Potential Application of Alternative Materials for Organic Pollutant Removal

Abstract

The work aims to synthesize and characterize vegetal charcoal (or biochar) from Syzygium cumini (AC-SC), evaluating the adsorption capacity for dexamethasone drug (DEX) removal, using the kinetic and equilibrium adsorption. The samples were characterized by N₂ porosimetry, X-ray diffraction, scanning electron microscopy with energy-dispersive spectroscopy, zeta potential, and zero charge point. Adsorption equilibrium was carried out applying the Langmuir, Freundlich, Redlich-Peterson, Sips, and Toth models, and kinetic adsorption applied the pseudo-first order, pseudo-second order, Elovich, Avrami, and Weber-Morris models. AC-SC showed a heterogeneous and porous surface, negatively charged, crystalline structure, specific surface area of the 2.14 m² g⁻¹ and pH_ZCP = 7.36. About the effect of the AC-SC concentration, 5.0 g L⁻¹ showed the best DEX removal (53.02%), about the others’ concentration (2.0 and 7.5 g L⁻¹). About the equilibrium and kinetic adsorption, the Sips model and pseudo-second order showed the best experimental data adjusted, indicating that the adsorption monolayer was dependent on the ions onto the biosorbent, with a maximum adsorption capacity of 0.744 mg g⁻¹ after 180 min. Therefore, AC-SC can be used as an alternative material in the removal of organic pollutants, such as drug removal.

Introduction

Water is an essential resource for the permanence of life on the Earth; however, due to the significant increase of pollution on hydro resources, it is necessary to act on preserving these resources to avoid social and environmental crises (Empinotti et al., 2019).

Concurrently, COVID-19 caused by the SARS-CoV-2 showed a serious problem about the drug consummation, mainly the dexamethasone, being a corticosteroid medication used for the treatment of chronic obstructive pulmonary diseases, reducing the inflammatory process associated with the exacerbated production of cytokines, as well as pulmonary edema and alveolar damage (Halpin et al., 2020; Sterne, 2020). However, around 65% of the dexamethasone, after being absorbed in the gastrointestinal tract, is excreted by the urine, being able to generate by-products after 24 h (Nunes & Lima, 2020; Wang et al., 2020). Thus, advanced processes for the treatment of drug wastewater have been studied to provide the correct and adequate treatments for these effluents (Oliveira et al., 2020) such as adsorption.

Adsorption is the physical or chemical adhesion of atoms, ions, or molecules of the organic pollutants onto the surface of a solid (labeled adsorbent) (Wang et al., 2019). Moreover, this process has as main characteristics a low complexity, low operating cost, and the possibility of the use of alternative materials (biosorbents) to remove drugs, such as cellulose sources (Zhu et al., 2018), chitosan (Yanyan et al., 2018), and residual biomass (Gallo-Cordova et al., 2017).

Biochar or vegetal charcoal has been used in the adsorption process because of its application variability of alternative materials, which can be synthesized with many (agro)industrial residues from high-carbon precursors, showing high porosity and specific surface area and stability (Srivatsav et al., 2020), being favorable in removing drugs (Kebede et al., 2020), such as tetracycline (Smiljanić et al., 2021), ibuprofen, and naproxen (Ahsan et al., 2018). Moreover, vegetal charcoal can be synthesized from different methods, highlighting the activation/carbonization process from various residual biomass using the step of thermal degradation and the activation with activating agent (Chi et al., 2021). Among the main novelties and advantages for the use of biochar are the following (Albanio et al., 2021; Costa et al., 2021): (a) economic: due to operational simplicity and the possibility of using different residual biomasses with high availability; (b) environmental: contributes to the mitigation of climate change and the reuse of residual biomass, preventing these residues from becoming possible environmental liabilities; and (c) high adsorption capacity to remove organic pollutants with easy adsorbate recovery and biosorbent reusability.

In this context, the present work aims to synthesize and characterize biochar (or vegetal charcoal) from Syzygium cumini leaves, to evaluate the adsorption capacity for dexamethasone drug (DEX) using kinetic and equilibrium study.

Materials and Methods

Preparation of Syzygium cumini Leaves

Syzygium cumini leaves were obtained from a local property (Santa Maria – RS, Brazil), where they were successively washed with potable water (about 5 times), drying (60 °C for 24 h), and ground in a knife mill for 15 min, being sieved for uniform particle size (#12).

Biochar Synthesis

The preparation of the biochar was carried out by the methodology adapted (Zhang, Zhao, et al., 2019), using Syzygium cumini leaves. Thus, initially, the leaves were placed under magnetic agitation with zinc chloride (ZnCl₂, CAQ, PA) with 1:2 w/w (leaves:ZnCl₂) for 30 min. After, the solution was placed on the furnace (heating rate of 30 °C min⁻¹) at 600 °C for 2 h. Figure 1 shows a schematic representation of the biochar preparation process. The sample was labeled as AC-SC.

Fig. 1.

Schematic representation of the AC-SC preparation process

Characterization

For the biochar crystallinity characterization, the X-ray diffraction (XRD) was applied using Bruker Optics equipment (D2 Advance, USA) with a copper tube (radiation K_α-Cu = 1.5418 Ǻ) and angle range 2θ of the 10° in 70°, acceleration tension and current applied of 30 kV and 30 mA. The N₂ porosimetry was used to determine the specific surface area, diameter, and volume pore, using the adsorption/desorption isotherms in the Gemini VII 2375 Surface Area Analyzer Micrometrics equipment. For the computation of the specific area (S_BET), Brunauer–Emmett–Teller equation (BET method) was used, in the relative pressure (P Po⁻¹ = 0.05 to 0.35), and for the diameter and volume pore diameter the Barret-Joyner-Halenda equation (BJH method). The surface charge was determined using zeta potential (ZP) using Malvern-Zetasizer® version nanoZS (ZEN3600, UK) with closed capillary cells (DTS 1060) (Malvern Instruments, UK) with laser He–Ne of 4 mW (633 nm). To investigate the morphologic properties of the AC-JL, scanning electron microscopy with energy-dispersive spectroscopy (SEM–EDS) was used in the Phenom Prox Scanning Electron Microscope (Thermo Fisher Scientific) using metalized with gold sputtering and submitted to a magnification of the 710 × under 15 kV of the voltage. The zero charge point (pH_ZCP) was determined according to the 11-point methodology, according to the literature (Bakatula et al., 2018). Thus, 0.1 g of AC-SC was added in solution with DEX at different pH (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12), where this initial pH was adjusted with HCl (Sigma-Aldrich, PA) and NaOH (Sigma-Aldrich, PA), both at 1.0 mol L⁻¹. The final pH was measured after 3 h at 120 rpm of rotation and room temperature (25 ± 2 °C).

Effect of the AC-SC Concentration and the Adsorption Tests

The adsorption tests were carried out in batch processing over 3 h, where aliquots were collected at predetermined times (0, 5, 15, 30, 45, 60, 75, 90, 120, 150, and 180 min) and evaluated the effect of the concentration of the biochar (2, 5, and 7.5 g L⁻¹). All samples were analyzed at the specific wavelength of the DEX drug (λ_max = 242 nm) (Sastry et al., 2016) and all of the adsorption tests were performed in duplicate (with error values less than 5%).

Equilibrium Adsorption

Adsorption equilibrium study correlates the number of available active sites present onto the biosorbent surface with the number of drug molecules. Thus, there are a series of models (denominated isotherms) that correlate the adsorption capacity with the equilibrium DEX concentration, mainly Langmuir (Langmuir, 1918), Freundlich (Freundlich, 1906), Sips (Sips, 1948), and Toth (Tóth, 1981), according to Eqs. (1)–(4), respectively.

$$q_e=\frac{q_m\ast C_e}{1+k_L\ast C_e}$$ 1

$$q_e=k_F\ast {C_e}^{\frac{1}{n}}$$ 2

$$q_e=\frac{{q_s\ast (k_S\ast C_e)}^{n_S}}{1+{(k_S\ast C_e)}^{n_S}}$$ 3

$$q_e=\frac{q_{\mathit{To}}\ast C_e}{{\left(b_{\mathit{To}},+,C_e^{n_{\mathit{To}}}\right)}^{n_{\mathit{To}}}}$$ 4

where q_e (mg g⁻¹) and C_e (mg L⁻¹) are adsorbed amount and adsorbate concentrations at equilibrium; q_m, q_S, and q_To (mg g⁻¹) are the maximum adsorbed amount to Langmuir, Sips, and Toth, respectively, where the maximum adsorbed amount is influenced according to the concentration of biochar (Bardestani et al., 2019; Zhang, Meng, et al., 2019); K_L is Langmuir constant (L mg⁻¹); k_F is the Freundlich constant ((mg g⁻¹) (mg L⁻¹)^−1/n) which indicates the relative adsorption/desorption capacity of the adsorbent in relation to the bonding energy; n is the constant related to the adsorption intensity that should be between 1 and 10, where it usually indicates physical adsorption (Dasgupta et al., 2018; Enaime et al., 2017); n_s is the heterogeneity factor, where if n_s = 1, the model is reduced to the Langmuir equation and if n_s < 1, there is an increase in heterogeneity, that is, the model approaches Freundlich (Kumar et al., 2019; Shahri et al., 2018); n_TO is the heterogeneity parameter, which can assume a value between 0 and 1; for n_TO = 1 the Langmuir model is obtained (characteristic for representing homogeneous surfaces) and if n_TO ≠ 1 represents a heterogeneous surface; and b_TO is the constant of the Toth isotherm (Al-Ghouti & D.A. Da’ana, 2020; Kumar et al., 2021).

The degree of development and spontaneity of the reaction of adsorption can be obtained from the evaluation of the parameter of equilibrium or separation factor (R_L), which indicates whether the adsorption reaction is favorable or unfavorable (Akrawi et al., 2021), according to Eq. (5).

$$R_L=\frac{1}{1+k_L\ast C_e}$$ 5

where the adsorption will be considered favorable if 0 < R_L < 1, unfavorable to R_L > 1, linear (R_L = 1), and irreversible (R_L = 0).

Kinetic Adsorption

The kinetic models describe the speed on which the reaction occurs, needing this way the respective times (t), being more usual pseudo-first order (PFO) (Lagergren, 1898), pseudo-second order (PSO) (Blanchard et al., 1984), Elovich (ELO) (Aharoni & Tompkins, 1970), Avrami (AVR) (Avrami, 1939), and Weber-Morris (WEM) (Weber & Morris, 1963), according to Eqs. (6)–(10), respectively.

$$q_t=q_1\ast (1-\mathrm{Exp}\left(-,k_1,,\ast ,t\right))$$ 6

$$q_t=k_2\ast \left(q_2^2\right)\ast \frac{t}{(1+k_2\ast q_2\ast t)}$$ 7

$$q_t=\left(\frac{1}{b_e}\right)\ast l\mathrm{n}(1+a_e\ast b_e\ast t)$$ 8

$$q_t=q_{\mathit{avr}}\ast (1-E\mathrm{xp}(-k_{\mathit{avr}}\ast t{)}^{n_{\mathit{avr}}}$$ 9

$$q_t=k_{\mathit{wm}}\ast t^{0.5}+B$$ 10

where k₁ (min⁻¹) is the rate constant of pseudo-first order; q₁, q₂, and q_avr (mg g⁻¹) are theoretical values of adsorption capacity; k₂ (g mg⁻¹ min⁻¹) is the rate constant of pseudo-second order; b_e (mg g⁻¹ min⁻¹) is the initial adsorption rate; a_e (g mg⁻¹) is the desorption constant of the Elovich model; k_avr (min⁻¹) is the rate constant of Avrami model; n_avr is a heterogeneity factor; and k_wm (mg g⁻¹ min^−0.5) is the intraparticle diffusion rate.

Statistical Evaluation of Adjusted Models

The kinetic and equilibrium parameters were determined by adjusting the models with the experimental data, using non-linear regression, by Statistic 9.1 software (StatSoft, USA) with the Quasi Newton method. The determination coefficient (R²), adjusted determination coefficient (R²_adj), root mean square error (RMSE), and error sum of squares (SSE) were used to evaluate the fit quality of the models, according to Eqs. (11)–(14) (Ceylan, 2020; Doiron, 2019; Roozbeh et al., 2020).

$${\text{R}}^2\text{=1-}\frac{{\sum }_{\text{i=1}}^{\text{n}}{\left({\text{y}}_{\text{exp}},-,{\text{y}}_{\text{pred}}\right)}^2}{{\sum }_{\text{i=1}}^{\text{n}}{\left({\text{y}}_{\text{exp}},-,{\stackrel{-}{\text{y}}}_{\text{exp}}\right)}^2}$$ 11

$$R_{\mathit{adj}}^2=1-\left(1,-,R^2\right).\left(\frac{n-1}{n-p}\right)$$ 12

$$\mathrm{RMSE}=\sqrt{\sum _{i=1}^n\frac{{(y_{\mathit{pred}}-y_{\mathit{exp}})}^2}{n}}$$ 13

$$\text{SSE}=\frac{1}{\text{n}}\sum _{\text{i=1}}^{\text{n}}{\left({\text{y}}_{\text{exp}},-,{\text{y}}_{\text{pred}}\right)}^2$$ 14

where y_exp is the experimental data, y_pred is the predicted value, n is the number of experimental values, and p is the number of parameters according to the model.

Results and Discussion

Characterization of the Biochar

Figure 2 represents the diffractogram pattern of the AC-SC, showing a crystalline structure of the biochar prepared using an active agent (ZnCl₂). Thus, wurtzite-type hexagonal crystalline structures of the ZnO with characteristic peaks and planes were indicated at 31.3° (1 0 0), 33.8° (0 0 2), 35.8° (1 0 1), 46.9° (1 0 2), 56.3° (1 1 0), 62.5° (1 0 3), 66.2° (2 0 0), 67.3° (1 1 2), and 68.5° (2 0 1), according to the Joint Committee on Powder Diffraction Standard (JCPDS—nº 01–075-0576), coming from the precursor of the chemical activation ZnCl₂ (Figueiredo et al., 2020; Zidi et al., 2019).

Fig. 2.

XRD pattern diffractogram of the AC-SC

Figure 3a shows the micrograph of AC-SC, indicating a heterogeneous and rough surface, with a series of cavities and porosity, with a measured average diameter of about 53.5 nm. Figure 3b represents the elementary composition (% weight) by EDS, indicating the presence of zinc (65.94%), oxygen (21.09%), magnesium (4.37%), chlorine (4.12%), calcium (2.74%), silica (0.88%), and potassium (0.86%).

Fig. 3.

a SEM micrograph at 710 × magnification and b elementary composition (% weight) obtained by EDS analysis for the AC-SC.

According to Fig. 3a, it was possible to identify the formation of the zinc oxide (ZnO) crystal onto the biochar surface in the wurtzite-type hexagonal crystalline structure with a lattice parameter of the a = 0.325 nm and c = 0.521 nm, and special group P63mc, indicating that the oxygen atoms are stacked in a compact hexagonal shape and the zinc atoms occupy half of the tetrahedral interstices (Bayan & Mohanta, 2010). Figure 3b indicates the majority presence of zinc and oxygen, confirming the effectiveness of the activation/carbonization process. The other elements found (such as magnesium, chlorine, calcium, silica, and potassium) come from the residual biomass used as a precursor for the preparation of biochar.

Figure 4 represents the N₂ adsorption/desorption isotherms used to determine the specific surface area (S_BET), pore diameter (Dp), and pore volume (Vp).

Fig. 4.

N₂ adsorption/desorption isotherms of the AC-SC

According to Fig. 4, AC-SC showed a structure with hysteresis type H3, indicating plate-like particle aggregates that give rise to wedge-shaped and cone-shaped pores. Moreover, the specific surface area was 2.14 ± 0.10 m² g⁻¹, diameter pore (Dp) of the 53.63 ± 0.04 nm and volume pore (Vp) of the 0.0054 ± 0.003 cm³ g⁻¹, indicating the mesoporous materials, but with smaller specific area and pore volume about other vegetal charcoals, due to the precursor chosen for synthesis and the possible sintering of the pores, decreasing in the surface area and porosity (Liu et al., 2020). However, it allows the possibility of a high removal capacity due to the selectivity of the AC-SC with the dexamethasone drug, allowing a physicochemical interaction and thus the possible removal of the organic pollutant (Pavlović et al., 2021). About the zeta potential (ZP) (pH = 5.5 ± 0.5), AC-SC showed negative surface charge (− 3.09 ± 0.21 mV), due to the composition of precursors (Syzygium cumini leaves), such as flavonoids and hydrolyzable tannins, as a group of polyphenols (phenolic rings), indicating the stability of the biosorbent and favoring the interaction between the drug and the surface of the AC-SC (Bernardo et al., 2021; Daniel & Devi, 2019; Sethy et al., 2020).

Figure 5 shows the zero charge point (pH_ZCP) of the AC-SC, indicating around 7.36, confirmed with the literature (Ghenaatgar et al., 2019). Moreover, when the pH is lower than pH_ZCP, AC-SC surface will be protonated, favoring the adsorption of compounds with negative charges (such as drugs), and many anions will be adsorbed to balance the positive charges. Thus, the adsorption process can be explained by the electrostatic attraction between the charge generated on the surface of the adsorbent material and the anionic group of the solution (Thiebault, 2020).

Fig. 5.

Zero charge point (pH_ZCP) of the AC-SC

Equilibrium Adsorption Isotherms

Table 1 presents the results of adsorption equilibrium parameters using biochar in the concentrations of the 2, 5, and 7.5 g L⁻¹ and [DEX] = 4 mg L⁻¹, while Table 2 presents the statistical parameters obtained by the adjustment of the experimental data for the equilibrium models. The drug concentration was optimized in a preliminary study and according to the literature (Asuha et al., 2019); thus, [DEX] = 4 mg L⁻¹ was chosen for the adsorption tests.

Table 1.

Parameter equilibrium obtained using Langmuir, Freundlich, Sips, and Toth models

[AC-SC] (g L−1)	Langmuir			Freundlich			Sips		Toth
	Qmax (mg g−1)	KL (L mg−1)	RL	KF ((mg g−1)(mg L−1)−1/n)	n	Qs (mg g−1)	Ks (L mg−1)	ns	Qto (mg g−1)	Bto (L mg−1)	nto
2	0.141	0.027	0.479	0.154	1.94	0.272	0.135	− 8.205	0.021	1.961	0.193
5	0.673	1.234	0.168	0.383	3.351	0.519	0.393	− 5.749	3031	19.87	2.902
7.5	0.744	0.041	0.849	1.9 × 10−4	0.191	0.769	4.985	− 5.337	3008	9250	0.731

Table 2.

Statistical parameters regarding the readjustment of experimental results

[AC-SC] (g L−1)	Langmuir				Freundlich				Sips				Toth
	R2	R2adj	RMSE	SSE	R2	R2adj	RMSE	SSE	R2	R2adj	RMSE	SSE	R2	R2adj	RMSE	SSE
2	0.270	0.210	0.147	0.068	0. 601	0.568	0.328	0.152	0.847	0.782	0.463	0.214	0.454	0.221	0.876	0.767
5	0.070	0.040	0.038	0.018	0.143	0.091	0.078	0.036	0.984	0.978	0.017	0.001	0.978	0.969	0.021	0.001
7.5	0.046	0.022	0.025	0.012	0.337	0.271	0.184	0.085	0.896	0.851	0.011	0.001	0.567	0.401	0.039	0.001

According to Table 1, about the Langmuir model, all the results showed the R_L values between 0 and 1, indicating the adsorption process was considered favorable. Moreover, the maximum adsorption capacity (Q_max) increased with the AC-SC concentration, due to the greater number of active sites available for adsorption of the drug and, thus, the greater the rate of diffusion and mass transfer (Bagheri et al., 2020). For Freundlich isotherm, only the concentration of 5 g L⁻¹ showed the heterogeneity of the material (1 < n < 10). Sips isotherm showed the K_s in the concentrations of 2 and 5 g L⁻¹ had values close to 0; thus, the equation can be reduced to the Freundlich isotherm, and n_s values were below 0, indicating an increase in heterogeneity. About the Toth isotherm, n_TO in the concentration of 5 g L⁻¹ showed greater heterogeneity due to being greater than 1, and the maximum adsorption capacity Q_TO obtained an increase with the increase in the concentration of biochar, where in the 7.5 g L⁻¹ there was a saturation due to more than biochar.

About the equilibrium model, the Sips model showed the best fit, according to the coefficient determination (R²) of 0.847, 0.984, and 0.896 to the 2, 5, and 7.5 g L⁻¹ respectively, with a maximum adsorption capacity (Qs) of 0.272, 0.519, and 0.769 mg g⁻¹. Thus, according to the literature, the results using biochar in the removal of drugs were promising, with excellent results for Q_s, K_s, and n_S using the Sips model that, in high concentrations of adsorbate, provides an adsorption capacity in monolayers, characteristic of the isotherm of Langmuir (Nguyen et al., 2021; Santos et al., 2020).

Kinetic Adsorption Models

For the kinetic study of the adsorption, the pseudo-first order, pseudo-second order, Avrami, and Weber-Morris models were used to obtain the kinetic parameters, according to Table 3. Moreover, Table 4 presents the statistical parameters obtained by the adjustment of the experimental data for the kinetic models. Thus, the pseudo-second order kinetic model showed the best experimental fit of the data for the biochar concentration of 5 (R² = 0.87) and 7.5 (R² = 0.73) g L⁻¹, indicating the adsorption mechanism that involves electron exchange and/or transfer between the biochar and the drug, suggesting chemical adsorption (Bullen et al., 2021; Chen et al., 2018; Ezzati, 2020; Hubbe et al., 2019).

Table 3.

Kinetic parameters obtained using the pseudo-first order, pseudo-second order, Elovich, Avrami, and Weber-Morris models

AC-SC (g L−1)	Pseudo-first order		Pseudo-second order		Elovich		Avrami			Weber-Morris
	q1 (mg g−1)	k1 (1 min−1)	q2 (mg g−1)	k2 (g (mg min−1)−1	ae (mg (mg min−1)−	be (mg g−1)	qavr (mg g−1)	kavr (L min−1)	navr	kwm (mg (g min0.5)−1	B (mg g−1)
2	1.850	0.123	1.704	1396	1.065	3.001	1.850	0.012	9.772	0.139	0.523
5	0.451	0.257	0.455	1.910	9.95 × 10−6	558.4	2601	1.7 × 10−4	0.008	0.015	0.282
7.5	0.093	0.362	0.101	2.952	0.896	90.23	228.0	1.9 × 10−7	20.01	0.006	0.039

Table 4.

Statistical parameters regarding the readjustment of experimental results

AC-SC (g L−1)	PFO				PSO				Elovich				Avrami				Weber-Morris
	R2	R2adj	RMSE	SSE	R2	R2adj	RMSE	SSE	R2	R2adj	RMSE	SSE	R2	R2adj	RMSE	SSE	R2	R2adj	RMSE	SSE
2	0.26	0.07	1.02	1.05	0.17	0.04	1.09	1.17	0.25	0.06	1.03	1.05	0.25	0.06	1.02	1.05	0.22	0.03	1.05	1.09
5	0.85	0.81	0.05	0.003	0.87	0.75	0.06	0.004	0.80	0.76	0.41	0.18	0.25	0.40	0.27	0.07	0.19	0.08	0.13	0.02
7.5	0.64	0.54	0.02	0.001	0.73	0.62	0.02	0.001	0.69	0.67	0.02	0.001	0.99	0.59	0.05	0.002	0.52	0.39	0.02	0.001

In other studies, Syzygium cumini leaves were also used to remove organic pollutants, where some equilibrium and kinetic models were used, as shown in Table 5.

Table 5.

Kinetic and equilibrium parameters in the removal of pollutants using Syzygium cumini leaves

AC-SC (g L−1)	Langmuir			Freundlich		Reference
	Qmax (mg g−1)	KL (L mg−1)	RL	KF ((mg g−1)(mg L−1)−1/n)	n
25	11.52	0.275	0.879	2.31	1.453	Keshtkar et al., 2019)
6.5	1.121	-	-	-	1.093	Tirkey et al., 2018)
	Pseudo-first order		Pseudo-second order
	q1 (mg g−1)	k1 (1 min−1)	q2 (mg g−1)	k2 (g (mg min−1)−1
25	0.547	0.021	2.34	0.126		Keshtkar et al., 2019)
6.5	0.0009	0.073	0.4991	2.38		Tirkey et al., 2018)

Table 5 shows the results of other research using Syzygium cumini leaves in the removal of pollutants; the q_max was better than that presented in the present study; that is, due to the difference in activation of the biosorbent, there is an increase in the chemical interaction; both obtained a n optimal value for Freundlich, the pseudo-second order (PSO) kinetic model was the best at the concentration of 6.5 g L⁻¹; this shows the similarity with the result obtained in the present work.

Conclusion

It was possible to verify the DEX removal capacity using the AC-SC, presenting a better fit in the Sips equilibrium model with the best R² of 0.962, that in high concentrations of adsorbate, provides an adsorption capacity in monolayers, characteristic of the isotherm of Langmuir, and a maximum Q_S adsorption capacity of 0.769 mg g⁻¹, n_S values were below zero, indicating a heterogeneous surface, as also showed by EDS and for the kinetic, the best model obtained was the PSO where the R² has a value of 0.87 indicating that the adsorption mechanism that involves electron exchange and/or transfer between the biochar and the drug, suggesting chemical adsorption, in the end of the tests showed removal of 53.02% of DEX drug. The AC-SC showed a heterogeneous surface and a percentage of presence of zinc (65.94%), according to the SEM–EDS, a negatively charged ZP and a zero charge point (pH_ZCP) of 7.36. Therefore, biochar presented good results in the removal of DEX, considering the AC-SC can be used as an alternative material in the removal of organic pollutants.

Author Contribution

M.L. da Costa: conceptualization, validation, writing—review and editing.

G. Pavoski: conceptualization, data curation, formal analysis, investigation and validation.

D.C.R. Espinosa: conceptualization, data curation, formal analysis, investigation and validation.

N.J.S. De Vasconcellos: conceptualization, validation, writing—review and editing.

W.L. da Silva: conceptualization, data curation, formal analysis, investigation, validation, writing—original draft, writing—review and editing.

Funding

This study was financially supported by the Foundation for Research of the State of Rio Grande do Sul (FAPERGS – Project 19/2551–0001362-0).

Availability of Data and Materials

The data that support the findings of this study are available on request from the corresponding author.

Declarations

Ethics Approval

Not applicable in this work.

Consent to Participate

Not applicable.

Consent to Publish

All authors read and approved the final manuscript.

Competing Interests

The authors declare no competing interests.