Behavior of Homoionic Clays prior to pH Adjustment

Before any pH adjustment, the dispersed clay percentages of the homoionic clays were in the order Na > K > Mg > Ca (Fig. 2), regardless of dominant mineralogy. Mg clays had 7.8–25.5% more dispersed clay than Ca clays, and K clays were 17.5–20.3% less dispersed than Na clays, depending on clay mineralogy. The results (Table 2) showed that the ζ-potential and mean particle size (d _g) of homoionic clay were in the order Na < K < Mg < Ca at pH control.

Fig. 2.

Comparison between the percentages of dispersed clay of the treated clays prior to pH manipulation. Bars indicate the standard error with all comparisons among the Na, K, Mg, and Ca clays within S1, S2, and S3

Table 2.

Original pH, ζ-potential, dispersed clay percentage, and particle size of clays before altering pH

The Effect of Mg and K on Clay Dispersion Behavior at Various pH Levels

The changes in behavior of the homoionic clays noted with change of pH are discussed in terms of the dominant mineralogy in the following section. General trends in terms of the dispersive behavior of homoionic clays can be observed from Figs 3, 4, and 5:

1. (1) The amount of dispersed clay (%) was in the order: Ca < Mg < K < Na;

2. (2) The ζ-potential of homoionic clays was in the order: Ca > Mg > K > Na; and

3. (3) The d _g of homoionic clays was in the order: Ca > Mg > K > Na.

Fig. 3.

a The dispersed clay in log scale, b ζ-potential, and c mean particle size of Na, K, Mg, and Ca homoionic kaolinite-dominant soil clay (S1) at varying pH. The bars represent the standard error and the lowercase letters represent significant differences between Na, K, Mg, and Ca clays at the same pH

Fig. 4.

a dispersed clay (log scale), b ζ-potential, and c mean particle size of Na, K, Mg, and Ca homoionic montmorillonite-dominant soil clay (S2) at various pH values. Partial flocculation was observed in Na montmorillonite clays at pH 3 and pH 4 without dispersed clay (%) data. The bars represent the standard error and the lowercase letters represent significant differences among Na, K, Mg, and, Ca clays at the same pH

Fig. 5.

a dispersed clay (log scale), b ζ-potential, and c mean particle size of Na, K, Mg, and Ca homoionic illite-dominant soil clay (S3) at various pH values. The bars represent the standard error and the lowercase letters represent significant differences among the Na, K, Mg, and Ca clays at the same pH.

These sequences occurred at the equivalent pH observation, irrespective of clay type, and showed equivalent trends to those samples without pH adjustment. The visual changes in dispersive behavior of Na, K, Mg, and Ca soil clays at pH 3 to pH 11 are presented in Appendix A as useful supporting information for confirmation of flocculation at a given pH.

The results showed that the behavior of Mg clays was in close agreement with that of Ca clay. For the same clay mineralogy, Mg and Ca clays had the same critical pH value that distinguished flocculation and dispersion at low pH: Mg- and Ca-S1 (kaolinite dominant) clays flocculated between pH 3 and 6 (Fig. 3); Mg- and Ca-S2 (montmorillonite dominant) clays flocculated at pH 3–6 (Fig. 4); and for Mg- and Ca-S3 (illite dominant) clay, flocculation took place at pH 3 to 4 (Fig. 5). In the present study, the clay suspension system was considered as flocculated when the percentage of dispersed clay was <5 (Zhu et al. Reference Zhu, Bennett and Marchuk2019). At neutral pH, Mg clays had slightly more dispersed clay (%) than Ca clays but the difference was insignificant under similar conditions. The greatest pH values for magnesic clay reported in this study were pH 7, 8, and 7 for S1, S2, and S3, respectively, due to the low solubility of Mg(OH)₂. At high pH, Ca clays tended to flocculate regardless of clay types with increasing ζ-potential and d _g. The flocculation tendency of Ca clays at high pH is related to the increased electrolyte concentration (Table 3) in suspension from the addition of alkali solution and/or mineral dissolution. Furthermore, the flocculation could be related to the charge reduction due to cementation or coating by the formation of CaCO₃ in Ca-clay suspensions with exposure to atmospheric CO₂ (Chorom and Rengasamy Reference Chorom and Rengasamy1995). When Mg cations were initially present in soil/soil water, an increase in pH upon alkalization could result in the precipitation of Mg(OH)₂, beginning at approximately pH 9.5, becoming significant above 10.5, and essentially completed at pH = 11–11.5 (Semerjian and Ayoub Reference Semerjian and Ayoub2003). The resulting Mg(OH)₂ was a gelatinous precipitate with a large adsorptive surface area and a positive superficial charge, which was found to serve as an efficient coagulant and flocculation aid (Semerjian and Ayoub Reference Semerjian and Ayoub2003). Therefore, under high pH conditions, one assumes that Mg should behave in a similar fashion to Ca, and tend to flocculate clay suspensions.

Table 3.

The measured EC (dS/m) of soil clays at the corresponding pH

Results from the present study further showed that Mg clays were in close agreement with Ca clay behavior, albeit with a net negative charge consistently more negative than Ca (greater electrophoretic mobility). The electrophoretic mobility of dispersed clay, which depends on ζ-potential, indicated the changes in net charge. The Ca system was conducive to flocculation conditions with ζ-potential >–30 mV (Molina Reference Molina2014). Results from the present study demonstrated that, while Mg-clay < Ca-clay in terms of ζ-potential, conditions for an unstable clay dispersion (prone to flocculation) were always obtained with ζ-potential of <–30 mV in the Mg system. This clearly suggests that the role of Mg is similar to the role of Ca, irrespective of clay type, and that Mg has a flocculative effect rather than a dispersive effect.

K-S1 and K-S3 clays flocculated completely within the same pH ranges as Na-saturated clay. K-S2 clay flocculated at pH ≤ 4, while partial flocculation was observed in the Na system over the same pH range. The ζ-potential of K clays was similar to but slightly greater than Na clays at each pH reading, regardless of clay mineralogy, which was consistent with less dispersion in K clays. Where the Na-clay system had ζ-potential <–30 mV (stable clay dispersion), the K-clay also had ζ-potential <–30 mV, indicating that the K clay behavior was largely in line with Na clay. Additionally, the d _g of K clays had a similar but slightly larger mean particle size than Na clays under equivalent conditions. The general results confirmed that K had similar dynamics with pH to those of Na, but induced less dispersion, which is consistent with the results of Marchuk and Rengasamy (Reference Marchuk and Rengasamy2011).

Monovalent clays behave differently from divalent clays. At pH 3, each homoinic S1 and S3 clay reached full flocculation. The volume of Na floccules was clearly larger than that of Ca, however. Na-S2 clay flocculated partially, while Ca-S2 reached full flocculation at pH 3. This observation is in line with findings from Greene et al. (Reference Greene, Posner and Quirk1973) that only Ca montmorillonite could form a quasi-crystal with a fixed distance of 10 Å between the particles, while Na montmorillonite floccules cannot condense into a primary minimum, resulting in a large volume of floccules. As noted at high pH, Ca clays tended to flocculate, while monovalent clays maintained a high degree of dispersion, e.g. K-S3 52.1%, 49.6%, and 45.7% at pH 9, 10, and 11. The monovalent clays were highly dispersive because of the strong negative charge, as the addition of OH^– in solution led to more OH^– ions being adsorbed on clay surfaces (Chorom and Rengasamy Reference Chorom and Rengasamy1995; Ibanez et al. Reference Ibanez, Wijdeveld and Chassagne2014; Parameswaran and Sivapullaiah Reference Parameswaran and Sivapullaiah2017). In support of this, a comparable ζ-potential was found in monovalent clays at neutral pH, in spite of the increased EC due to the addition of electrolyte at high pH (Table 3). Ibanez et al. (Reference Ibanez, Wijdeveld and Chassagne2014) found that when K cations were adsorbed on a kaolinite surface, its surface charge would be insensitive to the addition of extra K ions in suspension, but the addition of extra K ions helped to reduce the size of the diffuse double layer around the particles. Moreover, when pH ≥ 9, the d _g of monovalent saturated soil clays decreased. As pH increased, the bonding between organic matter and clay particles decayed, resulting in decreased d _g (Marchuk et al. Reference Marchuk, Rengasamy and McNeill2013a). In summary, the effects of Mg on clay dispersion will be similar but have less flocculative effect than those of Ca, while the effects of K will be close to those of Na, but inducing less dispersion, under similar pH conditions.

Application of the Ionicity Concept in Explaining Specific Clay Dispersive Behavior

In dilute electrolytes, the separation distance of clay particles often follows DLVO theoretical prediction (Hartley et al. Reference Hartley, Larson and Scales1997; Quirk and Marcelja Reference Quirk and Marcelja1997). The DLVO theory has its limitations, however, whereby it predicts that the double layer interactions depend on the valence of the electrolyte ions, regardless of their differences in size and ability to be polarized, etc. (Pashley and Quirk Reference Pashley and Quirk1984; Boström et al. Reference Boström, Williams and Ninham2001; Karraker and Radke Reference Karraker and Radke2002; Manciu and Ruckenstein Reference Manciu and Ruckenstein2003; Liu et al. Reference Liu, Li, Li, Xie, Ni and Wu2014; Hu et al. Reference Hu, Liu, Xu, Wang, Liu, Li and Zhao2018b). The different behavior of Mg and Ca clays, and of K and Na clays, apparently cannot be fully explained by DLVO theory. Strong correlations (0.86 < R ² < 0.99) were observed between the ionicity indices of the four cations and the percentage of dispersed clay at the control (without pH adjustment), pH 6, pH 7, and pH 8 (Table 4). Hence, the difference between Mg and Ca clays, and between K and Na clays can be explained by the differences in their degrees of ionicity of clay–cation bonds. The results further confirmed that clay–cation bonds are a mixture of covalent and ionic bonds, where the net ionicity of a clay–cation bond determined how water reacted with clays and the degree of hydration (Marchuk and Rengasamy Reference Marchuk and Rengasamy2011; Rengasamy et al. Reference Rengasamy, Tavakkoli and McDonald2016). Though this work was conducted in clay suspensions (i.e. not directly equivalent to an aggregated soil system), the mechanism of increasing covalency of clay–cation bonds will lead to enhanced potential for aggregation of clay particles, and/or retention of aggregation under some adverse conditions. This was reflected by the d _g of homoionic clays at each pH value being in the sequence of Na < K < Mg < Ca. Therefore, the ionicity approach demonstrates that soil stability under dynamic conditions will be useful in informing predictive models.

Table 4.

Relationship between the ionicity index (I) and zeta potential (ζ) as well as the dispersed clay percentage (D) of homoionic clays at pH control (without pH adjustment), pH 6, pH 7, and pH 8

Even though the ionicity index had a significant correlation with clay dispersive behavior, the gradient of the relationship between ionicity and clay dispersion parameters varied substantially (Table 4). The variability in coefficients suggested potential for other factors, or a specific effect. Firstly, while the clay suspensions were considered homoionic, removing all other ions from the system was impossible. This, and the fact that homoionic systems would not be expected in a natural soil environment, highlights the merit of developing an ionicity index approximation for mixed-ion systems. Secondly, the deviation in gradient may be partly related to the existence of iron oxide and organic matter within the colloidal system (Goldberg and Glaubig Reference Goldberg and Glaubig1987; Goldberg et al. Reference Goldberg, Kapoor and Rhoades1990; Marchuk et al. Reference Marchuk, Rengasamy and McNeill2013a), and the fact that clay structure varies in terms of mineralogy (Brigatti et al. Reference Brigatti, Galan and Theng2006), with natural soils not having a consistent mineralogy, but rather a possible dominant mineral and mixed mineral suite. As the mineral suite changes, the charge density and its distribution will also vary, even within the same dominant mineralogy (Lagaly Reference Lagaly1981; Saka and Güler Reference Saka and Güler2006). The suggestion is, therefore, that organic matter, sesquioxides, and clay source are also important factors explaining specific clay dispersive behavior. On such a basis, two key investigations are required: (1) the effect of variations within clays to establish the application of the ionicity concept; and (2) an ionicity index approximation for a mixed ionic system in order to determine the specific effect. Despite these variations, results from the current study confirm the first hypothesis that at equivalent pH the dispersive behavior of Na, K, Mg, and Ca homoionic clays is correlated with the ionicity indices of clay–cation bonds.

Predicting Dispersive Behavior of Clay Suspensions

The role of ionicity of clay–cation bonds in explaining dispersed clay (%) and ζ-potential for homoionic clay suspensions was investigated with the results presented in Table 4. No significant correlation was found for the data when treated as a single data set, or by soil, meaning that both the mineralogy and the pH affected the relationship between dispersed clay and the ionicity index. Dispersed clay was highly correlated with ionicity index at a given pH for a given clay, explaining >86% of variation, and was highly significant (p < 0.0001) in all instances. Strong relationships (R ² >0.94) were also found between ζ-potential and ionicity index for a given pH and clay mineralogy.

In order to explore generalized relationships for dispersed clay (%) against soil characteristics, step-wise regression was undertaken using the pH (explanatory variable) and EC (varied by necessity of treatment), as well as the ionicity index, given the results in Table 4, resulting in Eq. 1. In testing the hypothesis that dispersed clay is related to the ionicity index, the d _g was added as a factor, with the net negative charge (ζ-potential) also added, resulting in Eq. 2.

Dispersed clay (%), D, was explained by the pH, EC (C), and the ionicity index (I) as follows:

(1) $D=172I+3.27\mathrm{pH}-25.7C-121.3$

while net negative charge (ζ), mean particle size (d _g), and EC were shown to explain dispersed clay (%) as:

(2) $D=-0.777\zeta -0.0138d_g+38I-7.2C-12.122$

The statistics for Eqs 1 and 2 are given in Table 5. Both equations explain the majority of variation in dispersed clay as a function of their factors. However, Eq. 2 is clearly superior to Eq. 1. That said, the efficacy of Eq. 2 can only be considered as reasonable. While neither model is over parameterized (R ² ≈ R ²_ADJ), with both models having reasonable predictive capacity (R ²_PRED) and precision (C _p ≈ the number of model parameters), substantial deviation of predicted results from observed results occurred (Table 5). Equation 2 resulted in 92% of data being within ±15% dispersed clay, as compared to the true value, and 42% of data varying by less than ±5% dispersed clay; for Eq. 1, these values were 75% and 29% of data being within the ±15% and ±5%, respectively (Table 5). This indicates that neither equation provides exceptional predictive capacity, but that Eq. 2 is reasonable. Both equations also provided low confidence in predicting suspension conditions where the dispersed clay percentage was <12%. Below this concentration, the suspension system was unstable (i.e. clay flocs formed rapidly), indicating that the system was actually better explained by a two-phase model (flocculated and unstable suspension; dispersed and stable suspension), which should not be considered surprising. Separating the data into these phases did not improve predictive capacity, but <12% dispersed clay was clearly delineated as the boundary condition in terms of all regression models explored, with the caveat that this applies only for the data observed in this work. One is cautioned not to use these equations for aggregated soil systems, as these results are based on clay suspensions; the intention of the results is to facilitate mechanistic investigation.

Table 5.

Statistical characteristics pertaining to Eqs. 1 and 2 deviation from validation data

Dev. Refers to deviation of the predicted results from the true result observed; R ² explains the variation described by the model; R ²_ADJ is the adjusted R ²; R ²_PRED is the predicted R ²; C _p is Mallow’s measure of precision; N _Dev is the number of observation pertaining to the deviation data; and N is the total number of observations used in the construction and subsequent statistical assessment of model efficacy.

The present study demonstrates that the degree of clay dispersion is explained by the pH, EC, ionicity, ζ-potential, and mean particle size of the clay–cation system. Chorom and Rengasamy (Reference Chorom and Rengasamy1995) demonstrated that solution pH and EC had distinct effects on the level of stability within a clay suspension, while Marchuk and Rengasamy (Reference Marchuk and Rengasamy2011) showed that clay suspension dispersion was related to ionicity. Hence, the existence of a generalized relationship would be expected between pH, EC, and ionicity in explaining clay suspension dispersion, as found in Eq. 1. The subsequent incorporation of net negative charge and particle size resulted in pH being dropped from the model and substantial improvement in the predictability of new results. The removal of pH with the introduction of net negative charge is explained by the fact that net negative charge varies as a result of change in pH, as described by Chorom and Rengasamy (Reference Chorom and Rengasamy1995), meaning that net negative charge inclusion makes the pH term redundant. Interestingly, note that EC is not dropped, despite the relationship to net negative charge; when the EC increased, the double layer was compressed, resulting in a smaller net negative charge (Chorom and Rengasamy Reference Chorom and Rengasamy1995; Brown et al. Reference Brown, Goel and Abbas2016). This is probably because the more dominant mechanism of EC effect is via osmotic potential, as described by Quirk (Reference Quirk2001). Hence, the osmotic effect of EC on clay particle dispersion appears to be more important than the effect of EC on net negative charge.

Mineralogical Observations

For dispersed clay suspensions, the ζ-potential at each pH reading changed in response to clay mineralogy and was integral in explaining the actual dispersive extent. Distinct differences between kaolinite (S1) and montmorillonite (S2) clays were observed at low pH, due to different clay structures and charge changing when interacting with acid solutions. The alumina octahedral sheet of kaolinite was positively charged at pH < 6 (Gupta and Miller Reference Gupta and Miller2010), while the silica tetrahedral sheet was always negatively charged; its magnitude was pH-dependent, however, possibly due to broken bonds on the basal planes and/or hydrolysis of siloxane bonds (Zhou and Gunter Reference Zhou and Gunter1992). The surface charge of the kaolinite crystal edge was positive at pH < 4.5. Unlike kaolinite, montmorillonite possesses a large permanent negative charge and a small amount of variable charge (Tombácz and Szekeres Reference Tombácz and Szekeres2004; Weil and Brady Reference Weil and Brady2016). Thus, at low pH, the positively charged surface of the aluminum sheet of kaolinite and its highly exposed edge surface area with positive charge yielded less net negative charge than that of montmorillonite. This was supported in this work whereby the ζ-potential of each homoionic montmorillonite was more negative than kaolinite at pH ≤ 5.

Before any pH adjustment, each homoionic S1 clay had the largest d _g as compared to S2 and S3 for the same dominant cation (Table 4). This is related to the existence of amorphous material in S1 as indicated by its XRD pattern. Clay particles were attached firmly and consequently had a large mean particle size (Tisdall and Oades Reference Tisdall and Oades1982).

Homoionic illite (S3) clays were more dispersive than kaolinite (S1) and montmorillonite (S2) clays, with each treated illite clay having a greater percentage of dispersed clay than the corresponding montmorillonite and kaolinite clays at equivalent treatment. In addition, the treated illite clay showed notable dispersion over a wider pH range than those of montmorillonite and kaolinite. For example, Ca-illite had notable dispersion from pH 5 to pH 10, whereas Ca-kaolinite and Ca-montmorillonite showed visible dispersion only within the range of pH 7 to pH 9. The results in this study are in line with reports that illitic red-brown earths are susceptible to dispersion even at SAR < 3 and under weak mechanical forces (Rengasamy Reference Rengasamy1983; Rengasamy et al. Reference Rengasamy, Greene, Ford and Mehanni1984). Oster et al. (Reference Oster, Shainberg and Wood1980) and Greene et al. (Reference Greene, Posner, Quirk, Emerson, Bond and Dexter1978) suggested that the high dispersivity of illite was due to "bad" contact between the terraced planar surfaces of illite and its edges. Results from the present study also showed that each homoionic illite clay had lower comparative ζ-potential at equivalent conditions than kaolinite and montmorillonite. These results lend further weight to the requirement of incorporating a clay suite within predictive models, which will require some quantitative measure of mineralogy, or a probabilistic modeling approach.

Dispersed Suspension vs Aggregated System

Both the degree of ionicity of clay–cation bonds and the net negative charge have clear effects on the dynamics of clay particle dispersion. Note, however, that in the present study, the dynamics of flocculation conditions of clay suspensions were investigated under various solutions. Clearly, in soil structural science, clay flocculation is not equivalent to soil aggregation (Bradfield Reference Bradfield1936; Quirk Reference Quirk2001; Dang et al. Reference Dang, Bennett, Marchuk, Biggs and Raine2018). Clay suspensions were used in the current investigation to infer relative differences in fundamental particle dynamics as driven by solute conditions. These mechanisms will be valid within aggregated systems, but the predictive model derived in this work is not suitable for application in an aggregated system. Still, using this work in such an approach will be important in the prediction of soil-specific dispersive thresholds.