A Comparative Evaluation of Ulothrix sp. and Spirogyra sp. as Eco-Friendly Biosorbents for Methylene Blue Removal: Mechanistic Insights from Equilibrium, Kinetic, and Thermodynamic Analyses

Abstract

This study investigates two novel algal biosorbents (Ulothrix sp. and Spirogyra sp.) from Djelfa, Algeria, for methylene blue (MB) removal from aqueous solutions. A comprehensive characterization, including scanning electron microscopy–energy dispersive X-ray spectroscopy (SEM–EDS), Brunauer–Emmett–Teller (BET) analysis, porosity measurements, and Fourier-transform infrared spectroscopy (FTIR), revealed distinct physicochemical properties. Ulothrix exhibited a surface area of 5.35 m²/g with an average pore diameter of 32.77 nm, whereas Spirogyra showed values of 3.47 m²/g and 20.97 nm for the surface area and average pore diameter, respectively. Despite their modest surface areas, both algae demonstrated effective adsorption capacities (6.94 mg/g for Spirogyra vs. 6.38 mg/g for Ulothrix), with optimal doses of 0.01 g and 0.08 g (for 50 mL of MB solution), respectively. Kinetic analysis confirmed pseudo-second-order adsorption (R² > 0.97), indicating chemisorption dominance. Isotherm data best fit the Sips model (R² = 0.94), suggesting heterogeneous monolayer formation. Thermodynamic studies revealed an endothermic (ΔH° > 0), spontaneous (ΔG° < 0), yet favorable adsorption process, highlighting the potential of these naturally abundant algae as sustainable biosorbents for dye wastewater treatment.

1. Introduction

Organic synthetic dyes are chemical compounds widely used across various industrial sectors, including the automotive, chemical, paper, and textile industries in particular [1,2,3,4,5,6]. However, the presence of these dyes in textile wastewater poses significant environmental risks due to their high stability and low biodegradability [7,8]. Most of these dyes are discharged in liquid effluents, which are often released directly into waterways without proper treatment.

Methylene blue (MB), a commonly used dye in industries such as paper, silk, wool, and cotton processing, can cause permanent eye damage in humans and animals [9,10]. The treatment of industrial wastewater containing MB is critically important, as this compound exhibits carcinogenic and mutagenic properties with serious effects on human health [11,12]. Despite these risks, many industries continue to discharge untreated wastewater into rivers and seas, leading to contamination of both surface and groundwater sources [13].

Various physical, chemical, and biological methods have been employed for treating dye-containing wastewater, including electrodialysis [14], electrocoagulation [15], chemical precipitation [16], bioreduction using bacteria and fungi [17,18], ion exchange, membrane filtration, and photocatalytic oxidation processes, along with adsorption on commercial activated carbons. However, these conventional approaches are increasingly proving inadequate for effective treatment, remaining costly, particularly when applied to high-volume effluents [7,19,20]. Furthermore, such methods may generate byproducts more toxic than the original contaminants.

Among available treatment options, adsorption has emerged as particularly advantageous due to its cost-effectiveness, design simplicity, efficient dye removal capabilities, and operational ease [21,22,23]. This has driven research toward developing economical sorbent materials, including clays [24], wood derivatives [25], polysaccharides [26], and various biological materials ranging from microbial biomasses [27] to plant-based alternatives [28]. Despite these advantages, challenges persist regarding material regeneration and the practical recovery of adsorbent powders after treatment applications.

Over the past decade, scientific research has increasingly focused on developing novel adsorbent materials to overcome the limitations of conventional options. In this context, algae have emerged as particularly promising biosorbents due to their multiple advantages, including low cost, abundant availability, biocompatibility, non-toxicity, and bioaccumulation capacity [9,10]. Their effectiveness has been demonstrated in removing various water pollutants, including textile dyes [21,29,30,31]. These characteristics, combined with their operational simplicity, make algal biomass an attractive alternative for wastewater treatment applications.

Recent advances in algal biosorption research underscore its viability for dye and heavy metal removal, driven by unique physicochemical interactions and sustainable advantages [32,33]. Studies reveal that algal cell walls, rich in polysaccharides and functional groups (e.g., -OH and -COOH), facilitate contaminant uptake through both chemisorption and physisorption mechanisms [34]. This is evidenced by Plocamium cartilagineum’s pH-sensitive Cibacron Blue adsorption (25.83 mg/g capacity) [35], and Pterocladia capillacea’s efficient multilayer adsorption of crystal violet dye (98% removal efficiency) [36].

Performance varies significantly among algal species, with surface area and pore architecture being key determinants of adsorption capacity [37,38]. Critical operational parameters, including pH, temperature, and biomass dosage, demonstrate synergistic effects, although inherent trade-offs exist between process efficiency and scalability. While algae present eco-friendly, low-cost alternatives to conventional adsorbents (e.g., activated carbon), their industrial-scale application requires further optimization to overcome limitations in consistency and regeneration potential [39].

This study presents a novel approach to wastewater treatment through the development and characterization of two algal biosorbents (Ulothrix sp. and Spirogyra sp.) specifically tailored for leather industry dye removal [32,40]. Unlike conventional adsorbents, these algal biomasses offer sustainable advantages: (1) natural abundance and biodegradability with minimal processing requirements, (2) cost-effectiveness and potential for cultivation in wastewater systems, and (3) superior binding capacity due to high densities of functional groups (–OH, –COOH, and –NH₂) on cell walls, which enhance dye adsorption via multiple interaction mechanisms [37,41].

The following study systematically (1) develops comprehensive characterization protocols (SEM–EDS, BET, and FTIR) to identify the unique surface properties of these algal species, (2) establishes optimized conditions for methylene blue (MB) removal through kinetic, isotherm, and thermodynamic analyses, and (3) demonstrates the practical viability of algal biosorption for industrial wastewater treatment.

The key innovations include the following: (i) the comparative evaluation of Ulothrix sp. and Spirogyra sp. as biosorbents for leather dye removal, highlighting species-specific performance, (ii) the mechanistic elucidation of pH-dependent adsorption behavior, including surface charge effects and competitive ion interactions, and (iii) the comprehensive analysis of the adsorption nature (physisorption vs. chemisorption) and thermodynamics (including spontaneity, enthalpy, and entropy changes), conducted to characterize the adsorption process. This research significantly advances sustainable water treatment solutions by providing a renewable alternative to synthetic adsorbents, with particular relevance for the polluting leather industry. The findings bridge the gap between laboratory-scale biosorption studies and practical environmental applications.

2. Materials and Methods

2.1. Materials

Pure methylene blue (MB) dye (solid powder) was purchased from Biochem Laboratory Chemopharma (Montreal, QC, Canada). Sodium hydroxide (NaOH, 97%) was obtained from Biochem Chemopharma (Montreal, QC, Canada), and hydrochloric acid (HCl, 37%) was acquired from Sigma-Aldrich (Bangalore, India). All aqueous solutions were prepared using ultrapure water (resistivity = 15.0 MΩ·cm at 25 °C).

2.2. Adsorbent Preparation

Algal samples were collected from Oued Mellah and Oued Mgusba in the Taadmit commune of Djelfa province. The algae were manually cleaned to remove all epiphytes and debris adhering to their filaments, then rinsed on-site with stream water before being placed in plastic bags for transport. Upon arrival at the laboratory, the samples were sorted by species, sequentially rinsed with tap water followed by distilled water, and oven-dried at 105 °C for 24 h.

The dried algae were subsequently ground into a fine powder for use as an adsorbent material. For genus identification, fresh algal samples were examined under an optical microscope (MOTIC AE2000, MOTIC, Xiamen, China) at 40× and 100× magnification (Figure 1).

2.3. Characterization of Algae

The structural analysis of the studied material was performed using UV-visible spectrophotometry, scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDS), Brunauer–Emmett–Teller (BET) surface area analysis, and Fourier-transform infrared (FTIR) spectroscopy analysis.

The absorbance of methylene blue adsorbed by algae was determined using a UV-visible spectrophotometer (SP-UV500VDB, Spectrum Instrumentation GmbH, Großhansdorf, Germany). Specific surface area measurements were obtained through nitrogen adsorption isotherms analyzed using the BET method (Micromeritics Instrument, Norcross, GA, USA). Surface morphology and elemental composition were evaluated using scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (Thermo Scientific Quattro ESEM, Thermo Fisher Scientific Inc., Waltham, MA, USA). FTIR analysis was conducted in the range of 400–4000 cm⁻¹ with 20 cumulative scans using a Shimadzu IRSpirit-T spectrometer (Shimadzu Corporation, Kyoto, Japan).

2.4. Adsorption Experiments

Batch adsorption studies were conducted in 100 mL Erlenmeyer flasks containing 50 mL of MB solution (5–60 mg/L) mixed with algal adsorbent (0.01–1 g for Spirogyra and 0.05–0.7 g for Ulothrix) at varying pH levels (1.38–12 for Spirogyra and 2–11 for Ulothrix). The mixtures were agitated in a water bath shaker (Julabo SW22, Julabo GmbH, Seelbach, Germany) at 150 rpm and 20 ± 1 °C for 24 h. All experiments investigating contact time effects, algal dose effects, pH effects, and isotherm studies were performed in triplicate.

Following each adsorption experiment, the solid phase was separated by centrifugation (Sigma 3-16P, Sigma Zentrifugen, Osterode am Harz, Germany). The equilibrium concentrations of MB in solution were determined using a UV-visible spectrophotometer at the maximum absorption wavelength of 664 nm. The adsorption capacity (q_e (mg/g) was calculated using Equation (1):

$${\mathrm{q}}_{\mathrm{e}}={\displaystyle \frac{\left({\mathrm{C}}_0-{\mathrm{C}}_{\mathrm{e}/\mathrm{t}}\right)·\mathrm{V}}{\mathrm{m}}},$$

(1)

where C₀ and C_e/t are the initial and equilibrium concentrations at a specific time within the MB solution (mg/L), respectively, V represents the total solution volume (L), and m stands for the mass of algae (g). The removal efficiency was also calculated using Equation (2):

$$\mathrm{R}\mathrm{\%}={\displaystyle \frac{({\mathrm{C}}_0-{\mathrm{C}}_{\mathrm{e}})}{{\mathrm{C}}_0}}\times 100.$$

(2)

2.5. Data Analysis

The adsorption kinetics of MB onto the algal biosorbents were investigated by fitting the experimental data to four kinetic models: pseudo-first-order, pseudo-second-order, Elovich, and intraparticle diffusion (ID). The non-linear equations for these models and their corresponding parameters are presented in

Table 1. Summary of kinetic models used and associated parameters.

Non-Linear Kinetic Equations	Parameters	Reference
Pseudo-first-order
${\mathrm{q}}_{\mathrm{t}}={\mathrm{q}}_{\mathrm{e}}\left[1-\mathrm{e}\mathrm{x}\mathrm{p}(-{\mathrm{K}}_1\mathrm{t})\right]$	qt: sorption capacity (mg/g) time t K1: rate constant (min−1)	[42]
Pseudo-second-order
${\mathrm{q}}_{\mathrm{t}}={\displaystyle \frac{{\mathrm{q}}_{\mathrm{e}}^2{\mathrm{K}}_2\mathrm{t}}{1+{\mathrm{q}}_{\mathrm{e}}{\mathrm{K}}_2\mathrm{t}}}$	qt : equilibrium concentration (mg/g). k2 : rate constant (g mg−1 min−1)	[42]
Elovich
${\mathrm{q}}_{\mathrm{t}}={\displaystyle \frac{1}{\mathsf{\beta }}}\mathrm{l}\mathrm{n}(1+\mathsf{\alpha }\mathsf{\beta }\mathrm{t})$	α: initial adsorption rate constant (mg/(g.min)) β: the ratio between surface coverage and activation energy constant of chemisorption (g/mg)	[22]
Intraparticle diffusion (ID)
${\mathrm{q}}_{\mathrm{t}}={\mathrm{k}}_{\mathrm{i}\mathrm{n}\mathrm{t}}\sqrt{\mathrm{t}}+\mathrm{c}$	Kint: the rate constant for intraparticle diffusion (mg.g−1.min−1/2). C: the intercept relating to the amount of MB removed by rapid initial adsorption and/or the boundary layer thickness. (mg/g)	[43]

.

Previous studies have demonstrated that transforming adsorption isotherms into linearized forms frequently alters the error structure of experimental data [31]. To address this limitation, non-linear regression analysis is essential, as it provides a mathematically rigorous approach for determining adsorption parameters while maintaining the original form of isotherm equations [42,44].

For equilibrium adsorption studies, the adsorption capacity q_e was calculated using Equation (1). The experimental data were fitted to non-linear forms of four isotherm models: Freundlich, Langmuir, Sips, and Jossens. The complete non-linear equations for these models, along with their respective parameters and definitions, are presented in

Table 2. List of isotherm models and their respective parameters.

Non-Linear Isotherm Equations	Parameters	Reference
Langmuir
${\mathrm{q}}_{\mathrm{e}}={\displaystyle \frac{{\mathrm{q}}_{\mathrm{m}}{\mathrm{K}}_{\mathrm{L}}{\mathrm{C}}_{\mathrm{e}}}{1+{\mathrm{K}}_{\mathrm{L}}{\mathrm{C}}_{\mathrm{e}}}}$	qmax: maximum adsorption capacity (mg/g) bL: Langmuir coefficient (L/mg) Ce: equilibrium concentration (mg/L)	[44]
Freundlich
${\mathrm{q}}_{\mathrm{e}}={\mathrm{k}}_{\mathrm{f}}{\mathrm{c}}_{\mathrm{e}}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\mathrm{n}$}\right.}$	Kf: Freundlich adsorption coefficient (L/g) n: Freundlich isotherm exponent reflects adsorption intensity.	[44]
Sips
${\mathrm{q}}_{\mathrm{e}}={\displaystyle \frac{{\mathrm{K}}_{\mathrm{s}}{\mathrm{C}}_{\mathrm{e}}^{\mathsf{\beta }\mathrm{s}}}{1+{\mathrm{a}}_{\mathrm{s}}{\mathrm{C}}_{\mathrm{e}}^{\mathsf{\beta }\mathrm{s}}}}$	Ks: is the Sips adsorption constant (L/mg) βs: describes the surface heterogeneity as: Sips’ isotherm model constant (L/g)	[45]
Jossens
${\mathrm{q}}_{\mathrm{e}}={\displaystyle \frac{{\mathrm{K}}_{\mathrm{j}}{\mathrm{C}}_{\mathrm{e}}}{1+{\mathsf{\beta }}_{\mathrm{j}}{\mathrm{C}}_{\mathrm{e}}^{\mathrm{n}\mathrm{j}}}}$	Kj: Jossens constant, L/g βj: Jossens constant, L/g nj: dimensionless Jossens constant	[45]

.

A non-linear regression analysis was performed on selected kinetic and adsorption isotherm models to fit the experimental data using OriginPro 2023 software (OriginLab Corporation, Northampton, MA, USA). To assess the goodness of fit, the coefficient of determination (R²) [46] was determined as a statistical parameter using the following equation:

$${\mathrm{R}}^2=1-{\displaystyle \frac{\sum {\left({\mathrm{q}\mathrm{e}}_{\mathrm{c}\mathrm{a}\mathrm{l}}-{\mathrm{q}\mathrm{e}}_{\mathrm{e}\mathrm{x}\mathrm{p}}\right)}^2}{\sum {\left({\mathrm{q}\mathrm{e}}_{\mathrm{e}\mathrm{x}\mathrm{p}}-{\mathrm{q}\mathrm{m}}_{\mathrm{e}\mathrm{x}\mathrm{p}}\right)}^2}},$$

(3)

where qe_cal is the amount of adsorbate obtained by kinetic or isotherm models (mg/g), qe_exp is the amount of adsorbate adsorbed by the adsorbent during the experiment (mg/g), and qm_exp is the average of qe_exp (mg/g).

3. Results and Discussion

3.1. Material Characterization

Scanning electron microscopy (SEM) serves as a powerful analytical tool for investigating algal surface morphology, including porosity, roughness, morphological characteristics, and structure. Figure 2 presents the SEM images of the surface morphologies of Ulothrix and Spirogyra algae. The characteristic structure of these filamentous algae is observed to have relatively rough surfaces, with visible fragments or natural mineral deposits appearing as small, lighter, or darker grains depending on their density.

Following adsorption, the algae exhibited increased surface roughness and heterogeneity, likely due to the occupation of methylene blue (MB) molecules on active sites. Granular structures visible on both Ulothrix and Spirogyra surfaces may correspond to Fe- and Co-containing phases, as confirmed by EDS analysis (Figure 3). All samples showed non-uniform porosity distribution. Algal cell walls typically contain cellulose compounds, often reinforced with minerals such as silica and calcium, which are consistent with the Ca, Si, and Mg elements detected by EDS.

3.1.1. EDS Analysis

EDS provides elemental composition data for sample areas analyzed by scanning electron microscopy. Figure 3 presents the SEM–EDS elemental profiles of Ulothrix and Spirogyra. The analysis reveals distinct elemental distribution patterns between the two species. In Spirogyra, the elements follow the presented abundance order: oxygen > carbon > calcium, while Ulothrix shows oxygen > calcium > carbon. Both algae contain major elements including O, C, Ca, K, Mg, Fe, Si, Al, and Na, with oxygen and carbon predominating due to their organic composition (cellulose and polysaccharides) and structural hydroxyl groups. In Spirogyra, we often see a higher percentage of carbon than in Ulothrix, which may indicate a slightly different organic composition or a lower mineral content.

Presence of minerals (Ca, Si, Mg, Fe, Al, Na, and K): The mineral elements (Ca, Si, Mg, Fe, Al, Na, and K) originate from either the culture medium/environment or the algal cell wall architecture, where some species actively accumulate calcium or silica. Notably, Ulothrix contains significantly more calcium than Spirogyra, indicating either enhanced calcium incorporation into its structure or greater adsorption capacity from the medium.

Difference between Ulothrix and Spirogyra: Figure 3 clearly shows that Ulothrix possesses a stronger mineral signature (Ca, Mg, and Fe), while Spirogyra maintains higher carbon content. These compositional differences likely reflect distinct adsorption mechanisms and capacities, potentially involving variations in active sites or functional groups. The elevated calcium content in Ulothrix may particularly influence dye adsorption through complexation or ion exchange processes.

The BET surface area was determined through nitrogen adsorption at 77 K.

Table 3. Physicochemical properties of the studied algal biosorbents (Spirogyra sp. and Ulothrix sp.) with comparative literature data.

Samples	SBET (m2/g)	Smic [a] (m2/g)	Dave [b] (nm)	Pore Volume (cm3/g)	References
Spirogyra	3.4715 ± 0.1313	2.67	20.97	0.0095	This study
Ulothrix	5.3508 ± 0.0337	4.84	32.77	0.0252	This study
S. costatum	87.17	-	3.131	0.103	[47]
P. capillacea	87.1721	-	1.564	0.10368	[48]
Mougeotia robusta	3.1806	-	24.733	0.019666	[49]

^[a] micropore surface area. ^[b] average pore diameter.

presents the BET analysis and porosity results for Ulothrix and Spirogyra algae. Although both algal samples exhibit relatively small specific surface areas, they demonstrate notably large average pore diameters. This structural characteristic facilitates easier and faster diffusion of MB molecules into active adsorption sites, as evidenced by the strong observed affinity between the algal surfaces and MB molecules.

The BET analysis reveals distinct textural characteristics between Ulothrix (SBET = 5.35 m²/g, pore volume = 0.025 cm³/g) and Spirogyra (SBET = 3.47 m²/g, pore volume = 0.009 cm³/g), with Ulothrix exhibiting superior surface area and porosity. Both species display notably lower surface areas compared to Skeletonema costatum (87.17 m²/g) and Pterocladia capillacea (87.17 m²/g), but share similar ranges with Mougeotia robusta (3.18 m²/g). The average pore diameters (20.97 nm for Spirogyra and 32.77 nm for Ulothrix) suggest mesoporous structures, contrasting sharply with the microporous nature of S. costatum (3.13 nm) and P. capillacea (1.56 nm). This mesoporosity likely facilitates methylene blue diffusion, compensating for their modest surface areas. While both studied algae show limited surface areas relative to some literature-reported species, their hierarchical pore architecture may offer kinetic advantages for dye removal applications.

3.1.2. FTIR Analysis

The adsorption capacity of MB is strongly influenced by the surface chemistry of the adsorbent, particularly the presence of oxygen-containing functional groups. FTIR spectroscopy was used to characterize these functional groups in Spirogyra and Ulothrix algal samples. The FTIR spectra for both algal species are presented in Figure 4, with observed peaks corresponding to vibrational energy transitions of specific functional groups that are essential for MB adsorption.

The major spectral peaks, their corresponding functional group assignments, and their roles in the MB binding mechanisms are summarized in

Table 4. The main peaks observed in the FTIR spectra of Spirogyra and Ulothrix before adsorption.

Wavenumber (cm−1)	Assignment	Implication Adsorption
$~$2928	C-H stretching vibration (alkyl groups) [51]	Not directly involved in adsorption
$~$1647	C=O stretching vibration (carbonyls in proteins, amides) [6]	Interaction with MB via hydrogen bonds or electrostatic forces
$~$1510	C=C aromatic or N-H bending (secondary amines) [52]	Possible interaction with MB through electrostatic attraction
$~$1424 and 1418	Carboxylate(-COO-) bending vibration [51]	Involved in electrostatic interactions with MB
$~$1035 and 1029	C-O stretching vibration (polysaccharides alcohols) [52]	Possible hydrogen bonding with MB
$~$875, 709, 715 and 463	Out of plane deformation modes (aromatic or silicates) [51]	Changes after adsorption suggest interaction with MB

. Both Spirogyra and Ulothrix display similar FTIR spectra, suggesting comparable chemical compositions between the two algal species. The FTIR analysis confirms that MB adsorption occurs primarily through three mechanisms: (1) electrostatic interactions, (2) hydrogen bonding, and (3) π–π stacking. Key functional groups involved in these interactions include carbonyl (-C=O), amine (-NH), carboxyl (-COO⁻), and hydroxyl (-OH) moieties, which play essential roles in the adsorption process [50].

3.2. Contact Time Effect—Adsorption Kinetics Study

Contact time is a critical parameter for assessing adsorbent performance, enabling kinetic modeling and mechanistic analysis of the adsorption process. Figure 5 illustrates the temporal evolution of MB adsorption capacity over 240 min for Ulothrix and Spirogyra.

The adsorption capacity of MB increased progressively with contact time for both algal species (Ulothrix and Spirogyra), as demonstrated in Figure 5. The kinetic profile exhibited distinct biphasic behavior: (1) an initial rapid adsorption phase (0–60 min) accounting for approximately 85% of the equilibrium capacity, followed by (2) a gradual approach to equilibrium (60–240 min). The rapid initial uptake can be attributed to the abundance of available active sites and minimal mass transfer resistance. Subsequently, the adsorption rate decreased significantly as active sites became saturated, eventually reaching equilibrium. It appears that there is no significant difference in adsorption capacity between the two algae species during the first 40 min. However, in the final 100 min, Spirogyra exhibits a slightly higher adsorption performance, exceeding the other algae by approximately 0.5 mg/g. This result can be attributed to Spirogyra’s slightly higher oxygen content and greater abundance of oxygenated functional groups (C=C aromatic or N-H), as evidenced by EDS and FTIR analyses, respectively.

Adsorption kinetics examines the rate of contaminant uptake, providing critical insights into the underlying adsorption mechanisms. The kinetic models presented in

Table 5. Calculated parameters of non-linear kinetic models for methylene blue (MB) adsorption onto Ulothrix and Spirogyra algae.

Concentration	qexp (mg/g)	Pseudo-First-Order			Pseudo-Second-Order			Elovich
		R2	qm	k1	R2	qm	k2	R2	Ɓ	α
Ulothrix	6.38	0.95	5.92	0.21	0.98	6.27	0.048	0.98	1.64	126.23
Spirogyra	6.94	0.90	6.33	0.17	0.97	6.76	0.037	0.99	1.37	51.14

were evaluated for MB adsorption onto Ulothrix sp. and Spirogyra sp. biomasses, with experimental data and model fits compared in Figure 6. The adsorption kinetics of both biomasses were best described by the pseudo-second-order (PSO) model, as evidenced by higher non-linear regression coefficients (R² = 0.98 and 0.97 for Ulothrix and Spirogyra, respectively) compared to other kinetic models. The PSO-derived equilibrium adsorption capacities (q_m) showed better agreement with experimental values (q_exp) than those predicted by the pseudo-first-order (PFO) model (Table 5), with only minor variation in adsorption capacity observed between the two algal species.

These results suggest that chimisorption is involved in the adsorption of MB onto both algal species [53].

For Ulothrix algae, the PSO model demonstrated a particularly strong correlation (R² = 0.98) between model predictions and experimental data compared to the PFO model [54]. The low k₂ values obtained from the PSO model indicate decreasing adsorption rates proportional to the number of remaining unoccupied sites [55], while the low k₁ values from the PFO model suggest relatively slow adsorption kinetics for both species [56].

Kinetic analysis of the adsorption processes revealed important mechanistic insights through evaluation of four established models: pseudo-first-order, pseudo-second-order, Elovich, and intraparticle diffusion. The PFO model effectively described initial adsorption dynamics during the first 30–50 min of contact time [57], while the superior fit of the PSO model confirmed chemisorption as the primary adsorption mechanism [58].

The Elovich model describes heterogeneous adsorption systems where chemisorption processes dominate, based on the fundamental principle that adsorption rates decrease exponentially over time [59]. This equation is particularly effective for modeling adsorption on energetically heterogeneous surfaces with chemisorption-dominated mechanisms [22]. The model parameters α (initial adsorption rate in mg/g·min) and β (desorption coefficient in g/mg) quantitatively characterize the adsorption kinetics. Meanwhile, the intraparticle diffusion model serves as a valuable tool for identifying rate-limiting steps controlled by pore diffusion mechanisms during adsorption processes.

The Elovich parameters for Ulothrix (α = 126.23, β = 1.64) and Spirogyra (α = 51.14, β = 1.37) confirm the heterogeneous nature of the algal surfaces, consistent with FTIR analysis that revealed multiple functional groups (e.g., -OH at 3400 cm⁻¹, C=O at 1647 cm⁻¹, -COOH at 1424 cm⁻¹, C=C or N-H at 1510 cm⁻¹, and C-O at 1029 cm⁻¹). The high α values indicate a high density of functional groups and rapid initial adsorption, likely due to electrostatic interactions between cationic dyes (e.g., MB⁺) and negatively charged algal surface sites (e.g., deprotonated -COOH groups). The β parameters reflect a wide distribution of adsorption energies, attributed to both the diversity of functional groups (e.g., polar -OH versus charged -COO⁻) and their spatial arrangement on the algal biomass. Additionally, the cation-rich composition of algae may contribute to MB adsorption through cation exchange mechanisms.

The excellent model fit (R² = 0.98–0.99) confirms that MB adsorption onto algal biomasses follows Elovich kinetics, indicating a complex uptake mechanism involving both (1) surface chemical reaction control (chemisorption) and (2) heterogeneous diffusion processes, rather than simple first- or second-order kinetics [60]. For all studied materials, the α values (initial adsorption rates) exceeded the β values (desorption coefficients), suggesting strong MB adsorption affinity with reversible characteristics. This trend was particularly pronounced in the case of Ulothrix sp., demonstrating its superior adsorption properties.

The parameters derived from kinetic modeling are presented in Table 5. The pseudo-second-order (PSO) and Elovich models demonstrated superior fits to the kinetic data, as evidenced by their higher R² values, indicating that chemical interactions dominate over physical interactions in the adsorption of MB onto both Ulothrix sp. and Spirogyra sp. The PSO model yielded estimated adsorption capacities (qₑ_cal) of 6.27 mg/g for Ulothrix sp. and 6.76 mg/g for Spirogyra sp., which closely matched the experimental values (q_exp) of 6.38 mg/g and 6.94 mg/g, respectively. The higher k₁ values compared to k₂ values suggest rapid achievement of physisorption equilibrium.

Intraparticle diffusion modeling serves to elucidate mass transfer mechanisms within adsorbent pore structures and determine the rate-controlling steps governing adsorption kinetics [61]. The adsorption process typically occurs through three consecutive stages: (1) liquid film diffusion (transport from bulk solution to the adsorbent exterior), (2) intraparticle diffusion (migration into pore networks), and (3) surface adsorption onto active sites. As step (3) reaches equilibrium rapidly, it is generally not rate-limiting. Figure 7 reveals distinct kinetic profiles for the algal biomasses, with Ulothrix sp. exhibiting faster initial adsorption kinetics than Spirogyra sp. This enhanced uptake rate likely stems from the superior surface area and wider pore structure, which facilitate more efficient MB diffusion to adsorption sites.

3.3. Effect of Adsorbent Type and Dosage

The influence of adsorbent dosage was evaluated by testing algal biomass concentrations ranging from 0.01 to 1 g per 50 mL of MB solution (Figure 8). The results revealed a direct relationship between adsorbent mass and MB removal efficiency, resulting from the greater available surface area and adsorption sites at higher dosages. For Ulothrix sp., the adsorption capacity increased steadily with biomass dosage up to 0.1 g, followed by a subsequent decrease. This pattern reflects two key factors: insufficient active sites at low dosages (<0.05 g) to bind all MB molecules, and saturation effects at higher dosages (>0.1 g) where the adsorbate concentration becomes limiting relative to available surface sites [62]. Spirogyra sp. exhibited an inverse relationship between adsorbent dosage and adsorption capacity, mirroring the trend observed for Ulothrix sp. This consistent pattern across both algal species confirms that (1) at higher biomass dosages (>0.1 g/50 mL), the available MB molecules become insufficient to saturate all active sites, and (2) the fixed initial dye concentration leads to decreased adsorption capacity per unit mass of biosorbent. These results indicate that at low adsorbent dosages (<0.05 g/50 mL), Spirogyra exhibits higher adsorption capacity than Ulothrix, with a maximum difference of 66 mg/g recorded. This observation can be explained by the factors discussed in Section 3.2 regarding elemental composition and oxygenated functional groups in algal structures. For algal doses exceeding 0.1 g/50 mL, no significant difference in adsorption capacity was observed between species, with both showing similar decreasing trends in MB adsorption performance as the adsorbent dose increased.

3.4. Effect of pH

The influence of solution pH (ranging from 1 to 12) on the MB removal efficiency was investigated for Ulothrix sp. and Spirogyra sp., while constantly maintaining other parameters (Figure 9). Both algal biomasses exhibited basic points of zero charge (pH_PZC), with values of 7.8 for Ulothrix and 7.6 for Spirogyra. The adsorption studies revealed that MB removal was optimal under alkaline conditions, particularly for Spirogyra sp., which achieved a maximum adsorption capacity at pH 12.06. This enhanced performance results from two key factors occurring at pH > pH_PZC: (1) increased density of negatively charged surface sites, and (2) stronger electrostatic attraction between these sites and the cationic MB molecules. The alkaline pH range was specifically selected based on literature reports demonstrating superior adsorption capacity within this range [63,64]. Below the pH_PZC, MB adsorption is limited by electrostatic repulsion between the positively charged algal surface and MB⁺ cations, as well as competitive adsorption with H⁺ ions [65]. The electrostatic interactions (attraction or repulsion) between the algal surface and MB species in solution are pH-dependent. For instance, at pH 10, methylene blue (MB) exists entirely in its cationic form, whereas at pH 6, approximately 50% remains in a non-ionic, undissociated state [66]. Despite the positively charged surface of Ulothrix at pH 6, high MB adsorption was observed. This suggests that the algae may adsorb undissociated MB molecules through alternative mechanisms, such as hydrogen bonding, rather than electrostatic interactions alone [67]. Similar findings have been reported for MB adsorption on geopolymers derived from rice husk ash and alkali-activated blast furnace slag at a pH of 3 [68], as well as on lignin composite foams, where maximum uptake occurred at a pH of 4 [69].

The pH-dependent behavior clearly demonstrates how solution pH modulates the availability of anionic binding sites on the algal surface, with alkaline conditions promoting both increased dissociation of functional groups and enhanced electrostatic attraction to MB molecules [70]. These findings highlight the critical role of surface charge in determining adsorption efficiency.

The selection of pH 12.06 as the upper test limit was based on its demonstrated maximum adsorption efficiency for MB. These findings correlate well with previous studies by Hameed et al. (2008) and Doğan et al. (2007), who reported similar pH-dependent adsorption patterns for methyl violet and MB on sepiolite, confirming the general importance of pH control in cationic dye removal processes [63,71], and similar results was reported for the adsorption of BB 41 dye on silkworm pupa [64].

The high efficiency at pH = 12 is less feasible for real wastewater systems. To bridge this gap, localized pH adjustment (e.g., within reactor zones) or hybrid processes (e.g., combining adsorption with pH-tolerant oxidation) could enhance applicability without full-scale alkalization.

3.5. Adsorption Isotherms

The adsorption isotherm behavior of MB on Ulothrix sp. and Spirogyra sp. was evaluated using multiple isotherm models (Table 3), with experimental data and model fits presented in Figure 10. The corresponding fitting parameters and correlation coefficients for each model are detailed in

Table 6. Parameters of the applied isotherm models for MB adsorption onto Ulothrix algae.

		Calculated Parameters
Adsorbent: Ulothrix
Langmuir	qexp (mg/g): 14.36			qm:	22.43	KL:	0.065	R2:	0.60
Freundlich				KF:	2.39	n:	1.83	R2:	0.51
Sips		KS:	7.2 × 10−5	βS:	6.24	aS:	5.4 × 10−6	R2:	0.94
Jossens		Kj:	0.996	Bj:	1.41 × 10−6	nj:	4.06	R2:	0.76

and

Table 7. Parameters of the applied isotherm models for MB adsorption onto Spirogyra algae.

		Calculated Parameters
Adsorbent: Spirogyra
Langmuir	qexp (mg/g): 21.42			qm:	112.03	KL:	0.015	R2:	0.83
Freundlich				KF:	1.75	n:	1.08	R2:	0.82
Sips		KS:	0.022	βS:	3.89	aS:	0.001	R2:	0.95
Jossens		Kj:	1.46	Bj:	1 × 10−14	nj:	1 × 10−8	R2:	0.81

.

A fundamental characteristic of the Langmuir isotherm is the equilibrium parameter (R_L), a dimensionless constant calculated using Equation (4). This parameter provides critical insights into adsorption favorability and mechanisms:

$${\mathrm{R}}_{\mathrm{L}}={\displaystyle \frac{1}{1+{\mathrm{K}}_{\mathrm{L}}{\mathrm{C}}_0}},$$

(4)

where K_L is the Langmuir constant and C₀ is the initial MB concentration.

The value of R_L reflects the following trend of adsorption: irreversible (R_L = 0), favorable (0 < R_L < 1), linear (R_L = 1), and unfavorable (R_L > 1) [72,73]. In this study, the calculated R_L values were 0.43 and 0.76 for Ulothrix algae and Spirogyra algae, respectively. This shows that the adsorption process of MB onto the studied materials was thermodynamically favorable [74].

The Langmuir model predicted maximum adsorption capacities (q_m) of 22.43 mg/g for Ulothrix sp. and 112.03 mg/g for Spirogyra sp., both exceeding the experimentally observed values (q_exp = 14.36 mg/g and 21.42 mg/g, respectively). This discrepancy likely stems from the relatively low correlation coefficients obtained for Langmuir model fitting. A comparative analysis of the determination coefficients (R²) revealed that the Sips model provided superior agreement with experimental data for both algal species, suggesting it more accurately describes MB adsorption behavior than alternative isotherm models.

3.5.1. Sips Model

The Sips isotherm model is a hybrid model resulting from the combination of the Freundlich and Langmuir isotherms in a system operating under a wide range of conditions [75]. It is suitable for predicting the adsorption behavior of heterogeneous structures across a wide range of adsorbate concentrations [45]. This may explain why the R² coefficients of Sips are moderately better than the R² values for Langmuir and Freundlich.

The Sips isotherm model exhibits concentration-dependent behavior that bridges key features of both Freundlich and Langmuir models: (i) at low adsorbate concentrations, it simplifies to the Freundlich isotherm, describing heterogeneous surface adsorption, (ii) at high concentrations, it converges to Langmuir-type monolayer saturation behavior, and (iii) when the heterogeneity parameter (β = 1) equals unity, the Sips equation becomes mathematically equivalent to the Langmuir model. This adaptive characteristic makes the Sips model particularly suitable for systems exhibiting both multilayer adsorption at low concentrations and monolayer saturation at higher concentrations [76,77]. So, the results confirm that the adsorption of MB is homogeneous following the Sips and Langmuir isotherms, mainly occurring on a single layer.

3.5.2. Jossens Model

The Jossens isotherm model describes adsorption systems exhibiting heterogeneous surface energetics. This approach incorporates three fundamental assumptions: (i) a continuous distribution of adsorption site energies, (ii) non-uniform binding affinities between adsorbate molecules and surface sites, and (iii) variable interaction strengths dependent on local surface chemistry and topology. Unlike homogeneous surface models, the Jossens formulation explicitly accounts for the probabilistic nature of adsorbate-adsorbent interactions across energetically diverse surface sites [78,79].

Furthermore, the 1/n values derived from the Freundlich model were less than 1, indicating that the active sites on the studied algae’s surface have a strong adsorption affinity towards MB [80,81]. These results demonstrate that Spirogyra sp. exhibits greater MB adsorption affinity compared to Ulothrix sp., consistent with the formation of a monolayer dominated by chemical interactions. This finding aligns with the kinetic study results, further supporting the chemisorption mechanism.

3.6. Adsorption Thermodynamics

Temperature plays a critical role in adsorption processes by affecting the thermodynamic driving forces between adsorbate molecules and surface binding sites. Systematic temperature-dependent studies yield essential thermodynamic parameters (ΔG°, ΔH°, and ΔS°) that reveal three key aspects: (i) the spontaneity and favorability of adsorption through Gibbs free energy changes, (ii) the energetic nature of surface interactions (endothermic/exothermic), and (iii) molecular-level reorganization during adsorption (entropy changes). These parameters provide fundamental insights into the adsorption mechanism’s thermal sensitivity and the underlying energy landscape of adsorbent–adsorbate systems. The values of the standard enthalpy change (ΔH°) and standard entropy change (ΔS°) can be calculated from the temperature-dependent adsorption data using Equations (5)–(7) [82,83].

$$∆{\mathrm{G}}^0=-\mathrm{R}\mathrm{T}\mathrm{l}\mathrm{n}(1000\times {\mathrm{k}}_{\mathrm{d}}),$$

(5)

$${\mathrm{k}}_{\mathrm{d}}=1000\times {\displaystyle \frac{{\mathrm{q}}_{\mathrm{e}}}{{\mathrm{C}}_{\mathrm{e}}}},$$

(6)

$$\mathrm{l}\mathrm{n}{\mathrm{k}}_{\mathrm{d}}=\left({\displaystyle \frac{∆{\mathrm{S}}^0}{\mathrm{R}}}\right)-\left({\displaystyle \frac{∆{\mathrm{H}}^0}{\mathrm{R}\mathrm{T}}}\right),$$

(7)

where T is the temperature in Kelvin, ΔS° (J/mol·K) is the entropy change in the adsorption process, ΔH° (kJ/mol) is the adsorption enthalpy change, and K_d (mL/g) is the distribution coefficient provided by Equation (6) [84].

Here, q_e (mg/g) and C_e are the experimental equilibrium adsorption capacity and MB concentration at equilibrium, respectively.

The Gibbs free energy change (ΔG°, kJ/mol) can be obtained with the help of the parameter K_d, and the formula is presented in Equation (5) [34,84]. The ΔH° and ΔS° values were obtained from the slope and intercept of the linear plot of lnk_d versus 1/RT (Figure 11). Related thermodynamic parameters are summarized in

Table 8. Thermodynamic parameters for the MB adsorption onto Ulothrix algae and Spirogyra algae at different temperatures.

Adsorbents	T (K)	∆G° (kJ/mol)	∆H° (kJ/mol)	∆S° (kJ/mol.K)	R2
Ulothrix	293.15	−27.90541662	66.86865	0.20889673	0.965
	308.15	−33.00104698
	313.15	−35.09378418
	318.15	−35.59752841
Spirogyra	293.15	−30.85305612	37.45373	0.11822508	0.962
	308.15	−34.2596712
	313.15	−35.9287893
	318.15	−36.46073148

.

The thermodynamic analysis across the tested temperature range yielded negative Gibbs free energy values (ΔG° < 0) for all adsorbents. This result indicates a favorable and spontaneous adsorption process under the experimental conditions.

Positive ∆S° values indicate the increase in disorder at the adsorbate–adsorbent interface during the adsorption of MB on the surface.

The negative enthalpy change (ΔH° < 0) quantitatively confirms the exothermic nature of MB adsorption, indicating favorable energy release during solute desolvation from aqueous phase, surface binding at active sites, and structural reorganization of the adsorbent matrix [85]. This often indicates a weak physical interaction (physisorption) or, in some cases, chemisorption that requires energy to break certain initial bonds [86].

Also, a random interference at the solid–liquid interface was shown by the low positive values of the entropy variations ΔS.

As a result, the positive value of ΔS° for the two algae shows that the overall disorder of the system increases during adsorption. This may be due to the better organization of solvent molecules (water) in the medium after adsorption. The positive value of ΔS° shows that a certain degree of disorder is introduced into the system, which may be due to the reorganization of the molecules or the release of solvent molecules.

3.7. Adsorption Mechanism

The presence of both electron-donating groups (e.g., C=C and C=O) on the algal biosorbent and electron-accepting sites in MB molecules enhances π–π electron donor-acceptor (EDA) interactions (Figure 12). Specifically, the π-electron-rich surface of the algae interacts with the π-electron-deficient, positively charged MB molecules [87]. FTIR analysis confirmed the presence of -OH groups on the biochar surface, which can act as π-electron donors, while the nitrogen-containing heterocyclic ring in MB serves as the π-electron acceptor [88].

Furthermore, the abundance of hydroxyl and carboxylic functional groups (as evidenced by FTIR) promotes MB adsorption through multiple mechanisms, including (i) van der Waals forces, (ii) electrostatic attraction (where deprotonated -COO⁻ groups attract cationic MB⁺), and (iii) hydrogen bonding [89].

EDS analysis also revealed the presence of cations (Ca²⁺, K⁺, and Na⁺) on the algal surface, suggesting that cation exchange may further contribute to MB adsorption by releasing these ions in exchange for MB⁺. The results suggest that Spirogyra exhibits superior MB adsorption performance compared to Ulothrix. This enhanced adsorption capacity may be attributed to (1) its higher oxygen content (as evidenced by EDS analysis), and (2) the presence of unique functional groups (e.g., C=C and N-H) that are absent in Ulothrix’s structure.

3.8. Comparative Analysis of Adsorption Performance

Table 9. Comparison of Ulothrix and Spirogyra adsorption performance with other adsorbents.

Adsorbent	Pollutant (Concentration)	Adsorption Capacity (mg/g)	References
Algae
Spirogyra	Methylene blue (20 mg/L)	6.94	This study
Ulothrix	Methylene blue (20 mg/L)	6.38	This study
Skeletonema costatum	Crystal Violet Dye	6.410	[47]
Corallina officinalis	Malachite green dye (20 mg/L)	101.30	[90]
Red seaweed, Gracilaria corticata	Crystal Violet Dye (100 mg/L)	181.0	[36]
Red seaweed, Pterocladia capillacea	Crystal Violet Dye (0.3 g/L)	5.714	[48]
Sargassum Tenerrimum	Eosin yellow	5.18	[47]
Plocamium cartilagineum	Reactive red	34.72	[91]
Plocamium cartilagineum	Cibacron blue	25.83	[35]
Other adsorbents (activated carbon, biochar, etc.)
Sargassum weightii Activated Carbon	Crystal Violet Dye (80 mg/L)	3.306	[92]
green algae Ulva lactuca biochar-sulfur	Methylene blue (200 mg/L)	303.78	[39]
Yellow passion fruit waste	Methylene blue	44.7	[33]
Beech Biochar Flax Biochar Citric acid-treated Beech Biochar	Methylene blue	81.06 24.74 117.33	[89]
Shrimp carapace-derived chitosan	Orange G dye	34.63	[13]

presents a systematic comparison of adsorption capacities between the studied algal biomasses (Ulothrix sp. and Spirogyra sp.) and various adsorbents reported in the literature for organic pollutant removal. The adsorption capacities of Ulothrix (6.38 mg/g) and Spirogyra (6.94 mg/g) for methylene blue (20 mg/L) align with values reported for other algal biomasses like Skeletonema costatum (6.41 mg/g for crystal violet) but are significantly lower than those of red seaweeds (e.g., Gracilaria corticata: 181.0 mg/g for crystal violet). This disparity may stem from differences in cell wall composition, surface area, or functional group density. Notably, modified adsorbents, like sulfur-loaded Ulva lactuca biochar (303.78 mg/g) and Citric acid-treated Beech Biochar (117.33 mg/g), outperform most untreated algae, highlighting the impact of chemical activation on capacity enhancement. While Ulothrix and Spirogyra show moderate performance compared to specialized adsorbents, their natural abundance, low cost, and sustainability make them viable for decentralized wastewater treatment, particularly in low-concentration dye removal scenarios. Future studies could explore surface modification to improve their competitiveness against high-capacity alternatives.

4. Conclusions

This study examines the adsorption behavior of MB onto algal biomasses under selected operational conditions (pH, algae dose, and contact time), revealing specific trends in parameter effects. A comprehensive characterization of the adsorbents’ physicochemical properties was performed using multiple analytical techniques. Notably, Spirogyra sp. demonstrated superior MB affinity under alkaline conditions compared to Ulothrix sp., attributed to enhanced electrostatic interactions at high pH values.

Kinetic analysis revealed that MB adsorption on both algal species followed pseudo-second-order (PSO) kinetics (R² > 0.98), indicating chemisorption as the rate-limiting step. Isotherm modeling showed optimal fit with the Sips model, suggesting monolayer adsorption on heterogeneous surfaces. Maximum adsorption capacities reached 21.42 mg/g for Spirogyra sp. versus 14.36 mg/g for Ulothrix sp., reflecting differences in surface chemistry and pore structure. Thermodynamic parameters (ΔG° < 0 and ΔH° > 0) confirmed the spontaneous and endothermic nature of the adsorption process.

These findings not only advance the fundamental understanding of algal biosorption mechanisms but also demonstrate the practical potential of using these naturally abundant biomaterials for effective wastewater treatment, particularly for cationic dye removal.

This study offers fundamental insights into algal biosorbents for MB removal while acknowledging three key limitations. First, the work focused exclusively on batch systems rather than industrial-scale continuous-flow setups. Second, experiments used synthetic MB solutions rather than complex real wastewater, which contains competing pollutants. Third, the economic potential remains partially unexplored as adsorbent regeneration has not been assessed. These constraints will guide our future research priorities.

Our forthcoming work will systematically address these gaps through three approaches. We will employ response surface methodology to optimize multi-parameter interactions, validate findings using actual leather industry effluents, and conduct pilot-scale column tests, including regeneration protocols. This progression from fundamental research to practical applications aligns with our ultimate objective of developing scalable, cost-effective algal biosorption systems for industrial wastewater treatment.