Sustainable Adsorption of Rhodamine B and Heavy Metals Using Sewage Sludge-Derived Biochar

Abstract

The sustainable management of sewage sludge remains a key environmental challenge for rapidly urbanizing regions such as Kazakhstan. This study explores the potential of sewage sludge-derived biochar as an efficient, low-cost adsorbent for removing Rhodamine B (RhB) dye and toxic metals from water. Sewage sludge was pyrolyzed at 700 °C (BC) and subsequently activated with hydrochloric acid (BCH) and sodium hydroxide (BCN) to improve its surface functionality and porosity. The morphology, surface area, porosity, and functional groups of the obtained biochars were characterized using SEM-EDS, BET, FTIR, and XRD analyses. Batch adsorption experiments demonstrated that the pseudo-second-order kinetic model (R² = 0.99) best described the data, indicating chemisorption-controlled uptake. Experimental RhB adsorption capacity was 14.53 mg/g for BCH at RhB concentration of 75 mg/L after 120 min. Moreover, BCH exhibited enhanced metal adsorption capacities of 22.85 mg/g (Cu²⁺), 17.55 mg/g (Zn²⁺), 15.08 mg/g (Cd²⁺), 7.97 mg/g (Cr³⁺), and 3.68 mg/g (As³⁺). These results confirm that acid activation significantly improves adsorption efficiency compared with pristine biochar due to increased surface area and the introduction of oxygen-containing functional groups. Overall, sewage sludge-derived biochar shows strong potential as a sustainable adsorbent for dye and heavy metal removal.

1. Introduction

Kazakhstan’s accelerating urban development and population growth are contributing to a steady increase in wastewater volumes, particularly in its major urban centers such as Astana and Almaty. Municipal wastewater treatment facilities in the country predominantly rely on biological processes where organic contaminants are broken down by activated sludge, consisting largely of microbial communities [1,2]. To ensure operational efficiency, these plants must regularly remove excess biomass, resulting in the continuous generation of large quantities of sewage sludge. Currently, this sludge is only dewatered and then disposed of in landfills near urban areas, with an estimated 240–250 tons dumped daily in the vicinity of Astana [3,4,5]. This practice poses environmental and health risks, as untreated sludge may harbor both residual pollutants and harmful microorganisms [6].

Thermal conversion technologies such as pyrolysis offer a more sustainable route for sewage sludge management, aligning with global trends in waste valorization [7]. Pyrolysis decomposes organic matter under oxygen-limited conditions, typically within a temperature range of 400–900 °C, producing a solid, carbon-rich byproduct known as biochar. While a range of biomass feedstocks, such as agricultural residues and forestry waste, have been extensively studied for biochar production [8], the transformation of sewage sludge into functional biochar remains relatively underexplored, despite its potential to address both waste management and environmental remediation challenges.

Biochar is known for its large surface area, porosity, and diverse surface chemistry, which together enable it to adsorb a wide variety of pollutants from aqueous media. These properties make biochar a promising low-cost alternative to conventional adsorbents for the removal of both organic and inorganic contaminants [9,10,11]. In particular, the application of sewage sludge-derived biochar in water treatment has attracted increasing attention due to its dual role in waste recycling and pollution control. For instance, biochar produced from pulp mill sludge has demonstrated notable removal efficiencies for heavy metals such as Pb(II), Cd(II), Cu(II), and Ni(II), with Pb(II) adsorption reaching 256.4 mg/g based on Langmuir isotherms [9]. Other studies have shown that sewage sludge biochar can remove chromium, cadmium, copper, and lead with varying but significant capacities [10,11].

Beyond metal ion removal, removal of other organic pollutants is of great importance [12,13]. Biochar has also been explored for capturing synthetic dyes, such as RhB, which are widely used in the textile and printing industries and persist in aquatic systems due to their complex aromatic structures. Adsorption mechanisms in such cases typically involve π-π stacking, electrostatic interaction, and hydrogen bonding [14,15]. However, the specific potential of sewage sludge biochar for the adsorption of RhB and heavy metals remains inadequately addressed in the literature.

This study investigates the adsorption performance of biochar derived from municipal sewage sludge produced by the Astana wastewater treatment plant. The material’s efficiency in removing RhB and selected heavy metals from aqueous solutions is evaluated to explore its feasibility as a sustainable, locally sourced adsorbent for water purification applications in Kazakhstan. Systematic studies combining chemical activation strategies, advanced kinetic and multi-parameter isotherm modeling, and simultaneous evaluation of dye and heavy-metal adsorption using sewage sludge biochar derived from Kazakhstan municipal wastewater remain limited in the literature. This work directly supports the United Nations Sustainable Development Goals SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), and SDG 12 (Responsible Consumption and Production) by promoting wastewater valorization and sustainable resource management.

2. Materials and Methods

Sewage sludge samples were obtained from the municipal wastewater treatment plant “Astana su arnasy” (Astana, Kazakhstan). The as-received sludge exhibited a high moisture content of approximately 70–75%. The sludge was dried overnight at 105 °C and weighed into ceramic vessels, which were sealed with custom-made caps designed to prevent oxygen ingress at high temperatures. The vessels were then placed in a gradient temperature furnace (LH 60/12, Nabertherm GmbH, Lilienthal, Germany) and the dried sewage sludge was pyrolyzed at 700 °C with a heating rate of 10 °C min⁻¹ and a holding time of 1 h under oxygen-limited conditions. For acid activation (sample BCH), 5 g of biochar was dispersed in 100 mL of 1 M hydrochloric acid (HCl) solution and stirred continuously for 6 h at room temperature. For alkaline activation (sample BCN), 8 g of biochar was dispersed in 100 mL of 2 M sodium hydroxide (NaOH) solution and stirred continuously for 24 h at room temperature. After treatment, both suspensions were centrifuged to recover the solid fraction, which was subsequently washed several times with deionized water until the filtrate reached neutral pH. The resulting biochar samples were dried overnight at 105 °C and stored for further use.

Simultaneous thermal analysis (TGA) of the biochar samples was carried out using a STA 6000 analyzer (Perkin Elmer, Waltham, MA, USA). The surface morphology and elemental composition were examined with a JEOL JSM-IT200 scanning electron microscope (JEOL Ltd., Tokyo, Japan) equipped with energy-dispersive X-ray spectroscopy (EDX). The crystalline structure and phase composition were analyzed using an X-ray diffractometer (SmartLab, Rigaku Corp., Tokyo, Japan) with a Cu Kα radiation source. Specific surface area and pore size distribution were determined using nitrogen adsorption–desorption measurements on an Autosorb iQ porosimeter (Quantachrome Instruments, Boynton Beach, FL, USA). Functional groups present on the biochar surface were identified by Fourier transform infrared (FTIR) spectroscopy (Nicolet iS10, Thermo Fisher Scientific, Waltham, MA, USA) in the range of 4000–500 cm⁻¹. The metal content was determined using an Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) system (iCAP 6300, Thermo Scientific, Waltham, MA, USA). The elemental composition of carbon, hydrogen, nitrogen, and sulfur in the samples was determined using a CHNS-O Elemental Analyzer (Unicube, Elementar, Langenselbold, Germany).

Adsorption experiments were conducted using RhB as a model organic dye pollutant. For heavy metal adsorption tests, aqueous solutions were prepared from analytical-grade salts: zinc nitrate hexahydrate (Zn(NO₃)₂·6H₂O), chromium(III) chloride hexahydrate (CrCl₃·6H₂O), copper(II) nitrate trihydrate (Cu(NO₃)₂·3H₂O), cadmium chloride hemipentahydrate (CdCl₂·2.5H₂O), and sodium arsenite (NaAsO₂). All reagents were of analytical grade and used without further purification.

Batch adsorption experiments were carried out by adding 50 mg of biochar to 50 mL of a pollutant solution with a known initial concentration (10–75 mg/L) in 100 mL glass flasks. The mixtures were stirred at room temperature (298 ± 2 °K) for a predetermined contact time to reach adsorption equilibrium. For heavy-metal adsorption experiments, 50 mg of biochar was added to 500 mL of individual metal solutions (Zn, Cu: 25 mg/L; Cr, Cd, As: 10 mg/L) and stirred for 48 h. After adsorption, the suspensions were centrifuged to separate the solid and liquid phases. The residual concentrations of RhB and heavy metals were measured using a UV-Vis spectrophotometer (for RhB at 554 nm) and ICP-OES for metal ions. The amount of adsorbate removed per unit mass of biochar (q_e, mg/g) was calculated using mass balance equations.

To gain a deeper understanding of the adsorption kinetics, the experimental data were analyzed using kinetic models, namely the pseudo-first-order (PFO, Equation (1)), pseudo-second-order (PSO, Equation (2)), intra-particle diffusion (Equation (3)), and Elovich (Equation (4)) models [16,17,18].

$${\mathrm{q}}_{\mathrm{t}}={\mathrm{q}}_{\mathrm{e}}-{\displaystyle \frac{{\mathrm{q}}_{\mathrm{e}}}{{\mathrm{e}}^{{\mathrm{k}}_1\mathrm{t}}}}$$

(1)

$${\mathrm{q}}_{\mathrm{t}}={\displaystyle \frac{{\mathrm{k}}_2{\mathrm{q}}_{\mathrm{e}}^2\mathrm{t}}{1+{\mathrm{k}}_2{\mathrm{q}}_{\mathrm{e}}\mathrm{t}}}$$

(2)

$${\mathrm{q}}_{\mathrm{t}}={\mathrm{k}}_{\mathrm{d}\mathrm{i}\mathrm{f}\mathrm{f}}{\mathrm{t}}^{1/2}+\mathrm{C}$$

(3)

$${\mathrm{q}}_{\mathrm{t}}={\displaystyle \frac{1}{\mathsf{\beta }}}\mathrm{l}\mathrm{n}(1+\mathsf{\alpha }\mathsf{\beta }\mathrm{t})$$

(4)

where q_t and q_e (mg g⁻¹) represent the adsorption capacities at time t and at equilibrium, respectively; k₁ (min⁻¹) and k₂ (g mg⁻¹ min⁻¹) are the rate constants for the PFO and PSO models, respectively; k_diff (mg g⁻¹ min^−0.5) is the intraparticle diffusion rate constant; C (mg g⁻¹) denotes the boundary layer thickness; and α (mg g⁻¹ min⁻¹) and β (g mg⁻¹) are the initial adsorption rate and surface coverage/desorption constant in the Elovich model.

The adsorption equilibrium data were analyzed using two common isotherm models, namely the Langmuir, Freundlich, Temkin, Redlich-Peterson, Sips and Jovanovic models [17,18]. It is important to emphasize that both models are based on macroscopic equilibrium data and do not provide direct information about the underlying retention mechanisms [19]. The non-linear form of the Langmuir isotherm can be expressed as in Equation (5) [20]:

$${\mathrm{q}}_{\mathrm{e}}={\mathrm{q}}_{\mathrm{m}\mathrm{a}\mathrm{x}}{\displaystyle \frac{{\mathrm{K}}_{\mathrm{L}}{\mathrm{C}}_{\mathrm{e}}}{1+{\mathrm{K}}_{\mathrm{L}}{\mathrm{C}}_{\mathrm{e}}}}$$

(5)

where C_e represents the equilibrium concentration of the pollutant in solution (mg/L), q_e is the amount of pollutant adsorbed per unit mass of sorbent at equilibrium (mg/g), K_L is the Langmuir constant related to the adsorption energy (L/g), and q_max denotes the theoretical maximum adsorption capacity of the sorbent (mg/g).

Freundlich isotherm model is expressed in Equation (6) [20]:

$${\mathrm{q}}_{\mathrm{e}}={\mathrm{K}}_{\mathrm{F}}{\mathrm{C}}_{\mathrm{e}}^{1/\mathrm{n}}$$

(6)

where K_F and n are the Freundlich constants, representing the adsorption capacity and adsorption intensity of the sorbent, respectively.

The Temkin model accounts for indirect adsorbate-adsorbate interactions, assuming that the heat of adsorption decreases linearly with surface coverage. The fitting was performed using the non-linear form (Equation (7)):

$${\mathrm{q}}_{\mathrm{e}}={\mathrm{K}}_{\mathrm{T}}\ln {(\mathrm{A}}_{\mathrm{T}}{\mathrm{C}}_{\mathrm{E}})$$

(7)

where A_T (L/mg) is the Temkin binding equilibrium constant, and K_T (mg/g) is related to the heat of adsorption b_T by the relationship K_T = RT/b_T. R is the universal gas constant (8.314 J/molK), and T is the absolute temperature (K).

The Redlich-Peterson model is another three-parameter model applicable over a wide concentration range, incorporating features of both Langmuir and Freundlich models. The non-linear equation used was (Equation (8)):

$${\mathrm{q}}_{\mathrm{e}}={\displaystyle \frac{{\mathrm{K}}_{\mathrm{R}}{\mathrm{C}}_{\mathrm{e}}}{1+{\mathrm{A}}_{\mathrm{R}}{\mathrm{C}}_{\mathrm{e}}^{{\mathrm{B}}_{\mathrm{R}}}}}$$

(8)

where K_R (L/g) and A_R (L/mg) are the Redlich-Peterson constants, and B_R (unitless) is the exponent, which suggests the model’s tendency towards either Langmuir (B_R = 1) or Freundlich (B_R closer to 0).

The Sips model is a three-parameter hybrid that reduces to the Freundlich isotherm at low concentrations and the Langmuir isotherm at high concentrations, thus accounting for both homogeneous and heterogeneous surface characteristics. The non-linear form used for fitting was (Equation (9)):

$${\mathrm{q}}_{\mathrm{e}}={\displaystyle \frac{{\mathrm{q}}_{\mathrm{S}}{{(\mathrm{K}}_{\mathrm{s}}{\mathrm{C}}_{\mathrm{e}})}^{{\mathrm{B}}_{\mathrm{S}}}}{1+{{(\mathrm{K}}_{\mathrm{s}}{\mathrm{C}}_{\mathrm{e}})}^{{\mathrm{B}}_{\mathrm{S}}}}}$$

(9)

where q_s (mg/g) is the maximum adsorption capacity, K_S (L/mg) is the Sips equilibrium constant, and B_S (unitless) is the surface heterogeneity parameter. If B_S = 1, the model simplifies to the Langmuir model.

The Jovanovic model is an approach similar to Langmuir but includes a term for mechanical surface interaction that occurs at equilibrium. The model fitted was (Equation (10)):

$${\mathrm{q}}_{\mathrm{e}}={\mathrm{q}}_{\mathrm{m}\mathrm{a}\mathrm{x}}(1-{\mathrm{e}}^{{\mathrm{K}}_{\mathrm{J}}{\mathrm{C}}_{\mathrm{e}}})$$

(10)

where q_max (mg/g) is the maximum adsorption capacity and K_J (L/mg) is the Jovanovic constant.

3. Results

3.1. Characterization

First, characterization was performed using thermogravimetric analysis (TGA) and results are presented in Figure 1.

Thermogravimetric analysis shows that all biochars underwent gradual weight loss with increasing temperature up to 900 °C. The initial loss below 150 °C corresponds to the removal of adsorbed moisture, while the major decrease between 200 and 600 °C is due to the decomposition of surface oxygen-containing groups [22,23]. The pristine biochar exhibited the highest thermal stability, whereas the acid-activated sample showed the greatest weight loss, indicating a higher content of labile oxygenated functionalities. The alkaline-activated biochar demonstrated intermediate stability, consistent with partial ash removal and increased porosity. Overall, the thermal stability followed the order BC > BCN > BCH.

FTIR spectroscopy was used to identify the surface functional groups present in the biochar samples (Figure 2).

FTIR spectra (Figure 2) show characteristic bands at 3232 cm⁻¹ (O-H stretching), 2881 cm⁻¹ (C-H stretching), 1584 and 1410 cm⁻¹ (aromatic C=C and carboxylate groups), 973 cm⁻¹ (C-O or Si-O-Si), and 516 cm⁻¹ (metal-O) [24,25,26]. Compared with BC, BCH exhibited slightly more pronounced O-H and C=O bands, indicating increased oxygenated groups after acid activation, while BCN showed enhanced Si-O and metal-O peaks due to alkali-induced mineral transformations. These changes confirm surface modification and increased reactivity after activation.

Next, the XRD patterns of biochars produced are shown in Figure 3.

According to Figure 3, all samples exhibit multiple diffraction peaks in the range of 10–80°, indicating the presence of crystalline mineral phases inherited from the sludge. The major reflections near 26–30° correspond to SiO₂ (quartz) and Al₂O₃-containing phases [25,27]. After chemical activation, noticeable changes in peak intensity and sharpness are observed. The BCN sample shows slightly reduced crystallinity, suggesting partial dissolution of mineral components under alkaline treatment. In contrast, BCH displays more pronounced peaks, which may be attributed to the formation or enrichment of stable mineral residues (e.g., metal oxides and silicates) after acid washing. Overall, the XRD results confirm that both acid and base activations alter the mineral composition and structural order of the biochars.

The surface morphology of the biochar samples was investigated using SEM, as shown in Figure 4.

The pristine biochar exhibits a relatively compact and smooth surface with limited visible porosity. After alkaline activation, the surface becomes rougher and more fractured, indicating partial dissolution of mineral components and pore development [28,29]. Acid activation results in an even more porous and irregular structure, likely due to the removal of inorganic phases and the oxidation of the carbon matrix [30]. Overall, both treatments increase surface roughness and expose more active sites, which can enhance adsorption performance.

EDS analysis (

Table 1. EDS analysis results.

Elements	Content, %
	BC	BCN	BCH
C	31.02 ± 2.39	25.78 ± 1.52	23.41 ± 0.10
O	37.07 ± 0.02	29.66 ± 7.96	35.17 ± 0.13
Na	1.60 ± 0.91	0.00	0.29 ± 0.01
Mg	1.28 ± 0.35	1.14 ± 0.11	0.15 ± 0.01
Al	3.83 ± 0.45	4.60 ± 0.20	4.04 ± 0.03
Si	10.06 ± 0.55	11.08 ± 0.15	31 ± 0.07
P	2.58 ± 0.27	2.43 ± 0.81	0.00
K	1.33 ± 0.33	1.70 ± 0.37	5.02 ± 0.04
Ca	6.81 ± 1.39	13.74 ± 4.34	0.00
Ti	0.34 ± 0.03	0.67 ± 0.21	0.16 ± 0.01
Fe	3.89 ± 0.03	7.36 ± 2.15	0.76 ± 0.02
S	0.21 ± 0.1	1.56 ± 0.63	0.00
Cl	0.00	0.30 ± 0.11	0.00

) provides insight into the elemental composition of the pristine and modified biochars.

The main elements detected in all samples were C, O, Si, Al, Ca, and Fe, originating from the organic matrix and inorganic minerals of sewage sludge [28,31,32]. After acid and alkaline activation, notable compositional changes occurred. The carbon and oxygen contents decreased slightly after activation, indicating structural modification and partial oxidation or mineral enrichment. Acid treatment led to a significant increase in Si and K content but complete removal of Ca, P, and S, suggesting dissolution of carbonates and phosphates during HCl washing. Conversely, alkaline activation resulted in higher levels of Ca and Fe, likely due to mineral rearrangement and precipitation of oxides or hydroxides. The EDS results confirm that both activation methods effectively alter the surface composition and mineral phases of biochar, which can influence its adsorption behavior. Although heavy metals were not detected by EDS, ICP-OES analysis of the raw sludge (Table S1) confirms the presence of trace metals, which should be considered when applying sewage sludge-derived biochars in water treatment.

The nitrogen adsorption–desorption isotherms of the biochars are shown in Figure 5, while their corresponding textural parameters, such as BET surface area and pore volume, are listed in

Table 2. BET surface area and pore volume of the biochar samples. The data for BC corresponds to the previously reported B700 sample (taken from [21]).

Biochars	SBET, m2/g	Pore Volume, cm3/g
BC	92.33	0.0790
BCN	127.92	0.0835
BCH	198.33	0.0914

.

The N₂ adsorption–desorption isotherms of the biochars (Figure 5) exhibit type IV behavior with H₃-type hysteresis loops according to the IUPAC classification, indicating the presence of mesoporous structures [33]. The adsorption sharply increases at relative pressures (P/P₀) above 0.8, suggesting the coexistence of macropores formed by slit-like aggregates of plate-like particles. Among the samples, the pristine biochar displayed the lowest surface area (92.33 m²/g) and pore volume (0.0790 cm³/g). Alkaline activation with NaOH increased the surface area to 127.92 m²/g and slightly enlarged the pore volume to 0.0835 cm³/g, likely due to the etching and removal of amorphous carbon by hydroxide ions. Acid activation with HCl further enhanced the textural properties, achieving the highest surface area (198.33 m²/g) and pore volume (0.0914 cm³/g). This improvement can be attributed to the dissolution of inorganic components such as metal oxides and carbonates, which opened additional porosity and cleaned pore walls. Overall, the activation treatments significantly improved the surface area and pore structure of sewage sludge-derived biochar, with acid activation showing the most pronounced enhancement.

3.2. Adsorption Results

The adsorption performance of the biochar samples toward RhB was evaluated over a period of 120 min, and the corresponding results are presented in Figure 6. All experiments were conducted using 1000 mg/L of biochar and an initial RhB concentration of 10 mg/L. Adsorption efficiency (%) was calculated as the percentage decrease in pollutant concentration relative to its initial value.

The adsorption kinetics of RhB onto the biochars are shown in Figure 6. The adsorption proceeded rapidly within the first 20 min and gradually reached equilibrium after approximately 60 min. Among the samples, the acid-activated biochar exhibited the highest adsorption efficiency, achieving nearly complete RhB removal within 120 min, while the pristine and alkaline-activated samples showed comparatively lower removal efficiencies. To better understand the adsorption mechanism, the experimental data were fitted to the pseudo-first-order (PFO), pseudo-second-order (PSO), Elovich, and intraparticle diffusion kinetic models [17,18]. The fitting parameters are summarized in

Table 3. PFO and PSO kinetic model fitting results.

Kinetic Model	Kinetic Parameters at 298 °K		Statistical Analysis
PFO	qe, cal., mg/g	k1, min−1	R2	χ2
BC	2.79	0.2	0.8544	0.15633
BCH	6.95	0.55	0.9734	0.14844
BCN	4.5	0.33	0.9308	0.17689
PSO	qe, cal., mg/g	k2, g/mg min	R2	χ2
BC	2.98	0.1	0.8992	0.10826
BCH	7.22	0.14	0.9944	0.03115
BCN	4.78	0.1	0.9747	0.06468
Elovich	α (mg/g⋅min)	β (g/mg)	R2	χ2
BC	6.86	2.43	0.9254	0.0801
BCH	5238.94	1.85	0.9895	0.05859
BCN	40.51	1.79	0.9852	0.03782
Intra particle	Kdiff		R2	χ2
BC	0.23351		0.72793	0.29214
BCH	0.45173		0.52419	2.65496
BCN	0.34947		0.68484	0.80561

.

The PSO model exhibited higher correlation coefficients (R² = 0.9944 for BCH, 0.9747 for BCN, and 0.8992 for BC) and lower χ² values compared to the PFO model, indicating that the PSO model better describes the adsorption kinetics. This suggests that the adsorption of RhB onto the biochars is primarily governed by chemisorption involving valence forces through electron sharing or exchange between the dye molecules and the surface functional groups of the biochar [34,35].

The applicability of the Elovich model further supports this interpretation. The Elovich model provided high R² values (≥0.925) for all biochars, indicating adsorption on energetically heterogeneous surfaces. Notably, the exceptionally high initial adsorption rate (α) observed for BCH reflects the presence of a large number of highly reactive surface sites generated during acid activation. Such behavior is characteristic of chemically heterogeneous adsorbents and is consistent with the rapid initial uptake observed experimentally.

The intraparticle diffusion (IPD) model was employed to further examine the contribution of diffusion mechanisms to the overall adsorption process. As shown by the IPD fitting results (Table 3), the relatively low correlation coefficients and high χ² values indicate that intraparticle diffusion alone does not adequately describe the adsorption kinetics. Moreover, the deviation of the fitted lines from the origin suggests that boundary layer diffusion and surface adsorption play significant roles alongside intraparticle diffusion. These results imply that intraparticle diffusion is involved in the RhB adsorption process but is not the sole rate-limiting step.

Overall, the combined kinetic analysis suggests that RhB adsorption onto the biochar samples is governed by a complex mechanism involving rapid surface adsorption on heterogeneous active sites followed by diffusion-controlled processes, with chemisorption playing a dominant role, particularly for the acid-activated biochar. The superior performance of BCH can be attributed to its higher BET surface area (198.33 m²/g) and cleaner pore structure resulting from acid activation, which provides more active adsorption sites and facilitates better diffusion of RhB molecules.

The time-dependent adsorption of RhB with biochar samples are presented in Figure 7. As the equilibrium concentration increased from 10 to 75 mg/L, the adsorption capacity of all samples increased correspondingly, indicating that more adsorption sites were occupied at higher dye concentrations. Among the tested biochars, the acid-activated BCH exhibited the highest adsorption capacity, followed by BCN and BC.

To describe the adsorption behavior, the experimental data were fitted to the Freundlich, Langmuir, Temkin, Redlich-Peterson, Jovanovic and Sips isotherm models (Figure 8). The corresponding parameters are summarized in

Table 4. Adsorption isotherm parameters.

Model	Equation	Parameter	BC	BCH	BCN
Langmuir	${\mathrm{q}}_{\mathrm{e}}={\mathrm{q}}_{\mathrm{m}\mathrm{a}\mathrm{x}}{\displaystyle \frac{{\mathrm{K}}_{\mathrm{L}}{\mathrm{C}}_{\mathrm{e}}}{1+{\mathrm{K}}_{\mathrm{L}}{\mathrm{C}}_{\mathrm{e}}}}$	KL (L/mg)	0.3720	0.0777	0.0937
		qmax (mg/g)	4.5718	16.9413	9.7560
		R2	0.1735	0.9446	0.9185
Freundlich	${\mathrm{q}}_{\mathrm{e}}={\mathrm{K}}_{\mathrm{F}}{\mathrm{C}}_{\mathrm{e}}^{1/\mathrm{n}}$	KF [(mg/g)(L/mg)1/n]	2.3332	4.9278	2.5228
		n	5.8511	1.8494	2.3167
		R2	0.0914	0.8866	0.9033
Temkin	${\mathrm{q}}_{\mathrm{e}}={\mathrm{K}}_{\mathrm{T}}\ln {(\mathrm{A}}_{\mathrm{T}}{\mathrm{C}}_{\mathrm{E}})$	KT (mg/g)	0.3411	3.5474	1.8967
		AT (L/mg)	6055.6714	0.8769	1.3590
		R2	0.1061	0.9405	0.9280
Redlich- Peterson	${\mathrm{q}}_{\mathrm{e}}={\displaystyle \frac{{\mathrm{K}}_{\mathrm{R}}{\mathrm{C}}_{\mathrm{e}}}{1+{\mathrm{A}}_{\mathrm{R}}{\mathrm{C}}_{\mathrm{e}}^{{\mathrm{B}}_{\mathrm{R}}}}}$	KR (L/g)	0.5617	1.4965	1.5492
		AR (L/mg)BR	0.0308	0.1116	0.2952
		BR	1.3252	0.9483	0.8640
		R2	0.2855	0.9456	0.9284
Jovanovic	${\mathrm{q}}_{\mathrm{e}}={\mathrm{q}}_{\mathrm{m}\mathrm{a}\mathrm{x}}(1-{\mathrm{e}}^{{\mathrm{K}}_{\mathrm{J}}{\mathrm{C}}_{\mathrm{e}}})$	qmax (mg/g)	4.3279	14.0546	8.2802
		KJ (L/mg)	0.1599	0.0674	0.0751
		R2	0.2502	0.9112	0.8565
Sips	${\mathrm{q}}_{\mathrm{e}}={\displaystyle \frac{{\mathrm{q}}_{\mathrm{S}}{{(\mathrm{K}}_{\mathrm{s}}{\mathrm{C}}_{\mathrm{e}})}^{{\mathrm{B}}_{\mathrm{S}}}}{1+{{(\mathrm{K}}_{\mathrm{s}}{\mathrm{C}}_{\mathrm{e}})}^{{\mathrm{B}}_{\mathrm{S}}}}}$	qs, mg/g	4.3062	17.7576	12.3196
		Ks (L/mg)	0.1060	0.0708	0.0527
		Bs	20.9595	0.9049	0.6431
		R2	0.2849	0.9452	0.9303

.

The Langmuir model adequately described the adsorption behavior of RhB on the activated biochars, particularly BCH and BCN, with high correlation coefficients (R² = 0.9446 and 0.9185, respectively). In contrast, a poor fit was observed for pristine BC (R² = 0.1735), indicating that monolayer adsorption on a homogeneous surface is not a valid assumption for the untreated material. The maximum monolayer adsorption capacities obtained from the Langmuir model were 16.94 mg/g for BCH, 9.76 mg/g for BCN, and 4.57 mg/g for BC.

The significantly higher q_max and R² values for BCH confirm that acid activation markedly enhanced the adsorption performance of sewage sludge biochar. This improvement can be attributed to the development of a more homogeneous surface and the exposure of oxygen-containing functional groups, which promote electrostatic attraction and π–π interactions with RhB molecules [14].

The Freundlich model showed a reasonable fit for BCH and BCN (R² = 0.8867 and 0.9034, respectively), while exhibiting a very poor correlation for BC (R² = 0.0915). The Freundlich constants (n > 1) for all samples indicate favorable adsorption conditions; however, the highest adsorption intensity was observed for BC (n = 5.85), likely reflecting weak adsorption at low surface coverage rather than true sorption efficiency. The lower n values for BCH (n = 1.85) and BCN (n = 2.32) suggest increased surface heterogeneity following chemical activation.

The Temkin isotherm provided good agreement for BCH and BCN (R² = 0.9405 and 0.9280, respectively), indicating a relatively uniform distribution of adsorption energies up to moderate surface coverage. The higher Temkin constant (K_T = 3.55 mg/g) obtained for BCH further supports stronger adsorbate–adsorbent interactions induced by acid activation, whereas the model was not suitable for BC (R² = 0.1061).

Nonlinear three-parameter models further supported these observations. The Redlich–Peterson model exhibited excellent fits for BCH and BCN (R² = 0.9456 and 0.9284), with exponent values (B_R ≈ 1) approaching Langmuir-type behavior. Similarly, the Sips model yielded high correlation coefficients for BCH (R² = 0.9452) and BCN (R² = 0.9303), confirming adsorption on energetically heterogeneous surfaces that tend toward monolayer saturation at higher equilibrium concentrations. In contrast, the extremely high heterogeneity parameter obtained for BC (B_S ≫ 1) reflects the inadequacy of this model for the pristine biochar. The Jovanovic model followed a similar trend, with the highest adsorption capacity obtained for BCH (q_max = 14.05 mg/g), further illustrating enhanced surface affinity and reduced steric limitations after acid activation.

Overall, the isotherm analysis demonstrates that RhB adsorption on chemically activated sewage sludge-derived biochars is best described by Langmuir-based and hybrid nonlinear models, whereas pristine BC does not conform well to classical isotherm assumptions. The adsorption capacity followed the order: BCH > BCN > BC, highlighting the superior performance of acid-activated biochar [14].

Next, the BCH sample was used to absorb heavy metals. A lower biochar dose (100 mg/L) was used here compared with the RhB experiments (1000 mg/L) to evaluate sorption capacity under high pollutant-to-adsorbent loading; both the q_e values and percent removal are reported to clarify performance under these different dosing regimes (Figure 9).

Among the tested metals, Cu²⁺ exhibited the highest adsorption capacity (22.85 mg/g) and noticeable removal efficiency (8.59%), followed by Zn²⁺ (17.55 mg/g; 7.22%) and Cd²⁺ (15.08 mg/g; 13.13%). Lower adsorption values were observed for Cr³⁺ (7.97 mg/g; 8.26%) and As³⁺ (3.68 mg/g; 4.26%). These results suggest that the BCH demonstrates moderate affinity toward divalent cations, particularly Cu²⁺ and Zn²⁺, which may be attributed to electrostatic attraction and complexation with surface oxygen-containing functional groups. The relatively low adsorption of arsenic indicates weaker interactions due to its predominantly anionic species in aqueous media. It should be noted that the present study focuses on single-metal systems. In real wastewater, competitive adsorption may occur; therefore, future studies will investigate multi-component systems to better simulate realistic conditions.

Table 5. Comparison of RhB and heavy metal adsorption by different biochars reported in the literature.

Compound	Biomass	Pyrolysis Conditions	Dosage, g/L	C0, mg/L	te, h	qmax, mg/g	Ref.
RhB	Sewage sludge	at 500 °C for 1 h	1	0.14–15	5	4.29	[28]
	Sewage sludge	at 500 °C for 1 h and NaOH activation	1	0.14–15	5	8.72	[28]
	Coconut fruit shell	at 500 °C for 4 h	10–35	100–350	2	8.1	[12]
	Sewage sludge (beverage industry)	at 500 °C for 4 h, then at 800 °C for 1 h, and KOH activation	2–500	1	3	199.86	[36]
	Sewage sludge (beverage industry)	at 500 °C for 4 h, then at 800 °C for 1 h	2–500	4	3	22.59	[36]
	Sewage sludge	at 700 °C for 1 h	1	75	2	4.25	This work
	Sewage sludge	at 700 °C for 1 h and NaOH activation	1	75	2	8.63	This work
	Sewage sludge	at 700 °C for 1 h and HCl activation	1	75	2	14.53	This work
Cd2+	Corn stalks	at 300 °C for 2 h and amino modification	50	100–500	12	375.6	[37]
	Platanus orientalis Linn leaves	at 400 °C for 4 h and modification with orthophosphates	2	50	24	54.7	[38]
	Tea residue	at 400 °C and modification with magnetic particles	3	100	24	27.50	[39]
	Banana peel waste	at 400 °C for 5 h	0.5–4	10–80	1.5	20.63	[40]
	Sewage sludge	at 400 °C	2.5–7.5	20–200	24	46.64	[31]
	Sewage sludge	at 600 °C	4	100	24	5.33	[41]
	Sewage sludge	at 700 °C for 1 h and HCl activation	0.1	10	48	15.08	This work
Cu2+	Sargassum hemiphyllum	at 700 °C for 2 h	-	10–80	2	93.5	[42]
	Pinecone, white popinac, and sugarcane bagasse	at 550 °C for 4 h and modification with MnFe2O4	0.4	1–100	12	19.8	[43]
	Bamboo	at 550 °C for 1 h and modification with γ-Fe2O3	2.5	635	-	35.2	[44]
	Water hyacinth	at 425.27 °C for 3.09 h	2	20	4	9.9	[45]
	Sewage sludge	at 400 °C for 2 h	10	5–100	24	5.342	[46]
	Sewage sludge	at 400 °C	2.5–7.5	20–200	24	42.42	[31]
	Sewage sludge	at 600 °C	4	100	24	4.05	[41]
	Sewage sludge	at 700 °C for 1 h and HCl activation	0.1	25	48	22.85	This work
Zn2+	Paddy husk and mixed wood sawdust	at 450–550 °C	1	10	24	6.5	[47]
	Rice straw	at 500 °C	2	100	3	32.8	[48]
	Sewage sludge	at 400 °C for 2 h	10	5–100	24	5.905	[46]
	Sewage sludge	at 600 °C	4	100	24	6.14	[41]
	Sewage sludge	at 700 °C for 1 h and HCl activation	0.1	25	48	17.55	This work
Cr3+	Sewage sludge (Cr6+)	at 400 °C for 2 h	10	5–100	24	5.724	[46]
	Sewage sludge	at 400 °C	2.5–7.5	20–200	24	43.89	[31]
	Sewage sludge	at 450 °C for 1 h	1–16	10	2	2.324	[49]
	Food waste	at 450 °C for 30 min	4	75	12	21.456	[35]
	Sewage sludge	at 700 °C for 1 h and HCl activation	0.1	10	48	7.97	This work
As3+	Sewage sludge	at 400 °C for 2 h	2	0.1–30	24	6.04 *	[50]
	Peanut shell	at 400 °C and modification with KMnO4/KOH	0.3–3	0.05–1	4	1.76	[51]
	Canola straw	pyrolyzed using a 650 W (2.45 GHz) microwave and impregnated with Zn and Al oxides	1	1	12	7.6	[52]
	Distillers’ grain with electrolytic manganese residue	at 750 °C for 2 h	2.5	50	24	40.92	[53]
	Sewage sludge	at 700 °C for 1 h and HCl activation	0.1	10	48	3.68	This work

* The Langmuir sorption capacity was reported.

compares the adsorption performance of RhB and metals on sewage sludge-derived biochars from this study with data reported in the literature.

The biochar produced from sewage sludge at 700 °C in this work exhibited a moderate adsorption capacity of 4.25 mg/g, which increased markedly to 8.63 mg/g after NaOH activation and further to 14.53 mg/g following HCl activation. This enhancement can be attributed to the increase in surface functional groups and the development of porosity induced by chemical treatment. Compared with previously reported sewage sludge-based biochars, the adsorption capacity of the HCl-activated sample in this study is higher than that of unmodified sludge biochars pyrolyzed at 500 °C [28] and comparable to some chemically activated materials (NaOH-activated biochar: 8.72 mg/g). However, it remains lower than the KOH-activated sludge biochar derived from beverage industry sewage sludge reported by Tocharoen et al. (2019), which reached 199.86 mg/g due to more aggressive activation conditions and higher specific surface area [36].

Moreover, BCH demonstrated moderate adsorption capacities of 15.08 mg/g (Cd²⁺), 22.85 mg/g (Cu²⁺), 17.55 mg/g (Zn²⁺), 7.97 mg/g (Cr³⁺), and 3.68 mg/g (As³⁺). These values are higher than those of most unmodified sewage sludge biochars pyrolyzed at 400–600 °C (e.g., 4–6 mg/g; [41,46]), indicating that acid activation significantly enhances metal ion affinity by increasing surface area and generating oxygen-containing functional groups. Although some modified biochars from plant-based materials (e.g., amino- or MnFe₂O₄-functionalized) exhibit much higher adsorption capacities, the performance of the HCl-activated sludge biochar remains competitive, especially considering its low-cost origin and multi-metal sorption ability. Overall, the results highlight the potential of chemically activated sewage sludge biochars as effective and sustainable adsorbents for removing both dyes and toxic metals from aqueous systems.

4. Conclusions

This study demonstrates that sewage sludge-derived biochar can be effectively valorized into a sustainable and low-cost adsorbent for the removal of Rhodamine B and multiple toxic heavy metals from aqueous solutions. Chemical activation played a decisive role in enhancing adsorption performance, with the HCl-activated biochar exhibiting the highest adsorption capacities of 14.53 mg/g for RhB, 22.85 mg/g for Cu²⁺, 17.55 mg/g for Zn²⁺, 15.08 mg/g for Cd²⁺, 7.97 mg/g for Cr³⁺, and 3.68 mg/g for As³⁺. This improvement is primarily attributed to the substantial increase in specific surface area and pore volume, as well as the enrichment of oxygen-containing functional groups, as confirmed by BET and FTIR analyses.

Kinetic investigations revealed that RhB adsorption followed the pseudo-second-order model (R² ≈ 0.99), indicating that chemisorption dominates the uptake process. The good agreement with the Langmuir isotherm further suggests monolayer adsorption on energetically favorable sites, while complementary nonlinear isotherm models confirmed the heterogeneous nature of the activated biochar surface. The combined kinetic and equilibrium analyses highlight that adsorption proceeds through rapid surface interactions followed by diffusion-controlled steps, with acid activation providing a higher density of accessible and reactive sites.

Overall, the findings underscore the potential of chemically activated sewage sludge biochar as an environmentally friendly and resource-efficient adsorbent for wastewater treatment applications. Beyond effective pollutant removal, this approach contributes to sewage sludge valorization and circular resource management, directly supporting the United Nations Sustainable Development Goals 6 (Clean Water and Sanitation), 11 (Sustainable Cities and Communities), and 12 (Responsible Consumption and Production). Future work will focus on pH-dependent adsorption behavior, thermodynamic analysis, and competitive multi-component systems to further investigate adsorption mechanisms and assess performance under realistic wastewater conditions.