Enhancement of Tetradesmus obliquus Adsorption for Heavy Metals Through Lysine Addition: Optimization and Competitive Study

Abstract

Heavy metal wastewater often contains multiple metal ions, and competition among them reduces the adsorption efficiency of microalgae. Enhancing this efficiency is crucial for improving heavy metal removal. This study optimized lysine addition to facilitating the formation of a ternary complex between microalgae, lysine, and heavy metals, thereby enhancing adsorption in both single- and mixed-metal systems. In a single-metal system at 64 mg/L, lysine improved the removal rates of copper, zinc, and cadmium by 13.96%, 41.21%, and 33.26%, respectively. In binary systems (Cu-Zn, Cu-Cd, and Cu-Pb) at 32 mg/L, lysine increased copper adsorption by 11.81%, 15.71%, and 25.25%, while improving zinc, cadmium, and lead adsorption by 15.41%, 12.51%, and 3.93%, respectively. Competitive adsorption analysis revealed that lead most strongly inhibited copper adsorption, while copper significantly suppressed zinc adsorption. Mechanistic investigations using 3D-EEM, FTIR, and XPS demonstrated that humic substances in the extracellular polymeric substances (EPSs) of microalgae play a key role in lysine binding. This interaction increases the number of carboxyl functional groups on the cell surface, thereby enhancing the microalgae’s capacity to adsorb heavy metals.

1. Introduction

Heavy metal pollution has become a critical global environmental issue. Rapid industrialization, mining, and livestock activities have resulted in the release of large amounts of heavy-metal-containing wastewater. Due to their non-biodegradable nature and tendency to accumulate in the food chain, heavy metals pose significant risks to both ecosystems and human health [1]. Traditional methods for heavy metal removal, including chemical precipitation, ion exchange, membrane filtration, and adsorption, are commonly used but often involve high costs and the potential for secondary pollution [2,3]. Although various novel adsorbents have been developed, including amyloid fiber CsgA-Fe₃O₄ composites, cellulose hydrogel-coated nano-zero-valent iron intercalated montmorillonite, and carbon microspheres supported with sulfurized nano-zero-valent iron, their application remains limited to the laboratory stage [4,5,6]. In contrast, bioremediation has gained attention as an environmentally friendly and cost-effective alternative [7]. Among various microorganisms, microalgae have emerged as promising candidates for heavy metal remediation due to their efficient photosynthesis, simple cellular structure, and high metal tolerance. Furthermore, their ability to thrive in extreme environments, such as high salinity, nutrient deficiency, and fluctuating temperatures, makes them ideal for practical applications [8].

Microalgae have gained significant attention for their potential in bioremediation due to their strong absorption capabilities, high removal efficiency, and selective sensitivity to specific heavy metals [3,9]. For example, Liu et al. investigated the Cu(II) removal efficiency of Desmodesmus sp. CHX1 in piggery digestate, reporting an 88.35% removal rate after 4 days of treatment [10]. Similarly, Saavedra et al. investigated the removal of manganese, zinc, copper, arsenic, and boron by various green algae, with removal rates of up to 99.4%, 91.9%, 88%, 40.7%, and 38.6%, respectively [11]. However, the practical application of microalgae for heavy metal removal still faces significant challenges. Wastewater often contains a mixture of heavy metal ions in varying chemical forms and concentrations, which creates complex pollution. Removing mixed metals is more challenging than single-metal removal, as interactions between different metals can affect the adsorption capacity and removal efficiency of microalgae [12]. For instance, Gu et al. found that in binary systems with lead, zinc, and cadmium, lead inhibited the adsorption of both cadmium and zinc, reducing the adsorption capacities of Chlorella by 60.75% and 68.98%, respectively [13]. Furthermore, high metal concentrations or an increased number of metal species can further limit the adsorption capacity of microalgae, primarily due to competitive adsorption and the saturation of binding sites on the microalgae cell surface [14]. Therefore, optimizing microalgae cultivation conditions and developing effective strategies for heavy metal removal are critical for enhancing the efficiency of microalgae in treating mixed-metal wastewater.

Recent studies have explored various strategies to enhance the heavy metal removal capacity of microalgae, including immobilization techniques, biofilm formation, and metabolic regulation. For example, Ahmad et al. reported a maximum Zn(II) adsorption capacity of 105.29 mg/g within 300 min using microalgae immobilized in calcium alginate [15]. Similarly, Ma et al. developed a Tetradesmus obliquus biofilm on a loofah sponge, achieving a Cd adsorption capacity of 133.14 mg/g within 120 min [16]. However, these methods tend to increase the cost of biological remediation. Recently, there has been growing interest in surface and chemical modifications of microalgae, as well as the integration of other heavy metal removal technologies, to improve efficiency [8]. Amino acids, as a type of soluble organic matter (DOM), can both complex with heavy metals [17,18] and adsorb onto microalgal cell surfaces, providing additional functional groups that increase the number of binding sites and enhance the algae’s capacity for heavy metal adsorption [19]. In particular, studies by Fang et al. have shown that the amino group in amino acids plays a key role in the formation of ternary complexes [20]. Among amino acids, lysine (Lys) stands out due to its high number of amino groups, which significantly enhance the adsorption capacity of Chlorella vulgaris for Zn. Furthermore, the enhancement of microalgae’s adsorption capacity by lysine is primarily through extracellular processes, with minimal internalization. Lysine has low toxicity to microalgae and is also a component of microalgal extracellular polymeric substances (EPSs) [21]. The EPS plays a vital role in the adsorption and removal of heavy metals by microalgae [22]. It acts in two main ways: first, by interacting with the positively charged metal ions, and second, by providing binding sites on its surface for efficient metal ion chelation [12]. This suggests that modifying the microalgal surface with amino acids could be a promising approach to enhance their adsorption capacity. However, the formation of ternary complexes between algae, amino acids, and heavy metals is complex. For optimal formation, the binding constants between the amino acids, metals, and microalgae must be similar [23]; otherwise, free amino acids may compete with the microalgae for metal ions, leading to a decrease in removal efficiency [20,24]. This complexity limits the practical application of amino acids in microalgal-based heavy metal removal.

This study investigates the use of Tetradesmus obliquus for heavy metal removal through short-term adsorption experiments. The ability of lysine-treated microalgae to adsorb heavy metals was assessed, with the optimization of lysine addition explored by varying contact time and lysine concentration. The goal was to evaluate the potential of amino acids to enhance the heavy metal adsorption capacity of microalgae. The effectiveness of lysine-treated microalgae was tested in both single-metal systems (Cu, Zn, Pb, Cd) and binary mixed-metal systems (Cu + Zn, Cu + Cd, Cu + Pb). This study also examined the enhancement of adsorption capacity for different metals and compared microalgal performance across various heavy metals. Kinetic and isotherm models were used to fit the data and assess adsorption behavior. Additionally, the binding mechanism between microalgae and lysine was explored using 3D-EEM (three-dimensional excitation–emission matrix), FTIR (Fourier-transform infrared spectroscopy), and XPS (X-ray photoelectron spectroscopy).

2. Materials and Methods

2.1. Cultivation of Microalgae

The microalgae strain Tetradesmus obliquus (FACHB-14) was obtained from the Institute of Hydrobiology, Chinese Academy of Sciences (Wuhan, China). The algae were cultured in BG11 medium (see Supplementary Materials Table S1), which was sterilized at 121 °C for 30 min before use. After inoculation, the cultures were maintained at 25 ± 2 °C under continuous illumination (3500 lux) with constant aeration. Once the microalgae reached the logarithmic growth phase, they were harvested for use in heavy metal adsorption experiments.

2.2. Preparation of Stock Solutions

Cu(NO₃)₂, Zn(NO₃)₂, Cd(NO₃)₂, and Pb(NO₃)₂ (AR grade, Shanghai Macklin Biochemical Technology Co., Shanghai, China) were each dissolved in 1000 mL of ultrapure water to prepare 1000 mg/L heavy metal solutions. Lysine was prepared using the same method, with a final concentration of 0.01 M. The stock solutions were stored in 1000 mL white plastic bottles at room temperature.

2.3. Effect of Lysine Addition

2.3.1. Effect of Lysine Exposure Time

The removal efficiency of copper ions by Tetradesmus obliquus was used as the optimization criterion. The algae, at the logarithmic growth phase, were cultured in BG11 medium and adjusted to a concentration of 0.5 g/L. A 100 mL aliquot of this algae suspension was placed in a 150 mL beaker and stirred at 120 rpm on a magnetic stirrer. Lysine was added at a concentration of 0.03 mM, and the system was allowed to react for different time intervals (0, 1, 2, 3 h). After the specified exposure times, the heavy metal stock solution was added to adjust the initial copper ion concentration to 32 mg/L. The pH was then adjusted to 6.0 ± 0.2 before sampling. At various time points (0, 5, 10, 20, 30, 40, 50, 60, 90, 120, 180 min), algae suspensions were filtered using a 0.22 µm acetate fiber membrane. The filtered samples were acidified with 5% HNO₃ to prevent metal precipitation and stored at 0–4 °C. The residual copper ion concentration was measured using an atomic absorption spectrophotometer (Shimadzu AA-7000, Shimadzu, Shanghai, China).

2.3.2. Effect of Lysine Concentration

Under the optimal exposure time identified in Section 2.3.1 (3 h), lysine was added at varying concentrations of 0.01, 0.03, 0.05, and 0.1 mM. The initial copper ion concentration was kept at 32 mg/L, and the impact of different lysine concentrations on copper ion adsorption by the microalgae was assessed. All other procedures followed those outlined in Section 2.3.1. Subsequent experiments were conducted under the optimized conditions.

2.4. Adsorption of Single Heavy Metals

2.4.1. Biosorption Kinetics

The removal efficiency of different heavy metal ions was evaluated under pH 6 and an algal concentration of 0.5 g/L. Three initial metal concentrations—low (8 mg/L), medium (32 mg/L), and high (64 mg/L)—were tested to assess the adsorption kinetics of Tetradesmus obliquus for Cu, Zn, Cd, and Pb, both with and without lysine addition. Samples were collected at 0, 5, 10, 20, 30, 40, 50, 60, 90, 120, and 180 min to determine the residual metal ion concentrations. The experimental procedures followed those described in Section 2.3.1.

The equilibrium adsorption capacity and removal rates of the microalgae for heavy metals were calculated using Equations (1) and (2) as follows:

$$q_e={\displaystyle \frac{C_0-C_t}{x}}$$

(1)

$$\mathit{Removal}\left(\%\right)={\displaystyle \frac{C_0-C_t}{C_0}}\times 100\%$$

(2)

where C₀ is the initial heavy metal ion concentration in the solution at t = 0 min (mg/L). C_t is the residual heavy metal ion concentration in the solution at t min (mg/L). x is the biomass concentration of the microalgae (g DCW/L). q_e is the equilibrium adsorption capacity of the microalgae (mg/g).

2.4.2. Adsorption Kinetics Model

At an initial metal concentration of 32 mg/L, the adsorption kinetics of Tetradesmus obliquus for Cu, Zn, Cd, and Pb were examined with and without lysine. To evaluate the impact of lysine on the heavy metal adsorption process, the data were fitted to pseudo-first-order and pseudo-second-order kinetic models, representing physical and chemical adsorption, respectively [16].

The equation for calculating the pseudo-first-order rate is as follows:

$${\mathit{ln}(q}_e{-q}_t)={\mathit{ln}(q}_e{)-k}_{1}t$$

(3)

The equation for calculating the pseudo-second-order rate is as follows:

$${\displaystyle \frac{t}{q_t}}={\displaystyle \frac{1}{q_e^2{k}_2}}+{\displaystyle \frac{1}{q_e}}t$$

(4)

where q_t is the adsorption capacity of microalgae at t min (mg/g). q_e is the adsorption capacity at equilibrium (mg/g). k₁ is the pseudo-first-order adsorption rate constant (min⁻¹). k₂ is the pseudo-second-order adsorption rate constant (g/(mg·min)).

2.4.3. Adsorption Isotherm Models

The equilibrium adsorption capacity of microalgae was fitted to isothermal adsorption models to study the adsorption type on the cell surface under lysine exposure and to predict the maximum adsorption capacity. In this study, Langmuir, Freundlich, and Sips isotherm models were applied. The Langmuir model describes a scenario where adsorption sites on the surface of the adsorbent are uniformly distributed, allowing for monolayer adsorption, where each site can bind only one molecule of adsorbate [25]. Conversely, the Freundlich model characterizes adsorption on heterogeneous surfaces, which is associated with multilayer adsorption [26]. The three-parameter Sips model is applicable to both homogeneous and heterogeneous systems, indicating monolayer adsorption of an adsorbate molecule at a ratio of 1/ns [27]. The corresponding equations are as follows.

(1)

The Langmuir adsorption isotherm equation is as follows:

$$q_e={\displaystyle \frac{q_m{K}_L{C}_e}{1+K_L{C}_e}}$$

(5)

where q_e (mg/g) represents the equilibrium adsorption capacity, indicating the amount of heavy metal ions adsorbed per unit mass of the biosorbent; q_m (mg/g) denotes the maximum adsorption capacity; C_e (mg/L) indicates the residual concentration of heavy metals in the solution at equilibrium; and K_L (L/mg) is the Langmuir adsorption equilibrium constant, reflecting the affinity of heavy metal ions for the biosorbent [14].

(2)

The Freundlich adsorption isotherm equation is as follows:

$$q_e=K_f{C}_e^{1/n}$$

(6)

where K_f is the Freundlich equilibrium constant, which is associated with the adsorption capacity. The coefficient n indicates the favorability of the adsorption process, with higher values reflecting enhanced adsorption performance. Specifically, an n value between 2 and 10 suggests effective adsorption, while a value between 1 and 2 indicates moderate difficulty in adsorption. An n value of less than 1 implies that adsorption is unlikely to occur [13].

(3)

The Sips isotherm model is as follows:

$$q_e={\displaystyle \frac{q_{\mathit{mS}}{K}_S{C}_e^{\mathit{ns}}}{1+K_S{C}_e^{\mathit{ns}}}}$$

(7)

where q_mS (mg/g) represents the maximum adsorption capacity, while K_S (L^ns/mg^ns) and ns are the constants of the Sips model. C_e (mg/L) denotes the equilibrium concentration of heavy metal ions. Notably, when ns = 1, the Sips model simplifies to the Langmuir model.

2.5. Binary Heavy Metal Competitive Adsorption

At an algal concentration of 0.5 g/L, binary heavy metal mixtures of Cu + Zn, Cu + Cd, and Cu + Pb were prepared at equal concentrations to study the adsorption effects of Tetradesmus obliquus on mixed heavy metals, both with and without lysine. The concentrations of the metal mixtures were set to 4, 8, 16, and 32 mg/L. The focus was on evaluating the removal efficiency of microalgae for mixed heavy metals at different concentrations, as well as studying the adsorption kinetics at 32 mg/L.

2.6. Analytical Methods

2.6.1. Analysis of Three-Dimensional Fluorescence Excitation-Emission Matrix (3D-EEM)

The interaction between extracellular polymeric substances (EPSs) of Tetradesmus obliquus and lysine was analyzed using 3D-EEM (three-dimensional excitation–emission matrix). The experimental procedure was as follows. (1) To investigate the interaction over time, algal suspensions containing lysine were sampled at 0, 1, 2, and 3 h. Each sample was filtered through a 0.45 µm acetate fiber membrane, and the fluorescence intensity of the filtrate was measured. (2) To examine the fluorescence characteristics of extracellular organic matter before and after Cu²⁺ adsorption, 10 mL of algal suspension from both the lysine-treated and control groups was filtered, and the fluorescence properties were analyzed. A fluorescence spectrophotometer was used to scan the filtrate, with ultrapure water serving as the blank control to eliminate Raman scattering, generating the three-dimensional fluorescence spectra.

2.6.2. Analysis of Fourier-Transform Infrared Spectroscopy (FTIR)

Fourier-transform infrared spectroscopy (FTIR) spectra of the microalgae were obtained before and after the addition of lysine and copper ion adsorption using a Thermo Fisher Nicolet iS50 spectrometer. (Thermo Fisher Scientific Co., Ltd., Shanghai, China) Microalgae samples were collected through high-speed centrifugation and then freeze dried for further analysis. Detailed procedures can be found in previous studies [12].

2.6.3. Analysis of X-Ray Photoelectron Spectroscopy (XPS)

X-ray photoelectron spectroscopy (XPS) was employed to quantitatively analyze the elemental composition and surface functional groups of the microalgae. The freeze-dried algal powder was analyzed using a Thermo Scientific K-Alpha XPS (Thermo Fisher Scientific Co., Ltd., Shanghai, China), with a full spectrum scan energy set at 150 eV. The raw data were calibrated against the C 1s peak, which has a fixed binding energy of 284.8 eV. The area under each chemical peak was used to calculate peak intensity.

2.7. Statistical Analysis of Data

Three independent experiments were conducted to evaluate the effects of lysine on the removal of heavy metal ions by microalgae. The results from each analysis are presented as the mean ± standard deviation (SD) of three technical replicates. Statistical analysis was performed using one-way analysis of variance (ANOVA), with Origin 2021 (OriginLab Corporation, Northampton, MA, USA) and Excel (Microsoft, Redmond, WA, USA) as the software tools.

3. Results and Discussion

3.1. Optimization of Lysine Addition Conditions

Previous experiments focused on optimizing algal concentration, pH, and the type of amino acid used, with the optimal conditions identified as 0.5 g/L algal biomass, pH = 6, and lysine (see Supplementary Materials Figures S1–S3). Among these factors, pH significantly influences copper ion adsorption and the formation of the microalgae–lysine–metal complex. At lower pH, H⁺ competes with Cu²⁺ for binding sites on the algal surface, while in alkaline conditions, Cu²⁺ tends to precipitate [28,29]. Consequently, pH = 6 was selected as the optimal condition for copper ion adsorption. The subsequent sections focus on optimizing the lysine addition method by examining the effects of lysine interaction time with microalgae (Section 3.1.1) and lysine concentration (Section 3.1.2).

3.1.1. Optimization of Lysine Interaction Time

This study explores the effect of lysine interaction time on the removal efficiency of Cu²⁺ at an initial concentration of 32 mg/L using a lysine concentration of 0.03 mM. As shown in Figure 1a, after 5 min of adsorption, the remaining copper concentrations in the 0 h group (19.15 mg/L) and 1 h group (16.30 mg/L) were higher than the control group (15.95 mg/L) without lysine. These results suggest that at the beginning of the process, free lysine in the solution competes with microalgae for Cu²⁺ adsorption, which lowers the removal rate [30,31]. Over time, the 1 h group demonstrated improved removal efficiency compared to the 0 h group, likely due to the partial binding of lysine to the microalgal surface, reducing the concentration of free lysine in the solution. However, the remaining free lysine continued to interfere slightly with copper adsorption, resulting in lower removal rates than in the control group at the 5 min mark. As interaction time increased, more lysine adhered to the microalgae surface, significantly enhancing Cu²⁺ adsorption. After 5 min of adsorption, the 2 h and 3 h groups reduced copper concentrations to 11.19 mg/L, and 4.21 mg/L, respectively, corresponding to removal efficiencies that were 14.88% and 36.69% higher than the control group. These findings suggest that although lysine initially slows adsorption, this effect diminishes as interaction time increases and free lysine concentration decreases.

The time required to reach adsorption equilibrium varied across groups. The 3 h group achieved equilibrium within 40 min, the 2 h group in 120 min, and the 0 h group in 180 min. In contrast, the other two groups failed to reach equilibrium even after 180 min. The order of equilibrium times was 3 h > 2 h > 0 h > control > 1 h, with the 0 h group reaching equilibrium faster than the 1 h group. This indicates that under pH = 6.0, lysine binds more strongly to Cu²⁺ than microalgae do, limiting the ability of microalgae to adsorb copper ions already bound to lysine [32]. This finding aligns with prior research by Fang et al., which reported that aspartic acid (Asp) binds more strongly to Zn than Chlorella pyrenoidosa does, resulting in competition between Asp and microalgae for Zn ions and accelerating equilibrium [20]. The 1 h group exhibited more complex adsorption behavior due to the partial adsorption of lysine onto the microalgal surface, with remaining free lysine slowing the rate of equilibrium. Studies suggest that in ternary systems, adsorption involving DOM and heavy metals occurs more slowly than in binary systems [33]. The faster equilibrium times for the 2 h and 3 h groups support the idea that increased lysine adsorption reduces free lysine in the solution, minimizing competition and enhancing copper ion adsorption.

When microalgae, lysine, and copper ions coexist, microalgae can directly adsorb both heavy metals and amino acids. Additionally, heavy metals can bind to amino acids to form amino acid–algae–heavy metal ternary complexes [19]. The behavior of the system can be categorized into two scenarios. (1) Similar binding constants between amino acids and microalgae for metal ions promote the formation of ternary complexes, enhancing adsorption capacity but extending equilibrium time [23]. (2) Stronger binding constants of amino acids with metal ions compared to microalgae result in competitive adsorption, accelerating equilibrium but reducing overall adsorption capacity [20]. The interaction of lysine with copper ions falls under the second scenario, where lysine competes with microalgae for copper ions, similar to how fulvic acid (FA) outcompetes microalgae in copper binding [34]. This competition complicates the formation of ternary complexes. However, increasing the interaction time between lysine and microalgae and adjusting the binding sequence of amino acids, metal ions, and microalgae mitigates competitive adsorption. The results indicate that a lysine interaction time of 3 h significantly enhances both the removal rate and efficiency of Cu²⁺. Therefore, further optimization of the lysine concentration was conducted under this condition.

3.1.2. Optimization of the Lysine Concentration

The lysine concentration was further optimized at a copper ion concentration of 32 mg/L and a 3 h interaction time with Tetradesmus obliquus. The goal of this optimization was to identify the ideal lysine concentration that maximizes its adsorption by the microalgae while maintaining a constant interaction time. This ensures the maximum number of lysine molecules bind to the microalgal surface, minimizing the free lysine concentration in the solution and enhancing the algae’s ability to effectively remove heavy metal ions. As shown in Figure 2b, after 180 min of adsorption, the removal efficiency followed the trend 0.03 mM (96.22%) > 0.01 mM (95.4%) > 0.05 mM (94.14%) > 0.1 mM (90.53%), all exceeding the control group without lysine (89.23%). Compared to the control, lysine addition improved removal efficiency by 6.99%, 6.17%, 4.91%, and 1.3%, respectively, with 0.03 mM identified as the optimal concentration. As the lysine concentration increases, the copper ion removal efficiency by microalgae initially increases and then decreases. This trend is influenced by both the amount of free lysine in the solution and the lysine bound to the microalgae surface. At a concentration of 0.01 mM, the adsorption capacity for lysine is not saturated, allowing more lysine to bind to the microalgae surface. As a result, at 0.03 mM, more lysine is bound, which enhances the copper removal efficiency. However, with a constant interaction time between lysine and microalgae, the number of lysine molecules that can bind to the microalgae surface is limited [35]. As the lysine concentration increases to 0.05 mM and 0.1 mM, the binding sites become saturated, leading to an increase in free lysine in the solution. This surplus of free lysine competes with copper ions for adsorption sites, which reduces the removal efficiency. This observation aligns with findings by L. Luo et al., where high concentrations of fulvic acid (FA) formed Cr–FA complexes, competing with algal adsorption sites for chromium ions [36]. At 0.01 mM lysine, the remaining copper concentration in the solution after treatment is only 1.47 mg/L. The low concentration of copper ions creates significant mass transfer resistance, limiting the adsorption of copper ions by the microalgae. Consequently, while the microalgae show a stronger adsorption capacity at 0.03 mM lysine, the improvement in removal efficiency is less pronounced. In solutions with higher copper concentrations, the enhancement in removal efficiency is more evident. Based on these results, a lysine concentration of 0.03 mM and a 3 h interaction time were chosen as the optimal conditions for subsequent experiments.

3.2. Adsorption of Single Metals by Microalgae

3.2.1. Adsorption Kinetics

To investigate the effects of heavy metal concentration and metal ion type on lysine-assisted adsorption, this study examined the removal of Cu, Zn, Cd, and Pb by Tetradesmus obliquus at low (8 mg/L), medium (32 mg/L), and high (64 mg/L) concentrations. As shown in Figure 3, in the control group, the removal efficiencies for Cu were 84.33%, 89.23%, and 58.41% at 8, 32, and 64 mg/L, respectively. For Zn, the removal efficiencies were 72.83%, 31.66%, and 16.68%; for Cd, 14.47%, 8.00%, and 7.87%; and for Pb, 78.98%, 88.40%, and 97.41%. The removal efficiency of Tetradesmus obliquus for heavy metal ions followed the order Pb > Cu > Zn > Cd, aligning with findings from previous studies [12,14,37]. The addition of lysine enhanced the adsorption capacity for all four heavy metals, with this effect becoming more pronounced as the initial metal concentration increased. This is likely due to the promotion of microalgae–lysine–metal ternary complex formation at higher metal concentrations [19]. At an initial heavy metal ion concentration of 64 mg/L, the adsorption capacities of microalgae for Cu, Zn, Cd, and Pb were 74.76, 21.35, 10.07, and 124.68 mg/g, respectively. After the addition of lysine, the adsorption capacities for Cu, Zn, Cd, and Pb increased by 17.88, 52.76, 42.58, and 2.20 mg/g, respectively.

The differences in adsorption capacity among heavy metals are closely related to the functional groups on the microalgal surface. Studies have shown that carboxyl (-COOH), amino (-NH₂), and hydroxyl (-OH) groups are the primary functional groups responsible for surface charge [38]. Variations in metal affinity for these functional groups influence adsorption efficiency. Research by Li et al. demonstrated that carboxyl groups in microalgal surface proteins play a key role in Cd adsorption [39], which explains the significant increase in Cd uptake following lysine addition. Brinza et al. found that the carboxyl and hydroxyl groups in alginate were involved in the binding with zinc ions at low pH [40]. For Cu and Pb, multiple functional groups participate in the adsorption process, and Tetradesmus obliquus inherently exhibits high adsorption capacities for these metals. As a result, the enhancement effect at 64 mg/L was less pronounced. Previous studies indicate that Pb(II) has a high affinity for -COOH, C-OH, C-N, and P=O functional groups, contributing to its strong adsorption capacity [13].

The differences in the relative atomic masses of heavy metal ions may also partly explain the variation in the adsorption capacities of Tetradesmus obliquus. A lower initial molar concentration of a heavy metal ion corresponds to fewer ions in solution, while the biosorbent concentration remains constant for all four metals. If the biomass adsorbs a similar number of metal ions per unit mass, metals with lower initial molar concentrations tend to exhibit higher removal efficiencies. For example, at an initial concentration of 64 mg/L, the molar concentrations of Cu, Zn, Cd, and Pb were 1.00 mM/L, 0.98 mM/L, 1.33 mM/L, and 0.31 mM/L, respectively. The relatively low molar concentration of Pb may contribute to its higher removal efficiency.

3.2.2. Adsorption Kinetics Model Fitting

To investigate the adsorption kinetics of microalgal cells for different heavy metal ions, adsorption curves for Tetradesmus obliquus with and without lysine were fitted to kinetic models at a concentration of 32 mg/L. Both the control and lysine-treated groups achieved adsorption equilibrium within 180 min. As shown in

Table 1. Kinetic model fitting parameters for heavy metal adsorption by Tetradesmus obliquus before and after lysine modification. Values are the mean of three repeated measurements with standard error bars (n = 3).

Experimental Group	Pseudo-First-Order Model			Pseudo-Second-Order Model
	qe	k1	R2	qe	k2	R2
Algae (Cu)	26.92	0.145	0.909	30.07	5.72 × 10−3	0.998
Algae + Lys (Cu)	31.32	8.82	0.997	30.39	3.08 × 10−2	0.999
Algae (Zn)	-	-	0.867	11.75	2.12 × 10−3	0758
Algae + Lys (Zn)	51.31	0.37	0.913	61.91	1.84 × 10−3	0.987
Algae (Cd)	3.965	0.16	0.775	5.25	1.24 × 10−2	0.980
Algae + Lys (Cd)	26.630	0.33	0.847	34.25	2.32 × 10−3	0.971
Algae (Pb)	56.08	0.84	0.999	56.66	5.09 × 10−2	0.999
Algae + Lys (Pb)	63.42	2.58	0.999	62.77	4.65 × 10−2	0.999

, the pseudo-second-order kinetic model provided a better fit for the adsorption behavior before and after lysine addition, compared to the pseudo-first-order model. This is because the pseudo-first-order model primarily describes the initial phase of adsorption and does not accurately capture the entire adsorption process [41]. In contrast, the pseudo-second-order model accounts for all stages of adsorption, including external liquid film diffusion and internal particle diffusion [14,42]. Similar results were observed in the study by Zhang et al., where the pseudo-second-order model better described the microalgal adsorption of Zn and Ni in the presence of dissolved organic matter (DOM) [43]. These findings indicate that the addition of lysine enhances the chemical adsorption of heavy metal ions onto microalgae, likely through complexation or ion exchange via the functional groups on the cell surface [44]. Notably, the adsorption rate of Cu²⁺ was significantly enhanced by lysine, with the rate constant (k₂) increasing by an order of magnitude compared to the control group.

3.2.3. Adsorption Isotherm Modeling

The adsorption data of Tetradesmus obliquus for four heavy metal ions, both with and without lysine, were analyzed using Freundlich, Langmuir, and Sips isothermal adsorption models. The fitting results, shown in

Table 2. Adsorption isotherm model parameters for Tetradesmus obliquus before and after lysine addition. Values represent the mean of three replicates with standard error bars (n = 3).

Experimental Group	Langmuir Constant			Freundlich Constant			Sips Constant
	qm	KL	R2	Kf	n	R2	qmS	KS	ns	R2
Algae (Cu)	81.41	0.12	0.852	26.81	1.98	0.770	67.46	0.08	1.03	0.988
Algae + Lys (Cu)	93.60	0.82	0.922	62.17	7.75	0.805	84.98	1.58	1.92	0.984
Algae (Zn)	24.75	7.45	0.957	18.83	11.57	0.893	26.72	2.92	0.49	0.998
Algae + Lys (Zn)	79.49	2.68	0.982	41.05	4.69	0.728	78.04	3.26	1.14	0.969
Algae (Cd)	28.56	0.22	0.933	10.51	4.11	0.704	25.20	0.08	1.76	0.988
Algae + Lys (Cd)	76.09	0.06	0.981	8.26	1.95	0.996	71.23	0.03	1.22	0.959
Algae (Pb)	162.13	0.65	0.806	58.63	2.07	0.740	118.15	2.88	3.50	0.964
Algae + Lys (Pb)	165.56	5.87	0.996	174.47	2.22	0.975	178.30	4.23	0.92	0.995

, indicate that the Sips model provides the best fit, as demonstrated by the highest R² value. The Sips model represents the adsorption of a single metal ion on a monolayer of adsorption sites with a parameter of 1/ns [13,27]. When ns < 1, multiple binding sites contribute to the adsorption of a single adsorbate, while ns > 1 suggests that a single binding site is responsible for adsorbing multiple adsorbates. Notably, the addition of lysine led to a significant increase in the ns value, indicating that lysine altered the adsorption behavior on the microalgal surface. This is consistent with the proposed mechanism of lysine action, which increases the number of adsorption sites without occupying the existing ones [20]. The maximum adsorption capacities predicted by the Sips model were in the following order: Pb (118.15 mg/g) > Cu (67.46 mg/g) > Zn (26.72 mg/g) > Cd (25.20 mg/g), which aligns with the experimental results. Compared to the control group, the theoretical maximum adsorption capacities of the lysine-treated group increased by 60.15, 17.52, 51.32, and 46.03 mg/g for Pb, Cu, Zn, and Cd, respectively.

3.3. Binary Mixed-Metal Adsorption Study

To evaluate the role of lysine in enhancing the removal of mixed heavy metals and to understand the affinity of Tetradesmus obliquus for different metal ions, we investigated the adsorption rate and removal efficiency for binary metal mixtures, specifically Cu + Zn, Cu + Cd, and Cu + Pb.

3.3.1. Adsorption Rate

In a binary metal mixture with a concentration of 32 mg/L for each metal, we investigated the adsorption kinetics for both the lysine-treated and control groups. As shown in Figure 4, competition between the metal ions notably inhibited the algae’s ability to adsorb individual metals. In the presence of Cu, the residual concentrations after 5 min in the control group were as follows: Cd (30.53 mg/L) > Zn (29.84 mg/L) > Pb (17.16 mg/L). In a single-metal system, the concentration order was Cd (30.53 mg/L) > Zn (30.32 mg/L) > Pb (4.35 mg/L), with corresponding copper concentrations of 12.54, 27.03, and 23.76 mg/L. These results indicate that Tetradesmus obliquus preferentially adsorbs Cu, with Cu having the most significant impact on Pb adsorption, while the presence of Cd strongly inhibited Cu adsorption.

The addition of lysine improved the algae’s removal efficiency for both metals in the binary mixture. After 5 min, the residual concentrations of Zn and Cd in the lysine-treated group were 24.84 mg/L and 26.79 mg/L, respectively, showing removal efficiency increases of 15.6% and 11.68% compared to the control group. However, there was no significant improvement in Pb removal. For copper ions, in the presence of equal concentrations of Zn, Cd, and Pb, the lysine-treated group had residual concentrations of 8.97 mg/L, 13.61 mg/L, and 10.81 mg/L, respectively, resulting in removal efficiency improvements of 11.15%, 41.93%, and 40.46% compared to the control group. Notably, in the binary mixed solution, Zn and Cd reached adsorption equilibrium more quickly, while the equilibrium times for Cu and Pb were significantly delayed, which contrasts with the results observed in the single-metal systems.

3.3.2. Removal Efficiency

Figure 5 illustrates the removal efficiency of heavy metal ions from binary metal mixtures by Tetradesmus obliquus at equal initial concentrations. In the control group, the removal efficiencies for Zn at metal concentrations of 4, 8, 16, and 32 mg/L were 70.93%, 40.82%, 7.12%, and 6.74%, respectively. For Cd, the corresponding removal efficiencies were 28%, 27.23%, 9.94%, and 5.94%, while for Pb, they were 95.79%, 97.76%, 98.36%, and 82.18%. At lower Cu concentrations, the presence of Cu enhanced the adsorption of competing ions, whereas at higher Cu concentrations, it inhibited their removal. For instance, at 8 mg/L, Cd removal increased by 12.75%. The inhibitory effect of Cu on Zn and Cd was particularly pronounced. At 32 mg/L, the removal efficiency of Zn and Cd in the control group decreased by 24.92% and 2.03%, respectively. In contrast, Cu had a minimal impact on Pb removal, with only a 6.22% reduction at 32 mg/L. A similar trend was observed for Cu itself, where low concentrations of Zn, Cd, and Pb enhanced Cu adsorption, while high concentrations suppressed it. In the control group, the removal efficiency of Cu at 32 mg/L was 77.20% in the presence of Zn, 71.00% with Cd, and 62.28% with Pb. The addition of lysine improved Cu removal by 11.82%, 16.50%, and 22.49%, respectively. Similarly, lysine enhanced Zn, Cd, and Pb removal by 15.41%, 12.53%, and 3.94%, respectively, at the same concentration.

The enhancement in the removal of mixed heavy metals by lysine is reflected in two main aspects. (1) Lysine increases the adsorption capacity for both metals in the binary system. (2) It significantly raises the concentration at which the removal rate begins to decline due to competitive adsorption. For example, in the control group, the removal rate of Zn started to decrease as Cu concentration increased from 4 mg/L. However, in the lysine-treated group, the removal rate did not decrease until the Cu concentration reached 16 mg/L. This suggests that lysine, by providing a substantial number of additional adsorption sites, enhances the algae’s adsorption capacity, thereby reducing the competition between Cu and Zn for the limited adsorption sites at lower concentrations.

In summary, Cu exhibits the strongest inhibitory effect on Zn, while Pb has the strongest inhibition on Cu. This phenomenon can be explained by two factors, including (1) the selective affinity of different heavy metal ions for functional groups. Wang et al. found that the type of functional group determines the selectivity of cation exchange, with carboxyl groups showing a stronger affinity for Pb(II) than Cu(II) [45]. (2) The radius and electronegativity of the metal ions play a role. Smaller ions with higher electronegativity tend to have greater adsorption capacities. Pb(II) has a higher electronegativity, making it more easily adsorbed [46]. While the ionic radii of Cu(II) (73 pm) and Zn(II) (74 pm) are similar, Cu(II) has a higher electronegativity (1.95) compared to Zn(II) (1.65) [37], which results in a stronger adsorption capacity for Cu. This observation is supported by Areco et al., who found that microalgae exhibit a significantly higher adsorption capacity for Cu(II) than Zn(II) [47]. Moreover, the total adsorption of binary heavy metals by the algae is higher than that in single-metal systems, indicating that in addition to ion exchange, mechanisms such as electrostatic adsorption may also contribute to enhanced removal [48].

3.4. Analysis and Characterization

3.4.1. 3D-EEM

Extracellular polymeric substances (EPSs) of microalgae play a significant role in the adsorption of lysine and heavy metals, providing binding sites and contributing to electrostatic interactions [49]. In this study, the 3D-EEM (three-dimensional excitation–emission matrix) was used to investigate the binding process between Tetradesmus obliquus and lysine, as well as the mechanism of copper ion adsorption. As shown in Figure 6, the fluorescence spectrum can be divided into five regions based on the types of substances involved [50]. Notably, changes in fluorescence intensity predominantly occur in regions IV and V, which correspond to soluble biological metabolites and humic substances, respectively.

Figure 6a–d illustrate the changes in fluorescence intensity in the solution during the binding process between microalgae and lysine at 0, 1, 2, and 3 h. As time progressed, a noticeable increase in fluorescence intensity in region V was observed, indicating the involvement of humic substances in EPSs during the interaction with lysine. Simultaneously, the fluorescence intensity in region IV decreased, likely due to the reduction in lysine concentration in the solution, which resulted in a decrease in fluorescence intensity.

Figure 6e–h show the fluorescence spectra before and after copper ion adsorption in the control and lysine-treated groups. The results reveal that in the control group, the fluorescence intensity in region IV was minimal, while the lysine-treated group exhibited a prominent peak. This confirms the association of region IV with lysine and suggests that a significant amount of lysine was adsorbed onto the microalgal surface, playing a role in the copper ion adsorption process.

3.4.2. FTIR

Figure 7 presents the Fourier-transform infrared (FTIR) spectra of Tetradesmus obliquus before and after copper ion adsorption, comparing the changes in the algae’s surface functional groups in the presence and absence of lysine. When lysine is added, the absorption peak at 3400.6 cm⁻¹ in the control group shifts to 3408.6 cm⁻¹, corresponding to the -OH stretching vibration in the algae’s cell surface proteins and polysaccharides [12,24]. The shift at 1655 cm⁻¹ in the control group is attributed to C=O stretching in carboxyl groups, which are commonly found in amino acids, proteins, and carbohydrates [49,51]. This change likely results from lysine binding with the algae or the addition of carboxyl groups from lysine. Additionally, absorption peaks at 1546.9 cm⁻¹ and 1384.4 cm⁻¹ show noticeable shifts, with the peak around 1384 cm⁻¹ increasing after lysine addition. This is typically related to C=O stretching or N-H and C-N stretching in amide II [49]. It is speculated that lysine’s amino group interacts with the algae’s organic functional groups, leading to this shift. This mechanism is consistent with lysine’s role, where its -NH₃⁺ group electrostatically adsorbs to the algae, forming an algae-lysine complex [20].

Following copper ion adsorption, all the aforementioned peaks show varying degrees of stretching, indicating their involvement in the adsorption process. However, no significant change is observed at 2919 cm⁻¹, suggesting that the C-H bonds from proteins and carbohydrates in the cell wall are not significantly involved in copper ion adsorption [12]. Additionally, both the lysine-treated and control groups show a distinct shift at 1037 cm⁻¹, indicating that C-O-C bonds in polysaccharides secreted by the algae also participate in copper ion adsorption [52]. Notably, negatively charged functional groups, such as -COOH and -OH, on the algae’s surface may facilitate ion exchange with heavy metal ions. According to Gu et al., oxygen has the highest electronegativity among elements like C, N, O, P, and H, suggesting that an increase in oxygen-containing functional groups could enhance the algae’s adsorption capacity [13].

3.4.3. XPS

To further investigate the role of functional groups in the interaction between lysine and Tetradesmus obliquus and its copper ion adsorption process, X-ray photoelectron spectroscopy (XPS) was used for quantitative analysis of the relevant elements in the microalgae [53]. As shown in Figure 8a,b. display the full XPS spectra before and after copper ion adsorption for both the lysine-treated and control groups. Carbon (C), nitrogen (N), and oxygen (O) are the primary elements of the microalgal cells. Upon the addition of lysine, subtle shifts were observed in the C 1s, O 1s, and N 1s peaks. After copper ion adsorption, both the lysine-treated and control groups showed shifts in the N 1s and O 1s peaks toward higher binding energies, while the C 1s peak shifted to a lower binding energy. Shifts to higher binding energies indicate electron loss due to oxidation, while shifts to lower binding energies reflect electron gain during reduction [54]. This suggests that the N and O atoms participated in electron loss to facilitate the adsorption of positively charged metal ions.

Table 3. Elemental composition of Tetradesmus obliquus before and after Cu²⁺ adsorption under conditions with and without lysine.

Treatment	Elemental Composition (Atomic %)				Functional Group Composition (%)
	C 1s	O 1s	N 1s	Cu2+	C-C/C-H	C-N/ C-O-C	COOH	O-C=O
Algae	65.14	15.38	4.92	-	77.30	14.55	6.94	1.20
Algae + Lys	63.86	17.13	5.20	-	66.43	24.15	7.12	2.29
Algae + Cu	66.45	14.55	4.07	0.39	77.72	14.44	6.36	1.48
Algae + Lys + Cu	62.79	17.13	5.02	0.85	69.33	23.34	4.24	3.08

presents the elemental composition of C, N, and O before and after lysine treatment. Notably, the proportions of nitrogen and oxygen increased significantly, from 4.92% and 15.38% to 5.02% and 17.13%, respectively. This change is attributed to the bridging role of the amino group in lysine’s interaction with Tetradesmus obliquus, which increased the nitrogen content on the cell surface, while the carboxyl group on the lysine molecule contributed to the rise in oxygen content. Following copper ion adsorption, both the lysine-treated and control groups displayed characteristic Cu 2p peaks at 933.05 eV and 933.54 eV, respectively [10]. The lysine-treated group exhibited a higher copper content of 0.85% compared to 0.39% in the control group, which is consistent with the observed higher copper ion adsorption capacity of the lysine-treated microalgae.

Figure 8c–f present the deconvoluted C 1s spectra, showing four distinct peaks at 284.80 eV, 286.58 eV, 288.21 eV, and 289.18 eV, corresponding to C–C/C–H, C–N/C–O–C, –COOH, and O–C=O, respectively [13]. After lysine addition, the proportion of C–N/C–O–C and –COOH functional groups on the Tetradesmus obliquus surface increased from 14.55% to 24.15% and from 6.94% to 7.12%, respectively. This trend is consistent with the FT-IR analysis, suggesting that the presence of C–N may be a result of lysine binding to the microalgae. Following copper ion adsorption, both the lysine-treated and control groups exhibited a decrease in –COOH content, while the proportions of O–C=O and C–N/C–O–C functional groups increased. This indicates that deprotonated carboxyl groups, which carry a negative charge, play a key role in copper ion binding. Similar findings were reported by Que et al., who demonstrated that heavy metal adsorption capacity is positively correlated with the molar concentration of carboxyl groups per unit mass of dissolved organic matter (DOM) [33]. In the lysine-treated group, the –COOH content decreased by 0.58% compared to a 2.88% reduction in the control group, suggesting that more carboxyl groups in the lysine-treated microalgae were involved in copper ion adsorption.

4. Conclusions

This study evaluated the feasibility of modifying Tetradesmus obliquus with lysine to enhance heavy metal adsorption. The optimal conditions for lysine addition were determined to be 3 h of exposure at a concentration of 0.03 mM. Lysine significantly improved the adsorption capacities of Tetradesmus obliquus for Cu, Zn, Cd, and Pb. At an initial heavy metal concentration of 64 mg/L, the adsorption capacities increased by 17.88, 52.76, 42.58, and 2.20 mg/g for Cu, Zn, Cd, and Pb, respectively. This study also investigated competitive adsorption in binary heavy metal systems. Pb exhibited the strongest inhibitory effect on Cu adsorption, while Cu most strongly inhibited Zn adsorption. These variations were primarily attributed to differences in functional group affinity and the inherent properties of the metal ions. To further elucidate the mechanism of lysine-enhanced adsorption, 3D-EEM, FTIR, and XPS analyses were conducted, focusing on the roles of extracellular polymeric substances (EPSs) and functional groups.

The findings contribute to a deeper understanding of dissolved organic matter (DOM) interactions with Tetradesmus obliquus and provide preliminary insights into its potential application in heavy metal removal. However, several challenges remain for future research and practical implementation:

(i)

Mechanism of Amino Acid–Microalgae Interactions. Further studies should investigate the factors influencing the binding affinity of different amino acids to Tetradesmus obliquus and identify strategies to enhance this interaction. Additionally, research should differentiate between extracellular adsorption and intracellular uptake of heavy metals in response to amino acid modification.

(ii)

Competitive Adsorption Mechanisms. The specific functional groups involved in heavy metal adsorption should be identified, and their affinities for different metal ions should be evaluated. Selecting amino acids that target high-affinity functional groups could optimize microalgal surface modification for enhanced adsorption performance.

(iii)

Binary Isotherm Adsorption Model Analysis. Several binary isotherm models, including the Extended Langmuir and Extended Freundlich models, have been developed to predict microalgae’s efficiency in removing mixed heavy metals. However, the presence of DOM affects the adsorption behavior of microalgae, and there is limited research on how DOM influences the removal efficiency in binary metal mixtures. Future research should focus on assessing the performance of these models and refining them to improve their accuracy.

(iv)

Recovery and Reusability. Research is needed to explore methods for recovering microalgae after heavy metal adsorption and to assess the regeneration potential of the biosorbent. Notably, significant flocculation was observed during adsorption with lysine treatment, suggesting that self-flocculating sedimentation could serve as a potential harvesting method.