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Abstract

This paper aims to investigate the uptake of two basic dyes (methylene blue [MB] and brilliant cresyl blue [BCB]) using the formaldehyde-treated algae Fucus vesiculosus (FV-FORMALD). The surface and morphology of modified biomass was characterized by the point of zero-charge (pH_PZC), Brunauer–Emmett–Teller (BET) surface area (S_BET), scanning electron microscopy (SEM/EDS), and Fourier-transform infrared spectroscopy. The equilibrium data were best described by the Langmuir and Freudnlich isotherm model for MB and BCB, respectively. Kinetics modeling shows that the adsorption of both dyes onto FV-FORMALD was well described by pseudo-second order. The thermodynamics parameters validated the exothermic nature and the spontaneously of the adsorption of both dyes. The optimum conditions for the adsorption of MB and BCB were at pH = 8 and 4, adsorbent concentration = 2 and 1 g L⁻¹, and T = 313.15 and 300.15 K, respectively. The FV-FORMALD had reuse potential for at least three cycles with high adsorption capacities of MB and BCB dyes. A colored real wastewater was successfully treated using FV-FORMALD. According to the results, the brown algae FV-FORMALD could be considered appropriate for removing cationic dyes due to its high efficiency and an environmentally friendly, sustainable, and low-cost approach.

Keywords

formaldehyde brilliant cresyl blue methylene blue biomass

Introduction

Overpopulation, urbanization, and industrial development are the leading causes of environmental pollution. Pharmaceutical, cosmetic, agricultural, and textile industries are the main polluters because their wastewater effluents are discharged without or with poor treatment (Raj et al., 2020). The wastewater effluents from the textile industry are a mixture of recalcitrant pollutants such as surfactants, disinfectants, and high concentrations of dyes (Paździor et al., 2017). Dyes are applied in biology and medicine for microbiological and histological stains. Water that is not clear is associated with poor quality or polluted water. A low dye concentration (1 mg L⁻¹) is enough to be visually perceived (Malik et al., 2007) and may also present a risk to human health because some dyes are carcinogenic and mutagenic. Therefore, its regulation has been recently considered by the Zero Discharge of Hazardous Chemicals Program, which is an organization dedicated to eliminating hazardous chemicals in the leather, textile, and synthetic sectors (ZDHC, 2022).

The World Health Organization suggests drinking water should ideally have no visible color, or that levels of color below 15 TCU (True Color Units) are often acceptable to consumers (World Health Organization, 2022). The problem of color in water can be assumed as an aesthetic situation because water that is not transparent can be perceived as dirty water (Naseem et al., 2019). Furthermore, the loss of water transparency causes a decrease in the sunlight penetration, thus affecting the photosynthetic function of plants (Garg et al., 2004) and to a decrease in the amount of dissolved oxygen available to aquatic flora and fauna (Lakshmi et al., 2009). Therefore, color affects the balance of aquatic ecosystem and can lead to species turnover (U.S. Environmental Protection Agency, 1974).

The treatment of wastewater from the textile industry is one of the main environmental concerns today. Organizations such as Environmental, Health, and Safety (EHS) propose in their guidelines biological and physicochemical methods as the predominant control option for the treatment of the color parameter (EHS, 2007). Some biological materials, such as bacteria, fungi, yeasts, and algae, have demonstrated their ability to remove various synthetic dyes (Ali, 2010). These materials can break the bound of the chromophoric group by enzymatic action and remove the color (Satheesh Babu et al., 2015). The advantages of biological methods include that they are economically viable, environmentally friendly, and generate less sludge volume compared to other techniques, while their principal disadvantage is the transition from laboratory scale to real scale (Bhatia et al., 2017). Some bacteria have been successfully employed to eliminate different dyes. For example, the Bacillus sp bacteria was used in a study by Ezhilarasu (2016) to remove Methyl Red (Ezhilarasu, 2016). Adeeb et al. (2014) removed crystal violet dye using three different bacteria, Clostridium perfringens, Pseudomonas aeruginosa, and Proteus vulgaris.

The most applied physicochemical methods for dye removal are adsorption, ion exchange, oxidation process, and irradiation. In adsorption, the dye is adsorbed on the surface of the material through chemical or physical interactions. Various waste materials have been used as an adsorbent for dye removal. For example, sawdust was used to remove brilliant cresyl blue (BCB; Elhami et al., 2011) and methylene blue (MB; Garg et al., 2004). In addition, different types of resins were applied to remove basic dyes such as safranin T, Nile blue, and BCB (Iyim et al., 2008) and cationic dyes: brilliant green, methyl violet, MB, phenosafranine, and BCB (Çinar et al., 2018) using ion exchange.

Physicochemical techniques have a high dye removal efficiency, are easy to use, nevertheless require high production and operating costs, regeneration of the material is not always possible, and produce sludge or by-products.

Marine macroalgae such as Sargassum, in their raw state or after simple processing, such as washing and drying, present favorable chemical characteristics for adsorption of various pollutants from aqueous solution (Popa and Volf 2018). Therefore, the arrival of Sargassum to the coasts of Mexico and other parts of the world is seen as a socioeconomic and environmental problem. However, these seaweeds can be removed from beaches and applied as biosorbents with a simple process to remove dyes. Recently, Sargassum tenerrimum, Sargassum sp, and Sargassum ilicifolium were used to remove eosin yellow (Tukaram Bai et al., 2020), sulfur black (Vijayaraghavan and Shanthakumar, 2015), and five different dyes: methyl blue, fuchsin acid, rhodamine B, MB, bromocresol purple, and methyl orange (Tabaraki and Sadeghinejad, 2017). In addition, algae have shown promising results for the sorption of inorganic compounds such as heavy metals (Khajavian et al., 2019). Based on the above, algae have multiple applications, highlighting the removal of pollutants where high percentages of removal have been found. Furthermore, its main advantage is an economical alternative compared to other biosorbents.

Sargassum crasifolium was treated with formaldehyde and used to remove Pb (II) (Zein and Mawardi, 2009). Hypnea Valentiae and Gracilaria verrucose treated with formaldehyde were used to remove Co(II) (Vafajoo et al., 2018) and Cr (VI) (Ata et al., 2012), respectively. The amine groups present in the algae surface and the formaldehyde can form hydroxylamine (Zein and Mawardi, 2009). Moreover, formaldehyde can cause methylation of the amine groups (Jianlong, 2002). According to Park et al. (2005), methylation of amine groups can cause the reduction of the number of positively charged sites on the biomass surface. Based on the capacity of the formaldehyde of increase the binding sites in the surface of the algae due to cross-linking process. In this work, the brown algae Fucus vesiculosus was chemically treated with formaldehyde (FV-FORMALD) and used to remove two dyes of different chemical nature: MB and BCB in aqueous solution, to compare and evaluate the removal efficiency. The effects of the following parameters on the adsorption process were evaluated: contact time, dye concentration, temperature, and pH of the aqueous medium, as well as the biomass concentration. The thermodynamics adsorption parameters for both dyes were calculated. In order to evaluate the possibility of the reutilization of the loaded biomass, adsorption, and desorption cycles were performed.

Experimental

Dyes

Methylene Blue C.I. 52,015 (C₁₆H₁₈ClN₃S) and BCB C.I. 51,010 ((C₁₇H₂₀ClN₃O)₂·ZnCl₂) were procured from Merck and Fagalab, respectively, as a dye source, their chemical structures were drawn with ChemDraw professional software (Figure 1(a) and (b), respectively).

Figure 1.

Chemical structure of (a) brilliant cresyl blue and (b) methylene blue (ChemDraw professional software).

Preparation of biosorbent

Fucus vesiculosus was collected from the coast of El Recodo Beach, Playa del Carmen, Quintana Roo, Mexico, in March 2022. The collected algae were washed with distilled water several times and then sun dried for three days. Next, the biomass was washed again at room temperature (27 °C) in 1 L of 2% v/v formaldehyde solution for 24 h. Subsequently, the solution is decanted, and the biomass is thoroughly rinsed with deionized water. A few milliliters of washing solution are then collected and poured into a cell to scan in a UV-Vis spectrophotometer from 200 to 760 nm. The washing process was repeated three times until the spectrum showed no signal. The washed algae were then dried at 60 °C for 8 h. The dried algae FV-FORMALD were stored in a desiccator for later use in adsorption experiments.

Characterization techniques

pH_PZC

The pH_PZC of the raw and brown algae FV-FORMALD was determined using 0.01 M NaCl as the background electrolyte solution; the pH of the working solutions was fixed between 1 and 12 using 0.1 M NaOH or 0.1 M HCl. First, the initial pH of each solution was measured. Subsequently, solutions were put in contact with the algae at 120 rpm at room temperature for 1440 min. The solutions were decanted, and the pH values were measured using a Hanna instruments (model HI2020-01) pH meter.

Fourier-transform infrared spectroscopy

Fourier-transform infrared (FTIR) Fucus vesiculosus brown algae spectra (4000–400 cm⁻¹) before and after dye biosorption were recorded on a Nicolet Nexus 670 FTIR spectrophotometer. The samples were prepared using the KBr pellet method.

Scanning electron microscopy and EDS

The surface morphologies of the raw (FV) and modified seaweed (FV-FORMALD) were observed by scanning electron microscopy (SEM) using a JEOL JSM-IT300 electron microscope operating at 20 kV, and the superficial composition was estimated using an EDS system coupled to the electron microscope.

Brunauer–Emmett–Teller surface area (S_BET)

Brunauer–Emmett–Teller (BET) surface area and pore size were measured in a Nova 2200e equipment (Quantachrome). FV-FORMALD was pretreated under vacuum at 393 K for 16 h. The adsorption isotherm of N₂ onto FV-FORMALD was performed at 77 K. The BET theory and the BJH method were used to calculate the surface area and the pore size.

Sorption studies

Effect of contact time

Adsorption kinetics of both dyes (MB and BCB) was performed as follows: 0.01 g of FV-FORMALD and 10 mL aliquot of dye solution (1000 mg L⁻¹) were contacted for different time's intervals (5–1440 min) in a rotating incubator at 150 rpm at room temperature. After each contacted time, the samples were centrifuged and analyzed by UV-Vis spectrophotometry (Thermo Scientific, Evolution 220) at a wavelength of 664 and 635 nm for MB and BCB, respectively.

Effect of dye concentration

To study the effect of the initial dye concentration on the adsorption capacity of the algae, different MB and BCB dye concentrations were produced from 100 to 1000 mg L⁻¹. Then, 0.01 g of FV-FORMALD samples were added to 10 mL of each dye solution and kept in contact for 360 min, under the same conditions mentioned above.

Effect of pH

The initial pH medium's effect on MB and BCB adsorption was explored in the range of 2–8 for both dyes. Different dye concentrations were prepared (100–1000 mg L⁻¹). pH was adjusted by adding 0.1 M HCl or 0.1 M NaOH. The conditions were the same as for the sorption isotherm study.

Adsorbent concentration effect

The effect of FV-FORMALD algae concentration on the dye sorption was evaluated. An aliquot of 10 mL of each dye (1000 mg L⁻¹) was put in contact with the following amounts of biomass: 1 g L⁻¹ to 10 g L⁻¹ for 360 min, at room temperature, and pH = 4 for both dyes.

Effect of temperature

A volume of 30 mL of each dye at different concentrations (100–1000 mg L⁻¹) was placed in contact with 0.03 g of biomass for 360 min, 150 rpm agitation, and at the following temperatures: 300.15, 313.15, and 330.15 K.

Kinetic and isotherm models

The adsorption capacity (q_e₎ was calculated using the following equation:

q_{e} = \frac{(C_{o} - C_{e}) . V}{m}

(1)where C_o (mg L⁻¹) is the initial concentration, C_e (mg L⁻¹) is the equilibrium concentration, q_e (mg g⁻¹) is the adsorption capacity, V (L) is the volume, and m (g) is the mass of the adsorbent. The removal efficiency (% Removal) was calculated by equation (2):

% Removal = \frac{(C_{o} - C_{e})}{C_{o}} .100

(2)The experimental kinetics data were evaluated using linear and nonlinear equations of pseudo-first-order (equations (3) and (4)). For the linear model, the slope and intercept of the plot of

\log (q_{e} - q_{t})

vs t were used to calculate the kinetics parameters. Additionally, the pseudo-second-order linear and nonlinear models (equations (5) and (6), respectively) were used for kinetics study. For linear model, the plot of

\frac{t}{q t}

vs. t was used to calculate the kinetics parameters. Whereas kinetics parameters from nonlinear models were calculated applying the solver add-in from Microsoft's spreadsheet tool of Microsoft Excel:

\log (q_{e} - q_{t}) = \log q_{e} - (\frac{k_{1}}{2.303}) t

(3)

q_{t} = q_{e, cal} (1 - \exp (- k_{1} t))

(4)

\frac{t}{q t} = \frac{1}{k_{2} q_{e, cal}^{2}} + \frac{t}{q_{e, cal}}

(5)

q_{t} = \frac{k_{2} q_{e, cal}^{2} t}{1 + k_{2} q_{e, cal} t}

(6)where q_e,cal is the theoretical adsorption capacity (mg g⁻¹), k₁ (min⁻¹), and k₂ (g mg⁻¹ min⁻¹) are the pseudo-first and pseudo-second order rate constants, respectively.

The equilibrium data obtained from concentration, pH, and temperature effects mentioned above were fitted to the linear and non-linear Langmuir and Freundlich models. For linear form of Langmuir isotherm (equation (7)), the parameters were obtained from the intercept and slope of the plot of $\frac{C_{e}}{q_{e}}$ vs. $C_{e}$ . For linear form of Freundlich isotherm (equation (9)). The parameters were obtained from the intercept and slope of the plot of $\ln$ $q_{e}$ vs $C_{e}$ . The parameters of nonlinear equations (equations (8) and (10)) were obtained using the solver add-in from Microsoft's spreadsheet tool of Microsoft Excel:

\frac{C_{e}}{q_{e}} = \frac{1}{Q_{\max} K_{L}} + \frac{1}{Q_{\max}} C_{e}

(7)

q_{e} = \frac{Q_{\max} K_{L} C_{e}}{1 + K_{L} C_{e}}

(8)where K_L (L mg⁻¹) is the Langmuir equilibrium constant, and Q_max (mg g⁻¹) is the maximum adsorption capacity:

l n q_{e} = l n K_{F} + \frac{1}{n} C_{e}

(9)

q_{e} = K_{F} C_{e}^{1 / n}

(10)

where n (dimensionless) and K_F (mg^1−1/nL^1/ng⁻¹) are the exponent and the Freundlich parameter, respectively.

Error functions analysis

In order to confirm the accuracy of the measurement results and a good fit of the experimental adsorption data with the proposed kinetic and isotherm models, four error functions such as the average percentage error (ARE), nonlinear chi-square test (χ²) (Al-Ghouti and Da’ana, 2020), residual root mean square error (RMSE), normalized standard deviation (Δ(%)) (Rahman and Sathasivam, 2015) were calculated with the equations given below:

A R E (%) = \frac{100}{N} \sum_{i = 1}^{N} | \frac{q_{e \exp} - q_{e calc}}{q_{e \exp}} |

(11)

χ 2 = \sum_{i = 1}^{N} \frac{{(q_{e \exp} - q_{e calc})}^{2}}{q_{e calc}}

(12)

R M S E = \sqrt{\frac{1}{N - 2} \sum_{i = 1}^{N} {(q_{e \exp} - q_{e calc})}^{2}}

(13)

Δ q (%) = 100 \sqrt{\frac{1}{N - 1} \sum_{i = 1}^{N} {(\frac{q_{e \exp} - q_{e calc}}{q_{e \exp}})}^{2}}

(14)

where q_e_,exp is the adsorption capacity obtained from the batch experiment (mg g⁻¹), q_e_,calc is the adsorption capacity obtained with a mathematical model corresponding (mg g⁻¹), and N is the corresponding number of observations in the experiment. The ARE, χ², RMSE, and Δq (%) values obtained for the kinetic and isotherm models were compared to each other, and the lowest indicates best fitted model.

Reusability study

The reuse of the FV-FORMALD biosorbent and the recovery of MB and BCB are very important to assess the sustainability and cost-effectiveness of adsorbent. For this purpose, three adsorption/desorption cycles were carried. Many researchers have used HCl as the main eluent of MB dye (Hidayat et al., 2022; Momina et al., 2020; Tehubijuluw et al., 2021). In this case, 0.1 M HCl solution was used as eluent to desorb MB and BCB dye from the adsorbent surface.

For the adsorption step, 50 mL of each dye at the initial concentration of 1000 mg L⁻¹ was placed in contact with 50 mg of FV-FORMALD and stirred at room temperature for 6 h. The mixture was then centrifuged to obtain the adsorbent, and the residual concentration was measured by UV-vis spectrophotometric analysis. The quantity of MB or BCB adsorbed is calculated using equation (1). For desorption process, the loaded FV-FORMALD was added into 50 mL of 0.1 M HCl solution followed by continuous stirring at room temperature for 6 h. Next, the sample was centrifuged and the residual concentration of MB or BCB was measured. The quantity of dyes desorbed Q_des (mg g⁻¹) was calculated using equation (15) (Dada et al., 2021):

Q_{des} = C_{des} * \frac{V}{m}

(15)

where C_des (mg L⁻¹) is the concentration of dye left after desorption, V (L) is the volume of the dye solution, and m (g) is the FV-FORMALD mass.

The regenerated FV-FORMALD was washed severally with deionized water and dried at 60°C for 8 h before the next cycle of adsorption took place. Three cycles of consecutive adsorption–desorption studies were carried out.

Real colored wastewater treatment

The waste generated in our experiments was used as real colored wastewater. The sample has a blue color and pH = 2.4. A 10 mL aliquot of the wastewater was placed in contact with 0.01 g of FV-FORMALD at room temperature for 6 h. The sample was scanned in a UV-Vis spectrophotometer from 380 to 750 nm before and after the adsorption process.

Results and discussions

pH_PZC

The determination of the adsorbent's zero-point charge is useful because the dye's adsorption depends on the solution's pH. Figure 2 shows that pH_PZC= 7.2 for raw Fucus vesiculosus (Raw-FV) and pH_PZC= 4.7 for the algae after treatment with formaldehyde (FV-FORMALD). The pH_PZC of FV-FORMALD is 2.5 units lower than that raw brown algae, suggesting a change on the surface of algae after the treatment with formaldehyde. When the aqueous media has pH < pH_PZC, the algae surface is positively charged, favoring the biosorption of anions, while at pH > pH_PZC, the algae surface is negatively charged, favoring the biosorption of cations. Furthermore, the fact that the pH_PZC of FV treated with formaldehyde is lower than that of raw FV is an advantage, because it gives a higher range of pH solution values for the removal of cationic dyes than raw algae and, consequently, it can be suspected that the adsorption of MB and BCB could be improved for FV-FORMALD.

Figure 2.

pH_PZC determination of raw Fucus vesiculosus (Raw-FV) and Fucus vesiculosus treated with formaldehyde (FV-FORMALD).

Fourier-transform infrared spectroscopy

Fourier-transform infrared spectra of FV-FORMALD were recorded before and after adsorption of MB and BCB within the range of 650–4000 cm⁻¹ (Figure 3(a)–(c)). The vibrational spectra of FV-FORMALD before dye biosorption showed a broadband between the range 3200–3500 cm⁻¹, representing the O-H and -NH groups of the carbohydrates and protein present in the algae. The peak at 3300 cm⁻¹ corresponds to O-H vibrations of cellulose, pectin, and lignin, the peak at 2920 cm⁻¹ represents (C-H) of alkyl groups (Gokulan et al., 2019), a peak around 1600 cm⁻¹ could be attributed to the C=O stretch of the carboxylic or aldehyde functions. The peak at 1025 cm⁻¹ could be related to the C–O stretching vibration of carboxylic acids, alcohol, or phenol. After MB biosorption, the peaks shifted to 3275, 1595, and 1025 cm⁻¹, respectively, suggesting that these functional groups on the surface of the algae interacted with the dye. In addition, two new signals were identified, at 1330 cm⁻¹ (C–N stretching) and at 884 cm⁻¹ (C–H bending of arene rings), which identify the presence of the MB molecule (Salazar et al., 2017). After BCB biosorption, the peaks shifted to 3290, 1590, and 1010 cm⁻¹, respectively, indicating that the hydroxyl, amine, and carboxyl, are the main groups responsible for the biosorption of the two dyes (Table 1).

Figure 3.

FTIR of (a) FV-FORMALD (b) FV-FORMALD-MB and (c) FV-FORMALD-BCB.

Table 1.

FTIR bands before and after the adsorption process.

Functional group	Wavenumber (cm⁻¹)
Functional group	FV-FORMALD	FV-FORMALD-MB	FV-FORMALD-BCB
O-H	3300	3275	3290
C-O	1025	1030	1010

Scanning electron microscopy/EDS

Figure 4 shows the micrographs and respective EDS analysis of raw and treated algae FV before and after adsorption of BCB and MB dyes. As can be seen, the raw algae (Figure 4(a)) present a nonuniform and elongated particle; however, after washing with formaldehyde (Figure 4(c)), the FV-FORMALD shows a rougher and more heterogeneous surface. The morphology of the treated sample could improve the uptake process because it increases the surface area of the adsorbent (Lebron et al., 2021), and the cavities, which allow access to the dye molecule and its subsequent retention (Reck et al., 2018). EDS analysis of algae shows the approximate elemental analysis of the surface before and after biosorption. The results show that raw FV and treated with formaldehyde materials have mainly carbon and oxygen content (Figure 4(b) and (d)), a result expected for organic materials. After MB biosorption, an increase in sulfur percentage (from 1.3% to 1.5%) is observed in Figure 4(f); this element is present in the MB molecule (Figure 1(b)) and confirms the dye biosorption onto algae. After BCB biosorption, EDS analysis shows 0.4% of zinc and 0.1% of chlorine (Figure 4(h)); these two elements are characteristic of the BCB molecule (Figure 1(a)). Furthermore, an increase the percentage of carbon (from 59.4% to 61.4%), indicates that both dyes were adsorbed on the FV-FORMALD surface. Finally, SEM and EDS also analyzed the FV-FORMALD sample in contact with deionized water. The results are shown in Figure 4(i). The micrographs of the blank and those of the adsorbed dyes (Figure 4(c) and (e)) are different. Therefore, it is verified that the change in the biomass surface was due to the sorbed dyes and not to the biosorbent interaction with the aqueous solution.

Figure 4.

SEM images and EDS analysis of (a) and (b) raw seaweed Fucus vesiculosus, (c) and (d) Fucus vesiculosus treated with formaldehyde (FV-FORMALD), (e) and (f) FV-FORMALD after methylene blue (MB) biosorption, (g) and (h) FV-FORMALD after brilliant cresyl blue (BCB) biosorption and (i) and (j) FV-FORMALD-H₂O.

Brunauer–Emmett–Teller surface area (S_BET)

The adsorption–desorption isotherm of N₂ on the FV-FORMALD sample is shown in Figure 5. As can be seen in this figure, the shape of isotherm allows it to be classified as a type IV isotherm (IUPAC classification), this type of isotherm is characteristic of mesoporous materials (Barton et al., 1999).

Figure 5.

Adsorption–desorption isotherm of N₂ on the FV-FORMALD.

The results of surface area, pore volume, and mean pore diameter were calculated by BET and BJH methods and are shown in Table 2. The surface area of FV-FORMALD is similar with those of other algae species, slightly higher than of surface of the Sargassum horneri but slightly lower than that of Sargassum muticum. Based on the average pore size (7.48 nm), the pore of FV-FORMALD can be classified as mesoporous like the other algae species as shown in Table 2. Other mesoporous materials have been used to effectively remove MB. Pang et al. (2017) used a mesoporous mineral (birnessite) and achieved 95% of MB removal efficiency. Mesoporous activated carbon from dragon fruit peels achieved a maximum adsorption capacity (Q_max) of MB equal to 195.2 mg g⁻¹ (Jawad et al., 2021).

Table 2.

Results surface area, pore volume, and mean pore size for different algae materials.

Adsorbents	Surface area	Pore volume	Pore diameter	Reference
Adsorbents	(m² g⁻¹)	(cm³ g⁻¹)	(nm)	Reference
Sargassum muticum	0.503	1.43	12.8	Hannachi and Hafidh (2020)
Sargassum horneri	0.43	0.0051	21.77	Zeng et al. (2018)
FV-FORMALD	0.466	0.00087	7.48	This work

Effect of contact time

Figure 6(a) and (b) depicts the adsorption kinetics of BCB and MB dyes, respectively, by the FV-FORMALD and their modeling with the PFO and PSO order nonlinear equations. It was observed that BCB and MB were rapidly removed during the first 30 min; after this time, the adsorption capacity gradually reached equilibrium; this behavior is due to the that during the initial biosorption phase, dye particles are more present in the solution and the actives sites in algae are unoccupied and available (Silva et al., 2019). Adsorption equilibrium was reached in around 360 min for both dyes.

Figure 6.

Kinetic modeling of (a) BCB and (b) MB according to pseudo-first order and pseudo-second order nonlinear models by FV-FORMALD. (C_o, MB = 1000 mg L⁻¹; C_o, BCB = 1000 mg L⁻¹; pH MB = 4; pH BCB = 4; concentration of FV-FORMALD = 1 g L⁻¹; T = 300.15 K; contact time = 5–1440 min).

Kinetic parameters were calculated utilizing linear and nonlinear PFO and PSO models. Results are depicted in Table 3. For MB, PFO linear and nonlinear models show values of R²= 0.968 and R²= 0.913, respectively, suggesting a nongood fit to the PFO model. In other hand, the values of R²= 0.980 and R²= 0.999 obtained for PSO are higher to those obtained for PFO. In addition, the PFO model exhibits the lowest values of error functions (Table 4). The value of the equilibrium adsorption capacity (q_e_,cal) obtained with the pseudo-second-order nonlinear model was 799.4 mg g⁻¹ and is closer to the experimental equilibrium adsorption capacity at (q_e_,exp= 806.8 mg g⁻¹) than q_e_,cal obtained with the pseudo-first-order model (743.5 mg g⁻¹). Therefore, the best model to describe the kinetics of the adsorption of MB is the pseudo-second order. Similar behavior was observed for BCB kinetics, the equilibrium adsorption capacity (q_e_,cal) obtained with the pseudo-second-order nonlinear model was 914.3 mg g⁻¹ and is closer to the equilibrium experimental adsorption capacity (q_e_,exp= 908.6 mg g⁻¹) than q_e_,cal obtained with the pseudo-first-order model (Table 6) (847.0 mg g⁻¹). Therefore, the best model to describe the kinetics of the adsorption of both dyes is the pseudo-second order.

Table 3.

Kinetic parameters of MB and BCB biosorption onto Fucus vesiculosus treated with formaldehyde.

Dye		Pseudo-first order			Pseudo-second order
	q _e,exp	q _e,cal	k₁/10⁻³	R ²	q _e	k₂/(10⁻⁵)	R ²
	(mg g⁻¹)	(mg g⁻¹)	(min⁻¹)	R ²	(mg g⁻¹)	(g mg⁻¹ min⁻¹)	R ²
Nonlinear model
MB	806.8	743.5	45.32	0.913	799.4	8.493	0.980
BCB	908.6	847.0	42.91	0.947	914.3	6.811	0.988
Linear model
MB	806.8	284.4	11.961	0.968	816.0	7.306	0.999
BCB	908.6	301.5	11.130	0.951	918.7	6.638	0.999

Table 4.

Error function data of the kinetic models.

Dye	Model	Error functions
Dye	Model	ARE	χ²	RMSE	Δ(%)
MB	PFO	11.69	69.71	67.13	17.52
MB	PSO	5.128	17.53	32.63	8.820
BCB	PFO	8.216	56.12	61.77	15.67
BCB	PSO	4.687	15.73	29.74	8.467

Effect of dye concentration

The isotherm study was performed to describe the interaction between adsorbate and adsorbent surface, calculate the maximum biosorption capacity of adsorbent, and describe the dynamic equilibrium of the adsorption system. Figure 7(a) and (b) shows adsorption isotherms for BCB and MB, respectively, and the fit to Langmuir and Freundlich nonlinear isotherm models. Table 5 presents the parameters of the Langmuir and Freundlich isotherm obtained from their linear and nonlinear forms by fitting the experimental data.

Figure 7.

Fitting data for Langmuir and Freundlich nonlinear isotherms for (a) BCB and (b) MB adsorption onto FV-FORMALD (C_o, MB = 100–1000 mg L⁻¹; C_o, BCB = 100–1000 mg L⁻¹; pH MB = 4; pH BCB = 4; concentration of FV-FORMALD = 1 g L⁻¹; T = 300.15 K; contact time = 360 min.

Table 5.

Isotherm parameters obtained by linear and nonlinear model for methylene blue (MB) and brilliant cresyl blue (BCB) biosorption onto Fucus vesiculosus treated with formaldehyde.

Dye	Langmuir Model			Freundlich model
	Nonlinear model
	Q _max	K _L	R ²	K _F	n	R ²
	(mg g⁻¹)	(L mg⁻¹)	R ²	(mg g⁻¹ (L mg⁻¹)^1/n)	n	R ²
MB	791.4	0.106	0.987	243.4	4.534	0.869
BCB	1332	0.053	0.984	128.3	1.853	0.994
Linear model
MB	791.9	0.105	0.999	187.1	3.494	0.872
BCB	1290	0.059	0.976	118.6	1.765	0.991

Table 6.

Error function data of the isotherm models.

	Model	Error functions
	Model	ARE	χ2	RMSE	Δ(%)
MB	Langmuir	4.607	12.37	26.13	7.046
MB	Freundlich	17.17	134.1	82.89	25.02
BCB	Langmuir	6.856	23.02	37.88	8.457
BCB	Freundlich	3.998	10.47	21.91	6.393

For MB adsorption, both, linear and nonlinear forms of the Langmuir isotherm model fitted better than the Freundlich isotherm, given the R² value close to 1 (R²= 0.999 and R²= 0.987). Additionally, the ARE, χ², RMS, and Δq (%) values obtained for nonlinear model of Langmuir are lower than those obtained for Freundlich model (Table 6). The Langmuir isotherm model assumes a homogeneous biosorbent surface, an identical energy distribution on the biosorbent surface, and the formation of monolayer (Miraboutalebi et al., 2017). The maximum monolayer adsorption capacity (Q_max) obtained with the Langmuir nonlinear model was 791.4 mg g⁻¹. Regarding the Langmuir constant K_L, this parameter indicates the binding sites’ affinity and adsorption energy. A low value of K_L is related to a low binding site affinity (Zou et al., 2013). From Table 5, the K_L value obtained for MB is low (0.106). Therefore, a weak interaction between MB and FV-FORMALD can be assumed.

For BCB adsorption, as shown in Table 5, the R² values are 0.991 and 0.994 for linear and nonlinear Freundlich isotherm models, respectively. The results of R² for linear and nonlinear models of Langmuir isotherm are 0.976 and 0.984, respectively.

It can be assumed that both models are suitable to describe the adsorption of BCB dye onto FV-FORMAD. However, the values of the error functions are lower for the nonlinear Freundlich model, suggesting that this model better fits to the adsorption data. The Freundlich isotherm model assumes a heterogeneous surface, reversible adsorption, and multilayer formation (Özcan et al., 2004). Values of the parameter n between 1 and 10 indicate a favorable adsorption process (Lebron et al., 2021). Also, n = 1 indicates linear process, n > 1 indicates physical process, and when n < 1, indicates chemical process (Choi and Yu, 2019). In this study, the value of n obtained from the nonlinear model was 1.853, indicating that the biosorption of BCB onto FV-FORMALD is favorable. In addition, value of K_F = 128.3 (Table 5) indicates high affinity for BCB. According to the fit with the nonlinear Langmuir model, the maximum monolayer adsorption capacity of BCB dye (Q_max) is 1332 mg g-1, which is higher than the Q_max previously reported for the MB dye.

Based on the BCB and MB isotherms shapes and the results are shown above, it can be assumed that the Langmuir model describes the biosorption of MB onto FV-FORMALD, while Freundlich better describes the biosorption of BCB.

pH effect

Figure 8 shows the adsorption isotherms of BCB and MB fitted by Langmuir and Freundlich isotherms at different pH values. It can be seen that for all pH values, the same isotherm shapes are observed for both dyes.

Figure 8.

Effect of pH on the modeling of Langmuir and Freundlich nonlinear isotherms for MB and BCB biosorption onto FV-FORMALD (C_o, MB = 100–1000 mg L⁻¹; C_o, BCB = 100–1000 mg L⁻¹; concentration of FV-FORMALD = 1 g L⁻¹; T = 300.15 K; contact time = 360 min.

In the whole range studied (pH 2–8), the MB biosorption capacity increased with increasing solution pH values (Figure 8(b), (d), (e), and (h)), which also means that under low pH conditions, low adsorption capacity of MB was achieved. This occurred when the pH is acidic (low value); the electrostatic repulsion between the positive surface of the adsorbent and the positive charge of the cationic dyes (Figure 1(b)) inhibits dye adsorption, and excess of hydrogen ions can compete with the dye molecules for adsorption sites. Conversely, when the pH increases, the surface of the biosorbent becomes negatively charged due to the deprotonation of the surface (Pang et al., 2017), which facilitates the adsorption of MB. Other authors have reported similar behavior for the adsorption of cationic dyes onto other algae species (Caparkaya and Cavas 2008; Guarín et al. 2018; Liang et al. 2017). In other hand, in Figure 8, the effect of the pH solution on BCB adsorption is also shown. There is a minimal decreased in the adsorption capacity of BCB form pH = 2 to pH = 4 (Figure 8(a) and (c)), it could be said that in the whole studied range, there is no significant difference between the adsorption capacity of BCB by changing the pH of the solutin.

The results of the linear and nonlinear modeling of the Langmuir and Freundlich isotherm models are presented in Table 7. For the linear and nonlinear Langmuir models, R²> 0.950 were obtained for all pH solutions, and are higher than the R² values obtained for Freundlich models. In addition, the ARE, χ2, RMS, and Δq (%) values obtained for nonlinear model of Langmuir are lower than those obtained for Freundlich mode (Table 8). Thus, it confirms the tendency of fitting to the Langmuir isotherm model. According to the Langmuir nonlinear model, the maximum adsorption capacity of the MB monolayer (Q_max) increased substantially in the pH range 4–8; this is related to pH_PZC= 4.77 obtained for FV-FORMALD, when pH > pH_PZC algae surface is negatively charged, favoring cations biosorption. In this case, for MB at pH = 7 and 8, Q_max= 834.2 and 862.6 mg g⁻¹, respectively, confirm the increased biosorption of MB.

Table 7.

Isotherms parameters of MB and BCB biosorption onto Fucus vesiculosus treated with formaldehyde at different pH values.

Dye	pH	Langmuir Model			Freundlich model
		Q _max	K _L	R ²	K _F	n	R ²
		(mg g⁻¹)	(L mg⁻¹)		(mg g⁻¹ (L mg⁻¹)^1/n)	n	R ²
Nonlinear model
MB	2	791.9	0.049	0.966	154.8	3.358	0.956
	4	825.5	0.130	0.989	269.9	4.557	0.910
	7	834.2	0.124	0.987	277.7	4.763	0.904
	8	862.6	0.117	0.994	289.9	4.847	0.847
BCB	2	1528	0.031	0.990	120.1	1.954	0.954
	4	1332	0.053	0.984	128.3	1.853	0.994
	7	2153	0.035	0.991	106.6	1.415	0.979
	8	1341	0.063	0.987	134.6	1.743	0.983
Linear model
MB	2	851.6	0.041	0.994	113.5	2.700	0.948
	4	838.4	0.122	0.999	228.3	3.777	0.911
	7	848.1	0.115	0.999	242.2	4.064	0.898
	8	853.5	0.122	0.999	244.1	3.939	0.847
BCB	2	1590	0.028	0.971	82.34	1.623	0.937
	4	1940	0.029	0.996	151.7	2.193	0.950
	7	2431	0.029	0.912	85.14	1.263	0.981
	8	1348	0.063	0.974	124.1	1.651	0.977

Table 8.

Error function data of the isotherm models at different pH values.

Dye	pH		Error functions
Dye	pH	Model	ARE	χ²	RMSE	Δ(%)
MB	2	Langmuir	8.982	23.74	49.03	13.95
	2	Freundlich	12.91	47.62	56.22	22.83
	4	Langmuir	4.549	6.404	27.03	7.145
	4	Freundlich	11.82	39.85	75.40	16.83
	7	Langmuir	3.308	4.823	28.32	4.985
	7	Freundlich	10.56	38.07	76.19	15.78
	8	Langmuir	2.021	1.496	17.72	2.503
	8	Freundlich	13.21	55.77	93.31	18.73
BCB	2	Langmuir	5.948	23.02	36.65	14.42
	2	Freundlich	13.15	80.31	77.69	21.97
	4	Langmuir	6.856	23.02	37.88	8.457
	4	Freundlich	3.998	10.47	21.91	6.393
	7	Langmuir	4.961	10.21	29.83	6.901
	7	Freundlich	7.860	23.55	46.04	10.80
	8	Langmuir	3.667	5.274	23.55	4.244
	8	Freundlich	4.144	8.389	26.71	6.066

For BCB biosorption, when the pH increased from 2 to 4, Q_max calculated with Langmuir nonlinear model decreased from 1528 to 1332 mg g⁻¹. At higher pH values, Q_max = 2153 and 1341 mg g⁻¹ for pH values of 7 and 8, respectively. The highest value of Q_max obtained with nonlinear Langmuir model was 2153 at pH = 7, which could be considered erroneous and atypical because if we consider the results of linear model Q_max is 2431 mg g⁻¹ (Table 7), which is close to 2153 but the value of the determination coefficient (R²= 0.912) is low. Additionally, considering the results obtained from Freundlich nonlinear isotherm model, a slight change in the parameters (n and K_F) was observed. The parameter n decreased from 1.954 to 1.743 as the pH of the solution increased from 2 to 8 units. However, the values for n and K_F are still good and indicate a favorable biosorption of BCB. The highest value for Q_max obtained with linear model of Langmuir is 1940, and the highest value of n parameter obtained with linear Freundlich isotherm is 2.193 at pH = 4. According to the results of the evaluated isotherm models, the optimum pH condition is 8 for MB biosorption and 4 for BCB biosorption.

Adsorbent concentration effect

The effect of the concentration of the brown algae FV treated with formaldehyde on the dye's removal efficiency was evaluated in the range of 1.0–10 g L⁻¹. The results showed that the removal efficiency of MB increased from 78.85% to 98.56% when the adsorbent concentration increased from 1 to 2 g L⁻¹ (Figure 9); above 2 g L⁻¹, the removal efficiency increased slightly and then remained constant around 99%. Previous works have reported similar results (Caparkaya and Cavas, 2008; Liang et al., 2017). The increase in the removal efficiency as the biomass concentration increases are due to an increase in the number of active sites and enough surface to place the MB molecule on the biosorbent (Foroutan et al., 2019). The 99% removal of BCB was achieved using the lowest adsorbent concentration of FV-FORMALD (1 g L⁻¹). When the algae concentration increases, the removal efficiency remains almost constant. This uncommon behavior has been reported for MB biosorption by different brown algae (Pratiwi et al., 2020) and it may be attributed to high biosorbent amounts causing agglomeration and consequently obstructing the binding sites. According to the results, using the same initial concentration (1 g L⁻¹) of FV-FORMALD higher removal efficiency was obtained for BCB dye than MB which may indicates more affinity for BCB dye. The optimal adsorbent concentrations were 1 g L⁻¹ and 2 g L⁻¹ for BCB and MB, respectively.

Figure 9.

Effect of FV-FORMALD concentration on the percentage removal of MB and BCB (C_o, MB = 1000 mg L⁻¹; C_o, BCB = 1000 mg L⁻¹; pH MB = 4; pH BCB = 4; T = 300.15 K; contact time = 360 min).

Temperature effect

The effect of temperature on the biosorption capacity of MB and BCB onto FV-FORMALD was performed by an isotherm study. The experiments were carried out at 300.15, 313.15, and 330.15 K. As can be seen in Figure 10, the biosorption process of BCB dye continues fitting to both nonlinear isotherm models. However, experimental data are closer to the Freundlich model curve than the Langmuir curve, and the shape of the isotherm is similar for the three values of temperatures (Figure 10(a), (c) and (e)), where no significant change is observed in the adsorption capacity (qe). Figure 10(b), (d), and (f) shows the fitting isotherm curves of MB at 300.15, 313.15, and 330.15 K, respectively. It can be observed that the experimental curves are best fitted by the Langmuir model, with a slight increase in the adsorption capacity with increasing temperature.

Figure 10.

Temperature influence on BCB and MB uptake by the brown algae Fucus vesiculosus treated with formaldehyde (FV-FORMALD) (C_o, MB = 100–1000 mg L⁻¹; C_o, BCB = 100–1000 mg L⁻¹; pH MB = 4; pH BCB = 4; FV-FORMALD concentration = 1 g L⁻¹; T = 300–330 K; contact time = 360 min).

The Langmuir and Freundlich parameters obtained with the linear and nonlinear model for MB and BCB biosorption are shown in Table 9. For BCB adsorption, although the values of R² for Langmuir nonlinear model are good, and the experimental data seem to be closed (Figure 10(a), (c) and (e)), the determination coefficients obtained for the linear Langmuir model are R²= 0.935 and 0.831 for 313.15 and 330.15 K, respectively, could indicates that the Langmuir model is not the best to describe the adsorption of BCB at higher temperatures. However, the lowest values of error functions were obtained for the Freundlich isotherm model. The lower values of the error functions values corroborate the best fit of the Langmuir isotherm model for MB and Freundlich isotherm model for BCB (Table 10). Furthermore, the highest values of R² were obtained for linear and nonlinear Freundlich isotherm models. It was found that at 300.15 K, Freundlich parameters, K_F = 128.3 and n = 1.853 confirm a favorable adsorption process. In contrast, increasing temperature up to 313.15, K_F = 65.04 and n = 1.480. While for the highest studied temperature (330.15 K), K_F = 61.87 and n = 1.385. Therefore, biosorption of BCB is best performed at low temperatures, which implies energy savings. On the other hand, for MB biosorption, the results obtained from the linear Langmuir model indicate that Q_max increases with increasing temperature (Q_max= 791.9, 812.2 and 845.8 mg g⁻¹ for T = 300.15, 313.15, 330.15 K, respectively), because increasing temperature enhance the mobility of the adsorbate and provided sufficient energy for the interaction between the biosorbent surface and the adsorbate (Aichour and Zaghouane, 2020).

Table 9.

Isotherms parameters of MB and BCB biosorption onto Fucus vesiculosus treated with formaldehyde (FV-FORMALD) at different temperatures.

Dye	T(K)	Langmuir Model		Freundlich model
		Q _max	K _L	R ²	K _F	n	R ²
		(mg g⁻¹)	(L mg⁻¹)	R ²	(mg g⁻¹ (L mg⁻¹)^1/n)	n	R ²
Nonlinear model
MB	300.15	791.4	0.106	0.987	243.4	4.534	0.869
	313.15	866.3	0.050	0.990	159.3	3.276	0.884
	330.15	848.2	0.063	0.984	167.1	3.253	0.956
BCB	300.15	1332	0.053	0.984	128.3	1.853	0.994
	313.15	1991	0.017	0.985	65.04	1.480	0.993
	330.15	2239	0.016	0.985	61.87	1.385	0.982
Linear model
MB	300.15	791.9	0.105	0.999	187.1	3.494	0.872
	313.15	812.2	0.059	0.999	104.9	2.437	0.897
	330.15	845.8	0.066	0.997	128.2	2.653	0.962
BCB	300.15	1290	0.059	0.976	118.6	1.765	0.991
	313.15	1733	0.022	0.935	67.10	1.499	0.993
	330.15	2083	0.018	0.831	60.78	1.377	0.978

Table 10.

Error function data of the isotherm models at different temperatures.

Dye	Temperature	Model	Error functions
Dye	Temperature	Model	ARE	χ²	RMSE	Δ(%)
MB	300.15	Langmuir	4.607	12.37	26.13	7.046
	300.15	Freundlich	17.17	134.1	82.89	25.02
	313.15	Langmuir	7.204	14.73	35.46	10.50
	313.15	Freundlich	21.80	118.9	94.01	35.31
	330.15	Langmuir	10.16	26.43	39.24	17.28
	330.15	Freundlich	12.90	43.45	58.38	20.57
BCB	300.15	Langmuir	6.856	23.02	37.88	8.457
	300.15	Freundlich	3.998	10.47	21.91	6.393
	313.15	Langmuir	6.610	19.12	42.45	10.11
	313.15	Freundlich	4.019	6.753	28.87	4.773
	330.15	Langmuir	6.996	19.30	38.46	27.80
	330.15	Freundlich	6.368	17.32	41.52	8.441

Thermodynamic of Mb and BCB adsorption

According to Tran (2022), the constant K_L from the Langmuir model, in correct units (Lmol⁻¹), is appropriate for calculating the thermodynamics parameters of the solid-liquids adsorption process. The results obtained from the temperature effect were used to calculate K_L (Lmol⁻¹). Then, the linear relation between (ln K_L) and 1T⁻¹ (equation (16)) provided the thermodynamics parameters (ΔH°, and ΔS°). ΔG° was calculated using equation (17):

\ln (K_{L}) = \frac{Δ S \circ}{R} - \frac{Δ H \circ}{R} (\frac{1}{T})

(16)

Δ G \circ = - R T \ln (K_{L})

(17)

The thermodynamics parameters of the adsorption of both dyes are shown in Table 11. For both dyes, ΔG° presents negatives values for the three studied temperatures, which means that the adsorption of MB and BCB onto FV-FORMALD occurs spontaneously (Al-Ma’abreh et al., 2024). When ΔH° < 0, it indicates that the adsorption process is exothermic in nature (Al-Dalahmeh et al., 2024). ΔH° = −10.873 and −29.322 kJ^.mol⁻¹ for MB and BCB, respectively. Therefore, the adsorption of both dye is an exothermic process. Finally, ΔS° is positive for MB dye indicating a higher randomness at the interface of the dye and FV-FORMALD (Medhat et al., 2021). Whereas, for the BCB dye, ΔS° = −10.892 kJ.mol⁻¹K⁻¹, the negative value indicates a lower randomness at the interface of BCB dye and the adsorbent (Egbosiuba et al., 2020).

Table 11.

Thermodynamic parameters of MB and BCB adsorption by Fucus vesiculosus treated with formaldehyde (FV-FORMALD).

Dye	Temperature (K)	ΔG° (kJ^.mol⁻¹)	ΔH° (kJ^.mol⁻¹)	ΔS° (kJ^.mol⁻¹K⁻¹)
MB	300.15	−26.286	−10.873	49.233
	313.15	−25.510
	330.15	−27.569
BCB	300.15	−26.785	−29.322	−10.892
	313.15	−24.688
	330.15	−26.155

Comparison of the maximum adsorption capacities Q_m of MB and BCB onto different adsorbents

Table 12 compares the Q_max values of MB and BCB obtained using other biosorbents. It can be seen that the Q_max values for both dyes obtained in this study are higher than other types of seaweed. Lebron et al. (2021) used Raw FV for MB removal and obtained Q_max= 698.8 mg g⁻¹ under the following conditions: C_o,MB = 100–2500 mg L⁻¹; pH = 6; concentration of raw algae = 2 g L⁻¹; T = 303 K; contact time = 1 440 min. For vesiculosus treated with formaldehyde, we obtained Q_max= 866.3 mg g⁻¹ (167.58 mg g⁻¹ more than raw FV) at C_o, MB = 100–1000 mg L⁻¹; pH = 4; concentration of FV-FORMALD = 1 g L⁻¹; T = 313.15 K; and contact time = 360 min. Using the formaldehyde treated algae, the biomass concentration and contact time conditions were lower than those of raw FV, resulting in material and treatment time savings.

Table 12.

Comparison of the maximum adsorption capacity of MB and BCB onto different adsorbents.

Dye	Adsorbent	Q_max(mg g⁻¹)	References
	Sargassum latifolium	8.540	Fawzy and Gomaa (2021)
	Sugarcane Bagasse	9.410	Siqueira et al. (2020)
	Cystoseira barbata	14.97	Ozudogru et al. (2017)
	Carica papaya wood	32.25	Rangabhashiyam et al. (2018)
	Paspalum maritimum weed	56.18	Silva et al. (2019)
	Sargassum dentifolium	66.60	Moghazy et al. (2019)
	Coffee husks	79.60	Njanja et al. (2019)
	Gracilaria parvispora	83.08	Daneshvar et al. (2019)
	Calotropis gigantea seedpods	88.36	Ammar et al. (2021)
	Sargassum muticum	92.00	Hannachi and Hafidh (2020)
	Rosemary plant-activated carbon	110.7	Amin et al. (2017)
	Fava bean peel	140.0	Bayomie et al. (2020)
	Rice straw	158.0	Jawad et al. (2020)
MB	Soybean hulls	169.9	Cusioli et al. (2020)
	Watermelon (Citrullus lanatus) rinds	188.7	Jawad et al. (2018)
	Ulva fasciae	244.0	Moghazy et al. (2019)
	Sargassum ilicifolium	319.9	Tabaraki and Sadeghinejad (2017)
	Ulva lactuca-activated carbon	344.8	El Nemr et al. (2021)
	Corncob	417.1	Choi and Yu (2019)
	Chemical-modified Laminaria japonica	418.4	Shao et al. (2017)
	Magnetic Fucus vesiculosus	577.0	Jafari et al. (2020)
	Raw Fucus vesiculosus	698.8	Lebron et al. (2021)
	Durvillaea antarctica	702.9	Guarín et al. (2018)
	Sargassum hemiphyllum	729.9	Liang et al. (2017)
	Nizamuddinia zanardinii	863.4	Daneshvar et al. (2019)
	FV-FORMALD	866.3	This study
	Ulva fasciata	1514	Daneshvar et al. (2019)
	Sawdust	20.41	Elhami et al. (2011)
	Dimethyl terephthalate distillation residue	28.20	Güçlü (2010)
	Siliceous rocks	33.22	Rassol et al. (2020)
	Clay	42.00	Iyim and Güçlü (2009)
	Resin coated with nickel/nickel boride nanoparticles	88.502	Çinar et al. (2018)
BCB	Sulfonated phenol-formaldehyde resin	108.0	Iyim et al. (2008)
	Carboxymethyl cellulose incorporated acrylic hydrogels	209.3	Mandal and Ray (2016)
	Collagenic waste and rubber-based resin-cured	251.2	Roy et al. (2019)
	Regenerated spent bleaching earth	260.8	Shehab et al. (2019)
	Tamarix aphylla leaves	491.8	Ackacha and Drmoon (2012)
	Acid-base regulated anionic functionalized polyacrylonitrile fiber	890.1	Xiao et al. (2020)
	FV-FORMALD	1940	This study

Although there are not many studies of the removal of BCB dye. In this study, the Q_max obtained with the Langmuir isotherm model was higher than Q_max obtained using more complex adsorbents such as resins and hydrogels. Therefore, it can be assumed that formaldehyde-treated Fucus vesiculosus (FV-FORMALD) is a good option to remove large amounts of both dyes.

Reusability study

Reuse of adsorbents has been identified as an important parameter for economically and environmental valuable application. The result of the adsorption–desorption cycles of MB onto FV-FORMALD is shown in Figure 11(a). The figure shows that high adsorption capacities (q_ads) were obtained for at least three cycles, q_ads =726.9, 102.8, and 71.7 mg g⁻¹ for cycle 1, 2 and 3, respectively. For desorption capacity (q_des), values equal to 201.9, 202.3 and 184.3 mg g⁻¹ were obtained for cycle 1,2 and 3, respectively. Figure 11(b) shows the results for BCB adsorption–desorption cycles. Q_ads= 981.1, 645.9, and 212.36 mg g⁻¹ for cycles 1,2 and 3, respectively. While q_des= 28.1, 79.3, and 48.8 mg g⁻¹. After three cycles, high adsorption capacities (q_ads) were obtained for both dyes indicating their reusability to effectively remove cationic dyes.

Figure 11.

Adsorption/desorption cycles of (a) MB and (b) BCB onto FV-FORMALD using 0.1 M HCl as eluent (C_o, MB = 1000 mg L⁻¹; C_o, BCB = 1000 mg L⁻¹).

Real colored wastewater treatment

The waste generated from this research was treated with the FV-FORMALD biosorbent. The UV–Visible spectra of wastewater before and after the adsorption is shown in Figure 12. The maximum absorbance of the colored wastewater corresponds at λ_max= 655 nm. After adsorption, the absorbance was reduced from 1.129 to 0.089 a.u, indicating a removal efficiency of 92%. According to Sepulveda and Santana (2013), the λ_max= 655 nm could be attributed to basic blue 3, another cationic dye. Therefore, it can be assumed that the algae treated with formaldehyde can be used for the treatment of wastewater containing other catatonics dyes such as basic blue 3 efficiently, besides of MB and BCB removal.

Figure 12.

Adsorption of real colored wastewater onto FV-FORMALD.

Conclusions

The brown algae FV treated with formaldehyde (FV-FORMALD) presented high interaction with MB and BCB. Fourier-transform infrared spectroscopy characterization shows that the OH and COOH groups of FV-FORMALD exert a vital role in the uptake process of both dyes. Furthermore, the kinetic studies indicated that equilibrium biosorption of both dyes onto FV-FORMALD was reached within 6 h of contact time, and the pseudo-second-order model best describes the effect of contact time.

The fit to the Freundlich model and the adsorption/desorption study suggest a physical mechanism of BCB biosorption. The maximum adsorption (Q_max) of BCB obtained with the model of Langmuir was 1940 mg g⁻¹.

A chemical adsorption mechanism of MB is suggested as a result of the adjustment to the Langmuir model, the kinetics, FTIR, and the adsorption/desorption results. The maximum adsorption capacity (Q_max) of MB obtained with the Langmuir model was 866.3 mg g⁻¹. The interactions involved in the removal of MB and BCB were electrostatic attraction, hydrophobic attraction, hydrogen bonding and n-π and π-π interactions.

The optimal conditions for MB biosorption onto algae were at 313.15 K, adsorbent concentration = 2 g L⁻¹, and pH = 8. While for BCB biosorption, the optimal conditions were at 300.15 K, pH 4, and adsorbent concentration = 1 g L⁻¹. The adsorption of both dyes was exothermic and spontaneous.

Considering the high adsorption capacities of both dyes compared to other biosorbents, its reusability for at least three cycles, its successfully application in the treatment of the real wastewater, its cost–benefit compared to more complex and expensive methods, the brown algae FV treated with formaldehyde is an environmentally friendly, sustainable, and low-cost option for the removal of cationic dyes from water.

Footnotes

Acknowledgements

The authors thank the Congreso Nacional de Ciencia y Tecnología (CONACyT) for supporting the national master's scholarship. Rogelio Morán Elvira from IER-UNAM for BET characterization. CONAHCYT CF-2023-I-698, CF 2023-I-775, LNC 2023-33, and DGAPA PAPIIT IT100821.

Credit author statement

All authors have read and agreed to the published version of the manuscript.

Data availability statement

The datasets generated and used to support the findings of this study are available from the corresponding author upon request.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Mohamed Abatal

References

Ackacha

Drmoon

(2012) Effect of important selected parameters on adsorption capacity of brilliant cresyl blue dye onto novel adsorbent: Tamarix aphylla leaves. International Proceedings of Chemical, Biological and Environmental Engineering 48(14): 71–75.

Adeeb

Ali

Akthar

(2014) A study on bacterial decolorization of crystal violet dye by Clostridium perfringens, Pseudomonas aeruginosa and Proteus vulgaris. International Journal of Pharmacy and Biological Sciences 4(2): 89–96.

Aichour

Zaghouane

(2020) Single and competitive adsorption studies of two cationic dyes from aqueous mediums onto cellulose-based modified citrus peels/calcium alginate composite. International Journal of Biological Macromolecules 154: 1227–1236. Elsevier B.V.

Al-Dalahmeh

Hamadneh

Aburumman

, et al. (2024) Biosorption of hexavalent chromium by biochar prepared from the Ceratonia siliqua pod: Kinetic, equilibrium, and thermodynamics studies. Adsorption Science & Technology 42: 02636174241241946. SAGE Publications.

Al-Ghouti

Da’ana

(2020) Guidelines for the use and interpretation of adsorption isotherm models: A review. Journal of Hazardous Materials 393: 122383. Elsevier B.V.

Ali

(2010) Biodegradation of synthetic dyes—A review. Water, Air, and Soil Pollution 213: 251–273.

Al-Ma’abreh

AlKhabbas

Alawaideh

, et al. (2024) Investigation of kinetics, thermodynamics, and isotherms of the simultaneous removal of heavy metal ions by activated carbon from cypress fruit. Adsorption Science & Technology 42: 02636174241256853.

Amin

Alazba

Shafiq

(2017) Effective adsorption of methylene blue dye using activated carbon developed from the rosemary plant: Isotherms and kinetic studies. Desalination and Water Treatment 74: 336–345. Desalination Publications.

Ammar

El-Ghoul

Jabli

(2021) Characterization and valuable use of Calotropis gigantea seedpods as a biosorbent of methylene blue. International Journal of Phytoremediation 23(10): 1085–1094. Taylor and Francis Ltd.

10.

Ata

Nalcaci

Ovez

(2012) Macro algae Gracilaria verrucosa as a biosorbent: A study of sorption mechanisms. Algal Research 1(2): 194–204.

11.

Barton

Bull

Klemperer

, et al. (1999) Tailored porous materials. Chemistry of Materials 11(10): 2633–2656. American Chemical Society.

12.

Bayomie

Kandeel

Shoeib

, et al. (2020) Novel approach for effective removal of methylene blue dye from water using fava bean peel waste. Scientific Reports 10(1): 7824. Nature Research.

13.

Bhatia

Sharma

Singh

, et al. (2017) Biological methods for textile dye removal from wastewater: A review. Critical Reviews in Environmental Science and Technology 47(19): 1836–1876. Taylor and Francis Inc.

14.

Caparkaya

Cavas

(2008) Biosorption of methylene blue by a brown alga Cystoseira barbatula Kützing. Acta Chimica Slovenica 55: 547–553.

15.

Choi

(2019) Biosorption of methylene blue from aqueous solution by agricultural bioadsorbent corncob. Environmental Engineering Research 24(1): 99–106. Korean Society of Environmental Engineers.

16.

Çinar

Işlek Coşkun

Deniz Çift Çi

(2018) Removal of five cationic dyes using a resin coated with nickel/nickel boride nanoparticles. Turkish Journal of Chemistry 42(2): 505–519. TUBITAK.

17.

Cusioli

Quesada

Baptista

ATA

, et al. (2020) Soybean hulls as a low-cost biosorbent for removal of methylene blue contaminant. Environmental Progress and Sustainable Energy 39(2): e13328. John Wiley and Sons Inc.

18.

Dada

Adekola

Odebunmi

, et al. (2021) Two–three parameters isotherm modeling, kinetics with statistical validity, desorption and thermodynamic studies of adsorption of Cu(II) ions onto zerovalent iron nanoparticles. Scientific Reports 11: 1–15. Nature Research.

19.

Daneshvar

Vazirzadeh

Bhatnagar

(2019) Biosorption of methylene blue dye onto three different marine macroalgae: Effects of different parameters on isotherm, kinetic and thermodynamic. Iranian Journal of Science and Technology, Transaction A: Science 43(6): 2743–2754. Springer.

20.

Egbosiuba

Abdulkareem

Kovo

, et al. (2020) Ultrasonic enhanced adsorption of methylene blue onto the optimized surface area of activated carbon: Adsorption isotherm, kinetics and thermodynamics. Chemical Engineering Research and Design 153: 315–336. Institution of Chemical Engineers.

21.

EHS (2007) General EHS guidelines: environmental wastewater and ambient water quality.

22.

Elhami

Khaloo

Sharali

(2011) Sawdust as a natural and low cost adsorbent for the removal of brilliant cresyl blue dye from water samples. Journal of the Iranian Chemical Research 4: 281–286.

23.

El Nemr

Shoaib

AGM

El Sikaily

, et al. (2021) Evaluation of cationic methylene blue dye removal by high surface area mesoporous activated carbon derived from Ulva lactuca. Environmental Processes 8(1): 311–332. Springer Science and Business Media Deutschland GmbH.

24.

Ezhilarasu

(2016) Textile industry dye degrading by bacterial strain Bacillus sp. International Journal of Advanced Research in Biological Sciences 3(3): 211–226.

25.

Fawzy

Gomaa

(2021) Low-cost biosorption of methylene blue and congo red from single and binary systems using Sargassum latifolium biorefinery waste/wastepaper xerogel: An optimization and modeling study. Journal of Applied Phycology 33(1): 675–691. Springer Science and Business Media B.V.

26.

Foroutan

Mohammadi

Ramavandi

(2019) Elimination performance of methylene blue, methyl violet, and Nile blue from aqueous media using AC/CoFe2O4 as a recyclable magnetic composite. Environmental Science and Pollution Research 26(19): 19523–19539. Springer Verlag.

27.

Garg

Amita

Kumar

, et al. (2004) Basic dye (methylene blue) removal from simulated wastewater by adsorption using Indian rosewood sawdust: A timber industry waste. Dyes and Pigments 63(3): 243–250.

28.

Gokulan

Avinash

Prabhu

, et al. (2019) Remediation of remazol dyes by biochar derived from Caulerpa scalpelliformis—an eco-friendly approach. Journal of Environmental Chemical Engineering 7(5): 103297. Elsevier Ltd.

29.

Guarín

Moreno-Pirajan

Giraldo

(2018) Kinetic study of the bioadsorption of methylene blue on the surface of the biomass obtained from the algae D. Antarctica. Journal of Chemistry 2018(1): 2124845. Hindawi Limited.

30.

Güçlü

(2010) Removal of basic dyes from aqueous solutions by dimethyl terephthalate distillation residue. Desalination 259(1–3): 53–58.

31.

Hannachi

Hafidh

(2020) Biosorption potential of Sargassum muticum algal biomass for methylene blue and lead removal from aqueous medium. International Journal of Environmental Science and Technology 17(9): 3875–3890. Springer.

32.

Hidayat

Yonemura

Mitoma

, et al. (2022) Methylene blue removal by chitosan cross-linked zeolite from aqueous solution and other Ion effects: Isotherm, kinetic, and desorption studies. Adsorption Science and Technology 2022: 1853758. Hindawi Limited.

33.

Iyim

Acar

Özgümüş

(2008) Removal of basic dyes from aqueous solutions with sulfonated phenol-formaldehyde resin. Journal of Applied Polymer Science 109(5): 2774–2780.

34.

Iyim

Güçlü

(2009) Removal of basic dyes from aqueous solutions using natural clay. Desalination 249(3): 1377–1379.

35.

Jafari

Mahdavinia

Kazemi Heragh

, et al. (2020) Basic dyes removal by adsorption process using magnetic fucus vesiculosus (Brown algae). Journal of Water and Environmental Nanotechnology 5(3): 256–269. Iranian Environmental Mutagen Society.

36.

Jawad

Hum

NNMF

Farhan

, et al. (2020) Biosorption of methylene blue dye by rice (Oryza sativa L.) straw: Adsorption and mechanism study. Desalination and Water Treatment 190: 322–330. Desalination Publications.

37.

Jawad

Ngoh

Radzun

(2018) Utilization of watermelon ( Citrullus lanatus ) rinds as a natural low-cost biosorbent for adsorption of methylene blue: Kinetic, equilibrium and thermodynamic studies. Journal of Taibah University for Science 12(4): 371–381. Taylor and Francis Ltd.

38.

Jawad

Saud Abdulhameed

Wilson

, et al. (2021) High surface area and mesoporous activated carbon from KOH-activated dragon fruit peels for methylene blue dye adsorption: Optimization and mechanism study. Chinese Journal of Chemical Engineering 32: 281–290. Materials China.

39.

Jianlong

(2002) Biosorption of copper(II) by chemically modified biomass of Saccharomyces cerevisiae. Process Biochemistry 37(8): 847–850. Available at: www.elsevier.com.

40.

Khajavian

Wood

Hallajsani

, et al. (2019) Simultaneous biosorption of nickel and cadmium by the brown algae Cystoseria indica characterized by isotherm and kinetic models. Applied Biological Chemistry 62(1): 1–12. Springer Netherlands.

41.

Lakshmi

Srivastava

Mall

, et al. (2009) Rice husk ash as an effective adsorbent: Evaluation of adsorptive characteristics for Indigo Carmine dye. Journal of Environmental Management 90(2): 710–720.

42.

Lebron

YAR

Moreira

de Souza Santos

(2021) Biosorption of methylene blue and eriochrome black T onto the brown macroalgae fucus vesiculosus : Equilibrium, kinetics, thermodynamics and optimization. Environmental Technology 42(2): 279–297. Taylor and Francis Ltd.

43.

Liang

Xia

Long

(2017) Biosorption of methylene blue by nonliving biomass of the brown macroalga Sargassum hemiphyllum. Water Science and Technology 76(6): 1574–1583. IWA Publishing.

44.

Malik

Ramteke

Wate

(2007) Adsorption of malachite green on groundnut shell waste based powdered activated carbon. Waste Management 27(9): 1129–1138.

45.

Mandal

Ray

(2016) Removal of safranine T and brilliant cresyl blue dyes from water by carboxy methyl cellulose incorporated acrylic hydrogels: Isotherms, kinetics and thermodynamic study. Journal of the Taiwan Institute of Chemical Engineers 60: 313–327. Taiwan Institute of Chemical Engineers.

46.

Medhat

El-Maghrabi

Abdelghany

, et al. (2021) Efficiently activated carbons from corn cob for methylene blue adsorption. Applied Surface Science Advances 3: 100037. Elsevier B.V.

47.

Miraboutalebi

Nikouzad

Peydayesh

, et al. (2017) Methylene blue adsorption via maize silk powder: Kinetic, equilibrium, thermodynamic studies and residual error analysis. Process Safety and Environmental Protection 106: 191–202. Institution of Chemical Engineers.

48.

Moghazy

Labena

Husien

(2019) Eco-friendly complementary biosorption process of methylene blue using micro-sized dried biosorbents of two macro-algal species (Ulva fasciata and Sargassum dentifolium): Full factorial design, equilibrium, and kinetic studies. International Journal of Biological Macromolecules 134: 330–343. Elsevier B.V.

49.

Momina

Suzylawati

(2020) Study of the adsorption/desorption of MB dye solution using bentonite adsorbent coating. Journal of Water Process Engineering 34: 101155. Elsevier Ltd.

50.

Naseem

Begum

, et al. (2019) Catalytic reduction of toxic dyes in the presence of silver nanoparticles impregnated core-shell composite microgels. Journal of Cleaner Production 211: 855–864. Elsevier Ltd.

51.

Njanja

Mbokou

Pontie

, et al. (2019) Comparative assessment of methylene blue biosorption using coffee husks and corn cobs: Towards the elaboration of a lignocellulosic-based amperometric sensor. SN Applied Sciences 1(6): 1–14. Springer Nature.

52.

Özcan

Erdem

Özcan

(2004) Adsorption of acid blue 193 from aqueous solutions onto Na-bentonite and DTMA-bentonite. Journal of Colloid and Interface Science 280(1): 44–54.

53.

Ozudogru

Merdivan

Goksan

(2017) Removal of methylene blue from aqueous solutions by brown alga cystoseira barbata. Desalination and Water Treatment 62: 267–272. Desalination Publications.

54.

Pang

Ding

, et al. (2017) Adsorption behaviors of methylene blue from aqueous solution on mesoporous birnessite. Journal of the Taiwan Institute of Chemical Engineers 77: 168–176. Taiwan Institute of Chemical Engineers.

55.

Park

Yun

Jong

(2005) Studies on hexavalent chromium biosorption by chemically-treated biomass of Ecklonia sp. Chemosphere 60(10): 1356–1364. Elsevier Ltd.

56.

Paździor

Wrębiak

Klepacz-Smółka

, et al. (2017) Influence of ozonation and biodegradation on toxicity of industrial textile wastewater. Journal of Environmental Management 195: 166–173. Academic Press.

57.

Pratiwi

Poeloengasih

Suryani

, et al. (2020) Adsorption of basic dye methylene blue by brown algae Sargassum duplicatum. IOP Conference Series: Earth and Environmental Science 462(1): 1–7. Institute of Physics Publishing.

58.

Popa VI and Volf I (Eds.) (2018) Biomass as renewable raw material to obtain bioproducts of high-tech value. Elsevier.

59.

Rahman

Sathasivam

(2015) Heavy metal adsorption onto kappaphycus sp. From aqueous solutions: The use of error functions for validation of isotherm and kinetics models. Biomed Research International 2015(1): 126298. Hindawi Publishing Corporation.

60.

Raj

Singh

Trivedi

, et al. (2020) Biogenic synthesis of AgNPs employing Terminalia arjuna leaf extract and its efficacy towards catalytic degradation of organic dyes. Scientific Reports 10(1): 9616. NLM (Medline).

61.

Rangabhashiyam

Lata

Balasubramanian

(2018) Biosorption characteristics of methylene blue and malachite green from simulated wastewater onto Carica papaya wood biosorbent. Surfaces and Interfaces 10: 197–215. Elsevier B.V.

62.

Rassol

Kareem1

Mohammed

, et al. (2020) Removal of brilliant dyes from its aqueous solution by adsorption on siliceous rocks. Systematic Reviews in Pharmacy 11(4): 725–730.

63.

Reck

Paixão

Bergamasco

, et al. (2018) Removal of tartrazine from aqueous solutions using adsorbents based on activated carbon and Moringa oleifera seeds. Journal of Cleaner Production 171: 85–97. Elsevier Ltd.

64.

Roy

Dutta

Mahapatra

, et al. (2019) Collagenic waste and rubber based resin-cured biocomposite adsorbent for high-performance removal(s) of Hg(II), safranine, and brilliant cresyl blue: A cost-friendly waste management approach. Journal of Hazardous Materials 369: 199–213. Elsevier B.V.

65.

Salazar

Leyva-Ramos

Rivera-Utrilla

, et al. (2017) Biosorption mechanism of methylene blue from aqueous solution onto white pine (Pinus durangensis) sawdust: Effect of operating conditions. Sustainable Environment Research 27(1): 32–40. Chinese Institute of Environmental Engineering.

66.

Satheesh Babu

Mohandass

Vijayaraj

, et al. (2015) Detoxification and color removal of Congo red by a novel Dietzia sp. (DTS26) – A microcosm approach. Ecotoxicology and Environmental Safety 114: 52–60. Academic Press.

67.

Sepulveda

Santana

(2013) Effect of solution temperature, pH and ionic strength on dye adsorption onto Magellanic peat. Environmental Technology 34(8): 967–977. Taylor and Francis Ltd.

68.

Shao

Zheng

, et al. (2017) Removal of methylene blue by chemically modified defatted brown algae Laminaria japonica. Journal of the Taiwan Institute of Chemical Engineers 80: 525–532. Taiwan Institute of Chemical Engineers.

69.

Shehab

Abdel Basir

Abdel Khalek

, et al. (2019) Dye removal from aqueous solution by regenerated spent bleaching earth. International Journal of Chemical and Materials Engineering 13(9): 452–461.

70.

Silva

Nascimento

Brito

, et al. (2019) Biosorption of methylene blue dye using natural biosorbents made from weeds. Materials 12(15): 2486. MDPI AG.

71.

Siqueira

TCA

da Silva

Rubio

, et al. (2020) Sugarcane bagasse as an efficient biosorbent for methylene blue removal: Kinetics, isotherms and thermodynamics. International Journal of Environmental Research and Public Health 17(2): 526. MDPI AG.

72.

Tabaraki

Sadeghinejad

(2017) Biosorption of six basic and acidic dyes on brown alga sargassum ilicifolium: Optimization, kinetic and isotherm studies. Water Science and Technology 75(11): 2631–2638. IWA Publishing.

73.

Tehubijuluw

Subagyo

Yulita

, et al. (2021) Utilization of red mud waste into mesoporous ZSM-5 for methylene blue adsorption-desorption studies. Environmental Science and Pollution Research 28(28): 37354–37370. Springer Science and Business Media Deutschland GmbH.

74.

Tran

(2022) Improper estimation of thermodynamic parameters in adsorption studies with distribution coefficient K D(q e/ C e) or freundlich constant (K F): Considerations from the derivation of dimensionless thermodynamic equilibrium constant and suggestions. Adsorption Science and Technology 2022: 5553212. Hindawi Limited.

75.

Tukaram Bai

Anudeep

Raju

CAI

, et al. (2020) Decolourization of Eosin yellow (EY) dye using a variety of brown algae. Materials Today: Proceedings 42: 1130–1137. Elsevier Ltd.

76.

U.S. Environmental Protection Agency (1974) Development document for effluent limitations guidelines and new source performance standards for the textile mills .

77.

Vafajoo

Cheraghi

Dabbagh

, et al. (2018) Removal of cobalt (II) ions from aqueous solutions utilizing the pre-treated 2-hypnea valentiae algae: Equilibrium, thermodynamic, and dynamic studies. Chemical Engineering Journal 331: 39–47. Elsevier B.V.

78.

Vijayaraghavan

Shanthakumar

(2015) Removal of sulphur black dye from its aqueous solution using alginate from Sargassum sp . (Brown algae) as a coagulant. Environmental Progress and Sustainable Energy 34(5): 1427–1434. John Wiley and Sons Inc.

79.

World Health Organization (2022) Guidelines for drinking-water quality: fourth edition incorporating the first and second addenda. Geneva.

80.

Xiao

Wang

Ran

, et al. (2020) Highly selective removal of cationic dyes from water by acid-base regulated anionic functionalized polyacrylonitrile fiber: Fast adsorption, low detection limit, reusability. Reactive and Functional Polymers 146: 104394. Elsevier B.V.

81.

ZDHC (2022) ZDHC Wastewater guidelines.

82.

Zein

Mawardi

(2009) Chemical Modification of Some Functional Groups on Marine Algae (Sargassum crasifolium) Powder for the Evaluation of Lead(II) Uptake. Available at: https://www.researchgate.net/publication/287286056.

83.

Zeng

Lou

Ying

, et al. (2018) Preparation of microporous carbon from sargassum horneri by hydrothermal carbonization and KOH activation for CO2 capture. Journal of Chemistry 2018: 4319149. Hindawi Limited.

84.

Zou

Bai

Gao

, et al. (2013) Characterization of modified sawdust, kinetic and equilibrium study about methylene blue adsorption in batch mode. Korean Journal of Chemical Engineering 30(1): 111–122. Springer Science and Business Media, LLC.

Low-cost seaweed biomass treatment with formaldehyde for cationic dyes removal from aqueous solutions

Abstract

Keywords

Introduction

Experimental

Dyes

Preparation of biosorbent

Characterization techniques

pHPZC

Fourier-transform infrared spectroscopy

Scanning electron microscopy and EDS

Brunauer–Emmett–Teller surface area (SBET)

Sorption studies

Effect of contact time

Effect of dye concentration

Effect of pH

Adsorbent concentration effect

Effect of temperature

Kinetic and isotherm models

Error functions analysis

Reusability study

Real colored wastewater treatment

Results and discussions

pHPZC

Fourier-transform infrared spectroscopy

Scanning electron microscopy/EDS

Brunauer–Emmett–Teller surface area (SBET)

Effect of contact time

Effect of dye concentration

pH effect

Adsorbent concentration effect

Temperature effect

Thermodynamic of Mb and BCB adsorption

Comparison of the maximum adsorption capacities Qm of MB and BCB onto different adsorbents

Reusability study

Real colored wastewater treatment

Conclusions

Footnotes

Acknowledgements

Credit author statement

Data availability statement

Declaration of conflicting interests

Funding

ORCID iD

References

pH_PZC

Brunauer–Emmett–Teller surface area (S_BET)

pH_PZC

Brunauer–Emmett–Teller surface area (S_BET)

Comparison of the maximum adsorption capacities Q_m of MB and BCB onto different adsorbents