Physicochemical properties of compost
The textural analysis of the compost by N2 and H2O adsorption/desorption showed that the BET surface area was 14.54 and 119.13 m2/g, respectively, and the porosity determined by the mercury porosimeter was 0.49. The compost contained a high amount of ash, much higher than in biolithes (Kyziol-Komosińska et al. 2018b). The chemical composition of the inorganic part of the compost shows that besides Na2O, MgO and K2O, SiO2, Fe2O3 and CaO are the main components (Table 2). The extraction test shows that Fe is mainly present as amorphous Fe oxides (37.97% of total Fe), as separate particles or surface coating material of other particles and in complex bonds with organic matter (30.45% of total Fe). According to EU regulation (2019), only the Cd content exceeds the limit for an organic fertiliser (1.5 mg/kg dry matter).
Table 2
The textural, physicochemical and chemical properties of the compost
Textural and physico-chemical properties |
Specific surface area (m2/g) | Total porosity | pH (H2O) | pHPZC | AEC (mmol/g) |
Total SSA (H2O) | External SSA (N2) |
119.13 | 14.54 | 0.49 | 7.98 | 7.72 | 0.054 |
Chemical composition (%) |
Ash content | SiO2 | Al2O3 | Fe2O3 | CaO | MgO | K2O | Na2O |
69.53 | 10.82 | 3.36 | 4.86 | 5.24 | 1.34 | 2.98 | 0.21 |
Heavy metals content (mg/kg) |
Cu | Cd | Cr | Ni | Pb | Zn |
172.4 | 5.55 | 145.8 | 15.73 | 22.17 | 1112 |
Leaching of soluble components (mg/L) |
Cu | Cd | Cr | Ni | Pb | Zn | COD |
0.23 | 0.012 | 0.04 | 0.05 | 0.18 | 1.23 | 482 |
The value of the point of zero net charge (pHPZC) for the compost was 7.72 and was higher than for the humic substances (3.0-3.5) due to the presence of iron oxides, for which the pHPZC is 8.0-8.5. Iron oxides can modify the surface of organic particles and their properties, including pHPZC, and form new adsorption centres. The results showed that at solution pH lower than 7.72, the surface of compost particles was positively charged and favourable for the adsorption of anionic dyes (Fig. S1). At solution pH > pHPZC, the net surface charge is negative because the surface functional groups of the compost (–OH, –COOH) are deprotonated. Although the net charge of the compost can be negative, there are still local positive charges as indicated by the presence of amorphous Fe oxides. The pHPZC is slightly lower compared to the pHPZC of activated teak leaf powder (Gedam et al. 2019) and municipal solid waste compost (Al-Zawahreh et al. 2021). The pH of the compost suspension was high (7.98), indicating that the material was in the final stages of the composting process. The anion exchange capacity (AEC) was 0.054 mmol/g and was higher than that of cellulosic biochar at pH 6 and 8 (Lawrinenko et al. 2017). In addition, AEC decreases with increasing pH of water or soil (Kyzioł-Komosinska et al. 2018a). The low content of heavy metals in the solutions was observed as an effect of the high pH of the water suspension (pH of about 7.98). In addition, the COD values of the extracts were high, about 482 mgO2/L, due to dissolved humic acids (Table 2).
The compost has a good buffering capacity - after adding 50 ml of 0.1 M HCl solution to its suspension, the pH only decreased to 7.11 (Fig. S2). It can neutralise acidic wastewater during adsorption without further pH adjustment for already treated water.
FT-IR/ATR analysis
A detailed description of the FT-IR/ATR spectra can be found in the Supplement (Fig. S3). For better clarity, the FT-IR/ATR spectra of compost, dye and compost with adsorbed dye have been compared in the individual figures.
Study of kinetics
The effect of the dye structure and its initial concentration in solution (C0) on the equilibrium time of dye adsorption was observed (Fig. 1). The dye binding process is generally fast and a high level of adsorption is achieved within a short contact time. The equilibrium time was reached within 30 min for all dyes at C0 = 50 mg/L, whereas at C0 = 500 mg/L this time depended on the type of dyes and was 60 min for RR-198, RB-81 and ABk-194 and 300 min for DR-81 and DB-74. The adsorption capacity of the compost reached a plateau after this time for all dyes. The optimum contact time, determined to be 300 min, was used in the adsorption experiments (Fig. 1). A two-step adsorption was observed for both C0. Adsorption was rapid in the first contact time of 10 min for 50 mg/L and 30 min for 500 mg/L and then increased slowly in the following contact time. During this time, the dyes were bound at 80.5–91.2% and 59.7–95.7% of the total adsorbed amount, respectively. The monoazo dyes RB-81, RR-198 and ABk-194 with the smaller molecule size were adsorbed most rapidly (Table 1).
The dye adsorption rate was high due to the large surface area and pore volume of the compost. As the contact time increased, there were fewer vacant pores available, which slowed the movement of the dye into the pores of the adsorbent and the rate of the adsorption process. The two-step process indicates a rapid, diffusion-controlled surface reaction followed by a rate-limiting step that can be explained by various processes, including adsorption to sites with relatively high activation energy and diffusion into the micropores of the compost. A second, slow adsorption step indicates that equilibrium has been reached (Manera et al. 2019). The high adsorption rate indicated that the compost had a high efficiency in the removal of azo dyes, which determines the wastewater-compost contact time in the wastewater treatment process in real-world practice.
The pseudo-first order (PFO) and pseudo-second order (PSO) equations are adsorption-reaction models, but the intraparticle diffusion equation is defined as an adsorption-diffusion model. The kinetic models and the Weber-Morris intraparticle diffusion model were used to simulate the experimental data in order to gain a better understanding of the rate-controlling factors affecting the dye adsorption kinetics.
Regarding the adsorption kinetic models, according to the values of the coefficient of determination and the error functions (SSE and χ2), both the PFO and PSO models described well the adsorption of all the dyes at their low initial concentration (C0 = 50 mg/L). The adsorption capacity of compost estimated from both equations for all dyes was similar and very close to that determined directly from the laboratory experiment (Table 3). These results indicate that the adsorption of the studied dyes on the compost was dominated by valence forces through electron exchange between the dye anions and positively charged sites on the compost surface, such as iron oxides (Van et al. 2021).
At C0 = 500 mg/L, the experimental adsorption data fitted very well only with the PSO model with a very high coefficient of determination (R2 > 0.9821) (Table 3, Fig. 2). Statistical analysis also showed that the PSO model gave better correlations than the pseudo-first order model. There is a small difference between the experimental and estimated q values, confirming the applicability of the model (Jan et al. 2021).
A better pseudo-second order model fit may indicate the presence of two or more types of adsorption centres on the compost surface, whereas a pseudo-first order model fit suggests only one type of adsorption site or at least very homogeneous adsorption (Al-Zawahreh et al. 2021; McKay et al. 2011). Furthermore, the very good PSO fit suggests that the adsorption rate is controlled by chemisorption and the adsorption capacity is controlled by the number of active adsorption sites (Robati 2013). It is commonly observed that the removal of dyes from water and wastewater by adsorption on organic-rich material follows this model (Al-Zawahreh et al. 2021; Bouguettoucha et al. 2015; Guerrero-Coronilla et al. 2015; Ho and McKay 1999).
Table 3
Kinetics models fitting parameters for the adsorption of azo dyes on compost
Dyes | DR-81 | DB-74 | RB-81 | RR-198 | ABk-194 |
Pseudo-first and pseudo-second order models |
| PFO | PSO | PFO | PSO | PFO | PSO | PFO | PSO | PFO | PSO |
C0 50 mg/L |
qexp | 1.65 | | 1.88 | | 2.10 | | 1.51 | | 2.35 | |
qe1 / qe2 | 1.598 | 1.689 | 1.747 | 1.947 | 2.055 | 2.109 | 1.478 | 1.528 | 2.304 | 2.364 |
k1 / k2 | 0.1272 | 0.1055 | 0.2505 | 0.2514 | 0.4081 | 0.3648 | 0.3217 | 0.3441 | 0.3974 | 0.3142 |
h | - | 0.301 | - | 0.953 | - | 1.756 | - | 0.805 | - | 1.623 |
R2 | 0.9812 | 0.9821 | 0.9764 | 0.9736 | 0.9877 | 0.9976 | 0.9828 | 0.9926 | 0.9911 | 0.9934 |
SSE | 0.0632 | 0.0939 | 0.1051 | 0.1029 | 0.0526 | 0.0546 | 0.0368 | 0.0157 | 0.0443 | 0.0627 |
χ2 | 0.1054 | 0.1055 | 0.1062 | 0.1071 | 0.0268 | 0.0331 | 0.0259 | 0.0136 | 0.0208 | 0.0346 |
C0 500 mg/L |
qexp | 12.45 | | 14.14 | | 15.45 | | 8.85 | | 15.95 | |
qe1 / qe2 | 11.95 | 12.71 | 13.47 | 14.29 | 15.30 | 15.72 | 8.628 | 8.921 | 15.64 | 16.07 |
k1 / k2 | 0.0442 | 0.0054 | 0.1187 | 0.0012 | 0.3615 | 0.0414 | 0.3347 | 0.0639 | 0.4131 | 0.0482 |
h | - | 0.866 | - | 2.414 | - | 12.44 | - | 5.084 | - | 10.23 |
R2 | 0.9363 | 0.9838 | 0.9391 | 0.9873 | 0.9847 | 0.9887 | 0.9863 | 0.9983 | 0.9875 | 0.9966 |
SSE | 6.107 | 1.821 | 10.40 | 1.924 | 1.960 | 0.2641 | 0.9731 | 0.1227 | 2.824 | 0.2367 |
χ2 | 2.179 | 0.492 | 1.36 | 0.2931 | 0.1903 | 0.1481 | 0.1271 | 0.0178 | 0.1909 | 0.0556 |
Intra-particle diffusion model |
| Step 1 | Step 2 | Step 1 | Step 2 | Step 1 | Step 2 | Step 1 | Step 2 | Step 1 | Step 2 |
C0 50 mg/L |
Kp | 0.3710 | 0.0277 | 0.4057 | 0.0207 | 0.5059 | 0.0211 | 0.3511 | 0.0209 | 0.5693 | 0.0207 |
C | 0.0820 | 1.296 | 0.1263 | 1.451 | 0.2905 | 1.856 | 0.1633 | 1.274 | 0.3130 | 2.104 |
R2 | 0.8331 | 0.9173 | 0.9726 | 0.9349 | 0.9990 | 0.9390 | 0.9891 | 0.9495 | 0.9337 | 0.8540 |
C0 500 mg/L |
Kp | 1.679 | 0.6482 | 2.548 | 0.5640 | 6.087 | 0.1395 | 3.093 | 0.1159 | 6.076 | 0.1697 |
C | 0.9590 | 4.183 | 0.4645 | 7.822 | 0.3454 | 13.94 | 0.0473 | 7.579 | 0.1681 | 14.08 |
R2 | 0.9944 | 0.9820 | 0.9725 | 0.9494 | 0.9780 | 0.8793 | 0.9984 | 0.8278 | 0.9947 | 0.6956 |
qexp, qe1, qe2 (mg/g); k1 (1/min); k2 (g/mg min); h (mg/g min); Kp (mg/g min1/2); C (mg/g)
The values of the pseudo-second-order rate constants (k2) decreased with increasing initial dye concentration because the fraction of binding sites available to the dyes decreases with increasing C0. In addition, a higher value of q correlates with the value of k2 (Table 3). As C0 increases, the value of q increases and requires a longer time for the system to reach equilibrium, thus decreasing the value of k2 (Plazinski et al. 2009). This shows that the equilibrium time and adsorption rate are dependent on the amount of dye available, and the higher the initial dye concentration, the longer it takes to reach equilibrium conditions. The initial adsorption rate (h) values indicate that the dye removal rate was fastest for RB-81 and ABk-194, followed by the other dyes. The high initial adsorption rate indicated that the adsorbent had a strong affinity for azo dye anions. The results also showed that the pseudo-first order equation fitted the experimental data well for the first 5–15 min of the adsorption process, depending on the type of dye (Fig. 2). According to Ho and McKay (1998b), the pseudo-first order model is only applicable to a limited part of the reaction range.
In addition, the intraparticle diffusion model curves did not pass through the origin, indicating that intraparticle diffusion is not the main and only factor determining the adsorption rate and that the adsorption mechanism is very complex (Chen et al. 2021). The plots show two diffusion steps that can be attributed to adsorption stages, where intraparticle diffusion was the rate-limiting step (step 1) and external diffusion (surface adsorption and liquid film diffusion - step 2) can control the adsorption rate (Chen et al. 2019). The results also indicated that up to 10–15 min, the rate-determining step was intraparticle diffusion, and beyond that other surface phenomena were involved. Diffusion in solution was probably the limiting step in the process.
Adsorption capacity of the compost
The results showed that dye adsorption increased with increasing initial dye concentration due to the strong driving force provided by the concentration gradient (Salleh et al. 2011).
The experimental maximum adsorption capacities of compost for DR-81, DB-74, RB-81, RR-198 and ABk-194 were 7.81, 9.43, 11.22, 5.06 and 12.64 mg/g, respectively, at a compost dose of 50 g/L. A 2.5-fold reduction in the compost dose (to 20 g/L) increased the adsorption capacity from 1.64 (ABk-194) to 1.92 times (RR-198) (Fig. 3a). The dye uptake on the compost was 13.28, 15.97, 18.55, 9.73 and 20.76 mg/g, respectively. The order of experimental adsorption affinities to compost is ABk-194 > RB-81 > DB-74 > DR-81 > RR-198 for both compost doses. Expressing the adsorption capacity of the compost in units of mmol/g did not practically change the order of the dyes, only the direct dyes DR-81 and DB-74 replaced each other.
The dye removal efficiency (percentage of dye removal) remained consistently high (95 − 90%) up to an initial concentration of 25 mg/L and then declined rapidly as the initial concentration increased (Fig. 3b). This effect can be explained by the availability of active sites on the compost surface and their degree of saturation. Therefore, as the dye concentration increases, the saturation of the adsorbent changes, resulting in different removal percentages. At higher concentrations of the dye solution, the removal efficiency decreases because the surface of the adsorbent becomes saturated with dye molecules, making it unable to adsorb further (Salleh et al. 2011). The measured pH values in the equilibrium dye-compost suspension ranged from 7.95 to 8.37, although the pH of the solutions of the three dyes (RB-81, RR-198, ABk-194) was acidic (Fig. 3c). The pH values resulting from the pH of the compost suspension and the pH of the dye solution were close to the pH of the compost and resulted from the very good buffering properties of the compost. There was no effect of the initial dye concentration on the pH of the equilibrium solution. The pH of the suspensions was also similar for both composts, except for DR-81 and RR-198.
In addition, the pH values in the equilibrium solutions for all dye-compost systems were above the pHPZC of the compost, indicating an overall negative surface charge of the compost particles. At the same time, the pKa of the dyes was below the pH of the equilibrium solutions, also indicating a negative charge due to the dissociation of their acidic functional groups. Under these conditions, compost cannot bind anionic azo dyes by electrostatic interactions. Electrostatic repulsion significantly affects the adsorption of anionic dyes. However, it should be noted that compost is a heterogeneous adsorbent and the presence of amorphous iron oxides confirms the possibility of occasional binding by electrostatic interactions. In addition, the adsorption capacity of compost for the dyes DR-81, DB-74 and RR-198 (expressed in mmol/g) was lower than its AEC, while that for ABk-194 and RB-81 was higher than its AEC, suggesting that ion exchange may be one of the mechanisms of dye binding.
The molecular structure of the dyes and the structure of the organic matter with aromatic rings and surface functional groups, as described by FT-IR/ATR spectra (Fig. S3), indicated that the adsorption of dyes may also occur via:
-
- the dipole-dipole interaction or van der Waals attractions between a non-polar part of the dye and the hydrophobic part of the organic matter,
-
- hydrogen bonds between –OH groups of organic matter and nitrogen, oxygen or hydrogen atoms of dyes,
-
- π-π and π-hydrogen bonds between the π system of compost and dye molecules with benzene rings containing C = C or naphthyl groups (Chen et al. 2019; He et al. 2020).
The results showed that hydrophobic forces between organic matter and dyes are very important for the adsorption of dyes (Hu et al. 2013). In addition, the structure of the dye molecules and the formation of hydrogen bonds between the –NH2, –NH- and –OH groups in dyes and the surface functional –COOH and –OH groups in organogenic adsorbents affect their adsorption capacity. According to Reife and Freeman (1996), the presence of hydroxyl and nitro groups in the dye molecule increases the adsorption rate, whereas the presence of sulphonic acid groups decreases it. In contrast, Kyziol-Komosinska et al. (2018b) showed that the adsorption capacity of peat depends on the ratio of donor to acceptor sites in the dyes. Therefore, similar calculations were performed for the azo dye-compost system and confirmed the relationship between the adsorption capacity of the compost (experimental and estimated from the Langmuir equation) and the ratio of the number of donors to the number of acceptors of the hydrogen atom in the dye functional groups for both compost doses. The highest correlation coefficient was found between the ratio of the number of donors to the number of acceptors and the adsorption capacity of the compost expressed in molar units (Table 4). Moreover, for a constant number of donor centres (DB-74, RB-81 and RR-198 of 3 donor centres) in the dyes, the adsorption capacity of the compost was inversely proportional to the number of acceptor centres (correlation coefficient of 0.9717). On the other hand, no relationship was found between the number of azo groups in the dyes and the adsorption capacity of the compost.
Table 4
Pearson’s linear correlation coefficient for adsorption capacity of compost and number of proton donor to acceptor ratio in dyes (p < 0.05)
Adsorption capacity of compost | Donor/Acceptor (compost dose of 50 g/L) | Donor/Acceptor (compost dose of 20 g/L) |
qmax (mg/g) | 0.8045 | 0.7905 |
qL (mg/g) | 0.7788 | 0.8088 |
qL (mmol/ g) | 0.9671 | 0.9660 |
Isotherms of adsorption |
The adsorption isotherms of the dyes for both compost doses belong to the L-type isotherms according to the classification of Giles et al. (1974), indicating a high affinity between compost particles and dye molecules and the absence of strong competition between dye molecules and water for occupying adsorption active sites (Fig. 4). This suggests that the adsorption of dyes by organic matter occurs by monolayer formation, with a high affinity between dyes and composts at low concentrations and surface saturation at higher concentrations, which is typical of the Langmuir model (Guerrero-Coronilla et al. 2015).
The parameters of the isothermal models estimated by nonlinear regression analysis, the values of the coefficient of determination and the nonlinear error functions are summarised in Table 5, and the fits of the experimental data to the four models are shown in Fig. 4. The results showed that among the isotherms used, the Langmuir (Eq. S2) and Sips (Eq. S5) models showed a very good fit to the behaviour of the experimental equilibrium data with R2 > 0.99. From a practical point of view, the Langmuir model is simpler than the three-parameter Sips model and, according to Guerrero-Coronilla et al. (2015), is therefore easier to apply and interpret, which has practical implications for engineering design.
The values of maximum adsorption capacity (qL) estimated from the Langmuir equation were close to or higher than the experimental q values for all dye-compost systems at both compost doses. This observation suggests monolayer adsorption on a homogeneous surface with finite identical adsorption sites. The maximum adsorption capacities can be useful to compare the adsorption capacities of composts for dyes. In addition, the affinity of the compost surface for dyes and the binding energy (KL) depended on the dye structure and ranged from 0.00454 L/mg (RR-198) to 0.376 L/mg (ABk-194), but there was no effect of compost dose (Table 5). The Sips model gave an improved fit to the experimental data in the higher curvature zone of the adsorption isotherms only for direct dyes (Fig. 4). The use of a third parameter logically improves the quality of the mathematical fit. This model (also called the Langmuir-Freundlich equation) is a combination of the two models and represents systems where an adsorbed molecule can occupy more than one adsorption site. The maximum adsorption capacities (qS) for the dyes were higher than those obtained using the Langmuir isotherm, and the KS parameter changed in the same way as the KL constants of the Langmuir model equation (Table 5). The adsorption capacity obtained from the Sips equation may be more realistic than that obtained from the Langmuir equation (He et al. 2020).
The results of the error analysis showed that the Freundlich equation (Eq. S1) simulated the adsorption isotherms less well than the Langmuir (Saeed et al. 2010) and Sips models (Table 5). As shown in Fig. 4, the Freundlich model described the experimental data well up to an initial dye concentration of 150 mg/L. The estimated values of 1/n were below unity (0.3369–0.5070), indicating that the dyes were easily adsorbed by the compost and the adsorption process was favourable, and a good adsorption capacity of the compost was indicated by an n value above 2. Fitting the adsorption data to the Freundlich isotherm, assuming surface heterogeneity with adsorption sites of different energies and confirming the porous character of the compost, indicates that the adsorption was heterogeneous and not limited to monolayer adsorption.
Table 5
The isotherm constants for the adsorption of azo dyes on compost
Dyes | DR-81 | DB-74 | RB-81 | RR-198 | ABk-194 |
Compost dose | 50 g/L | 20 g/L | 50 g/L | 20 g/L | 50 g/L | 20 g/L | 50 g/L | 20 g/L | 50 g/L | 20 g/L |
Freundlich isotherm |
KF | 0.4764 | 0.7489 | 0.6589 | 1.299 | 0.7344 | 1.624 | 0.1891 | 0.3739 | 1.446 | 2.716 |
1/n | 0.4423 | 0.4461 | 0.4336 | 0.3936 | 0.4578 | 0.3886 | 0.5074 | 0.5002 | 0.4014 | 0.3369 |
R2 | 0.9801 | 0.9822 | 0.9741 | 0.9764 | 0.9928 | 0.9729 | 0.9797 | 0.9836 | 0.9937 | 0.9877 |
SSE | 0.6683 | 4.184 | 1.771 | 5.298 | 3.661 | 6.144 | 0.4727 | 2.611 | 6.793 | 8.611 |
χ2 | 0.5243 | 0.9298 | 0.9065 | 1.816 | 1.712 | 1.072 | 0.4003 | 0.8263 | 0.2354 | 2.473 |
Langmuir isotherm |
qexp | 7.81 | 13.28 | 9.43 | 15.97 | 11.22 | 18.55 | 5.06 | 9.73 | 12.64 | 20.76 |
qL | 8.91 | 16.09 | 10.78 | 17.25 | 12.95 | 19.74 | 6.441 | 12.64 | 14.16 | 20.92 |
KL | 0.0088 | 0.0067 | 0.0114 | 0.0127 | 0.0129 | 0.0161 | 0.0049 | 0.0045 | 0.0376 | 0.0364 |
R2 | 0.9995 | 0.9979 | 0.9986 | 0.9971 | 0.9941 | 0.9977 | 0.9952 | 0.9953 | 0.9981 | 0.9979 |
SSE | 0.4851 | 0.8297 | 0.4897 | 1.416 | 0.2603 | 0.199 | 0.0221 | 0.1485 | 0.2491 | 0.4791 |
χ2 | 0.3231 | 1.1328 | 0.3771 | 0.846 | 0.1108 | 0.486 | 0.2812 | 0.5453 | 0.3139 | 0.7674 |
Dubinin-Raduskevich isotherm |
qD·10− 3 | 0.0432 | 0.0765 | 0.0377 | 0.0528 | 0.0589 | 0.0719 | 0.0247 | 0.0469 | 0.0771 | 0.0874 |
β | 0.0042 | 0.0045 | 0.0039 | 0.0035 | 0.0041 | 0.0035 | 0.0048 | 0.0048 | 0.0034 | 0.0029 |
E | 10.89 | 10.59 | 11.37 | 11.87 | 11.05 | 11.90 | 10.15 | 10.14 | 12.03 | 13.06 |
R2 | 0.9899 | 0.9901 | 0.9849 | 0.9969 | 0.9968 | 0.9844 | 0.9893 | 0.9909 | 0.9982 | 0.9937 |
SSE | 0.3467 | 2.031 | 0.7312 | 2.032 | 1.614 | 1.635 | 0.1971 | 1.381 | 2.728 | 2.006 |
χ2 | 0.1753 | 0.3056 | 0.2804 | 0.687 | 0.8062 | 0.517 | 0.1956 | 0.3587 | 1.846 | 1.384 |
Sips isotherm |
qS | 10.57 | 18.19 | 14.05 | 23.49 | 14.31 | 23.26 | 7.012 | 12.57 | 14.08 | 25.43 |
KS | 0.0191 | 0.0108 | 0.0219 | 0.0282 | 0.0176 | 0.0286 | 0.0065 | 0.0044 | 0.0369 | 0.0639 |
mS | 0.6016 | 0.8476 | 0.7278 | 0.6632 | 0.8733 | 0.7632 | 0.9109 | 1.007 | 1.012 | 0.6768 |
R2 | 0.9995 | 0.9982 | 0.9997 | 0.9971 | 0.9981 | 0.9981 | 0.9962 | 0.9962 | 0.9997 | 0.9975 |
SSE | 0.3546 | 0.5130 | 0.2725 | 0.4368 | 0.0607 | 0.1281 | 0.0078 | 0.1482 | 0.0799 | 0.3934 |
χ2 | 0.2030 | 0.3851 | 0.085 | 0.2241 | 0.0604 | 0.0983 | 0.1144 | 0.2798 | 0.1035 | 0.1934 |
KF ((mg/g)·(L/mg)1/n); qexp, qL (mg/g); KL (L/mg); qD (mol/g); β (mol2/J2); E (kJ/mol); qS (mg/g); KS (L/mg)m
The mean values of the free energy (E) (Eq. S4) estimated from the Dubinin-Radushkevich isotherm were used to assess the nature of the adsorption, and the values in the range 10.14–13.06 kJ/mol were within the energy range for ion-exchange reactions of 8–16 kJ/mol (Hu and Zhang 2019). A similar fit of isotherms to experimental data using linear regression was observed for dyes adsorbed on sewage sludge-rice husk biochar (Chen et al. 2019) or activated carbon prepared from coal (Jan et al. 2021), but in both cases the adsorption process was chemical.
As different studies have used different concentrations, dose of adsorbent, temperature and pH range, it is sometimes difficult to make an accurate comparison of monolayer adsorption capacity. The adsorption capacity is increased by increasing the concentration and decreasing the adsorbent dose. The determined and estimated adsorption capacity of the studied compost was similar to that reported for some other biosorbents, such as low-moor peat, wood industry by-products and agro-waste materials, even though the studies were conducted at a solution pH above 7 (Table S4). Furthermore, it can be concluded that it was also comparable to the adsorption capacity of activated carbon, but only for DR-81 (Bhatt et al. 2013; Sohrabi et al. 2016).
Computational calculations
The results of DFT method are showed in Supplement (Table S5 and Fig. S4).