doses of 20 L/m 3 and 0.6 L/m 3 of the paste's volume, respectively. Finally, Caltite was used as a hydrophobic agent in the last mixture at a dose of 30 L/m 3 . The doses were chosen according to the recommendation of the manufacturers.

Samples Preparation and Testing
ASTM does not provide a way to measure the exact binding capacity. It provides a measure of the total chloride (free and bound) or the water-soluble chloride, which contains parts of the free and bound chlorides [33]. One of the reliable methods found in the literature that measures the chloride binding capacity is a test developed by Nilsson and Luping in 1993 [26]. The method tests the binding capacity of pastes and mortars by submerging them in known concentrations of chloride solutions and estimates the binding capacity by calculating the difference between the known concentration and the measured concentration of the solution after exposure. Chloride is bound in two forms: irreversibly bound chloride that reacts with the (C-S-H) gel and reversibly bound chloride that can unbind when the concentration of chloride in the environment surrounding the concrete is decreased [26]. The test showed that with higher concentrations of chloride in the environment, the binding capacity became higher and irreversible with the increase in the chloride concentration [26]. The test can be summarized as follows: First, cylindrical paste samples were made (75 mm × 150 mm) using deionized water to eliminate any disturbance in the results caused by the chlorides in the mixing water. Next, samples were cured in lime water (saturated solution of Ca(OH) 2 ) for 14 days in de-aerated containers to avoid any carbonation that acidifies the paste and led to a reduced chloride-binding capacity according to Saillio et al. [34]. Then, the central portion of each sample was wet crushed and ground to pass through No. 100 size mesh. After that, samples were dried in a desiccator with a bed filled with activated silica gel desiccant, treated at 110 • C for one day to ensure it has no moisture content before use, for one week under vacuum pressure. After drying, samples were treated in a desiccator with a relative humidity content of 11% at room temperature for two weeks, using a saturated solution of Lithium Chloride (LiCl) in the bed of the desiccator (Figure 1a). Finally, they were submerged in a salt and lime solution at different NaCl concentrations.
In this study, free-chloride concentrations of 0.1, 0.3, 0.5, 1, and 4.2 Molar (mol/L) were used. A lower free chloride molarity equal to 3 Molar was used for mix SC, 70% slag, to limit the expected bound chloride due to the instrument limitation. The exposure lasted for 14 days to reach the maximum binding capacity [26]. Then, the samples were filtered and the final chloride concentration was measured using a potentiometric titration of the filtrate after dilution against a solution of silver nitrate with a known concentration and a silver/silver chloride reference electrode (Figure 1b). and the final chloride concentration was measured using a potentiometric titration of the filtrate after dilution against a solution of silver nitrate with a known concentration and a silver/silver chloride reference electrode (Figure 1b).

Results and Discussion
As shown in Figure 2, the binding capacity is proportional to the concentration of chloride in the solution. This was exceptionally highlighted in cases of SC and FA pastes, which both exhibited a large increase in the binding capacity with the increase in free chloride in the exposure environment. Their capacities at the highest concentrations increased by 100% and 50%, respectively, compared to Mix I (control mix). Furthermore, SC exhibited the highest binding capacity with increasing concentrations of NaCl in the environment compared to the other mixes. This increase coincides with the reported properties of SC having high alumina and ferric-alumina salts, which help in binding chloride with no reversible mechanism by forming Friedel's salts [10,26,28,35]. This was the case with FA as well, which showed a significant improvement in chloride binding but less than SC overall.
On the other hand, Type V cement, as expected, had a low binding capacity due to the lower amount of alumina in the cement compared to Type I cement, as was reported in the literature [27,35,36]. The reduction was consistent at all concentrations, which ranged from 30% to 50%, compared to the control mix. Moreover, the addition of silica fume was shown to negatively impact the binding chloride, which agrees with what was reported by Thomas et al. [9] in pastes with 0.5 w/c, and can be attributed to either the lower amount of alumina and ferric-alumina hydration products or the dilution of C3A that causes a release of chloride [37]. The addition of CNI and MCI followed the same trend as silica fume in having slight to no effect on the binding properties, especially at higher salt concentrations since their role of inhibiting corrosion has minimal effect on the hydration process and hydration results. Finally, Caltite was shown to reduce the binding significantly, especially at higher concentrations of chloride in the environment, which can be owed to the hydrophobic nature of concretes containing it, which limits the contact between hydration products and chloride, and thus reduces the chance of binding chloride in the first place.

Results and Discussion
As shown in Figure 2, the binding capacity is proportional to the concentration of chloride in the solution. This was exceptionally highlighted in cases of SC and FA pastes, which both exhibited a large increase in the binding capacity with the increase in free chloride in the exposure environment. Their capacities at the highest concentrations increased by 100% and 50%, respectively, compared to Mix I (control mix). Furthermore, SC exhibited the highest binding capacity with increasing concentrations of NaCl in the environment compared to the other mixes. This increase coincides with the reported properties of SC having high alumina and ferric-alumina salts, which help in binding chloride with no reversible mechanism by forming Friedel's salts [10,26,28,35]. This was the case with FA as well, which showed a significant improvement in chloride binding but less than SC overall. In terms of the relative performances of the mixes compared to each other, SC was shown to have the best binding results out of all the mixes at all environmental salt concentrations, followed by FA as illustrated in Figure 3. Type V, SF, and Caltite had the  On the other hand, Type V cement, as expected, had a low binding capacity due to the lower amount of alumina in the cement compared to Type I cement, as was reported in the literature [27,35,36]. The reduction was consistent at all concentrations, which ranged from 30% to 50%, compared to the control mix. Moreover, the addition of silica fume was shown to negatively impact the binding chloride, which agrees with what was reported by Thomas et al. [9] in pastes with 0.5 w/c, and can be attributed to either the lower amount of alumina and ferric-alumina hydration products or the dilution of C 3 A that causes a release of chloride [37]. The addition of CNI and MCI followed the same trend as silica fume in having slight to no effect on the binding properties, especially at higher salt concentrations since their role of inhibiting corrosion has minimal effect on the hydration process and hydration results. Finally, Caltite was shown to reduce the binding significantly, especially at higher concentrations of chloride in the environment, which can be owed to the hydrophobic nature of concretes containing it, which limits the contact between hydration products and chloride, and thus reduces the chance of binding chloride in the first place.
In terms of the relative performances of the mixes compared to each other, SC was shown to have the best binding results out of all the mixes at all environmental salt concentrations, followed by FA as illustrated in Figure 3. Type V, SF, and Caltite had the lowest binding capacity overall compared to the other mixes. CNI and MCI did not show a pronounced difference from Mix I, and finally, FA and SC showed the highest binding capacities. In terms of the relative performances of the mixes compared to each other, SC was shown to have the best binding results out of all the mixes at all environmental salt concentrations, followed by FA as illustrated in Figure 3. Type V, SF, and Caltite had the lowest binding capacity overall compared to the other mixes. CNI and MCI did not show a pronounced difference from Mix I, and finally, FA and SC showed the highest binding capacities.

Available Binding Models
There are several models in the literature that express the chloride-binding phenomenon as a relationship between the bound and free chloride. As previously mentioned, none of the available models in the literature predict the bound chloride; instead, they

Available Binding Models
There are several models in the literature that express the chloride-binding phenomenon as a relationship between the bound and free chloride. As previously mentioned, none of the available models in the literature predict the bound chloride; instead, they find a relationship between experimental values of bound and free chlorides through curve fitting constants α and β, whose values differ according to experimental results.
First, the Freundlich isotherm, which is an exponential model, is expressed as where C b and C f are the bound and free chloride, respectively, and α and β are binding constants. Second, the Langmuir model, which is a non-linear model, is expressed as where α and β are constants as explained by Luping and Nilsson [26]. Third, the Tuutti linear model [38] is expressed as where α is the slope of the binding curve. Fourth, the modified Tuutti model, was suggested by Arya et al. [36] and expressed as where α is the slope of the curve and β is the y-intercept. Figure 4 illustrates the performance of the four models against two experimental mixes, and Table 3 and Figure 5 show the statistical performance of the four models.
find a relationship between experimental values of bound and free chlorides through curve fitting constants α and β, whose values differ according to experimental results. First, the Freundlich isotherm, which is an exponential model, is expressed as where Cb and Cf are the bound and free chloride, respectively, and α and β are binding constants. Second, the Langmuir model, which is a non-linear model, is expressed as where α and β are constants as explained by Luping and Nilsson [26]. Third, the Tuutti linear model [38] is expressed as where α is the slope of the binding curve. Fourth, the modified Tuutti model, was suggested by Arya et al. [36] and expressed as where α is the slope of the curve and β is the y-intercept. Figure 4 illustrates the performance of the four models against two experimental mixes, and Table 3 and Figure 5 show the statistical performance of the four models.
Mix I Mix FA      The Freundlich isotherm showed the best fit to the experimental data, follo the Langmuir model. The worst fit for a model was found for the Tuutti's mode to the linearity of the model. At concentrations higher than 0.5 Molar, the Langmuir isotherm seems to c less and produces results that are further away from the actual data, while linear seem to fit the data the least. This conclusion agrees with what was reported by and Nilsson [26] regarding the Freundlich, Langmuir, and Tuutti models and d with Mohammed and Hamada [39], who reported a linear relationship between and bound chloride. A statistical linear-regression analysis also corroborated t ranking of the models' performance, with the Freundlich model yielding the h coefficient, implying the best performance.
Besides the experimental cases in this study, thirty-two data sets from the li detailed in Table 4, were used in regression analysis. The linear models are the consistent, especially for data that have a wider range of free chloride concentratio the Freundlich and Langmuir models are more consistent and produce less var shown in Table 5. The linear regression, Figure 6, supported the superior perform the Freundlich model, which had β-coefficient and standard error equal to 0. 0.0042, respectively. The β-coefficients are similar for the Langmuir and modifie models; however, the standard error of modified Tuutti is higher.   The Freundlich isotherm showed the best fit to the experimental data, followed by the Langmuir model. The worst fit for a model was found for the Tuutti's model, owing to the linearity of the model. At concentrations higher than 0.5 Molar, the Langmuir isotherm seems to correlate less and produces results that are further away from the actual data, while linear models seem to fit the data the least. This conclusion agrees with what was reported by Luping and Nilsson [26] regarding the Freundlich, Langmuir, and Tuutti models and disagrees with Mohammed and Hamada [39], who reported a linear relationship between the free and bound chloride. A statistical linear-regression analysis also corroborated the same ranking of the models' performance, with the Freundlich model yielding the highest β coefficient, implying the best performance.
Besides the experimental cases in this study, thirty-two data sets from the literature, detailed in Table 4, were used in regression analysis. The linear models are the most inconsistent, especially for data that have a wider range of free chloride concentration, while the Freundlich and Langmuir models are more consistent and produce less variance as shown in Table 5. The linear regression, Figure 6, supported the superior performance of the Freundlich model, which had β-coefficient and standard error equal to 0.996 and 0.0042, respectively. The β-coefficients are similar for the Langmuir and modified Tuutti models; however, the standard error of modified Tuutti is higher.
Overall, the Freundlich model has more stability, thus, representing the relationship between the bound and free chloride much better than the other models considered in this study.

New Model Development
According to the literature, the chemical-oxide compositions of cement, i.e., Al2O3, SiO2, and CaO content and their ratios are significant parameters in determining the binding capacity [8,19] because they impact the ratios of calcium, silicate, and alumina in the C-S-H and C-A-S-H. To quantitatively identify the influential trends of the content of the oxides on the binding capacity, the analysis performed here conducts different comparisons: first, a comparison between experimental cases and their control mixes where CaO/SiO2 is the main changing variable when silica fume is used, or where Al2O3 is the main variable when fly ash and slag cement are used. The second comparison is between the bound chloride and different oxide ratios for all of the data gathered in Table 4 and presented in this paper, without isolation of variables to define the most dominant affecting parameters. Figure 7 compares the bound chloride against CaO/SiO2 for cases in Ref [9,30,46,48], and Mix SF where silica fume is used. Here, the CaO/SiO2 ratio is the main varying parameter; for instance, Mix SF is compared to its control mix (Mix I) and so forth. Increasing the CaO/SiO2 ratio increases the bound chloride, and this is in agreement with the literature because even while increasing the silicate, increasing the ratio of CaO/SiO2 changes the nature and composition of C-S-H gel and causes it to bind less chloride [9,17,18,27]. Only one case reports the opposite, and it is noteworthy that this case is measured using a new untraditional technique [48]. Similarly, in Figure 8, which compares bound chloride to Al2O3 content for cases in [9,17,34,45,50] and Mixes SC and FA, where slag cement and fly ash are used, bound chloride values increase with increasing Al2O3 content. Only one case reports the opposite, in which the w/c ratio was as little as 0.3. It is worth mentioning that CaO/SiO2 ratio also decreased because of the SiO2 content of fly ash and slag cement; however, it did not affect the results. This might imply that the Al2O3 content, when present, is the main driving parameter in determining the binding capacity, and CaO/SiO2 becomes the dominant parameter when Al2O3 quantity does not noticeably change, i.e., when silica fume is used.

New Model Development
According to the literature, the chemical-oxide compositions of cement, i.e., Al 2 O 3 , SiO 2 , and CaO content and their ratios are significant parameters in determining the binding capacity [8,19] because they impact the ratios of calcium, silicate, and alumina in the C-S-H and C-A-S-H. To quantitatively identify the influential trends of the content of the oxides on the binding capacity, the analysis performed here conducts different comparisons: first, a comparison between experimental cases and their control mixes where CaO/SiO 2 is the main changing variable when silica fume is used, or where Al 2 O 3 is the main variable when fly ash and slag cement are used. The second comparison is between the bound chloride and different oxide ratios for all of the data gathered in Table 4 and presented in this paper, without isolation of variables to define the most dominant affecting parameters. Figure 7 compares the bound chloride against CaO/SiO 2 for cases in Ref [9,30,46,48], and Mix SF where silica fume is used. Here, the CaO/SiO 2 ratio is the main varying parameter; for instance, Mix SF is compared to its control mix (Mix I) and so forth. Increasing the CaO/SiO 2 ratio increases the bound chloride, and this is in agreement with the literature because even while increasing the silicate, increasing the ratio of CaO/SiO 2 changes the nature and composition of C-S-H gel and causes it to bind less chloride [9,17,18,27]. Only one case reports the opposite, and it is noteworthy that this case is measured using a new untraditional technique [48]. Similarly, in Figure 8, which compares bound chloride to Al 2 O 3 content for cases in [9,17,34,45,50] and Mixes SC and FA, where slag cement and fly ash are used, bound chloride values increase with increasing Al 2 O 3 content. Only one case reports the opposite, in which the w/c ratio was as little as 0.3. It is worth mentioning that CaO/SiO 2 ratio also decreased because of the SiO 2 content of fly ash and slag cement; however, it did not affect the results. This might imply that the Al 2 O 3 content, when present, is the main driving parameter in determining the binding capacity, and CaO/SiO 2 becomes the dominant parameter when Al 2 O 3 quantity does not noticeably change, i.e., when silica fume is used.
To confirm the aforementioned statement, Figures 9-14 compare the bound chloride to different oxide contents and ratios, such as SiO 2 , Al 2 O 3 , CaO, CaO/SiO 2 , CaO/Al 2 O 3, and Al 2 O 3 /SiO 2 , for different chloride concentration ranges, regardless of other elements' percentages. For illustration, Figure 9 plots the bound chloride versus CaO content of all experimental cases in Table 4 and this study, regardless of the variation of other oxides to capture the dominance of the CaO. The Al 2 O 3 content ratio, as opposed to other oxides, shows a persistent proportional tendency with bound chloride to reflect a strong correlation to binding capacity as the regression analysis shows in Figure 11, complying with [27]. Figures 13 and 14 confirm that even if the ratio of Al 2 O 3 changes against CaO or SiO 2 , the trend is still the same as presented in Figure 11. Thomas et al. [9] shows that mixes with metakaolin, which contain almost equal amounts of Al 2 O 3 and SiO 2 , have higher binding capacity compared to the control mix, which implies that Al 2 O 3 is a dominant factor in determining the binding capacity. This observation cannot be confirmed for the other two oxides that show an inverse relation to CaO at small chloride concentration and direct relationship at higher concentration, i.e., 1.25 Molar or above, as shown in Figure 9. The opposite relationships are observed for SiO 2 in Figure 10. Figure 12 speculates that the CaO/SiO 2 effect becomes pronounced at high chloride concentrations , which agrees with Figure 2 in that the binding curve for Mix SF deviates away from the control mix at a concentration of 1 Molar. Results from the literature are similar to this observation, which might be attributed to the role of Al 2 O 3 in binding until it is completely exhausted.  To confirm the aforementioned statement, Figures 9-14 compare the bound chloride to different oxide contents and ratios, such as SiO2, Al2O3, CaO, CaO/SiO2, CaO/Al2O3, and Al2O3/SiO2, for different chloride concentration ranges, regardless of other elements' percentages. For illustration, Figure 9 plots the bound chloride versus CaO content of all ex- Bound Cl (mg cl/g sample)

Al 2 O 3 Content
Ref [17] Mix I-FA-SC Ref [34] Ref [50] Ref [9] Ref [45]   To confirm the aforementioned statement, Figures 9-14 compare the bound chloride to different oxide contents and ratios, such as SiO2, Al2O3, CaO, CaO/SiO2, CaO/Al2O3, and Al2O3/SiO2, for different chloride concentration ranges, regardless of other elements' percentages. For illustration, Figure 9 plots the bound chloride versus CaO content of all ex- Bound Cl (mg cl/g sample) Ref [17] Mix I-FA-SC Ref [34] Ref [50] Ref [9] Ref [45]  opposite relationships are observed for SiO2 in Figure 10. Figure 12 speculates that the CaO/SiO2 effect becomes pronounced at high chloride concentrations, which agrees with Figure 2 in that the binding curve for Mix SF deviates away from the control mix at a concentration of 1 Molar. Results from the literature are similar to this observation, which might be attributed to the role of Al2O3 in binding until it is completely exhausted.    opposite relationships are observed for SiO2 in Figure 10. Figure 12 speculates that the CaO/SiO2 effect becomes pronounced at high chloride concentrations, which agrees with Figure 2 in that the binding curve for Mix SF deviates away from the control mix at a concentration of 1 Molar. Results from the literature are similar to this observation, which might be attributed to the role of Al2O3 in binding until it is completely exhausted.              For further confirmation, the data in Table 4 are regrouped according to CaO/SiO2 ratios and Al2O3 content, and bound chloride is plotted against the free chloride in Figures  15 and 16, respectively. For further confirmation, the data in Table 4 are regrouped according to CaO/SiO 2 ratios and Al 2 O 3 content, and bound chloride is plotted against the free chloride in Figures 15  and 16, respectively. For further confirmation, the data in Table 4 are regrouped according to CaO/SiO2 ratios and Al2O3 content, and bound chloride is plotted against the free chloride in Figures  15 and 16, respectively.   It is clear from Figure 16 that increasing the Al2O3 content increases the binding capacity, even when other contents in the mixes change, which confirms that the Al2O3 content is the main driving parameter. On the contrary, Figure 15 did not evidence the same trend for the CaO/SiO2 ratio, which might imply that it is a dominant factor only when it is the main varying parameter, such as in cases with silica fume. Based on Figure 16 and by conducting a regression analysis, α and β relate to Al2O3 content ratio (AC) by the following equations:  It is clear from Figure 16 that increasing the Al 2 O 3 content increases the binding capacity, even when other contents in the mixes change, which confirms that the Al 2 O 3 content is the main driving parameter. On the contrary, Figure 15 did not evidence the same trend for the CaO/SiO 2 ratio, which might imply that it is a dominant factor only when it is the main varying parameter, such as in cases with silica fume. Based on Figure 16 and by conducting a regression analysis, α and β relate to Al 2 O 3 content ratio (AC) by the following equations: α = 1.3 AC + 3.44. (5) β = 0.0077 AC + 0.30 (6) This empirical model solves for α and β in Equations (5) and (6) using AC as an input, and then solves for the bound chloride using the Freundlich relation in Equation (1) using the free chloride value, α, and β. Figure 17 shows a β coefficient of 0.99 between theoretical values found using the proposed model and experimental values of bound chloride. The correlation between the model and experimental results is 0.834. The T-value is 0.35, which is smaller than the t-critical of 1.964 at a 95% confidence level, and the mean absolute error is 0.15. Figure 18 demonstrates the validity of the proposed model by plotting one case from each Al 2 O 3 content group shown in Figure 16. The proposed model has a satisfactory correlation to the experimental data given the wide range of experimental results. Thus, the model can be used in the service life of modeling concrete.

Conclusions
In this study, the binding capacity of pastes containing different mineral and chemical admixtures is evaluated using the process developed by Luping and Nilsson at differ- Bound Chloride (mg Cl/g paste) Free Chloride (M=mol/L) Mix SF Ref [48] Ref [34] Ref [27] Ref [26]

Conclusions
In this study, the binding capacity of pastes containing different mineral and chemical admixtures is evaluated using the process developed by Luping and Nilsson at different free-chloride concentrations ranging from 0.1 Molar to 4.2 Molar. In addition, the relationship between binding capacity and free chloride concentration is investigated using four models from the literature. Furthermore, the investigation of the relationship is ex- Bound Chloride (mg Cl/g paste) Free Chloride (M=mol/L) Mix SF Ref [48] Ref [34] Ref [27] Ref [26]