A comparison of the influence of flocculent and granular structure of sludge on activated carbon: preparation, characterization and application

Abstract

The objective of the present study was to evaluate the influence of the flocculent and granular structure of sludge on sludge-based activated carbon (AC) preparation, characterization and application. Four kinds of sludge, aerobic activated flocculent sludge (AS), anaerobic activated flocculent sludge (AnS), aerobic granular sludge (AGS) and anaerobic granular sludge (AnGS), were selected as precursors for carbon production by using phosphoric acid activation. The physicochemical properties of prepared ACs were determined by Brunauer–Emmett–Teller (BET) surface area, scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), Thermal Gravimetric Analysis (TGA) and zeta potential analysis. The results implied that sorption equilibrium data of malachite green (MG) adsorbed onto four kinds of AC fitted well with the Langmuir model. Granular sludge ACs expressed higher adsorption capacities than those of flocculent sludge ACs. Electrostatic attraction and surface complexation were two possible mechanisms for MG sorption onto the carbons. The obtained results could provide useful information on the development of ACs from excess sludge as attractive biosorbents for dye removal by considering the type of sludge structure.

---

1. Introduction

Recently, dye contamination has seriously affected the quality of the water environment and human health as well as other forms of natural life. As a typical cationic dye, malachite green (MG) has been widely used in the aquaculture industry, commercial fish hatcheries and animal husbandry as an antifungal, antiseptic and fungicidal therapeutic agent.¹ However, MG is toxic to freshwater fish in both acute and chronic exposure, cytotoxic to mammalian cells as well as a multi-organ toxin, which affects the liver, spleen, kidney, heart, eye, skin, lung and bone, etc.² Thus, it is necessary to treat this kind of wastewater before it released into the aquatic environment. Conventional physical and chemical treatment methods for dye-contaminated wastewater include flocculation, coagulation, flotation, oxidation, adsorption, ozonation and membrane separation.³

Among all the above methods, adsorption, being an effective and simple method for removing dye wastewater, is now widely applied in sewage treatment.⁴ Many biosorbents have been developed and applied for treating dye-containing wastewater, including bacteria, fungi, yeast and algae.⁵ Biologically activated carbon (AC) is a well-known adsorbent with a high adsorption capacity for dye treatment due to its immense surface area and poly-porous nature.⁶ To date, ACs have been prepared from different precursors, including shells,⁷ sewage sludge⁸ and seaweeds.⁹ Zhang et al.¹⁰ evaluated Arundo donax root as a precursor for preparing carbon in a tube furnace, and applied the carbonated product for the treatment of MG-polluted effluents. Deng et al.¹¹ investigated the preparation of AC from cotton stalks for removing methylene blue (MB) in aqueous solution, suggesting that AC could be regarded as an excellent adsorbent for removing dye-contaminated water.

Excess sludge is inevitably produced from wastewater treatment plants (WWTP), which may cause serious pollution and turn into an ecological burden if disposed of improperly. As an uncompact precursor of AC, aerobic and anaerobic activated flocculent sludge (AS and AnS) have been widely generated in municipal and industrial treatment. Since it is enriched with organic matter and microorganisms, sludge can directly serve as an efficient adsorbent or it can be carbonized to AC by chemical, physical, or physicochemical activation methods. Moreover, it has been realized that the resource utilization of excess sludge converted into AC is economical and environment-friendly.¹² Li et al.¹² studied the biosorption of methylene blue (MB) by AC made from paper mill sewage sludge, suggesting the considerable application potential of sludge-based ACs. Kacan¹³ evaluated the optimum preparation conditions for ACs from textile sewage sludge, suggesting that carbonized sludge was suitable for dye removal from aqueous solutions.

Compared with activated flocculent sludge, granular sludge, including aerobic and anaerobic granular sludge (AGS and AnGS), has also been widely used for wastewater treatment. AGS is considered to be a special case of a biofilm composed of self-immobilized cells, which are regular, smooth and nearly round in shape. Wei et al.¹⁴ evaluated the biosorption of MB onto AGS, suggesting that the advantages of the dense structure and excellent settling ability of AGS from treated effluent make it more feasible as an effective adsorbent for dye removal. In contrast to AGS, AnGS exhibited a higher carrying capacity and lower energy consumption in the treatment of high-concentration organic wastewater. Until now, most research has been reported for AC production derived from flocculent sludge. However, granular sludge shows a significant difference in operational performance, ability to settle and biological community due to its unique granular structure. It is of great interest that the distinction of structural form between granular sludge and flocculent sludge may affect the ACs' morphology formed in the carbonization process.

Based on the above discussion, four typical kinds of sludge with activated flocculent and granular structures (AS, AGS, AnS, AnGS) were compared as raw carbonized materials for ACs preparation. The prepared ACs were characterized by using Brunauer–Emmett–Teller surface area (S_BET), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), Thermal Gravimetric Analysis (TGA) and zeta potential analysis. The sorption process of MG onto ACs was investigated by considering operating variables for the adsorption, adsorption kinetics and adsorption isotherm. The obtained results could provide useful information about the preparation, characterization and application of sludge-based ACs by considering the type of structure of sludge.

2. Materials and methods

2.1 Sludge sources

AGS and AS were obtained from our lab sequencing batch reactors (SBRs), and the working volume of each SBR was about 17 L. AnGS and AnS were obtained from upflow anaerobic sludge blanket (UASB) treating food wastewater, located at Dezhou, Shandong Province. Before preparation of the ACs, four kinds of raw sludge were washed three times by using deionized water to remove the surface soluble ions.

2.2 Synthesis of activated carbons

Sludge samples were pretreated as follows: firstly, the granular and flocculent sludges were sifted by using a particle size of 1 mm and 0.3 mm standard test sieve, respectively. Then the four kinds of sludge were transferred into an oven for 24 h to dehydrate the sludge until a constant weight was achieved.

The commonly used activating agents for chemical activation include H₃PO₄, KOH and ZnCl₂. It has been well reported that phosphoric acid activation had the advantages of lower activation temperatures, higher yields and less environmental effect.¹⁵ Therefore, the method of H₃PO₄ activation was selected for sludge-based carbon preparation. Then AS, AnS, AGS and AnGS (50 g) were impregnated with a 45% H₃PO₄ solution at a ratio of 1 : 2 (g sludge/g H₃PO₄) for 10 h. After pre-treatment, the samples were loaded into crucibles placed in a muffle furnace. It was reported by Chen et al. that within the scope of 10–30 °C min⁻¹ the BET surface area and total pore volume increased with an increase in the heating rate.¹⁶ Therefore, samples were heated up with a heating rate of 25 °C min⁻¹. After the AC samples had cooled to room temperature, the four products were thoroughly washed with deionized water until a steady pH was achieved.

2.3 Analysis methods

Surface area calculations were analyzed by the BET equation with N₂ gas with a BET sorptometer (Micromeritics ASAP 2020 system, USA) at 77 K. Before the BET measurement, the AC samples were dried at 200 °C for 12 h. The pore distributions and pore volume were calculated by using the N₂ desorption isotherm based on the Barrett–Joyner–Halenda (BJH) model. The surface physical morphology of the ACs was observed by using a scanning electron microscope (SEM, JEOL JSM-6700F microscope, Japan). All the AC samples were gold-coated for good electrical conduction. The functional group was obtained by using a Fourier transforms infrared (FTIR) analyzer (VERTEX70 spectrometer, Bruker Co. Ger) in the spectral range of 4000–450 cm⁻¹. Thermal behavior was analyzed by using Diamond High Temperature Type TG/DTA (PerkinElmer, Inc., USA) from 25 to 550 °C with a heating rate of 25 °C min⁻¹ under N₂ atmosphere. Zeta potential was measured with a Malvern zetameter (Zetasizer 2000) with pH values from 2.0 to 10.0. MG concentration was determined at 618 nm by using a UV/Vis spectrophotometer (TU-1901, Purkinje General Instrument Co. Ltd, Beijing).

2.4 Batch sorption experiments

For the adsorption isotherm, samples of 20 mg AC were added to 50 mL conical flasks containing 40 mL at various concentrations (40–480 mg L⁻¹). Then the conical flasks were placed inside a water bath shaker for 12 hours at 25 °C. Kinetic experiments were conducted at time intervals ranging from 5 to 240 min. After the MG sorption achieved equilibrium, the supernatant liquid was separated by centrifugation at 8000 rpm for 10 min. The adsorption capacity (q_e) and removal efficiency (R) can be calculated from the following eqn (1) and (2):where C₀ (mg L⁻¹) is the original concentration of the MG, C_e (mg L⁻¹) is the amount of MG adsorbed under equilibrium. V represents the solution volume (L), and m is the weight of adsorbent (g).

3. Results and discussion

3.1 Characterization of sludge-based carbons

3.1.1 Physical properties of the adsorbents. The N₂ adsorption/desorption isotherms and the pore size distributions for the four kinds of sludge-based AC are shown in Fig. 1. The N₂ adsorption/desorption isotherms of the ACs displayed characteristics of type IV. At high relative pressures a small H₄ hysteresis loop is observed, which gives evidence about the existence of mesopores.¹⁷ The details of the textural parameters of the prepared carbons are summarized in Table 1, including S_BET, pore volume, pore area and diameter (D_p). The data implied that the surface area of the AC samples followed the order of AGS-AC (506.6 m² g⁻¹) >AnGS-AC (242.4 m² g⁻¹) >AnS-AC (198.9 m² g⁻¹) >AS-AC (62.9 m² g⁻¹). It could be seen that granular sludge-based ACs exhibited a higher surface area than those of flocculent sludge-based ACs, which was beneficial for surface sorption. It was found that the V_mic of AS-AC and AGS-AC was 0.0106 and 0.0570 cm³ g⁻¹, respectively. The majority of the pores of the carbons mainly fell into the range of mesopores (<16 nm). The average pore diameter (D_p) of AS-AC (6.79 nm) was much higher than those of the other sludges (2.58, 2.72 and 2.69 nm for AnS-AC, AGS-AC and AnGS-AC, respectively). The differences in surface area among the sludge-based ACs were attributed to the porosity and permeability of the sludge. These results implied that the surface properties were significantly different between flocculent sludge-based and granular sludge-based ACs.

Fig. 1 The N₂ adsorption/desorption isotherms and the pore size distributions (inset) for the four kinds of sludge-based AC.

Table 1 Comparison of the textural properties of the ACs prepared from sludge^a

Samples	SBET (m^2 g^−1)	Smic (m^2 g^−1)	Sext (m^2 g^−1)	Smic/SBET (%)	Vmic (cm^3 g^−1)	Vtot (cm^3 g^−1)	Dp (nm)
a BET surface area (SBET), external surface area (Sext), micropore surface area (Smic), micropore volume (Vmic), total pore volume (Vtot), and average pore diameter (Dp).
AS-AC	62.9	23.7	39.2	37.7	0.0106	0.1068	6.79
AnS-AC	198.9	107.6	91.3	54.1	0.0490	0.1284	2.58
AGS-AC	506.6	301.4	205.2	59.5	0.1382	0.3995	2.72
AnGS-AC	242.4	125.6	116.8	51.8	0.0570	0.1627	2.69

---

3.1.2 Surface morphology of the adsorbents. Fig. 2 shows the SEM micrographs of the carbonated products. The flocculent sludge-based AC expressed a loose structure and different sizes of pores distributed unevenly on the surface (Fig. 2A and B), while the granular sludge-based AC presented a dense structure with no obvious pores (Fig. 2C and D). The pore structure was due to the decomposition of organic matter during the carbonization process. However, the surface area of AC derived from granular sludge was larger than that of the AC derived from flocculent sludge. The surface of AGS-AC and AnGS-AC was coarse and filled with wrinkles which increased the specific surface area considerably, indicating that gasification of phosphate compounds occurs mainly in the interior of the particles.¹⁸

Fig. 2 SEM micrographs of carbonated products: AS-AC (A); AnS-AC (B); AGS-AC (C); AnGS-AC (D).

3.1.3 FTIR spectra. FTIR spectra of four kinds of raw sludge and carbonized products are presented in Fig. 3. It can be seen from Fig. 3A that the four kinds of raw sludge showed similar functional groups. The peaks at 3412–3420 and 2927–2930 cm⁻¹ could be attributed to the stretching vibrations of –OH and C–H from methyl and methylene groups, respectively.^19,20 The peak at 1638 cm⁻¹ may be the stretch vibrations of the C=C group in olefinic structures.²¹ Another peak at 1410 cm⁻¹ could be attributed to the symmetric stretching of the –C=O group belonging to the carboxyl groups.²² The peak around 1104 cm⁻¹ was related to the C–O–C vibration of polysaccharides. A weak peak at 617 was attributed to C–O–H twist bending vibration.²³

Fig. 3 FTIR spectra of four kinds of raw sludge (A) and carbonized products (B).

Compared to the FTIR spectra of raw sludge, it was found that the characteristic peaks of –CH disappeared for the carbonized products, indicating that the hydrogen element was removed to a great extent during the carbonization process (Fig. 3B). The disappearance of the peaks at 1385–1422 cm⁻¹ indicated some changes in the –C=O group. The peak of –OH became sharper, which may be a result of the moisture decreasing during the carbonation process. The weak peak at 1638 cm⁻¹ shifted to shorter wavelength, indicating the chemical changes in the C=C group.

3.1.4 TGA of sludge sample. Fig. 4 shows TGA curves of the sludge activated by phosphate. As the temperature rises from room temperature to 550 °C, four kinds of sludge adsorbent were accompanied by significant weight loss. That could relate to the acceleration of H₃PO₄ for the bond cleavage reactions, which bring about the inchoate evolution of volatiles from the sludge.²⁴ The TGA curves exhibit three temperature zones, which mirrors the fact that the activation of four kinds of sludge with H₃PO₄ occurs in three main stages. In the first stage, the mass loss on the curves as the temperature reaches to 170 °C corresponded to moisture evaporation. The second loss phase between 170 to 350 °C might be attributed to the decomposition of organic matter, such as carbohydrates, lipids and protein in the sludge and dehydration of phosphoric acid.²⁵ This loss in the curves may be associated with a transition period in the activation.²⁶ In the third stage, a slight weight loss was observed which could be assigned to the volatilization of phosphorus compounds. The residue percentages of AS, AnS, AGS and AnGS were 51.8%, 54.7%, 61.6%, and 64.5%, respectively. It was found that the weight loss of flocculent sludge was higher than that of granular sludge, which indicates that granular sludge had a better thermal stability than flocculent sludge. It was reported by Wei²⁷ et al. that the contents of proteins and polysaccharides of AS were lower than AGS. The study of Basuvaraj²⁸ et al. suggested that flocculent sludge contains higher water content. As temperature increased, these substances volatilize gradually. Therefore, the weight loss of flocculent sludge was higher than that of granular sludge.

Fig. 4 TGA curves of the sludge activated by phosphate.

3.2 Effect of pH value

Fig. 5 shows the effect of pH on the adsorption of MG onto ACs in the range of 3.0 to 10.0. It was found that MG could be effectively removed by using AGS-AC over the whole pH range. Dye removal by AS-AC was a minimum (45.6%) at pH 3.0, and increased gradually to 92.37% at a pH of 10.0, demonstrating that the adsorption of MG onto AS-AC was strongly pH-dependent. As for the removal of MG by AnGS-AC and AnS-AC, it increased in the pH range of 3.0 to 7.0 and the maximum removal efficiency was observed at pH 7.0. The reason may be the protonation of the functional groups on the adsorbent surface in an acidic medium. At low pH values, H⁺ ions compete with dye cations for appropriate sites on the adsorbent surface, which results in lower removal efficiency.²⁹ However, in the basic medium, the formation of an electric double layer changes its polarity and consequently the dye uptake increases.³⁰

Fig. 5 Effect of pH value on the adsorption of MG onto four kinds of ACs.

3.3 Effect of contact time

The effect of contact time is essential in an evaluation of the adsorption process of MG. It was observed from Fig. 6 that the adsorption of MG onto AS-AC and AGS-AC quickly reached an equilibrium in the first 30 min; for AnS-AC and AnGS-AC, the equilibrium time was about 120 min, longer than those of AS-AC and AGS-AC. It is worth noting that the removal efficiency of MG by AGS-AC quickly reached 97% and remained at a high level, which indicates that it has the potential to be used as a fast and efficient adsorbent. The dramatic increase in the efficiency of MG dye adsorption could be attributed to the available adsorption sites on the exterior surface during the initial period. Accompanying the reaction, surplus sites became progressively covered, presenting a relatively slow adsorption rate.³¹ Therefore, the adsorption of MG exhibited saturation when all binding sites were occupied. These conclusions were in accordance with the literature reported by Khattri and Singh³² for the adsorption of MG onto neem sawdust.

Fig. 6 Effect of contact time on the adsorption of MG onto ACs.

3.4 Adsorption kinetics

In order to investigate the mechanism of adsorption and to determine the rate-controlling step, the pseudo-first-order kinetic model and pseudo-second-order kinetic model were first used to test the experimental data. The above models have been well reported in the sorption process of Cr(VI) onto multi-walled carbon nanotubes and mesoporous magnetic carbon nanocomposite fabrics.^33,34 The models are given as follows:

Pseudo-first-order kinetic model:³⁵

Pseudo-second-order kinetic model:³⁶

where q_e and q_t are the amount of MG adsorbed (mg g⁻¹) at equilibrium and time t (min), respectively, k₁ (min⁻¹) is the rate constant of pseudo-first-order kinetics of MG for the adsorption process and k₂ (g mg⁻¹ min⁻¹) is the pseudo-second-order rate constant.

Fig. 7A and B show the pseudo-first-order and pseudo-second-order kinetic plots for the removal of an initial concentration of 40 mg L⁻¹ MG onto ACs at 25 °C. Parameters of pseudo-first-order and pseudo-second-order models are presented in Table S1.† It can be seen that the pseudo-second-order model agreed well with the experimental data for the four carbon biosorbents. The R² values of AS-AC, AnS-AC, AGS-AC and AnGS-AC in the pseudo-second-order model were 0.9997, 0.9992, 0.9999, and 0.9997, respectively. In addition, the experimental q_e values were consistent with the calculated values obtained from linear plots of the pseudo-second-order model. The kinetics results suggested that MG sorption onto the four ACs can be described as a chemical adsorption processes, as similarly reported in the Cr(VI) sorption onto polyethylenimine-facilitated ethyl cellulose and cellulose-derived magnetic mesoporous carbon nanocomposites by Qiu et al.^37,38

Fig. 7 (A) Pseudo-first-order kinetics plots, (B) pseudo-second-order kinetics plots for MG sorption onto ACs.

In order to investigate the diffusion-controlled adsorption system, the value of the rate constant of intra-particle diffusion was calculated. The intra-particle diffusion equation is expressed as:³⁹

q_t = k_pit^1/2 + C_i (5)

where k_pi (mg g⁻¹ min^−1/2) is the intra-particle diffusion rate constant of stage i and t^1/2 is the square root of the contact time, C_i is the intercept that represents the boundary layer effect: the larger the C_i value, the greater the resistance to the external mass transfer.

Fig. S1† shows multilinearity for MG adsorption onto ACs, indicating that three steps take place. As observed from the plot, the first sharper region was the instantaneous adsorption or external surface adsorption stage, which was probably due to electrostatic attraction between MG and ACs. The second region was a gradual adsorption stage, which could be attributed to intra-particle diffusion as the adsorbent particles were loaded with MG. The third region was the final equilibrium stage signified by the formation of a plateau, in which intra-particle diffusion gradually became slow due to the low MG concentration.

That the q_t versus t^1/2 plots (Fig. S1†) do not pass through the origin means that the intra-particle diffusion is not the only the rate-determining step and the boundary layer diffusion may be involved, concurrently operating during the interactions between MG and ACs.⁴⁰

3.5 Adsorption isotherm

As an efficient evaluation for the applicability of the adsorption process, the adsorption isotherm could provide essential physiochemical information that reflects the relationship between pollution and adsorbent in the adsorption process. Two commonly used isotherm models, the Langmuir and Freundlich isotherms, were selected to analyze the equilibrium experimental data of MG sorption onto the four carbons. The nonlinear Langmuir and Freundlich isotherms are expressed in the following eqn (6) and (7):

q_e = K_FC^1/n_e (7)

where C_e (mg L⁻¹) is the equilibrium concentration of the MG, q_e (mg g⁻¹) is the amount of MG adsorbed, q_m (mg g⁻¹) is the theoretical maximum monolayer sorption capacity and K_L (L mg⁻¹) is the Langmuir adsorption equilibrium constant, K_F (mg g⁻¹ (L mg⁻¹)^1/n) is the sorption capacity of the adsorbent and n is adsorption intensity of the adsorbent.

Fig. 8 shows the adsorption isotherms of MG onto ACs derived from sewage sludge. As the initial MG concentration increased, the amount of MG adsorbed onto ACs increased, which might be attributed to an improvement in the driving force of the concentration gradient.⁴¹ The constants and parameters for adsorption of MG are shown in Table 2. It was found that the equilibrium data of the AC samples were simulated better by the Langmuir model than by the Freundlich model. This demonstrated that the uptakes of the adsorbates were mainly due to the monolayer coverage on the surface of the adsorbents rather than multilayer adsorption. The calculated value of q_m was 283.79 mg g⁻¹ for MG absorbed by AGS-AC, which was larger than that of AS-AC. Correspondingly, the values of q_m for AnGS-AC and AnS-AC were 148.4 and 114.5 mg g⁻¹, respectively. The results implied that the ACs derived from granular sludge (AGS-AC and AnGS-AC) had better sorption capacities than those of flocculent sludge (AS-AC and AnS-AC). Moreover, the values of 1/n of the four carbons were smaller than 1, which reflected the high nonlinearity of the sorption. It is found from Table 3 that ACs derived from sludge have higher maximum MG adsorption capacities than other reported adsorbents,^1,2,10,46 suggesting that sludge-based ACs were promising adsorbents for removing MG in application. Although AnS has been reported as an effective biosorbent for cationic red X-GRL treatment,⁴² the conversion of sludge into AC expands the scope of application and adsorption for biosorbents.

Fig. 8 Langmuir isotherm (A) and Freundlich isotherm (B) of MG sorption onto ACs.

Table 2 The constants of Langmuir and Freundlich isotherms for MG adsorption

Sample	Langmuir model			Freundlich model
	qm (mg g^−1)	KL (L mg^−1)	R^2	KF (L g^−1)	1/n	R^2
AS-AC	97.74	0.0033	0.9793	0.7065	0.7618	0.9505
AnS-AC	114.5	0.0812	0.9858	44.28	0.1633	0.9161
AGS-AC	283.8	0.1780	0.9626	105.0	0.1830	0.8069
AnGS-AC	148.4	0.0858	0.9859	50.00	0.1913	0.9161

---

Table 3 Comparison of the maximum adsorption capacities of MG onto various adsorbents

Adsorbents	Adsorption capacity (mg g^−1)	Reference
Arundo donax root carbon	8.69	10
Sewage sludge bio-char	10–40	46
Borassus aethiopum carbon	48.48	1
Rattan sawdust	62.71	2
AS-AC	97.74	This study
AnS-AC	114.5	This study
AnGS-AC	148.4	This study
AGS-AC	283.8	This study

---

3.6 Adsorption mechanisms

3.6.1 FTIR. Fig. 9 shows the FTIR spectra of AGS-AC, AS-AC, AnGS-AC and AnS-AC after the adsorption of MG. The peaks between 700 and 900 cm⁻¹ in the four carbon samples were attributed to the vibration of aromatics substituted by aliphatic groups.⁴³ For AGS-AC, no absorption is observed in this region or it might be overlapped. These weaker peaks in the FTIR spectra indicated that the four carbons reacted with MG and generated aromatic substances. Moreover, the peaks at 3402, 3408, 3403 and 3413 cm⁻¹ shifted to 3419, 3406, 3415 and 3419, respectively. The slight shift of –OH after the adsorption of MG revealed the possibility of interactions between the dye ions and hydroxyl groups.⁴⁴ The peaks at 1620, 1587, 1579, and 1610 cm⁻¹ were shifted to 1617, 1582, 1583, and 1613, respectively. The weak band at 1582–1617 cm⁻¹ might due to aromatic ring stretching vibrations and the small red shift indicates an enlargement of the aromatic ring system.⁴⁵ One phenomenon was the sharp increase in the peak intensity of C–O–C and –OH after MG binding, which could well define the adsorption of MG by surface complexation.⁴⁶

Fig. 9 FTIR spectra of AGS-AC, AS-AC, AnGS-AC and AnS-AC after the adsorption of MG.

3.6.2 Electrostatic attraction. The pH_PZC is an important parameter to characterize the pH of an adsorbent surface.⁴⁷ It refers to the pH value when the positive and negative charges of the solution are kept equal. As shown in Fig. 10, the pH_PZC of AS-AC, AnS-AC, AGS-AC and AnGS-AC are 2.33, 3.28, 2.39, and 3.15, respectively. The surface of the adsorbent presents a negative charge and this may enhance the positively charged MG due to electrostatic attraction. In general, the pH of wastewater is neutral, approximately larger than the pH_PZC of ACs, making the ACs predominantly negatively charged in water solution. Hence, the positively charged pollutant (MG) may be more easily adsorbed by ACs. When it comes to the pH_PZC after the MG has been adsorbed, the zeta potentials of the carbons were significantly increased at the same pH values. This may be attributed to the fact that the electrostatic attraction between the absorbent site and the positively charged dye cations was strengthened, resulting in an increase in adsorption.⁴⁸ Therefore, it can be speculated that electrostatic attraction was responsible for the MG adsorption.

Fig. 10 Zeta potential of activated carbons before (a) and after (b) adsorption of MG.

4. Conclusion

In this study, four kinds of sludge-based ACs were successfully prepared with phosphoric acid activation. Granular sludge ACs exhibited higher surface area and adsorption capacity than those of flocculent sludge ACs. The adsorption capacities of MG onto ACs were significantly influenced by the contact time and pH value. Adsorption kinetics and isothermal data of four carbons followed the pseudo-second-order model and Langmuir model, respectively. The mechanisms for the adsorption of MG onto ACs included electrostatic attraction and surface complexation. The comparison of adsorption performance of sludge-based ACs for MG indicated that granular sludge could be a better choice than flocculent sludge for AC preparation and dye wastewater treatment.

Acknowledgements

This study was supported by the National Natural Science Foundation of China (21377046, 21607055), Special project of independent innovation and achievements transformation of Shandong Province (2014ZZCX05101), Science and technology development plan project of Shandong province (2014GGH217006), Shanghai Tongji Gao Tingyao Environmental Science & Technology Development Foundation (STGEF) and QW thanks the Special Foundation for Taishan Scholar Professorship of Shandong Province and UJN (No. ts20130937).

References

1. S. Nethaji, A. Sivasamy, G. Thennarasu and S. Saravanan, J. Hazard. Mater., 2010, 181, 271–280.
2. B. H. Hameed and M. I. El-Khaiary, J. Hazard. Mater., 2008, 159, 574–579.
3. R. Han, J. Zhang, W. Zou, S. Jie and H. Liu, J. Hazard. Mater., 2005, 125, 266–271.
4. S. Chen, Z. Jian, C. Zhang, Q. Yue, L. Yan and L. Chao, Desalination, 2010, 252, 149–156.
5. R. Han, W. Yi, W. Zou, Y. Wang and S. Jie, J. Hazard. Mater., 2007, 145, 331–335.
6. K. Vijayaraghavan and Y. S. Yun, Biotechnol. Adv., 2008, 26, 266–291.
7. H. Aydın and G. Baysal, Desalination, 2006, 196, 248–259.
8. W. Li, Q. Yue, P. Tu, Z. Ma, B. Gao, J. Li and X. Xu, Chem. Eng. J., 2011, 178, 197–203.
9. W. Man, H. Fang, L. Gang, H. Ji, B. Nan and L. Huang, Ecotoxicol. Environ. Saf., 2014, 104, 254–262.
10. J. Zhang, Y. Li, C. Zhang and Y. Jing, J. Hazard. Mater., 2008, 150, 774–782.
11. D. Hui, G. Li, H. Yang, J. Tang and J. Tang, Chem. Eng. J., 2010, 163, 373–381.
12. W. H. Li, Q. Y. Yue, B. Y. Gao, X. J. Wang, Y. F. Qi, Y. Q. Zhao and Y. J. Li, Desalination, 2011, 278, 179–185.
13. E. Kacan, J. Environ. Manage., 2015, 166, 116–123.
14. D. Wei, B. Du, J. Zhang, Z. Hu, S. Liang and Y. Li, Bioresour. Technol., 2015, 190, 474–479.
15. H. Liu, X. Wang, G. Zhai, J. Zhang, C. Zhang, N. Bao and C. Cheng, Chem. Eng. J., 2012, 209, 155–162.
16. D. Chen, Y. Li, K. Cen, L. Min, H. Li and B. Lu, Bioresour. Technol., 2016, 218, 780–788.
17. B. Qiu, Y. Wang, D. Sun, Q. Wang, X. Zhang, B. Weeks, R. Connor, X. Huang, S. Wei and Z. Guo, J. Mater. Chem. A, 2015, 3, 9817–9825.
18. H. Teng, T. S. Yeh and L. Y. Hsu, Carbon, 1998, 36, 1387–1395.
19. S. Stankovich, R. D. Piner, S. B. T. Nguyen and R. S. Ruoff, Carbon, 2006, 44, 3342–3347.
20. S. H. Min, J. S. Han, E. W. Shin and J. K. Park, Water Res., 2004, 38, 1289–1295.
21. J. Jaramillo, P. M. Álvarez and V. Gómez-Serrano, Fuel Process. Technol., 2010, 91, 1768–1775.
22. H. Liu, J. Zhang, N. Bao, C. Cheng, R. Liang and C. Zhang, J. Hazard. Mater., 2012, 235–236, 367–375.
23. T. Yang and A. C. Lua, Mater. Chem. Phys., 2006, 100, 438–444.
24. M. Jagtoyen and F. Derbyshire, Carbon, 1993, 31, 1185–1192.
25. M. A. Nahil and P. T. Williams, Biomass Bioenergy, 2012, 37, 142–149.
26. T. H. Liou, Chem. Eng. J., 2010, 158, 129–142.
27. D. Wei, L. Shi, T. Yan, G. Zhang, Y. Wang and B. Du, Bioresour. Technol., 2014, 171, 211–216.
28. M. Basuvaraj, J. Fein and S. N. Liss, Water Res., 2015, 82, 104–117.
29. V. K. Garg, R. Kumar and R. Gupta, Dyes Pigm., 2004, 62, 1–10.
30. V. K. Gupta, I. Ali, Suhas and D. Mohan, J. Colloid Interface Sci., 2003, 265, 257–264.
31. W. H. Li, Q. Y. Yue, B. Y. Gao, Z. H. Ma, Y. J. Li and H. X. Zhao, Chem. Eng. J., 2011, 171, 320–327.
32. S. D. Khattri and M. K. Singh, J. Hazard. Mater., 2009, 167, 1089–1094.
33. H. Gu, S. Rapole, Y. Huang, D. Cao, Z. Luo, S. Wei and Z. Guo, J. Mater. Chem. A, 2013, 1, 2011–2021.
34. J. Zhu, H. Gu, J. Guo, M. Chen, H. Wei, Z. Luo, H. A. Colorado, N. Yerra, D. Ding and T. C. Ho, J. Mater. Chem. A, 2014, 2, 2256–2265.
35. N. M. Mahmoodi, B. Hayati and M. Arami, Ind. Crops Prod., 2012, 35, 295–301.
36. A. Özcan and A. S. Özcan, J. Hazard. Mater., 2005, 125, 252–259.
37. B. Qiu, J. Guo, X. Zhang, D. Sun, H. Gu, Q. Wang, H. Wang, X. Wang, X. Zhang and B. L. Weeks, ACS Appl. Mater. Interfaces, 2014, 6, 19816–19824.
38. B. Qiu, H. Gu, X. Yan, J. Guo, Y. Wang, D. Sun, Q. Wang, M. Khan, X. Zhang and B. Weeks, J. Mater. Chem. A, 2014, 2, 17454–17462.
39. J. Zhu, S. Wei, H. Gu, S. B. Rapole, Q. Wang, Z. Luo, N. Haldolaarachchige, D. P. Young and Z. Guo, Environ. Sci. Technol., 2012, 46, 977–985.
40. T. Santhi, S. Manonmani and T. Smitha, J. Hazard. Mater., 2010, 179, 230–244.
41. S. Chowdhury, R. Mishra, P. Saha and P. Kushwaha, Desalination, 2011, 265, 159–168.
42. B. Qiu, C. Xiang and D. Sun, Bioresour. Technol., 2011, 113, 102–105.
43. G. C. Chen, X. Q. Shan, Z. G. Pei, H. Wang, L. R. Zheng, Z. Jing and Y. N. Xie, J. Hazard. Mater., 2011, 188, 156–163.
44. T. Maneerung, J. Liew, Y. Dai, S. Kawi, C. Chong and C. H. Wang, Bioresour. Technol., 2015, 200, 350–359.
45. A. M. Puziy, O. I. Poddubnaya, A. MartíNez-Alonso, F. Suárez-GarcíA and J. M. D. Tascón, Carbon, 2002, 40, 1493–1505.
46. L. Leng, X. Yuan, H. Huang, J. Shao, H. Wang, X. Chen and G. Zeng, Appl. Surf. Sci., 2015, 346, 223–231.
47. Y. Kan, Q. Yue, J. Kong, B. Gao and L. Qian, Chem. Eng. J., 2015, 260, 541–549.
48. C. Akmil-Başar, Y. Önal, T. Kılıçer and D. Eren, J. Hazard. Mater., 2005, 127, 73–80.

---

Footnote

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c6ra18881h

---