Modern Treatment Using Powdered Chlorella vulgaris for Adsorption of Heavy Metals from Freshwater

Abstract

In the face of current challenges related to climate change, maintaining the appropriate quality of freshwater becomes crucial. This study examined the effectiveness of removing heavy metals (Cu(II) and Co(II)) using Chlorella vulgaris biosorbents (dietary supplements in the form of powder). This study determined the parameters of the biosorbent (point of zero charge (PZC) analysis using scanning electron microscopy with back-scattered electron (SEM-BSE) and Fourier transform infrared spectroscopy (FT-IR) analysis). Batch tests were also performed to determine the kinetic constants and adsorption equilibrium of Cu(II) and Co(II) ions. Based on the conducted research, it was found that a pseudo-second-order equation describes the kinetics of the biosorption process. Among the studied adsorption isotherms, the Langmuir and Freundlich models fit best. The results indicate that single-layer adsorption took place and Chlorella vulgaris is a microporous adsorbent. The maximum sorption capacity in the single-component system for Cu(II) and Co(II) was 30.3 mg·g⁻¹ and 9.0 mg·g⁻¹, respectively. In contrast, in the binary system, it was 20.8 mg·g⁻¹ and 19.6 mg·g⁻¹ (extended Langmuir model) and 23.5 mg·g⁻¹ and 19.6 mg·g⁻¹ (Jain-Snoeyinka model). Chlorella vulgaris is an effective biosorbent for removing heavy metals from freshwater. This technology offers an ecological and economical solution for improving water quality, making it a promising alternative to traditional purification methods.

1. Introduction

Adsorption and ion exchange processes are methods of removing heavy metal ions that are used in both traditional methods and biosorption techniques. Biosorbents can remove heavy metals with comparable efficiency to physical methods, which makes biosorption an attractive alternative to traditional methods [1,2,3,4,5]. Biosorption can occur in two ways, passively, using dead biomass, and actively, through bioaccumulation involving living microorganisms. Biosorption is a physicochemical process in which metal ions are adsorbed onto the surface of a sorbent. Biosorption is the first step in the bioaccumulation process, which in turn requires the participation of living microorganisms and involves the transport of contaminants into the cell [2,6,7]. Any biomass can bind metal ions, but the manner and capacity of this binding can vary depending on the type of biomass. Biosorbents can be different forms of biomass, such as moss, leaves, trees, algae, bacteria, fungi, or yeast [8]. Each of these groups can vary in their ability to bind heavy metals. The structure and chemical composition of the cell wall play a key role in the amount of heavy metals bound. The ability of the biomass to effectively bind metals, its ability to renew itself, and its availability are key characteristics to consider when selecting a biosorbent. In addition to sorption capacity, economic considerations are also important. Biosorbents, which are costly, should have high pollutant removal efficiency and the ability to be easily regenerated [6].

In the context of searching for effective and economical biosorbents, microalgae deserve special attention. Chlorella vulgaris (C. vulgaris) is one of the most well-known and widely studied microalgae species due to its large specific surface area, the rich chemical composition of the cell wall, and its ability to accumulate pollutants [9]. Additionally, microalgae can be easily cultivated on a large scale in various environmental conditions, which makes them an accessible and renewable source of biomass. Their flexibility in adapting to different conditions makes them competitive compared to other biosorbents. C. vulgaris can be cultivated in autotrophic, mixotrophic, or heterotrophic conditions, providing flexibility in biomass production growth media [8,10]. In addition, microalgae can photosynthesize, which not only supports their growth but also contributes to a reduction in carbon dioxide, which gives additional ecological value to their use in biosorption processes [11]. Chlorella is widely produced and used worldwide, with the largest producers being Japan, Taiwan, and South Korea. Chlorella is used for biofuel, animal feed, and dietary supplements, among other things [6,7,8]. Chlorella vulgaris has a rich chemical composition including proteins (approx. 60%), polyunsaturated omega-3 fatty acids, polysaccharides (including β-1,3-glucan), vitamins, and minerals. Clinical studies suggest that supplementation with C. vulgaris brings health benefits such as supporting the treatment of hyperlipidemia, hyperglycemia, asthma, ulcers, and hemorrhoids and protects against oxidative stress and cancer [12,13]. A diverse morphological structure characterizes Chlorella, so it is often used in the food, pharmaceutical, cosmetics, and aquaculture industries [14]. In addition, it can also be used as a biosorbent for removing organic compounds [15,16,17] and heavy metals [15,18,19,20,21,22]. Pollution of fresh waters with metals is one of the most serious environmental problems due to their toxicity and ability to bioaccumulate in the food chain of aquatic organisms [23].

Considering the above advantages of C. vulgaris, in this work we focused on studying its ability for biosorption of two heavy metals, namely cobalt and copper. The choice of these metals was deliberate, as they are common environmental contaminants, and their excess can lead to serious health problems and ecosystem degradation. Although cobalt and copper are essential trace elements, they become toxic at higher concentrations [18,24,25]. Therefore, understanding the mechanisms by which C. vulgaris can effectively remove these metals from polluted waters is crucial for developing new, sustainable methods of freshwater purification. This study aimed to investigate the biosorption in the single-component and binary system of copper and cobalt using C. vulgaris. Detailed studies were conducted to determine the kinetics and equilibrium of the biosorption process. The results of our research can contribute to the further development of bioremediation technologies, offering ecological and effective solutions to the problem of environmental pollution with heavy metals.

2. Materials and Methods

2.1. Subject of Research: Algal Biomass

Chlorella vulgaris biomass was used to prepare biosorbents, which is a commercial product sold as a dietary supplement (Bio Planet, Superfoods).

2.2. Analysis of Algal Biomass Properties

2.2.1. Determination of Point of Zero Charge (PZC) of Chlorella vulgaris

A total of 0.5 g of C. vulgaris powder was weighed into 10 Erlenmeyer flasks. Each flask had 50 mL of NaNO₃ added at a concentration of 0.1 M. The pH in each flask was then determined so that the values were 2, 3, 4, 5, 6, 7, 8, 9, 10, and 11. In total, 0.1 M HNO₃ and 0.1 M NaOH were used to determine the pH values. The samples were left on an Elpin Plus laboratory shaker for 24 h. After this time, the pH values in each sample were measured again [26]. Based on the obtained initial and final pH values, the value of ∆pH = pH₁ − pH₀ was calculated.

2.2.2. Scanning Electron Microscopy with Back-Scattered Electron (SEM-BSE)

The morphological and textural observation of the surface was made by scanning electron microscope (SEM) (TESCAN VEGA 3, Brno, Czech Republic). The SEM was used also with a back-scattered electron detector (BSE) (INCA x-act, Oxford Instruments, High Wycombe, UK) to broaden the scope of the element content analysis.

2.2.3. Fourier Transform Infrared Spectroscopy FT-IR

The FTIR spectra of C. vulgaris were obtained with an Alpha spectrometer (Bruker, Billerica, MA, USA). The experiments were conducted using the transmission method, where samples were pressed with potassium bromide. The compressed adsorbent samples were mixed with KBr, maintaining a consistent ratio of 0.25% adsorbent weight to KBr weight, and then pressed into pellets. The FTIR spectra were employed in a spectral range of 4000 to 400 cm⁻¹ [27].

2.3. Batch Studies of the Adsorption Process

2.3.1. Equilibrium Studies

A total of 6 conical flasks of 250 mL capacity were prepared. They were labeled accordingly and 0.5 g of C. vulgaris was weighed into each. Copper and cobalt solutions of given concentrations were added to each sample: 50, 100, 200, 600, 2000, and 5000 mg·L⁻¹. Then, 2 drops of 1 M NaOH were added to maintain an alkaline pH. The prepared flasks were shaken for 2 h, and then the contents of the flasks were filtered through a medium quantitative filter 055 FILTRAK (Chem-Land). The obtained filtrate was further analyzed and the precipitate was discarded.

2.3.2. Determination of Cu(II) and Co(II) Concentration by Flame Atomic Absorption Spectrometry (F-AAS)

A PERKIN ELMER model 3100 F-AAS spectrometer was used to determine the concentration of Cu(II) and Co(II) in the aqueous phase after adsorption. A hollow cathode lamp for copper and cobalt was used as the radiation source. The measurement was carried out with acetylene–air flame activation. Initially, calibration of the apparatus was performed on standard solutions. The number of ions adsorbed by the algal biomass and the percentage of adsorption were calculated using the following Formulas (1) and (2), respectively:

$$q={\displaystyle \frac{({\mathrm{C}}_0-{\mathrm{C}}_{\mathrm{e}})·{\mathrm{V}}_0}{\mathrm{m}}}$$

(1)

$$A={\displaystyle \frac{({\mathrm{C}}_0-{\mathrm{C}}_{\mathrm{e}})}{{\mathrm{C}}_0}} ·100\%$$

(2)

where q is the adsorption capacity (in mg·g⁻¹), C₀ and C_e are, respectively, the initial concentration of the metal ion in solution and after adsorption over a specified period (in mg·L⁻¹), V (in L) is the volume of solution, and m (in grams), is the amount of C. vulgaris used.

2.3.3. Adsorption Isotherm Models in a Single-Component and Binary System

The adsorption isotherm in the studied system was obtained by applying the Freundlich [28], Langmuir [29], Brunauer Emmett, and Teller (BET) [30] equal (

Table 1. Lists of adsorption isotherm models in a single-component system [14,28,29,30].

Isotherm	Abbreviations
Freundlich Nonlinear form: ${\mathrm{q}}_{\mathrm{e}}={\mathrm{K}}_{\mathrm{F}}·{{(\mathrm{C}}_{\mathrm{e}})}^{\frac{1}{\mathrm{n}}}$ Linear form: $\mathrm{l}\mathrm{o}\mathrm{g}{q}_e={\displaystyle \frac{1}{n}}\mathrm{l}\mathrm{o}\mathrm{g}{C}_e+\mathrm{l}\mathrm{o}\mathrm{g}{K}_F$	qe—the amount of adsorbate adsorbed [mg∙g−1], KF—Freundlich adsorption constant [mg1−1/n∙L1/n∙g−1], Ce—the concentration of adsorbate remaining in the solution equilibrium [mg∙L−1], n—an empirical parameter related to adsorption intensity (in Freundlich), qmax—maximum adsorption capacity [mg∙g−1], KL—Langmuir adsorption constant [L∙mg−1], C0—initial concentration of the substance [mg∙L−1], KBET—adsorption equilibrium constant [L−1∙mg], ε—Dubinin–Radushkevich adsorption constant [mol2∙J−2], E—free energy [kJ∙mol−1].
Langmuir Nonlinear form: ${\mathrm{q}}_{\mathrm{e}}={\mathrm{q}}_{\max }\frac{{\mathrm{K}}_{\mathrm{L}}·{\mathrm{C}}_{\mathrm{e}}}{1+{\mathrm{K}}_{\mathrm{L}}·{\mathrm{C}}_{\mathrm{e}}}$ Linear form: ${\displaystyle \frac{C_e}{q_e}}={\displaystyle \frac{1}{K_L{·q}_{\max }}}+{\displaystyle \frac{C_e}{q_{\max }}}$
Brunauer, Emmett, and Teller (BET) ${\displaystyle \frac{\frac{{\mathrm{C}}_{\mathrm{e}}}{{\mathrm{C}}_0}}{{\mathrm{q}}_{\mathrm{e}}\left(1-\frac{{\mathrm{C}}_{\mathrm{e}}}{{\mathrm{C}}_0}\right)}}={\mathrm{q}}_{\max }{\displaystyle \frac{{\mathrm{K}}_{\mathrm{B}\mathrm{E}\mathrm{T}}-1}{{\mathrm{K}}_{\mathrm{B}\mathrm{E}\mathrm{T}}{·\mathrm{q}}_{\max }}}·{\displaystyle \frac{{\mathrm{C}}_{\mathrm{e}}}{{\mathrm{C}}_0}}+{\displaystyle \frac{1}{{\mathrm{K}}_{\mathrm{B}\mathrm{E}\mathrm{T}}{·\mathrm{q}}_{\max }}}$
Dubinin–Radushkevich (R-D) Nonlinear form: ${\mathrm{q}}_{\mathrm{e}}={\mathrm{q}}_{\max }\exp (-{\mathsf{\beta }\mathsf{\epsilon }}^2)$ $\mathsf{\epsilon }=\mathrm{RT} (1+\frac{1}{C_e})$; E = $\frac{1}{\sqrt{2\beta }}$ Linear form: $\mathrm{l}\mathrm{n}{q}_e=\mathrm{l}\mathrm{n}{q}_{max}-{\mathsf{\beta }\mathsf{\epsilon }}^2$

). The coefficient of determination (R²) and the Chi-square statistic reduced by the number of degrees of freedom (χ²/DoF) were used to define the fit of the models to the experiment’s results. The adsorption isotherm in the studied binary system was obtained using the Jain–Snoeyink and extended Langmuir [31] equations (

Table 2. Lists of adsorption isotherm models in a binary system [31].

Isotherm	Equations
Jain–Snoeyink	${\mathrm{q}}_{\mathrm{e}1}={(\mathrm{q} }_{ꝏ 1 }-{\mathrm{q} }_{ꝏ 2 }){\displaystyle \frac{{\mathrm{K}}_{\mathrm{L}1}·{\mathrm{C}}_{\mathrm{e}1}}{1+{\mathrm{K}}_{\mathrm{L}1}·{\mathrm{C}}_{\mathrm{e}1}}}+{\mathrm{q} }_{ꝏ 2 }{\displaystyle \frac{{\mathrm{K}}_{\mathrm{L}1}·{\mathrm{C}}_{\mathrm{e}1}}{1+{\mathrm{K}}_{\mathrm{L}1}·{\mathrm{C}}_{\mathrm{e}1}+{\mathrm{K}}_{\mathrm{L}2}·{\mathrm{C}}_{\mathrm{e}2}}}$ Where, ${\mathrm{q} }_{ꝏ 1 }>{\mathrm{q} }_{ꝏ 2 }$ ${\mathrm{q}}_{\mathrm{e}2}= {\mathrm{q} }_{ꝏ 2 }{\displaystyle \frac{{\mathrm{K}}_{\mathrm{L}2}·{\mathrm{C}}_{\mathrm{e}2}}{1+{\mathrm{K}}_{\mathrm{L}1}·{\mathrm{C}}_{\mathrm{e}1}+{\mathrm{K}}_{\mathrm{L}2}·{\mathrm{C}}_{\mathrm{e}2}}}$
Extended Langmuir	${\mathrm{q}}_{\mathrm{ei}}= {\mathrm{q} }_{ꝏ \mathrm{i} }{\displaystyle \frac{{\mathrm{K}}_{\mathrm{Li}}·{\mathrm{C}}_{\mathrm{ei}}}{1+{\sum }_{j=1}^n{\mathrm{K}}_{\mathrm{Lj}}·{\mathrm{C}}_{\mathrm{ej}} }}$

Note: explanation of abbreviations in Table 1.

).

2.3.4. Kinetic Studies

An array of 6 flasks corresponding to times of 15, 30, 60, 90, 120, and 180 min was prepared. A total of 0.5 g of C. vulgaris and 50 mL solution of Cu(II) and Co(II) ions at 600 mg·L⁻¹ were weighed into each. 2 drops of 1M NaOH were added to maintain an alkaline pH (pH = 8). The prepared C. vulgaris solutions were shaken for the appropriate time, and then the contents of the flasks were filtered through a medium quantitative filter 055 FILTRAK (Chem-Land). The adsorption process was carried out at 20 °C. To study the mechanism of Cu(II) and Co(II) ion adsorption, pseudo-first-order, and pseudo-second-order kinetic models were used, and the rate-controlling stage, i.e., intra-particle diffusion, was also determined [32,33,34,35,36,37] (

Table 3. Lists of kinetic models [32,33,34,35,36,37].

Kinetic Model	Pseudo-First-Order (PFO)	Pseudo-Second-Order (PSO)	Intra-Particle Diffusion
Equation	dqt/dt = k1(qe − qt) ln(qe − qt)= −k1t + ln qe	dqt/dt = k2(qe − qt)2 t/qt = 1/k2qe2 + t/qe	qt = k’it1/2 + b

).

3. Results and Discussion

3.1. Characterization of the Biosorbent

The value of the zero point of the Chlorella vulgaris load determined by the suspension method was 8.0 (Figure 1). The zero point of electric charge (PZC) is the point of intersection of the relationship ∆pH = f(pH₀) with the OX axis and is, therefore, equivalent to the zero point of this function. The point of zero electrical charge is the pH for which the electrical charge of a surface or suspended solid in water is zero. Knowing the PZC value makes it possible to determine the type of groups that predominate on the surface of the adsorbent and their presumed interactions with other ions. As the pH value of C. vulgaris is above the determined PZC, this means that its surface is negatively charged and will, therefore, have a higher cation exchange capacity [26].

The structure of C. vulgaris is shown in Figure 2. The photos were taken using an SEM VEGA3 TESCAN microscope at various resolutions. These photos are characterized by a large depth of field, which allows for an accurate assessment of porosity on the surface. The structure of the tested biological material can be described as an aggregate of particles of various sizes and microspheres in the form of irregular shapes [38,39,40]. Based on microscopic C. vulgaris images, BSE spectra were made, thanks to which the elemental composition of the biosorbent was determined. The results indicate that C. vulgaris mainly contained carbon and oxygen, and trace amounts of phosphorus, sulfur, silicon, calcium, and magnesium in its composition (Figure 3).

FTIR analysis showed the presence of different absorption bands on the surface of the biosorbent (Figure 4). A broad band at about 3485 cm⁻¹ belongs to the –NH and –OH groups, with a stretching frequency of about 2945 cm⁻¹ attributed to –CH. The band at 1674 cm⁻¹ shows the frequency of the carbonyl group (C=O), then the band at 1267 cm⁻¹ was assigned to the –CH₃ group [41]. The other lower bands were assigned to the C–N and C=S groups. After the absorption of Co(II) and Cu(II) ions, the various vibrational frequencies decreased, confirming the bond formation by Co(II) and Cu(II) ions to carboxyl, carbonyl, hydroxyl, amine, and amide groups [42].

3.2. Sorption Batch Model

Isotherms determine the equilibrium between the concentration of adsorbate in the solid phase and its concentration in the liquid phase. We can obtain information on the maximum adsorption capacity based on the course of isotherms. In addition, they provide information on the power of the sorbent or the amount required to remove a unit mass of pollutants under the conditions studied [10,38]. Experimental data were processed using various adsorption isotherms models in the solid–liquid system (

Table 4. The values of isotherm adsorption parameters for Cu(II) and Co(II) ions by Chlorella vulgaris in a single-component system.

Isotherm	Parameter	Cu(II)	Co(II)
Freundlich	KF [mg1−1/n·L1/n·g−1]	2.034	1.148
	n	2.4	2.9
	R2	0.954	0.704
	χ2/DoF	0.42	5.2
Langmuir	KL [L·mg−1]	0.055	0.376
	qmax [mg·g−1 ]	30.3	9.0
	R2	0.971	0.670
	χ2/DoF	4.6	9.5
Brunauer, Emmett, and Teller	KBET [L·mg−1]	1.00	3.44
	qmax [mg·g−1 ]	0.018	0.280
	R2	0.568	0.480
	χ2/DoF	8.7	9.2
Dubinin–Radushkevich	E [kJ·mol−1]	0.14	0.09
	qmax [mg·g−1]	25.7	9.78
	R2	0.539	0.276

). The comparison of isothermal models for adsorbed Cu(II) and Co(II) ions is presented in Figure 5, Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10.

By analyzing adsorption isotherms, it was possible to adjust the model accordingly and reflect the adsorption studies carried out for Cu(II) and Co(II) ions using Chlorella vulgaris. Each graph shows the equilibrium between the concentration of the adsorbate in the solid phase and its concentration in the liquid phase. Comparing these results, it can be concluded that a more efficient adsorption process occurred for Cu(II) ions. The maximum adsorption capacity for Cu(II) and Co(II) ions was obtained according to the Langmuir model. The maximum adsorption capacity was about 30.3 mg·g⁻¹ and 9 mg·g⁻¹ for Cu(II) and Co(II) ions, respectively. Equilibrium experiments showed that C. vulgaris’s selectivity towards Cu(II) ions is greater than that of Co(II) ions, which is related to their hydrated ionic radius and first hydrolysis equilibrium constant. For the BET isotherm, q_max values were also compared. For adsorption of Cu(II) ions, a value of q_max = 0.018 mg·g⁻¹ was obtained, and for adsorption of Co(II) ions, q_max = 0.280 mg·g⁻¹ (Table 4). On this basis, it was concluded that the efficiency of the adsorption process for this isotherm is poor, especially in the first case. Another isotherm discussed is the Freundlich isotherm, for which the dimensionless parameter n allows the determination of the intensity of adsorption; if this parameter is in the range 1 < n < 10, it means that the adsorption process is effective and efficient. In the case of the adsorption process of Cu(II) ions, a value of n = 2.4 was obtained, while for Co(II) ions a value of n = 2.9 was obtained. Based on this, it was concluded that the process was efficient, as it fell within the above-described range (Figure 5 and Figure 6). Then, calculating the inverse of the parameter n, information about the degree of diversity of sorption sites on the sorbent surface was obtained. The results were obtained for Cu(II) ions 1/n = 0.42, for Co(II) ions 1/n = 0.34. These values are closer to zero than unity, which allows us to predict that the adsorption surface with C. vulgaris is significantly homogeneous. The coefficients of determination for each isotherm were also compared. The results obtained were: R² = 0.9539 and 0.7040 for the Freundlich isotherm (Figure 5 and Figure 6), R² = 0.9710 and 0.6700 for the Langmuir isotherm (Figure 7), R² = 0.5684 and 0.4802 for the BET isotherm (Figure 8), and R² = 0.539 and 0.276 for the R-D isotherm (Figure 9). The Freundlich and Langmuir isotherms showed the best coefficient of determination. However, the experimental data did not correlate so well with the D-R model (Figure 9). The D-R model often fits the data at high concentrations well but has poor performance at low metal ion concentrations. The energy (E) was determined to be 0.14 kJ·mol⁻¹ and 0.09 kJ·mol⁻¹ for Cd(II) and Cu(II) indicating that physisorption may play a significant role in the metal adsorption process.

For binary mixtures, isotherm Jain–Snoeyinka and extended Langmuir models allow direct calculation of the concentrations of adsorbed components based on knowledge of isotherms in a single-component system. The total amount adsorbed can be determined based on the sum of the concentrations of the adsorbed components (

Table 5. The values of isotherm adsorption parameters for Cu(II) and Co(II) ions by Chlorella vulgaris in a binary system.

Isotherm	Parameter	Cu(II)	Co(II)
Jain-Snoeyinka	KLi [L·mg−1]	6.1·10−2	5.8·10−3
	q∞i [mg·g−1]	23.5	16.7
	R2	0.8531	0.7520
Extended Langmuir	KLi [L·mg−1]	7.5·10−2	3.8·10−2
	q∞i [mg·g−1]	20.8	19.6
	R2	0.7233	0.5745

).

Adsorption proceeded rapidly in the initial phase and gradually slowed down once equilibrium was reached. This phenomenon is very common due to the saturation of the available active surface centers. The experiments showed that an equilibrium state was reached within 2 h and 30 min for Cu(II) and Co(II) ions, respectively after which saturation of the adsorbent surface can be expected to occur (Figure 11).

3.3. Kinetic Adsorption

To investigate the mechanism and determine the rate of the adsorption process, pseudo-first-order and pseudo-second-order kinetic models, and diffusion within the particles were developed. Both the adsorbent and the adsorbate molecules could participate in the adsorption of Cu(II) and Co(II) ions on the C. vulgaris (

Table 6. Kinetic model constants and linear correlation coefficients for the adsorption system studied.

Kinetic Model	Parameter	Cu(II) Ions	Co(II) Ions
Pseudo-first-order (PFO)	R2 k1 [min−1]	0.8272 6·10−4	0.2344 0.014
Pseudo-second-order (PSO)	R2 k2 [g·mg−1·min−1]	0.9979 2.7·10−3	0.9746 2.8·10−4
Intra-particle diffusion	R2 k’1 [mg·g−1·min−1/2] b1 [mg·g−1]	0.6157 0.4563 24.18	-
	R2 k’2 [mg·g−1·min−1/2] b2 [mg·g−1]	1.000 - 27.000	-

).

Based on the graphs produced and the kinetic model results tabulated, it appears that the adsorption process of both copper and cobalt ions is best described by a pseudo-second-order equation. Analysis of the PSO model yielded R² determination coefficients approaching unity—0.9979 for Cu(II) ion adsorption, and 0.9746 for Co(II) ion adsorption (Figure 12). In the case of a PFO model, the coefficient of R² is much smaller than 1. The kinetic curves do not follow a linear path, so the experimental results do not satisfy an equation of this order (Figure 13).

The intra-particle diffusion model shows that there are two or three separate steps in the sorption process, namely external diffusion and intra-particle diffusion. The non-linear course of the entire adsorption process indicates multi-step adsorption of Cu(II) ions by C. vulgaris. The fit of the multilinear dependence of q_t concerning t1/2 is shown in Figure 14. It can be observed that there are two or even three distinct stages in the adsorption of copper ions. The sharp linear progression of the first part of the process is related to the diffusive boundary layer (film), so-called external diffusion, external surface adsorption, or external mass transfer effect. The second stage describes the gradual adsorption, surface diffusion, and adsorption on the pore surface, while the third linear relationship is responsible for the diffusion into the pore, which represents the final stage of the equilibrium state, where adsorption becomes very slow, stable, and assumes a maximum value.

Chlorella vulgaris is one of the biosorbents that can be used in wastewater treatment processes thanks to its favorable physicochemical properties and ability to bind metals. Studies indicate that C. vulgaris effectively bioremediates water from pollutants, constituting a promising alternative to traditional treatment methods. Adsorption, as a process characterized by simplicity, low operating costs, and the availability of various adsorbents, including biosorbents of natural origin, is particularly promising in the removal of heavy metals from aqueous solutions. Different values of adsorption capacity have been reported in the scientific literature depending on the adsorbent used, experimental conditions, and specific chemical and physical properties of the metal ions tested (

Table 7. Adsorption capacity of different adsorbents for the removal of Cu(II) and Co(II).

Adsorbent		qmax [mg g−1]	References
Chlorella vulgaris (this work)	Cu and Co	30.3 and 9.0	-
Stems and seed hulls of Cicer arietinum	Cu	18	[43]
Ulva lactuca (in suspension and fixed in agar)		32.80 and 10.01	[44]
Chlorella pyrenoidosa		11.88	[45]
Saccharomyces cerevisiae		4.73	[46]
Orange peel-derived biochar		72.99	[47]
Luffa cylindrica	Co	2.53	[48]
Chrysanthemum indicum flower (raw and biochar)		4.84 and 28.34	[49]
Natural hemp fibers		13.58	[50]
Cells of Saccharomyces cerevisiae		0.68	[51]
Ficus benghalensis L.		5.65	[52]

).

4. Conclusions

In this study, Chlorella vulgaris was used as an easily available sorbent for the removal of Cu(II) and Co(II) ions in one- and two-component systems. Among the analyzed isotherm models, the Freundlich and Langmuir isotherms best fit the indicated experimental points. The coefficient of determination for the Freundlich isotherm was R² = 0.95 and 0.70, and for the Langmuir isotherm R² = 0.97 and 0.6 for Cu(II) and Co(II) ions, respectively. The obtained results indicate that single-layer adsorption occurred and Chlorella vulgaris is a microporous adsorbent. The value of the dimensionless parameter n in the Freundlich equation indicates that the adsorption process is effective, and the adsorption surface is homogeneous. Cu(II) ions) were retained more effectively on the surface of Chlorella vulgaris (q_max = 30.3 mg·g⁻¹ than Co(II) ions (q_max = 9.0 mg·g⁻¹). However, in the binary system, these values were 20.8 and 19.6 [mg/g] for the extended Langmuir model, and 23.5 and 19.6 mg·g⁻¹ for the Jain-Snoeyinka model for Cu(II) and Co(II). The adsorbent surface was saturated with adsorbed anions after 2 h for Cu(II) and 0.5 h for Co(II). The obtained results indicate that copper ions had easier access to the activity centers of Chlorella vulgaris. The analysis of the intraparticle diffusion model showed that the sorption process of heavy metal ions is controlled by diffusion in the pores. The obtained sorption kinetic data were well expressed by a pseudo-second-order model, while they showed a very poor fit to a pseudo-first-order model, as evidenced by the analysis of the linear regression coefficient values. Based on the results obtained, it can be concluded that Chlorella vulgaris becomes an attractive material for purifying water containing Cu(II) and Co(II) ions.