In order to utilize the discarded Chaenomeles sinensis seed (CSS) and develop low-cost biochar for heavy metal pollution control, this study pyrolyzed CSS to prepare biochar at three different temperatures (300, 450 and 600 °C). The physicochemical properties of CSS biochar such as elemental composition, surface area, surface morphology and surface functional groups were characterized. Its adsorption properties including kinetics, isotherms and thermodynamics were studied. The results showed that the adsorption equilibrium was reached at 5 h, which was relatively fast. CSS biochar prepared at 450 °C (CSS450) had the maximum adsorption capacity for Cr(VI) and Cu(II), which was 93.19 mg/g and 105.12 mg/g, respectively. The thermodynamic parameter ΔG0 < 0 and the isotherm parameter RL between 0 and 1 all revealed the feasibility and spontaneity of the adsorption process. The removal of Cr(VI) exhibited high efficiency in a wide pH range (1–10), while the removal of Cu(II) was pH-dependent and optimal at pH = 6. The coexisting ions in the solution showed slight inhibition of the adsorption of Cr(VI) and Cu(II). Additionally, Cu(II) exhibited better affinity for CSS450 than Cr(VI) in dynamic adsorption. This is the first study to prepare biochar from CSS and confirms its potential application for heavy metal remediation.

Chaenomeles sinensis is a deciduous or semi-evergreen tree in the family Rosaceae (Du et al. 2013). Its fruit is often used for food and medicinal purposes, and has been used extensively to treat some diseases such as rheumatoid arthritis, hepatitis, asthma and cough (Hamauzu et al. 2005; Zhang et al. 2010). However, in the process of processing Chaenomeles sinensis fruit, its seeds are often discarded (Wang et al. 2018), which not only pollutes the environment but also wastes natural resources. In order to make use of the waste, this study tries to use the seeds to produce biochar and solve the increasingly serious environmental problems, so as to make them a low-cost and environmentally friendly source of biochar.

Biochar is a carbon-rich material obtained by pyrolysis of biomass in an oxygen-limited environment (Chen et al. 2012). It is widely used in soil remediation and water pollution control to adsorb organic matter and heavy metal ions (Ahmad et al. 2014; Zhao et al. 2017). In recent years, biomass sources of biochar production have included agricultural crops, plant residues, animal manure and wastewater sludge (Ahmad et al. 2014; Wei et al. 2019b). Waste biomass has been widely used in biochar production due to its cost and environmental benefits compared with other feedstock (Chen & Chen 2009; Jalayeri & Pepe 2019). The characteristics of biochar are greatly affected by the pyrolysis temperature (Chen et al. 2011). The specific surface area, aromatic structural components, porosity and mineral contents of biochar increase with pyrolysis temperature (Li et al. 2017; Wei et al. 2019a), while the oxygen-containing functional groups, hydrophilicity, polarity and yield decrease (Wang & Wang 2019; Wei et al. 2019a; Zhang et al. 2019a). The pyrolysis temperature influences the capacity of biochar to adsorb heavy metals by affecting these properties (Wei et al. 2019a). Therefore, it is necessary to find suitable conditions to produce biochar with better adsorption performance.

Heavy metals are increasingly becoming common contaminants in the environment and a major concern worldwide due to their properties of non-biodegradability and long-term accumulation (Li et al. 2018). Hexavalent chromium is a carcinogen with strong toxicity and oxidization (Zhang et al. 2018b). Low doses of copper are essential to humans, but high concentrations of it may be associated with many diseases (Ippolito et al. 2012; Jalayeri & Pepe 2019). At present, the methods for the treatment of heavy metal contaminants such as chemical precipitation, ion exchange, reverse osmosis and membrane separation have the shortcomings of high cost and low efficiency when used extensively (Li et al. 2017; Ahmad et al. 2018). As an adsorbent, biochar is attracting more and more attention in the field of heavy metal treatment due to its characteristics of environmental protection, low cost and simple operation.

Chaenomeles sinensis seed (CSS) is a major residue abundantly generated from Chaenomeles sinensis production procedures. Producing biochar by CSS is an effective way of waste recycling and pollutant removal. Extensive studies have been carried out to investigate the remediation of heavy-metal-contaminated water by biochar derived from different waste biomass (Peng et al. 2017; Hong et al. 2019; Shen et al. 2019; Wei et al. 2019a). But there is no relevant research about the use of CSS as feedstock to produce biochar. Therefore, this study attempts to directly pyrolyze CSS to produce biochar and to investigate the possibility of its adsorbing heavy metals. The objectives of this study are: (1) to investigate the physicochemical properties of CSS biochars; (2) to evaluate the adsorption performance of CSS biochars for Cr(VI) and Cu(II); (3) to analyze the potential mechanisms of heavy metal adsorption onto CSS biochars.

Materials

All the reagents used in this work were of analytical grade, and purchased from Sinopharm Chemical Reagent Co., Ltd, China. The solutions were prepared using deionized water. The CSS used in this work were obtained from a beverage factory using Chaenomeles sinensis as raw material in Henan province, China.

Production of biochars

The waste was collected from the beverage factory and CSS were picked out. Then they were washed with deionized water and dried in an oven at 80 °C to a constant weight. Then the treated CSS were stored in a desiccator for later use. Biochars were produced by pyrolyzing the treated materials in a tube furnace under N2 gas conditions for 2 h at 300 °C (CSS300), 450 °C (CSS450) and 600 °C (CSS600), respectively. The three kinds of biochar were ground separately using a mortar, and samples of 0.9–1.0 mm were sieved out and placed in a desiccator for subsequent experiments.

Characterization of biochars

The ash content of the biochars was determined by heating the biochar samples at 800 °C for 4 h (Chen & Chen 2009). The contents of C, H and N in the biochars were measured using an elemental analyzer (RARIO EL III, Elementar, Germany). The content of O was calculated by subtracting the C, H, N and ash contents from the biochar samples (Song et al. 2019). The atomic ratios of H/C, O/C, and (N + O)/C were calculated. The yield was calculated by the mass ratio of the obtained biochars to the raw materials. The surface area was measured with N2 adsorption determined by a multi-channel specific surface area analyzer (TriStar II 3020, Micromeritics, USA) using the Brunauer–Emmett–Teller (BET) method. A scanning electron microscope (SEM, JEM-6700F, Japan) and a Fourier transform infrared spectroscope (FTIR, Thermo Nicolet, 6700, Madison, WI, USA) were used to measure the surface morphology and surface functional groups, respectively.

Adsorption kinetics, isotherms and thermodynamics

For adsorption kinetics, 0.1 g biochar samples were mixed with 50 mL Cr(VI) and Cu(II) solution at an initial concentration of 50 mg/L, respectively. Then the solutions were placed in 250 mL sealed conical bottles and shaken in a shaker at a speed of 120 rpm and temperature of 25 ± 0.5 °C. Samples were taken at intervals of 5, 10, 20, 30, 60, 120, 300, 600, 900, 1,440 and 2,880 minutes. For adsorption isotherms, 0.1 g biochar samples were mixed with 50 mL Cr(VI) and Cu(II) solutions at an initial concentration of 10, 20, 40, 80, 160, 200, 300, 400, 500 mg/L, respectively. The thermodynamics experiments were the same as the kinetics experiments except that they were performed at three different temperatures (25, 35 and 45 °C). After filtration through 0.45 μm filters, the residual concentration of heavy metal ions in the filtrate was measured by inductively coupled plasma–atomic emission spectrometry (ICP-OES, Optima 4300DV, PerkinElmer SCIEX, USA).

The amounts of heavy metal ions adsorbed per unit of CSS biochar mass at each time point were calculated using the formula:
formula
(1)
where is the adsorption amount at time (mg/g); and are the concentrations of heavy metal ions at initial and time (mg/L), respectively; V is the volume of solution (L); and m is the mass of CSS biochar (g).
In order to study the mechanisms of Cr(VI) and Cu(II) adsorption onto CSS biochars and the rate-controlling step for the adsorption process, as well as the rate at which the target heavy metal ions were adsorbed, the experimental kinetic data were fitted by the following four model equations:
formula
(2)
formula
(3)
formula
(4)
formula
(5)
where is the adsorption amount at equilibrium (mg/g); and are the first-order adsorption rate constant and the second-order adsorption rate constant (h−1), respectively; is the intra-particle diffusion rate constant [mg/(g·min1/2)]; and are the initial adsorption rate constant (mg/kg) and the desorption constant (kg/mg), respectively; and is the intercept representing the thickness of the boundary layer.
The adsorption isotherm data were fitted by two typical isotherm models (Langmuir and Freundlich). The equations of the two models were as follows:
formula
(6)
formula
(7)
where is the maximum adsorption capacity (mg/g); is the sorbate concentration in solutions under equilibrium conditions (mg/L); K and are the Langmuir bonding term related to interaction energies (L/mg) and the Freundlich affinity coefficient (mg(1−n) Ln/g), respectively; and n is the Freundlich linearity constant.
Thermodynamic parameters including free energy (ΔG0), enthalpy (ΔH0) and entropy (ΔS0) could be calculated using the following formulae (Li et al. 2018):
formula
(8)
formula
(9)
formula
(10)
where is the equilibrium constant; R is the universal gas constant [8.314 J/(mol·K)]; T is the absolute temperature in Kelvin (K); and is the adsorbent dose (g/L).

Effects of pH and ionic strength

The initial concentrations of Cr(VI) and Cu(II) solutions were set to 100 mg/L. The initial pH of Cr(VI) and Cu(II) solutions varied between 1–10 and 1–7, respectively. And the NaCl concentration varied between 0 and 0.5 mol/L. Other experimental conditions were the same as that in the preceding subsection.

Dynamic sorption experiments

Biochar samples of 3 g were filled into a glass column with the specification of D × H = 16 mm × 240 mm. A peristaltic pump was used to make the mixed solution with Cr(VI) and Cu(II) concentrations both of 100 mg/L flow through the CSS biochars from bottom to top of the glass column at a flow rate of 20 mL/h. The samples were collected at the top of the adsorption column and the concentration of Cr(VI) and Cu(II) ions remaining was examined to investigate the efficiency and prioritization of Cr(VI) and Cu(II) ions during dynamic adsorption.

Characterization of biochar

The physicochemical properties of biochars produced at different pyrolysis temperatures are shown in Table 1. As shown, the yields of the three biochars were in the range of 25.04–59.94% and negatively correlated with pyrolysis temperature (300–600 °C), which was consistent with previous literature (Uchimiya et al. 2011; Zhao et al. 2018). In addition, the pyrolysis temperature had significant effects on the elemental compositions and ash contents of the biochars. Specifically, the increasing of pyrolysis temperature caused an increase in the C content and decreases in the H and O contents. As the temperature rose from 300 °C to 600 °C, the C content fluctuated between 67.82% and 71.63%, and the contents of H and O decreased from 6.82% to 2.33% and 18.13% to 6.76%, respectively. These might be due to an increase in the degree of carbonization of plant-derived biochar at higher temperatures (Wei et al. 2019b), the reduction of volatiles, and the destruction of surface hydroxyl groups and H and O atoms in combination with C in biochar (Novak et al. 2009). Temperature had no significant effect on the change of N content (Ahmad et al. 2014). This resulted in lower H/C and O/C ratios, and (N + O)/C also decreased. These indicated that the aromaticity of the biochars was enhanced and the polarity reduced. The ash contents increased with the pyrolysis temperature. Especially when the pyrolysis temperature was raised from 300 °C to 450 °C, the ash contents were greatly increased. The ash was mainly inorganic salts in CSS. The increasing of pyrolysis temperature led to the loss of volatile organic compounds and other organic matter, and thus the relative content of ash in the biochars increased.

Table 1

Yields, elemental compositions, atomic ratios, ash contents and BET surface area of the CSS biochars

BiocharYield (%)CHNO(N + O)/CO/CH/CAshBET surface area (m²/g)
CSS300 59.94 70.30 6.82 3.36 18.13 0.306 0.258 0.097 1.38 0.61 
CSS450 26.87 67.82 3.11 4.34 11.68 0.236 0.172 0.046 13.05 3.24 
CSS600 25.04 71.63 2.33 3.93 6.76 0.149 0.094 0.033 15.35 80.55 
BiocharYield (%)CHNO(N + O)/CO/CH/CAshBET surface area (m²/g)
CSS300 59.94 70.30 6.82 3.36 18.13 0.306 0.258 0.097 1.38 0.61 
CSS450 26.87 67.82 3.11 4.34 11.68 0.236 0.172 0.046 13.05 3.24 
CSS600 25.04 71.63 2.33 3.93 6.76 0.149 0.094 0.033 15.35 80.55 

Table 1 also shows that the BET surface area of CSS450 (3.24 m2/g) was about five times larger than that of CSS300 (0.61 m2/g). As the pyrolysis temperature rose to 600 °C, the BET surface area of CSS600 (80.55 m2/g) increased markedly. Many amorphous carbons remained in the biochars at lower temperatures and hindered the formation of pores, so the biochars produced at lower temperatures had a small BET surface area. As the temperature increased, the gas released by the destruction of the unstable volatile components thus produced many pore structures, causing an increase in the BET surface area (Kumar et al. 2017).

Figure 1 shows the morphologies of the three biochars. As can be seen, the surface of CSS300 was smooth and had irregular protrusions, but no shaped pores were visible. When the pyrolysis temperature rose to 450 °C, pores having a diameter of about 10 μm with a thickness interval of about 4 μm were formed. The surface of CSS600 had a large number of dense pores, and micropores could be observed on it. This further demonstrated an increase in the BET surface area, which could provide a larger contact area and more adsorption sites for heavy metals.

Figure 1

The SEM images of (a) CSS300, (b) CSS450 and (c) CSS600.

Figure 1

The SEM images of (a) CSS300, (b) CSS450 and (c) CSS600.

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The FTIR spectra of CSS biochars are presented in Figure 2. As the pyrolysis temperature increased, the functional groups on the surface of the biochars were significantly reduced, especially when the pyrolysis temperature rose from 300 °C to 450 °C. The stretching vibration of alcoholic and phenolic –OH occurred at 3,459 cm−1. The vibration of aliphatic C–H occurred at 2,929 cm−1. The skeleton vibration at 1,589 cm−1 was attributed to C = C and C = O in aromatic rings of conjugated ketones and quinines. The stretching vibration of –COOH occurred at 1,396 cm−1, and the stretching vibration at 1,106 cm−1 was attributed to C–O–C in cellulose and hemicellulose (Chen & Chen 2009; Tong et al. 2011; Zhang et al. 2018a; Chen et al. 2019). The variations of peak intensity reflected the gradual condensation of aromatic structures and the gradual decomposition of lignin, hemicellulose and cellulose with the increase of temperature (Keiluweit et al. 2010). The presence of a large number of oxygen-containing functional groups in the CSS biochars provided the possibility of adsorbing heavy metals.

Figure 2

FTIR spectra of CSS300, CSS450 and CSS600.

Figure 2

FTIR spectra of CSS300, CSS450 and CSS600.

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Adsorption kinetics, isotherms and thermodynamics

It was clear that both the adsorption process of Cr(VI) and Cu(II) by CSS biochars could be divided into an initial rapid adsorption phase and a slow phase after equilibrium (Figure 3(a) and 3(c)) (Wang et al. 2015). It was found that CSS450 had the maximum adsorption capacity for Cr(VI) and Cu(II). The adsorption capacity of Cr(VI) on CSS450 was 15.04 mg/g at 1 h, and 18.61 mg/g at equilibrium. The adsorption capacity of Cu(II) on CSS450 was 13.76 mg/g at 2 h, and 15.78 mg/g at the final equilibrium phase. It can also be seen that adsorption equilibrium was almost reached at 5 h and complete adsorption was achieved, which was faster than that in some previous studies (Tytłak et al. 2015; Jalayeri & Pepe 2019).

Figure 3

The pseudo-first-order, the pseudo-second-order and Elovich equations of (a) Cr(VI) and (c) Cu(II) and the intra-particle diffusion model of (b) Cr(VI) and (d) Cu(II).

Figure 3

The pseudo-first-order, the pseudo-second-order and Elovich equations of (a) Cr(VI) and (c) Cu(II) and the intra-particle diffusion model of (b) Cr(VI) and (d) Cu(II).

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The fitting results of the pseudo-first-order, pseudo-second-order and Elovich equations for the kinetic experimental data are shown in Figure 3 and Table 2. The correlation coefficients of the pseudo-second-order model for the adsorption of Cr(VI) and Cu(II) on the CSS biochars were both R2 > 0.98, which were higher than the other two models. And the equilibrium adsorption amounts calculated by the pseudo-second-order model were also close to the experimental data. This suggests that the adsorption process of Cr(VI) and Cu(II) on the CSS biochars was controlled by a chemical adsorption mechanism, and the adsorption rate was related to the adsorption sites on the adsorbent surface (Li et al. 2018; Zhang et al. 2019b). It could also be found that the increase of pyrolysis temperature was not conducive to the adsorption of Cr(VI) and Cu(II) on CSS biochars, which might be due to the destruction of functional groups on the surface of the biochars at high temperatures and that was detrimental to the chemical interaction of heavy metal ions with biochars. At the same time, the adsorption performance of Cr(VI) and Cu(II) by CSS biochars was not ideal when the pyrolysis temperature was 300 °C. This might be because the temperature was too low to generate enough pores to provide sufficient contact area.

Table 2

Kinetic parameters of Cr(VI) and Cu(II) adsorption by CSS biochars

AdsorbateBiocharKinetics modelsParameter-1Parameter-2R2
Cr(VI) CSS300 Pseudo-first-order k1 = 1.4538 ± 0.0952 qe = 15.9624 ± 0.2421 0.9919 
Pseudo-second-order k2 = 0.1213 ± 0.0079 qe = 16.7232 ± 0.1853 0.9964 
Elovich α = 158.4268 ± 87.6556 β = 0.4372 ± 0.0495 0.9462 
Intra-particle diffusion K3,1 = 1.2824 ± 0.1883
K3,2 = 0.0082 ± 0.0048 
C1 = 0.5688 ± 1.1838
C2 = 15.8648 ± 0.1688 
0.9206
0.4967 
CSS450 Pseudo-first-order k1 = 2.2667 ± 0.1197 qe = 18.3229 ± 0.2212 0.9941 
Pseudo-second-order k2 = 0.1825 ± 0.0103 qe = 19.0092 ± 0.1686 0.9972 
Elovich α = 732.2588 ± 622.8323 β = 0.4606 ± 0.0625 0.9336 
Intra-particle diffusion K3,1 = 1.4445 ± 0.3052
K3,2 = 0.0087 ± 0.0054 
C1 = 2.8679 ± 1.9182
C2 = 18.2596 ± 0.1914 
0.8485
0.4593 
CSS600 Pseudo-first-order k1 = 1.5822 ± 0.1246 qe = 12.1774 ± 0.2228 0.9877 
Pseudo-second-order k2 = 0.1771 ± 0.0105 qe = 12.7298 ± 0.1268 0.9969 
Elovich α = 163.2056 ± 88.1087 β = 0.5995 ± 0.0625 0.9546 
Intra-particle diffusion K3,1 = 0.9150 ± 0.1310
K3,2 = 0.0099 ± 0.0080 
C1 = 1.1000 ± 0.5270
C2 = 12.0103 ± 0.2827 
0.9235
0.3384 
Cu(II) CSS300 Pseudo-first-order k1 = 2.3162 ± 0.2708 qe = 11.0375 ± 0.2946 0.9708 
Pseudo-second-order k2 = 0.3081 ± 0.0254 qe = 11.4778 ± 0.1479 0.9941 
Elovich α = 460.5486 ± 292.4894 β = 0.7630 ± 0.0769 0.9626 
Intra-particle diffusion K3,1 = 0.6992 ± 0.1744
K3,2 = 0.0179 ± 0.0096 
C1 = 2.6729 ± 1.0967
C2 = 10.7332 ± 0.3385 
0.8006
0.5375 
CSS450 Pseudo-first-order k1 = 1.9490 ± 0.2604 qe = 15.4417 ± 0.4785 0.9603 
Pseudo-second-order k2 = 0.1922 ± 0.0260 qe = 15.9951 ± 0.3466 0.9831 
Elovich α = 586.6551 ± 330.5864 β = 0.5472 ± 0.0496 0.9691 
Intra-particle diffusion K3,1 = 0.9725 ± 0.0845
K3,2 = 0.0195 ± 0.0138 
C1 = 3.5128 ± 0.5312
C2 = 15.0474 ± 0.4856 
0.9707
0.4015 
CSS600 Pseudo-first-order k1 = 3.0935 ± 0.5221 qe = 12.1968 ± 0.4399 0.9406 
Pseudo-second-order k2 = 0.4259 ± 0.0612 qe = 12.5965 ± 0.2566 0.9829 
Elovich α = 2,579.3351 ± 1,657.3766 β = 0.8293 ± 0.0666 0.9815 
Intra-particle diffusion K3,1 = 0.5578 ± 0.1302
K3,2 = 0.0120 ± 0.0085 
C1 = 5.0515 ± 0.8181
C2 = 12.26334 ± 0.3012 
0.8212
0.3970 
AdsorbateBiocharKinetics modelsParameter-1Parameter-2R2
Cr(VI) CSS300 Pseudo-first-order k1 = 1.4538 ± 0.0952 qe = 15.9624 ± 0.2421 0.9919 
Pseudo-second-order k2 = 0.1213 ± 0.0079 qe = 16.7232 ± 0.1853 0.9964 
Elovich α = 158.4268 ± 87.6556 β = 0.4372 ± 0.0495 0.9462 
Intra-particle diffusion K3,1 = 1.2824 ± 0.1883
K3,2 = 0.0082 ± 0.0048 
C1 = 0.5688 ± 1.1838
C2 = 15.8648 ± 0.1688 
0.9206
0.4967 
CSS450 Pseudo-first-order k1 = 2.2667 ± 0.1197 qe = 18.3229 ± 0.2212 0.9941 
Pseudo-second-order k2 = 0.1825 ± 0.0103 qe = 19.0092 ± 0.1686 0.9972 
Elovich α = 732.2588 ± 622.8323 β = 0.4606 ± 0.0625 0.9336 
Intra-particle diffusion K3,1 = 1.4445 ± 0.3052
K3,2 = 0.0087 ± 0.0054 
C1 = 2.8679 ± 1.9182
C2 = 18.2596 ± 0.1914 
0.8485
0.4593 
CSS600 Pseudo-first-order k1 = 1.5822 ± 0.1246 qe = 12.1774 ± 0.2228 0.9877 
Pseudo-second-order k2 = 0.1771 ± 0.0105 qe = 12.7298 ± 0.1268 0.9969 
Elovich α = 163.2056 ± 88.1087 β = 0.5995 ± 0.0625 0.9546 
Intra-particle diffusion K3,1 = 0.9150 ± 0.1310
K3,2 = 0.0099 ± 0.0080 
C1 = 1.1000 ± 0.5270
C2 = 12.0103 ± 0.2827 
0.9235
0.3384 
Cu(II) CSS300 Pseudo-first-order k1 = 2.3162 ± 0.2708 qe = 11.0375 ± 0.2946 0.9708 
Pseudo-second-order k2 = 0.3081 ± 0.0254 qe = 11.4778 ± 0.1479 0.9941 
Elovich α = 460.5486 ± 292.4894 β = 0.7630 ± 0.0769 0.9626 
Intra-particle diffusion K3,1 = 0.6992 ± 0.1744
K3,2 = 0.0179 ± 0.0096 
C1 = 2.6729 ± 1.0967
C2 = 10.7332 ± 0.3385 
0.8006
0.5375 
CSS450 Pseudo-first-order k1 = 1.9490 ± 0.2604 qe = 15.4417 ± 0.4785 0.9603 
Pseudo-second-order k2 = 0.1922 ± 0.0260 qe = 15.9951 ± 0.3466 0.9831 
Elovich α = 586.6551 ± 330.5864 β = 0.5472 ± 0.0496 0.9691 
Intra-particle diffusion K3,1 = 0.9725 ± 0.0845
K3,2 = 0.0195 ± 0.0138 
C1 = 3.5128 ± 0.5312
C2 = 15.0474 ± 0.4856 
0.9707
0.4015 
CSS600 Pseudo-first-order k1 = 3.0935 ± 0.5221 qe = 12.1968 ± 0.4399 0.9406 
Pseudo-second-order k2 = 0.4259 ± 0.0612 qe = 12.5965 ± 0.2566 0.9829 
Elovich α = 2,579.3351 ± 1,657.3766 β = 0.8293 ± 0.0666 0.9815 
Intra-particle diffusion K3,1 = 0.5578 ± 0.1302
K3,2 = 0.0120 ± 0.0085 
C1 = 5.0515 ± 0.8181
C2 = 12.26334 ± 0.3012 
0.8212
0.3970 

The intra-particle diffusion models of Cr(VI) and Cu(II) are shown in Figure 3(b) and 3(d), respectively. Neither for Cr(VI) nor for Cu(II) did the linear portions pass through the origin. This indicates that intra-particle diffusion was not the only rate-controlling step, and the adsorption of Cr(VI) and Cu(II) onto the CSS biochars was controlled by more than one process (Shakya & Agarwal 2019). The adsorption process of Cr(VI) and Cu(II) onto the CSS biochars mainly consisted of two stages: the initial linear portion related to surface diffusion and the latter linear portion related to pore diffusion (Venugopal & Mohanty 2011). As shown in Table 2, the larger rate constant K3,1 corresponds to a fast diffusion process of Cr(VI) and Cu(II) from the liquid to the surface of the CSS biochar. The smaller rate constant K3,2 in the second stage indicates that intraparticle diffusion was relatively slow and gradually reached equilibrium. The increasing values of the boundary layer constants indicate the greater boundary effect of the adsorption of Cr(VI) and Cu(II) onto the CSS biochars.

Adsorption isotherms and models play an important role in understanding the adsorption mechanism. Models such as Langmuir, Freundlich, Dubinin–Radushkevich, Temkin and some statistical physics models have been used to simulate the adsorption isotherms (Dotto et al. 2015; Sellaoui et al. 2015; Niazi et al. 2018). In this study, the adsorption isotherm experimental data were investigated by two very common models (Langmuir and Freundlich) and the fitting results of Cr(VI) and Cu(II) adsorption onto CSS biochars are shown in Figure 4 and Table 3. The adsorption amounts of Cr(VI) and Cu(II) on the CSS biochars were gradually increased as the concentrations of Cr(VI) and Cu(II) increased from 0 to 500 mg/L. When the concentration of heavy metal ions was small, the adsorption sites on the surface of the biochars were sufficient to react with heavy metals quickly. When the concentration continued to increase, the number of available adsorption sites was limited. The adsorption capacity order of Cr(VI) and Cu(II) adsorbed by CSS biochars produced at different pyrolysis temperatures was CSS450 > CSS600 > CSS300. The results were consistent with the adsorption kinetics that CSS450 had the largest adsorption capacity for Cr(VI) and Cu(II).

Table 3

Isotherm parameters of Cr(VI) and Cu(II) adsorption by CSS biochars

AdsorbateBiocharIsotherm modelsParameter-1Parameter-2RLR2
Cr(VI) CSS300 Langmuir K= 0.0009 ± 0.0003 Smax = 84.54 ± 17.01 0.6897–0.9911 0.9956 
Freundlich Kf= 0.1477 ± 0.0453 n = 0.8442 ± 0.0525  0.9914 
CSS450 Langmuir K= 0.0037 ± 0.0009 Smax = 93.19 ± 11.89 0.3509–0.9643 0.9909 
Freundlich Kf= 1.5148 ± 0.0665 n = 0.6095 ± 0.0079  0.9854 
CSS600 Langmuir K= 0.0015 ± 0.0003 Smax = 90.76 ± 13.69 0.5714–0.9852 0.9944 
Freundlich Kf= 0.3367 ± 0.1058 n = 0.7749 ± 0.0544  0.9885 
Cu(II) CSS300 Langmuir K= 0.0059 ± 0.0024 Smax = 22.69 ± 3.44 0.2532–0.9443 0.9331 
Freundlich Kf= 0.8711 ± 0.1903 n = 0.4874 ± 0.0382  0.9801 
CSS450 Langmuir K= 0.0039 ± 0.0016 Smax = 105.12 ± 22.08 0.3390–0.9625 0.9583 
Freundlich Kf= 2.2767 ± 0.7242 n = 0.5621 ± 0.0572  0.9752 
CSS600 Langmuir K= 0.0012 ± 0.0004 Smax = 93.42 ± 19.85 0.6250–0.9881 0.9921 
Freundlich Kf= 0.3028 ± 0.0697 n = 0.7723 ± 0.0397  0.9938 
AdsorbateBiocharIsotherm modelsParameter-1Parameter-2RLR2
Cr(VI) CSS300 Langmuir K= 0.0009 ± 0.0003 Smax = 84.54 ± 17.01 0.6897–0.9911 0.9956 
Freundlich Kf= 0.1477 ± 0.0453 n = 0.8442 ± 0.0525  0.9914 
CSS450 Langmuir K= 0.0037 ± 0.0009 Smax = 93.19 ± 11.89 0.3509–0.9643 0.9909 
Freundlich Kf= 1.5148 ± 0.0665 n = 0.6095 ± 0.0079  0.9854 
CSS600 Langmuir K= 0.0015 ± 0.0003 Smax = 90.76 ± 13.69 0.5714–0.9852 0.9944 
Freundlich Kf= 0.3367 ± 0.1058 n = 0.7749 ± 0.0544  0.9885 
Cu(II) CSS300 Langmuir K= 0.0059 ± 0.0024 Smax = 22.69 ± 3.44 0.2532–0.9443 0.9331 
Freundlich Kf= 0.8711 ± 0.1903 n = 0.4874 ± 0.0382  0.9801 
CSS450 Langmuir K= 0.0039 ± 0.0016 Smax = 105.12 ± 22.08 0.3390–0.9625 0.9583 
Freundlich Kf= 2.2767 ± 0.7242 n = 0.5621 ± 0.0572  0.9752 
CSS600 Langmuir K= 0.0012 ± 0.0004 Smax = 93.42 ± 19.85 0.6250–0.9881 0.9921 
Freundlich Kf= 0.3028 ± 0.0697 n = 0.7723 ± 0.0397  0.9938 
Figure 4

Sorption isotherm data and fitted models of (a) Cr(VI) and (b) Cu(II) onto CSS biochars.

Figure 4

Sorption isotherm data and fitted models of (a) Cr(VI) and (b) Cu(II) onto CSS biochars.

Close modal

For the adsorption of Cr(VI) onto the CSS biochars, the Langmuir model was more suitable for describing it with R20.9909. It indicated that the adsorption of Cr(VI) was monolayer adsorption. After a certain number of adsorption sites on the surface of the CSS biochars were occupied by Cr(VI) ions, ions could not be accepted again (Zhou et al. 2016). For the adsorption of Cu(II) on the CSS biochars, the correlation coefficient fitted by the Freundlich model was higher than that of the Langmuir with R2 = 0.9752–0.9938, reflecting that the Freundlich model was more suitable for describing it. It indicated that the adsorption of Cu(II) onto the CSS biochars was chemical adsorption on a heterogeneous surface, which might have a greater relationship with the functional groups on the surface of the CSS biochars and the interaction between adsorbent and adsorbate (Deng et al. 2009).

Based on the Langmuir model, the feasibility of adsorption was further explored using the dimensionless separation factor RL (RL = 1/(1 + KC0). The values of RL between 0 and 1 indicated the feasibility of the adsorption process (Kołodyńska et al. 2012). As shown in Table 3, the values of RL were all within the range of 0–1, indicating the favorability of Cr(VI) and Cu(II) adsorption onto the CSS biochars. Furthermore, the values of RL of Cu(II) adsorption onto CSS450 were lower than those of Cr(VI), which corresponded to a higher affinity between Cu(II) and CSS450.

According to the fitting results, the calculated maximum adsorption capacities Smax of CSS450 for Cr(VI) and Cu(II) were 93.19 mg/g and 105.12 mg/g, respectively, which were higher than those of direct pyrolytic biochars or the biochars obtained by some modification methods reported in many other studies (Table 4). It indicated that the biochars obtained by pyrolyzing CSS directly had great potential as a cost-effective adsorbent for treating heavy metal contamination in the environment.

Table 4

Comparison of maximum adsorption capacities of biochars in the literature

AdsorbateAdsorbentAdsorption capacity (mg/g)Reference
Cr(VI) Modified Enteromorpha prolifera biochar 91.5 Chen et al. (2018)  
Modified corn stover biochar 24.5 Li et al. (2018)  
Coconut coir biochar 31.1 Shen et al. (2012)  
Ramie biochar 82.23 Zhou et al. (2016)  
Wheat straw biochar 24.6 Tytłak et al. (2015)  
Chaenomeles sinensis seed biochar 93.19 This study 
Cu(II) Modified corn stover biochar 91.2 Li et al. (2018)  
Corn straw biochar 12.52 Chen et al. (2011)  
Hardwood biochar 6.79 Chen et al. (2011)  
Pistachio green hull biochar 19.84 Jalayeri & Pepe (2019)  
Cauliflower leaves biochar 53.96 Ahmad et al. (2018)  
Chaenomeles sinensis seed biochar 105.12 This study 
AdsorbateAdsorbentAdsorption capacity (mg/g)Reference
Cr(VI) Modified Enteromorpha prolifera biochar 91.5 Chen et al. (2018)  
Modified corn stover biochar 24.5 Li et al. (2018)  
Coconut coir biochar 31.1 Shen et al. (2012)  
Ramie biochar 82.23 Zhou et al. (2016)  
Wheat straw biochar 24.6 Tytłak et al. (2015)  
Chaenomeles sinensis seed biochar 93.19 This study 
Cu(II) Modified corn stover biochar 91.2 Li et al. (2018)  
Corn straw biochar 12.52 Chen et al. (2011)  
Hardwood biochar 6.79 Chen et al. (2011)  
Pistachio green hull biochar 19.84 Jalayeri & Pepe (2019)  
Cauliflower leaves biochar 53.96 Ahmad et al. (2018)  
Chaenomeles sinensis seed biochar 105.12 This study 

The thermodynamic studies on the adsorption of Cr(VI) and Cu(II) onto CSS biochars are shown in Figure 5 and Table 5. Obviously, the equilibrium constant K0 increased with the temperature in the range of temperature studied, indicating that the higher temperature strengthened the adsorption process. A negative value of ΔG0 shows that the adsorption of Cr(VI) and Cu(II) onto the CSS biochars was feasible and spontaneous. In addition, the value of ΔG0 was reduced with temperature increase which mainly reflects the greater drive force and higher adsorption capacity (Zhang et al. 2019b). The positive values of ΔH0 and ΔS0 indicate that all the adsorption process was endothermic and the randomness at the solid–liquid interface increased, respectively (Li et al. 2018). At the same temperature, the minimum values of ΔG0 of the adsorption of Cr(VI) and Cu(II) onto CSS450 revealed better spontaneity than the other two kinds of biochar, and further corresponded to the better adsorption performance of CSS450. The values of ΔH0 of Cr(VI) (25.6714 kJ/mol) and Cu(II) (29.2075 kJ/mol) adsorption onto CSS450 indicated the existence of chemisorption (Zhang et al. 2018b).

Table 5

Thermodynamic parameters of Cr(VI) and Cu(II) adsorption by CSS biochars

AdsorbateBiocharTemperature (°C)lnK0ΔG0 (kJ/mol)ΔH0 (kJ/mol)ΔS0 [J/(mol·K)]
Cr(VI) CSS300 25 0.8932 −2.2140 21.9379 80.8451 
35 1.1211 −2.8722 
45 1.4508 −3.8376 
CSS450 25 1.4833 −3.6769 25.6714 98.3309 
35 1.7811 −4.5631 
45 2.1352 −5.6478 
CSS600 25 0.1740 −0.4312 18.5637 66.2149 
35 0.4253 −1.0896 
45 0.6638 −1.7557 
Cu(II) CSS300 25 0.0045 −0.0112 9.5349 31.9469 
35 0.1032 −0.2645 
45 0.2469 −0.6530 
CSS450 25 0.6269 −1.5540 29.2075 102.8695 
35 0.8968 −2.2975 
45 1.3701 −3.6240 
CSS600 25 0.0935 −0.2318 23.3257 78.7905 
35 0.3171 −0.8124 
45 0.6869 −1.8168 
AdsorbateBiocharTemperature (°C)lnK0ΔG0 (kJ/mol)ΔH0 (kJ/mol)ΔS0 [J/(mol·K)]
Cr(VI) CSS300 25 0.8932 −2.2140 21.9379 80.8451 
35 1.1211 −2.8722 
45 1.4508 −3.8376 
CSS450 25 1.4833 −3.6769 25.6714 98.3309 
35 1.7811 −4.5631 
45 2.1352 −5.6478 
CSS600 25 0.1740 −0.4312 18.5637 66.2149 
35 0.4253 −1.0896 
45 0.6638 −1.7557 
Cu(II) CSS300 25 0.0045 −0.0112 9.5349 31.9469 
35 0.1032 −0.2645 
45 0.2469 −0.6530 
CSS450 25 0.6269 −1.5540 29.2075 102.8695 
35 0.8968 −2.2975 
45 1.3701 −3.6240 
CSS600 25 0.0935 −0.2318 23.3257 78.7905 
35 0.3171 −0.8124 
45 0.6869 −1.8168 
Figure 5

The van 't Hoff plot for the adsorption of Cr(VI) and Cu(II) onto CSS biochars.

Figure 5

The van 't Hoff plot for the adsorption of Cr(VI) and Cu(II) onto CSS biochars.

Close modal

As mentioned above, CSS450 had sufficient surface area, a certain number of surface functional groups and the largest adsorption capacities for Cr(VI) and Cu(II). And the adsorption process showed a better spontaneity. Thus CSS450 was selected for the further adsorption test.

Effects of pH and ionic strength

The pH had significant effects on the adsorption of Cr(VI) and Cu(II) by CSS450 in aqueous solution, which was mainly due to the speciation of metal ions and the charge on the surface of biochars depending on the pH values. The maximum adsorption amounts of Cr(VI) by CSS450 occurred at pH 1 and decreased significantly during the increase of pH from 1 to 2. As the pH continued to rise to 10, the adsorption amounts showed a slight decrease but tended to be stable (Figure 6(a)). In general, Cr(VI) could be effectively removed in a wide pH range (1–10), indicating that CSS450 could be applied in wider pH conditions compared with pineapple peel biochar (Shakya & Agarwal 2019).

Figure 6

Effects of initial pH on (a) Cr(VI) and (b) Cu(II) adsorption onto CSS450.

Figure 6

Effects of initial pH on (a) Cr(VI) and (b) Cu(II) adsorption onto CSS450.

Close modal

It has been reported that electrostatic attraction and complexation are the two main mechanisms by which biochars adsorb Cr(VI) in aqueous solution (Li et al. 2017). At pH <6.0, Cr(VI) existed mainly in the form of Cr2O72− and HCrO4 (Richard & Bourg 1991). The functional groups on the surface of the biochars were protonated and positively charged under low pH conditions, which was beneficial to electrostatic attraction with negatively charged chromium. When pH >6.0, CrO42− was the main form of Cr(VI) (Richard & Bourg 1991), and the deprotonation of the functional groups on the surface of the biochars was not conducive to electrostatic attraction with negatively charged chromium, which hindered further complexation.

The adsorption amounts of Cu(II) onto CSS450 increased from 2.96 mg/g at pH = 1 to 41.45 mg/g at pH = 7 (Figure 6(b)). This was consistent with previous reports that the adsorption of copper onto biochars was pH-dependent (Ippolito et al. 2012). Most of the copper was present in its free ionic form (Cu2+) (Chen et al. 2011), and partially hydrolyzed to form Cu(OH)+ as the solution pH increased (Tong et al. 2011). Because the electronegativity of CSS450 increased with pH, electrostatic attraction between biochars and copper ions was enhanced. The organic functional groups on the surface of CSS450 were dissociated with rising pH, making it easier to combine with Cu2+ to form surface complexes. And at higher pH conditions, the affinity of the adsorbent surface for Cu(OH)+ increased (Tong et al. 2011). These all led to a higher adsorption capacity for Cu(II) by CSS450 at higher pH conditions.

As shown in Figure 7, the coexisting ion Na+ in the solution caused a slight inhibition in the adsorption of Cr(VI) and Cu(II) by CSS450, and the inhibition increased with the Na+ concentration. The same positively charged Na+ would compete with heavy metal ions for adsorption sites on the surface of the biochars, while the number of adsorption sites was limited. The increase of Na+ concentration made the competition more intense, and the adsorption sites that could bind to heavy metal ions decreased, resulting in a decrease in the adsorption of Cr(VI) and Cu(II). Moreover, sodium ions might exchange heavy metal ions that had been adsorbed (Liu et al. 2014; Wang et al. 2015), which was one of the possible reasons for the decrease in adsorption amounts.

Figure 7

Effects of solution ionic strength on (a) Cr(VI) and (b) Cu(II) adsorption onto CSS450.

Figure 7

Effects of solution ionic strength on (a) Cr(VI) and (b) Cu(II) adsorption onto CSS450.

Close modal

Dynamic sorption experiments

In the first five minutes, the removal of Cr(VI) and Cu(II) reached 88.2% and 93.4%, respectively, and peaked at 30 min (Figure 8). At the beginning of the reaction, there were excessive adsorption sites on the surface of CSS450, so Cr(VI) and Cu(II) could be rapidly adsorbed onto different adsorption sites. Then the removal rates and the adsorption amounts decreased due to the limited adsorption sites on the surface of CSS450. And there might be competition between the two ions (Yang et al. 2016; Han et al. 2017; Ji & Pei 2019). Desorption occurred at 6 h, but the phenomenon was not greatly aggravated at 8 h. This indicated that the adsorption sites on the surface of CSS450 were saturated and could no longer be combined with heavy metal ions. A small amount of desorption showed that the combination of Cr(VI) and Cu(II) onto CSS450 was stable. During the dynamic adsorption test, Cu(II) showed a better affinity for CSS450 compared with Cr(VI). This finding was in harmony with the adsorption isotherm results and previous research (Song et al. 2019).

Figure 8

Dynamic adsorption experiments of Cr(VI) and Cu(II) onto CSS450.

Figure 8

Dynamic adsorption experiments of Cr(VI) and Cu(II) onto CSS450.

Close modal

Sorption mechanisms

Although the surface area of the CSS biochars increased with pyrolysis temperature, the adsorption capacities of heavy metal ions decreased, because the surface oxygen-containing functional groups were lost as the pyrolysis temperature increased. It was obvious that the adsorption of heavy metal ions onto the CSS biochars was associated with oxygen-containing functional groups which could combine with heavy metal ions through a complexation mechanism. There was an initial rapid adsorption phase in the adsorption kinetics, and it could be seen from the FTIR spectrum that CSS300 had the largest number of functional groups, but sufficient pores had not been formed to provide space for heavy metal ions, indicating the existence of physical adsorption. The effects of pH on the adsorption indicated that electrostatic attraction was also one of the main mechanisms for CSS biochars adsorbing heavy metal ions.

The physicochemical properties of CSS biochar produced at different temperatures and its adsorption performance for Cr(VI) and Cu(II) removal were investigated. The increase in the pyrolysis temperature resulted in an increase in the surface area and a decrease in surface functional groups of the CSS biochar. Adsorption processes of Cr(VI) were strongly correlated with the pseudo-second-order kinetic model and the Langmuir isotherm model, while the adsorption of Cu(II) correlated with the pseudo-second-order kinetic model and Freundlich isotherm model. Thermodynamic studies revealed the feasibility and spontaneity of the adsorption process. Moreover, it exhibited highly efficient Cr(VI) removal in a wide pH range (1–10), while it possessed pH-dependent Cu(II) adsorption characteristics optimal at pH = 6. Crucially, ionic strength could slightly affect the adsorption performance. Results also indicated that the mechanism of Cr(VI) and Cu(II) sorption by CSS biochar is a combination of chemical adsorption and physical adsorption. This study confirmed the potential of CSS as feedstock to prepare low-cost and high-efficiency biochar.

This work was financially supported by the CRSRI Open Research Program (SN: CKWV2016399/KY).

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