Values of the linear correlation coefficients for the different kinetic models.
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Article Type: Research Paper
Date of acceptance: May 2025
Date of publication: May 2025
DoI: 10.5772/geet.20250022
copyright: ©2025 The Author(s), Licensee IntechOpen, License: CC BY 4.0
Water contamination by dyes is a pressing issue that must be addressed to ensure quality of life for humans, animals, and the environment. Adsorption, due to its versatility and low cost, is a promising method for removing dyes from contaminated water. Carbon-based materials have proven to be excellent dye adsorbents. In this study, commercial-grade graphene nanoplatelets (GNPs) without any surface modifications were used to adsorb malachite green dye from model solutions. Using a mass loading of 2 g⋅L−1 and an initial dye concentration of 600 ppm, nearly 90% removal was achieved within two hours, without adjusting the pH of the dye solution. The kinetic data fitted well with the pseudo-second-order model, while the adsorption isotherms followed the Langmuir model, indicating a maximum adsorption capacity (qmax) of 1000 mg⋅g−1. Thermodynamic analysis indicated that the adsorption process was spontaneous, endothermic, and irreversible. Overall, the findings suggested that the process is viable and corresponds to a chemisorption mechanism. The chemical structures of malachite green and GNPs favour adsorption via 𝜋–𝜋 interactions, eliminating the need for surface modification of GNPs. This not only enhances the adsorption process but also avoids the generation of byproducts and residues, aligning with the principles of green chemistry.
adsorption
graphene nanoplatelets
kinetics
malachite green dye
thermodynamics
Author information
Water contamination has become a significant challenge, primarily driven by the widespread use of chemicals across various industries and agricultural practices [1]. Many pollutants including heavy metals such as Pb, Hg, and As [2] as well as industrial residues, solvents, oils, and dyes affect water bodies [3, 4]. Dyes may cause dermatitis, allergic reactions, and even have carcinogenic effects [5]. Synthetic and aromatic dyes are remarkably stable and are not easily biodegradable [6], as is the case with malachite green (MG), a water-soluble, aromatic basic dye with applications in food processing, healthcare, textiles, and aquaculture as a parasiticide, among other purposes [7].
Several techniques are being used for wastewater treatment, including membrane filtration, electrocoagulation, ion exchange, advanced oxidation, and adsorption [8]. Among these, adsorption is one of the most environmentally friendly, easy-to-operate, and cost- effective methods [9–11]. The adsorbent material plays a crucial role in the process, requiring specific structural characteristics such as appropriate pore size, high specific surface area, good selectivity, thermal and chemical stability, and low cost [12]. Carbon-derived materials are highly effective in dye removal, including activated carbon, biochar, graphite, activated carbon fibers, fullerenes, carbon nanotubes, graphene oxide, and graphene nanoplatelets [13–18]. Graphene nanoplatelets (GNPs) are graphene-based materials consisting of thin layers of carbon atoms arranged in a hexagonal sheet-like structure. Their excellent structural properties make them promising candidates for dye adsorption in water treatment applications [15].
GNPs have attracted growing interest due to their nanopowder form and attractive physicochemical properties, making them a preferred material for advanced applications. GNPs exhibit interesting properties such as lightness, high aspect ratio, electrical and thermal conductivity, mechanical toughness, low cost, and a flat structure. GNPs are more economical than carbon nanofibers and nanotubes and are comparable to these tubular nanofillers in modifying mechanical properties in composite materials. Furthermore, the electrical conductivity of GNPs is in the order of magnitude higher than that of graphene oxides [19].
Adsorption, particularly using carbon absorbents, is an alternative for removing pollutants from wastewater. The adsorbent properties of GNPs have been shown to depend on their surface area, functional groups, their 𝜋-electron system capable of forming strong bonds with various pollutants, and their porous structure. Furthermore, stability, concentration, and overall quality of the carbon nanomaterial dispersion are also critical to the adsorption process, especially for the removal of organic dyes such as methylene blue (MB) or malachite green (MG) and other popular pharmaceutical compounds [20–22].
Recent studies have used different types of adsorbent materials for the remediation of contaminated water, such as pistachio shells [23], residual biomass [24], wood waste [25], carbon soot [26], carbon nano-onions [27], metal oxide/biochar nanocomposites [28], biochar [29], and magnetic carbon-based adsorbents [30]. Al-Zawahreh and Paradelo [31] carried out a critical analysis of MB adsorption on C-based adsorbents based on three criteria: maximum adsorption capacity, reaction time, and process equilibrium time. In their analysis, a composite of graphene oxide with diethylenetriamine turned out to be the best.
In earlier studies, GNPs have been studied as adsorbents with good adsorption capacity for uremic toxins [32, 33]. MG has three aromatic rings in their structure, its adsorption onto a laminar structure with conjugated rings, such as that possessed by GNPs is considered viable, meeting the aforementioned criteria.
Based on these considerations, this study aimed to evaluate the use of commercial-grade GNPs without any modification to adsorb MG dye from model water solutions and investigate the kinetic and thermodynamic parameters of the system.
Commercial-grade GNPs were procured from Cheap Tubes Inc. (VT, USA). The provider data indicated that they were Grade 3, with 97% purity, average thickness of 8–15 nm, 2 μm in length, and specific surface area (SSA) of 500–700 m2⋅g−1. They were used as received. MG dye was commercially sourced from Jalmek Scientific (NL, MX), and deionized water was used as the solvent. The spectrophotometer employed for analyte quantification was a Thermo Scientific model, Multiskan SkyHigh (MA, USA).
Figure 1 presents a general overview of the methodology followed for the various adsorption tests conducted in this study.
Schematic representation of the general procedure of the methodology applied for the adsorption experiments.
For the experiments, 10, 20, and 30 mg of GNPs were used, with a contact time of one hour at room temperature under constant agitation. The initial concentrations of MG dye used were 250–750 ppm, using a total volume of 5 mL. After the contact time, the samples were filtered and analyzed at a wavelength of 620 nm. The obtained absorbance readings were evaluated using the MG calibration curve to determine the dye concentrations in the solution.
To determine the optimal pH for the best adsorption of MG dye, experiments were conducted at a dye concentration of 500 ppm, using 10 mg of GNPs and varying pH levels of 4, 7, and 9.
An adsorption kinetics study was performed to observe the adsorption behavior of MG dye on GNPs over time. The initial concentration was 600 ppm, and contact time ranged from 0 to 180 minutes. Each trial used a fixed mass of adsorbent and 5 mL of MG solution.
Adsorption kinetics describes the rate at which the solute adsorbs and the contact time of the adsorbate at the solid-liquid interface, i.e., it is related to the efficiency of the adsorption process. The adsorption rate depends on the number of particles adsorbed onto the adsorbent surface per second and the number of particles in contact with a unit area per second [34].
The Elovich model assumes that the rate of the adsorption reaction decreases with increasing surface area of the adsorbent. The kinetic model represents the chemisorption of gases onto solid surfaces, with no desorption of the product [35], and the mathematical equation representing this model is given by Equation (1). This model is commonly used, assuming the adsorbent surface is energetically heterogeneous [34].
The pseudo-first-order model (PFO), Equation (2), was also applied. It is suitable for describing physisorption and reversible interactions between the gas and solid surface component.
The reaction time is directly proportional to the difference in concentration and the rate at which the adsorbate is removed over time [34].
The pseudo-second-order (PSO) kinetic model is a widely used model to fit rate data for the adsorption of metal ions, dyes, and other compounds from aqueous solutions. A literal interpretation of the model assumes that different adsorption sites on a solid substrate collide with each other [36].
The published evidence on the validity of the mechanistic assumptions underlying the application of the PSO model for adsorption kinetics has been considered. This mathematical model is expressed in Equation (3).
After the results from the contact time experiments were obtained, the data were tested against Elovich, PFO, and PSO kinetic models, which were the basis for the equations used to analyze the obtained data.
Adsorption isotherms describe the interaction between the adsorbent and the adsorbate, making them essential for optimizing the use of any adsorbent. The shape of an isotherm provides information about the stability of these interactions and the adsorption affinity of molecules. Adsorption isotherms are described in various mathematical models; few are based on simplified physical descriptions of adsorption, while others are empirical and must be correlated with experimental data [34].
To understand the behavior of the system at equilibrium, isotherms were performed at three different temperatures (10, 40, and 50 °C), with initial concentrations ranging from 600 to 2100 ppm using a fixed mass of adsorbent and a volume of 5 mL.
The Freundlich adsorption isotherm is used to quantify the equilibrium relationship between the amount of adsorbate removed per unit weight of adsorbent and the concentration remaining in the solution. The mathematical equation that describes the Freundlich adsorption model is given in Equation (4).
The Temkin isotherm is described in Equation (5).
The Langmuir isotherm is described in Equation (6) [34, 39].
Adsorption enthalpy (𝛥H°) provides insights into whether the process is exothermic or endothermic. It allows for estimating energy activation and helps differentiate whether the process occurs via physisorption or chemisorption. If the enthalpy values range between −40 and −120 kJ⋅mol−1, the adsorption is classified as chemical.
Adsorption entropy (𝛥S°) predicts the changes occurring on the adsorbent surface. Significant surface structure alterations affect the process’s reversibility, resulting in a negative adsorption entropy value. Conversely, a positive 𝛥S° indicates a high likelihood of reversibility [40].
Using van’t Hoff’s assumptions, it is possible to estimate thermodynamic properties such as enthalpy, entropy, and Gibbs free energy of adsorption processes. Once the data are plotted, the thermodynamic parameters can be determined using the intercept and slope of the linear equation, along with the ideal gas constant R.
The Gibbs free energy (𝛥G°) determines whether a process is spontaneous. Negative 𝛥G° values indicate a spontaneous process, whereas positive values imply that energy input is required to proceed. 𝛥G° is calculated using Equation (7), leading to Equation (8) [40].
Adsorption tests for dye removal are essential for scientific research and industrial applications to eliminate contaminants from water bodies and enhance industrial processes. In these experiments, key factors such as adsorbent mass, solution pH, and contact time play a crucial role in determining the efficiency of the adsorption process.
The adsorbent mass is critical in determining the maximum adsorption capacity. Varying the amount of adsorbent allows for evaluating its effectiveness and optimizing material usage, which is particularly important for large-scale applications. Additionally, the solution pH influences the ionization of the adsorbent and the dye, thereby modifying their affinity and ultimately affecting the amount of dye adsorbed. This is especially relevant for dyes exhibiting different adsorption behaviours in acidic or basic media [27].
Adsorption experiments were conducted using adsorbent masses of 10, 20, and 30 mg of GNPs, with initial dye concentrations of 250, 500, and 750 ppm, as described in the methodology section.
The adsorption percentages of MG with GNPs for the three tested masses (10, 20, and 30 mg) are presented. Although adsorption reached 100% with 20 and 30 mg, non-total adsorption was required for subsequent experiments. Therefore, the lowest mass (10 mg of GNPs) was selected, which exhibited a removal efficiency of 87%, corresponding to approximately 652 ppm.
The adsorbent dosages used in this study were 2, 4, and 6 g⋅L−1, with complete dye adsorption occurring at the two highest dosages. Consequently, the lowest dosage was selected. Fadhila et al. [5] used a CoCr2O4/ZnO nanocomposite for MG degradation, with adsorbent loadings ranging from 0.04 to 0.12 g⋅L−1. However, their initial concentration was only 5 ppm, and the highest degradation efficiency (90%) was achieved using 0.08 g⋅L−1, removing 4.5 ppm of the dye. Similarly, Piriya et al. [28] reported using acid-activated coconut shell charcoal for MG adsorption. Their initial dye concentrations ranged from 20 to 80 ppm, achieving a maximum adsorption capacity of 32 mg/g. The adsorption capacity of the GNPs in MG in percentage and in mass, of the present study are shown in Figure 2.
Mass test for MG adsorption with GNPs, (a) percentage and (b) mass.
Adsorption tests were conducted at three different pH values: 4, 7, and 9, as detailed in the methodology section. During the quantification process, a precipitate was observed at acidic pH, making dye quantification unreliable despite achieving removal of up to 230 ppm. At neutral pH, a removal efficiency of approximately 60% (300 ppm) was obtained, while at basic pH, the removal decreased to 150 ppm (∼30%). Consequently, neutral pH was selected for further experiments. In the present study the influence of pH on adsorption capacity of the GNPs in MG in percentage and in mass is shown in Figure 3.
Effect of pH on adsorption capacity, (a) percentage and (b) mass.
These findings align with those reported by Bulut et al. [41], who also determined that pH 7 was optimal for MG removal using bentonite as an adsorbent. Likewise, Fu et al. [42] reported that the most efficient pH for MG removal with activated carbon was 7, while adsorption rates significantly decreased at pH values below 4 or above 9. MG dye reacts with H+ and OH− ions, influencing its adsorption rate.
To determine the optimal contact time, adsorption kinetics were evaluated at an initial MG concentration of 600 ppm. Under ambient temperature and stirring at 250 rpm, contact times ranged from 0 to 180 minutes.
As shown in Figure 4, the adsorption process occurred rapidly, with adsorption percentages approaching 40% (equivalent to 240 ppm) within the first 15 minutes. The removal efficiency gradually increased, reaching approximately 90% (540 ppm) at 180 min. Based on these results, a contact time of 2 h is recommended for optimal adsorption performance.
Effect of contact time, (a) percentage and (b) mass.
Choudhary et al. [8] posited that activated biochar derived from Opuntia ficus-indica adsorbed MG dye, Cu2+, and Ni2+ from water. The material achieved an adsorption capacity of 165 ppm within a maximum contact time of 120 minutes and a removal efficiency of 99%. In the present study, a removal efficiency of 87% was obtained under the same contact time, corresponding to 522 ppm. This indicates that the material used in this work is more efficient for MG removal.
The kinetic analysis data were applied to the Elovich, PFO, and PSO models, as described by Equations (1), (2) and (3), to better understand the adsorption behavior of MG onto GNPs. These models help determine whether the adsorption mechanism is chemisorption or physisorption. PSO model provided the best fit (Table 1), as shown in Figure 5, indicating that adsorption depends on the number of available active sites on the adsorbent surface [43].
Pseudo-second-order kinetic model.
Kinetic model | R2 |
---|---|
Elovich | 0.9750 |
Pseudo first order | 0.8642 |
Pseudo second order | 0.9883 |
Values of the linear correlation coefficients for the different kinetic models.
Although acceptable R2 values were obtained for all three kinetic models, the experimental data for the kinetics from 0 to 180 minutes, at ∼40 °C, and with an initial analyte concentration of 600 ppm, fit best to the PSO model (98.8%). This indicates that the rate-controlling factor during the first 3 h was related to the adsorption mechanism, leading to the inference that the adsorption of the dye onto GNPs can be described as a chemical process [7, 44]. Furthermore, using the slope of the linear plot, it was possible to calculate the rate constant for the PSO model (k2), which was determined to be 35.71 mg/g. This relatively high value confirmed the rapid adsorbent and adsorbate interaction.
Adsorption isotherms were conducted at 10, 40, and 50 °C, as shown in Figure 6. The initial concentrations ranged from 600 to 2100 ppm, with 10 mg of GNPs and 5 mL of solution rendering different R2 for the Langmuir isotherm. The values were 0.9896, 0.9345 and 0.9899, respectively.
Langmuir isothermal models for MG adsorption at 10, 40 and 50 °C.
Another parameter that can be determined using the Langmuir model is the maximum adsorption capacity of the material. As presented in Equation (10), the method for obtaining qmax is outlined, providing a quantitative measure of the adsorption potential of the material.
Adsorbent material | Ml (g) | Ci (ppm) | T (°C) | qmax (mg/g) | Reference |
---|---|---|---|---|---|
Hevea brasiliensis root | – | – | 25 | 259 | [45] |
Bamboo | – | 25 | 25 | 263 | [45] |
Coffee peel | 0.0025 | – | – | 264 | [45] |
Activated carbon with mesoporous structure | 0.0100 | 400 | 55 | 738 | [46] |
Activated graphene with fractal structure for adsorption | 0.0150 | 300 | – | 791 | [47] |
Oxidized mesoporous carbon | 0.0100 | 700 | 40 | 963 | [46] |
Graphene nanoplatelets | 0.0100 | 900 | 40 | 1000 | This work |
Sulfonated reduced graphene oxide | 0.0100 | 30 | 25 | 1111 | [48] |
Carbon nanoparticles with large pores | 0.0100 | 500 | 25 | 1829 | [46] |
Zeolite-imidazolate modified with graphene oxide | 0.0300 | 100 | – | 3300 | [46] |
Comparison of qmax values of different materials reported in the literature.
Ml: Material load, Ci: Initial concentration, qmax: Maximum adsorption capacity.
Adsorption enthalpy (𝛥H°) provides information about the exothermic or endothermic nature of the process. It can estimate the activation energy and elucidate whether the process occurs via physical or chemical adsorption. If the 𝛥H° values range between −40 and −120 kJ/mol, the adsorption is classified as chemical. Using van’t Hoff’s assumptions, it is possible to estimate the range of thermodynamic properties, such as enthalpy, entropy and Gibbs free energy, for the adsorption process, as shown in Figure 7.
Van’t Hoff equation model for MG adsorption with 10 mg of GNPs.
Based on the slope and intercept values of the graph and considering the ideal gas constant R, it is possible to determine the adsorption enthalpy (𝛥H°) and adsorption entropy (𝛥S°). Once these values are calculated, the Gibbs free energy change (𝛥G°) can be determined. Table 3 presents these values, where the mass of 10 mg was consistent in all cases.
Temperature (°C) | 𝛥H° (kJ⋅mol−1) | 𝛥S°(J⋅ mol−1 ⋅ K−1) | 𝛥G°(kJ⋅ mol−1) |
---|---|---|---|
10 | 10.3 | ||
40 | 7.8 | −8.95 | 11.6 |
50 | 10.7 |
Thermodynamic parameters of the MG dye adsorption process on GNPs.
A positive enthalpy value indicated that the adsorption process was endothermic, i.e., it required energy to occur. Similarly, a negative entropy value suggested a decrease in system randomness, indicating that the adsorption process was ordered and irreversible. This also confirmed that GNPs have a strong affinity for MG dye possibly due the interaction of 𝜋–𝜋 bonds in their chemical structure. The Gibbs free energy value also confirmed that the system required an external energy input and that the process was non-spontaneous [49]. These thermodynamic parameters indicated that this adsorption model required an external energy supply for optimal performance. The optimal temperature was 40 °C.
This study investigated graphene nanoplatelets (GNPs) as an adsorbent for malachite green dye, a typical water pollutant, to improve water quality per the United Nation’s Sustainable Development Goals (SDGs). GNPs were chosen due to their increased surface area, large pore volume, and functionalized surface groups and the ability to be used without any surface modification to take advantage of the aforementioned characteristics. Adsorption tests using 10, 20, and 30 mg of GNPs showed 100% removal at higher masses, with 10 mg selected for further study. The optimal pH was neutral (∼7), as acidic conditions caused precipitation, while basic conditions did not enhance efficiency. Adsorption kinetics revealed 80% removal within one hour and 90% after two hours (600 ppm). The process followed a pseudo-second-order model, indicating chemical adsorption. Using Langmuir, Freundlich, and Temkin isotherms, the thermodynamic analysis showed Langmuir as the best fit, with a qmax of 1000 mg/g at 40 °C. The process was endothermic, non-spontaneous and increased system order, confirming GNPs as an efficient and viable adsorbent for malachite green removal. Further studies of desorption or re-adsorption processes are recommended to determine reuse of adsorbent for more cycles or introducing dye-saturated GNPs in optical applications.
The authors thank Janett A. Valdez, Maria L. Salazar and Jesús F. Lara for their technical support.
de la Peña-Aguirre, Daniel: Conceptualization, Methodology, Supervision; Cano-Salazar, Lucía Fabiola: Conceptualization, Methodology, Writing – review & editing; Covarrubias-Gordillo, Carlos Andrés: Writing – review & editing; Flores-Guía, Tirso Emmanuel: Data analysis, Writing – review & editing. Claudio-Rizo, Jesús Alejandro: Data analysis, Writing – review & editing; Cabrera-Munguia, Denis Aidee: Data analysis, Writing – review & editing. Ávila-Orta, Carlos Alberto: Writing – review & editing, Resources; Cruz-Delgado, Víctor Javier: Supervision, Project administration.
Authors thank the Universidad Autónoma de Coahuila for the financial support provided through the grant DIP-UADEC C01-2024-9.
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The authors declare no conflict of interest.
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Article Type: Research Paper
Date of acceptance: May 2025
Date of publication: May 2025
DOI: 10.5772/geet.20250022
Copyright: The Author(s), Licensee IntechOpen, License: CC BY 4.0
© The Author(s) 2025. Licensee IntechOpen. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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