Effect of montmorillonite content in natural Saudi Arabian clay on its adsorptive performance for single aqueous uptake of Cu(II) and Ni(II)

2.1 Preparation and characterization of clay and bentonite

Natural Saudi Arabian clay was obtained from Al-Hassa city near Jabal Al Qarah caves of Hofuf. This area is known for its rich in mineral deposits (Al-Safarjalani, 2004; Hussain et al., 2006). Meanwhile the bentonite was supplied by Riyadh Geotechnical and Foundations Company. Prior to using the two adsorbents for the adsorption experiments, the minerals were powdered and sieved through mesh to obtain particles size less than 200 mesh (i.e. 63 µm) and mildly washed with 0.1 N HNO_3, then followed by deionized water to remove any attached impurity. Afterwards, samples surface state and structure of the sample were characterized using a SM-6610LV JEOL with an energy dispersive X-ray (EDX) detector (Oxford INCA Penta FETx3) attached to enable determination of the samples elemental compositions. In order to minimize charging effect, the samples were gold (thin layer) for the SEM analyses. An accelerating voltage of 5 kV was used at a short working distance of 2 mm undertaken under magnifications ranging from x500 to x40,000. The accelerating voltage of the SEM was maintained at 20 kV during microchemical analysis. For determining the phase constitution of the samples, a Rigaku Ultima IV MPD X-ray diffractometer (XRD) fitted with a monochromator was employed. The samples diffraction spectra peaks were indexed using Rigaku PDXL software which were generated under Cu Kα radiation (λ = 1.54184 Å) source operating at 40 mA and 40 kV. The scanning was undertaken for 2theta diffraction angles (2θ) between 5 to 80 degrees at a rate of 1 degree per minute. The area and average diameter were measured using the Brunauer-Emmet-Teller (BET) method that is depended on N₂ gas adsorption technique using ASAP 2020, Micromeritics. The cation exchange capacity (CEC) was determined by EPA 9081 method (USEPA, 1986).

2.2 Chemical reagents

All chemicals used were of analytical grade from which standard solution (1000 mg/L; pH ≤ 2) of Cu(II) and Ni(II) were prepared separately from Cu (NO₃)·3H₂O and Ni (NO₃)·6H₂O respectively, using deionized water prepared from a Millipore Ultrapure instrument. The different pH of stock solutions were adjusted to target values by using 0.1 N HNO₃ or 0.1 N NaOH.

2.3 Batch adsorption experiments

Batch adsorption experiments in 125 mL glass bottles containing a constant volume of 50 mL were conducted on shaker under constant stirring at 200 rpm at room temperature 22 ± 3 °C. The experiments were conducted in four different sets to investigate the effect of contact time (5, 10, 15, 30, 45, 60, 120, 240, 360, and 480 min), bentonite to clay mass ratio, B/C, (which represents the montmorillonite content) of 0, 10, 30, 50, 100%, adsorbate concentration (20, 40, 60, 80, and 100 mg/L), total mass of adsorbent (0.2. 04, 0.6, 0.8, and 1 g), and the solution pH (3.5, 5, 6.5 and 8). The removal efficiency (R), adsorption capacity at any time t (q_t) and equilibrium (or maximum) adsorption capacity (q_e) all in mg/g were calculated using Eqs. (1), (2) and (3), respectively (Lukman et al., 2013).

2.4 Kinetics study

Based on the batch experiments, the kinetics study was undertaken at constant conditions of pH 5, amount of adsorbent 0.2 g, and initial concentration 100 mg/L considering different equilibrium time of 240 min and 360 min for Cu(II) and Ni(II), respectively. Pseudo first order and pseudo second order kinetic models were employed for estimating the kinetic coefficients using Eq. (4) (Plazinski et al., 2009; Sen Gupta and Bhattacharyya, 2011) and Eq. (5), respectively.

2.5 Isotherms study

The adsorption isotherms for copper and nickel were studied at their respective equilibrium time, pH 5, amount of adsorbent 0.2 g and at different initial concentrations (0–100 mg/L). The Langmuir and Freundlich isotherms given in Eqs. (6) and (7) (Malamis and Katsou, 2013; Zubair et al., 2017), respectively, were used for calculating different equilibrium isotherms parameters.

2.6 Heavy metals analysis

Immediately, after any of the above adsorption experiments, all the samples were filtered through a 0.45 μm membrane filter using a syringe filter. The filtrates were quantitatively analyzed for concentration of Cu(II) and Ni(II) using well calibrated inductively coupled plasma mass spectrometry instrument (Agilent 710 ICP-OES).

3.1 Characterization of clay and bentonite

Table 1 and Figs. 1–2 show some characterization results obtained from numerous analyses techniques as discussed earlier. The XRD pattern in Fig. 1 shows that the clay was mainly composed of kaolinite (47.27%), muscovite (23.53%), 7.63% quartz and11.57% calcite. Muscovite is one of clay mineral which belongs to mica group while quartz can be present in clay materials in lower proportion (Rowse and Jepson, 1972; Sayin and Jackson, 1979). Meanwhile the bentonite was dominantly composed of montmorillonite mineral (87.32%). Similarly, the microgram scan and surface structure of clay and bentonite captured by the SEM analysis shown in Fig. 2A shows marked differences in surface structure and morphology between clay and bentonite. Apparently, the chemical compositions breakdown of the two studied minerals obtained from EDX analysis provided in Fig. 2B shows the main dominant elements in the clay and bentonite were oxygen, silica, aluminum and iron. Thus, the clay SEM picture (Fig. 2A) suggests an entirely amorphous material with high presence silicon and oxygen as supported by the EDX chemical composition (Musić et al., 2011; Ogunmodede et al., 2015). Also, the bentonite possessed a discrete and rough pattern indicating the presence of higher iron oxide (Fig. 2B) (Oliveira et al., 2003; Ogunmodede et al., 2015). Interestingly, the clay sample elemental composition indicated presence of gold (Au) which agrees with earlier documented study (Al-Safarjalani, 2004). This indicated a slightly higher Si/Al (2.9%) for the bentonite compared to that of the clay (2.3%). As shown in Table 1, both the bentonite BET surface area (44.278 m2/g) and CEC (101.3 meq/100 g) were high, about 2 two folds and 5 folds than that of the clay, respectively. These values of the CEC are within the typical values reported in literature (Rollins and Pool, 1968, 1999; Dohrmann et al., 2012). Meanwhile, the mean particles sizes in clay were almost twice that of the bentonite. These characteristics indicate that both the two minerals have sorption potentials for heavy metals removal from water (Bhattacharyya and Gupta, 2008b,a).

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Fig. 1

XRD Results showing mineralogical compositions for (A) Clay and (B) Bentonite [Mu'azu, 2018].

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Fig. 2

Clay and bentonite (A) high resolution SEM showing microgram structure and (B) EDX elemental compositions analysis [Mu'azu, 2018].

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3.2 Effect of operating conditions on adsorption of copper and nickel

3.2.1

3.2.1 Effect of contact time

For the different bentonite-clay blends, Fig. 3A and Table 2 show the effect of contact time on adsorptive capacity and removal efficiency for adsorption of Cu(II) and Ni(II) at fixed 100 mg/L initial concentration and solution pH 5. Both the trends for Cu(II) and Ni(II) depicted in Fig. 4A indicate that as the montmorillonite content was increased, the time for attaining equilibrium decreased. Respectively, for 100% clay, the equilibrium time was established at 240 min and 360 min which reduced (rapidly for Cu) to 30 min and 45 min when the adsorbent used was 100% bentonite. As given in Table 2, both the Cu(II) removal efficiency (64 to 34.85%) as well as adsorption capacity (16.38 to 8.7 mg/g) was higher – almost doubling that of Ni(II)- and they decreased continuously as the montmorillonite content was increased from 0 to 100%. Conversely, Ni sorption capacity increases as the bentonite ratio increase and also its adsorption process was much slower (Fig. 3A) yielding lower equilibrium adsorption capacity and removal efficiency (Table 2) which slightly improved within narrow range as the montmorillonite content was increased. Moreover, the highest Ni adsorptive capacity was observed when montmorillonite content was 50% as results of the decline of capacity at longer contact time when the adsorbent was 100% bentonite. Comparable and divergent behaviors during adsorption of metal ions on different clay minerals have been reported elsewhere (Bhattacharyya and Gupta, 2006, 2008c; Jiang et al., 2010; Liu and Zhou, 2010; Aljlil and Alsewailem, 2014). Increase in agitation time is expected to induce decrease in the adsorption sites as they are gradually filled up by metal ions and the adsorption kinetics become dependent on the transport of the metal ions from the bulk liquid phase to the actual adsorption sites (Bhattacharyya and Gupta, 2006, 2008b; Jiang et al., 2010).

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Fig. 3

The effect of (A) contact time and (B) initial concentration on adsorption capacity for copper and nickel for the different adsorbents.

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Table 2 Maximum removal efficiency and adsorption capacity at equilibrium time and initial concentration for the different adsorbents.

Mixture ratio of adsorbent	Cu(II)			Ni(II)
	te (min)	qe (mg/g)	Removal efficiency %	te (min)	qe (mg/g)	Removal efficiency %
100% clay	240	16	64	360	7.7	30.7
90% clay + 10% bentonite	240	15.6	62.6	360	8.2	32.9
70% clay + 30% bentonite	240	14.7	59	360	8.8	35.3
50% clay + 50% bentonite	120	10.8	43.2	360	9.8	39.3
100% bentonite	30	8.7	34.85	45	8.9	35.7
		Cu(II)		Ni(II)
	C0, mg/L	qe (mg/g)	Removal efficiency %	qe (mg/g)	Removal efficiency %
100% clay	20	4.97	99.47	2.83	56.6
	100	15.84	63.38	7.51	30
90% clay + 10% bentonite	20	4.96	99.21	3.1	62.6
	100	15.65	62.6	8.2	32.9
70% clay + 30% bentonite	20	3.8	76.1	3.68	73.7
	100	14.97	59.9	8.9	35.7
50% clay + 50% bentonite	20	3.5	69.9	3.9	78.2
	100	10.5	42.2	10.2	40.9
100% bentonite	20	2.5	50.2	2.56	51.2
	100	8.2	32.7	8.63	34.5

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Fig. 4

The effect of (A) mass of adsorbent and (B) initial pH on adsorption capacity for copper and nickel for the different adsorbents.

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3.3 Kinetics study

Due to its successful in industrial applications for the adsorption reactors design, the kinetics of adsorption of Cu(II) and Ni(II) were investigated using the different blends of the clay minerals. The kinetic study experimental data obtained were fitted to the linearized forms of pseudo-first-order and pseudo-second-order models (Eqs. (4) and (5)) and presented in Fig. 5A and 5B, respectively. The kinetic parameters as well as the model’s predicted equilibrium adsorption capacity obtained from the slope and intercept of the plots are given in Table 4. Apparently, that the pseudo-second-order model parameters are well in agreement with the experimental data for all the different adsorbents tested (Fig. 6B). The R² ranged from 0.995–1 and with from 0.96–0.999 for Cu(II) and Ni(II) respectively. Moreover, the equilibrium adsorption capacity q_e obtained from pseudo-second-order model were in good agreed with the experimental data with very low percent error ranging between 0.07 to 4.47%. The pseudo-second order rate constant, k₂, varying from 0.102 to 3.558 to g-mg⁻¹ min⁻¹ and from 0.0452 to 0.885 g-mg⁻¹ min⁻¹ as the montmorillonite content was increased for Cu(II) and Ni(II), respectively (Table 4). Inferably, the bentonite catalyzed the adsorption process to reached equilibrium faster for both the two adsorbents, though dwindling the equilibrium uptake capacity (i.e. q_e) in case of Cu(II). This is clearly, manifested in the steeper slope in the plots in Fig. 5B as the montmorillonite content increases for Cu(II) and vice-verse for Ni. This implies that the kinetic behavior of adsorption of both ions was similar which was likely to be controlled by chemical process such as ion exchange between adsorbent and adsorbate (Ho and McKay, 1999; Plazinski et al., 2009). Similarly, other researchers (Bhattacharyya and Gupta, 2008b; Liu and Zhou, 2010; Vieira et al., 2010; Koyuncu and Kul, 2014) reported the suitability of the pseudo-second-order model compared with pseudo-first-order model for adsorption of Cu(II) and Ni(II) onto different types of clays.

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Fig. 5

(A) Pseudo-first order and (B) Pseudo-second order for the adsorption of copper and nickel onto the different adsorbents.

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Fig. 6

(A) Freundlich isotherm and (B) Langmuir isotherm plots for the adsorption of copper and nickel onto the different adsorbents.

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3.4 Isotherm study

Adsorption isotherms are usually employed to evaluate the applicability of adsorption processes as a unit operation and to find the relation between the distributed adsorbate within the liquid phase and the solid phase at equilibrium. To study the adsorption isotherm of Cu and Ni ions on studied adsorbents, the isotherm experimental data for both ions were fitted to the linearized form of Langmuir and Freundlich models (Eqs. (6) and (7)) with the later model’s plot provided in Fig. 6A and B, respectively. According to Table 5, both the adsorption data of the Cu(II) and Ni(II) fitted both the Langmuir and Freundlich isotherm models for all the different adsorbents with high R² of 0.941–0.996 and 0.93–0.974 for Cu(II) and of 0.957–0.997 and 0.914–0.997 for Ni(II) respectively. This could be attributed to the fact that adsorption sites can be naturally characterized by both monolayer and hetero-layer as reported by Malamis and Katsou (2013). From Table 5, the Freundlich isotherm coefficients n_f ranged between 1.53–5.34 and 1.6–2.96 for Cu(II) and Ni(II), respectively. These n_f values which are larger than unity indicate a decreasing in affinities with increasing in adsorption density i.e. conformity of the data to multilayer formation at the adsorbent surface (Sawyer and Parkin, 2003). Meanwhile, it can be observed that the Freundlich adsorption capacity coefficients k_f has values of 1.48–8.38 and of 0.6–2.23 for Cu(II) and Ni(II), respectively. Higher k_f values was obtainable for Cu(II) are than the corresponding values for Ni with the highest value (8.38 mg/g) associated with clay alone which drastically, decreased as the montmorillonite content was increased. However, in the case of Ni, increasing in the montmorillonite content slightly changed lower recorded kf values. On the other hand, the Langmuir equilibrium coefficients k_L has values of 0.022–5.466 and 0.025–0.157 for Cu(II) and Ni(II), respectively. According to Vieira et al. (2010) and Gupta and Bhattacharyya (2006), this parameter determine the direction of movement the equilibrium interaction; clay (adsorbent) + metal (adsorbate) = clay-metal with higher values indicate that the equilibrium shifts to the right side resulting formation of the adsorbate–adsorbent complex. Accordingly, the k_L obtained here suggest that the interaction between and the adsorbents was greater when the montmorillonite content is small with highest interaction obtainable when the absorbent was 100% clay (5.466). However, when the montmorillonite content increased above 10% in case of Cu(II) and also for all the five different adsorbents for Ni, there appeared to be very low interactions between adsorbate–adsorbent complexes as indicated by the lower values of k_L. Furthermore, the separation factor, R_L, for Langmuir isotherm ranged from 0.0036–0.485 and from 0.113–0.442 for Cu(II) and Ni(II), respectively. As the values of the R_L were less than one, the adsorption of Cu(II) and Ni(II) on the different blends of the clay bentonite were favorable (Vieira et al., 2010).

Jarrah (2010) has shown that decrease in Si/Al ratio resulted in increase in clay minerals CEC of clays which is expected to result in higher metal ions uptake. Considering the clay possessed much lower CEC compared to the bentonite (Table 1), however, the slightly lower Si/Al ratio was attributed to the bentonite as observed earlier (Fig. 2). Thus, in this case the higher CEC of the bentonite could be attributed to its higher organic matter which was susceptible to surpass the mineralogical compositions contribution to the CEC value (Parfitt et al., 1995; Soares and Alleoni, 2008). Additionally, CEC is also known to be highly influenced by other factors such as the organic matter-clay particles interactions and physico-chemical besides the Al/Si ratio (Ma and Eggleton, 1999).

Generally, the uptake of the Cu ions was by far higher than that of Ni when the proportion of the bentonite (BET surface area of 42.13 m²/g) was low despite the more than 4 folds’ lower clay BET surface area (9.07 m2/g). Conversely, at higher bentonite ratio, all the obtained results consistently suggest slight improvement of Ni(II) uptake compared to that of Cu(II). This clearly demonstrated that the Cu ions had more attracted to the active sites of the clay rather than onto the bentonite. This observed phenomenon of Cu ions having tendency towards higher uptake onto natural clay compared Ni have been reported by many other authors (Malandrino et al., 2006; Mishra and Patel, 2009; Lukman et al., 2013; Sis and Uysal, 2014). Bhattacharyya and Gupta (2006), Gupta and Bhattacharyya (2006) observed that different types of montmorillonite (natural and modified) had better affinity towards both Cu(II) and Ni(II) compared to natural and modified kaolinite clay minerals. Thus, these results presented here show that Cu(II) and Ni(II) exhibited divergent behaviors with distinctive affinity towards the clay and bentonite surfaces, respectively. Consequently, the distinctive behaviors of uptake of Cu(II) and Ni(II) onto different adsorbents could be attributed to the differences in the dominant adsorption mechanisms as well as the characteristics and mineralogical composition of the two types of clay investigated. This suggests that proportion of different mineralogical components in clay adsorbents would differently, affect heavy metal ions removal from water (as well as other processes such as electro-kinetic remediation of soil and groundwater).