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. 2023 May 20;8(22):20125–20137. doi: 10.1021/acsomega.3c02788

Adsorption and Removal of Tetrabromobisphenol A by Adsorption on Functionalized Mesoporous Silica Nanotubes: Isotherms, Kinetics, Thermodynamics, and Optimization via Box–Behnken Design

Huda Salem AlSalem †, Faisal K Algethami ‡, Soha Talal Al-Goul §, Ahmed Shahat ∥,*
PMCID: PMC10249080  PMID: 37305314

Abstract

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In pursuit of environmental safety, a novel and efficient method—dispersive solid-phase extraction based on functionalized mesoporous silica nanotubes (FMSNT nanoadsorbent)—was developed to remove tetrabromobisphenol A (TBBPA) from water samples. Characterization and comprehensive analysis of the FMSNT nanoadsorbent, including maximum adsorption capacity of 815.85 mg g–1 for TBBPA and its water stability, confirmed its potential. Subsequent analysis revealed the impact of multiple factors, for instance pH, concentration, dose, ionic strength, time, and temperature, on the adsorption process. The findings revealed that the adsorption of TBBPA followed the Langmuir and pseudo-second-order kinetics models while primarily driven by hydrogen bond interactions between bromine ions or hydroxyl groups of TBBPA and amino protons around the cavity. The novel FMSNT nanoadsorbent showed high stability and efficiency even after five times of recycling. Moreover, the overall process was identified as chemisorption, endothermic, and spontaneous. Finally, the Box–Behnken design was applied to optimize the results, confirming good reusability even after five cycles.

1. Introduction

The damage caused by water contamination is alarming, and the risk of exposure to toxic elements is real. Water contamination can be caused by a variety of sources, such as agricultural and industrial runoff, wastewater, and oil and gas production. Contaminated water can contain dangerous levels of heavy metals, chemicals, and other contaminants. This can lead to health problems such as skin rashes, respiratory illnesses, neurological damage, and even cancer. To protect our health and environment, it is essential to reduce water contamination and seek solutions where possible.1 By working together, we can help to ensure that our water resources remain clean and safe for generations to come.

Industries have long been contributing to the contamination of water both on land and in the oceans. With increasing industries and expanding consumer base, water is often exposed to toxic chemicals and other pollutants that can pose significant risks to both human and environment health.2 The most common ways for industries to contribute to water contamination are insufficiently treated wastewater discharge, disposal of toxic chemicals to groundwater, and runoff from industrial, manufacturing, and construction sites. Each of these practices must be actively monitored and regulated in order to reduce contamination and protect our most precious resource.3

Tetrabromobisphenol A is an essential compound applied in a wide range of industries. It is found in plastics, circuit boards, wires, and cables as well as textiles, paints, and coatings. It not only helps to reduce the risk of fire-related damage but also increases the product’s lifetime. TBBPA can also be found in healthcare products such as IV bags and medical electrodes due to the chemical’s nontoxicity. With its wide range of applications, TBBPA is critical in helping to protect human safety and daily life.4

Tetrabromobisphenol A has been contaminating our waterways for years without us realizing it. It is a chemical found in plastics that can be broken down and transferred through the environment, entering our rivers, lakes, and seas. When we dispose of plastic bottles and containers, the chemical can leech out and enter the water source or from the chemical industry that produces plastics directly.5 However, it does not stop there, tetrabromobisphenol A lasts in the water for a long time and has been found in sea-life, birds, and in the bodies of humans across the globe. We have to start taking action now to protect our environment and reduce the amount of plastic and toxic chemicals entering our water sources.6 It is considered an emerging contaminant. It has been detected in various environmental media, such as soil, water, and air, as well as in biota, including fish and bird eggs. TBBPA has been shown to have potential toxic effects on human health and the environment, which has led to increasing concern over its presence as a contaminant. As a result, efforts are underway to better understand TBBPA’s fate and transport in the environment and to develop strategies for its remediation and control.7,8

Exposure to TBBPA can occur through water contamination, making its removal from water systems an important task. In addition to its direct health risks, TBBPA can also indirectly affect public health by encouraging the growth of potentially hazardous microbial species. Removing TBBPA from contaminated water can help lessen these risks, preserving human health and a safe, clean environment.9,10

However, due to its potential adverse effects on human health and the environment, there has been a growing interest in developing effective methods for its removal. One of the most common methods for removing TBBPA from water is adsorption, which involves the use of adsorbents that can capture the chemical from the water. Some of the adsorbents that have been used for TBBPA removal include activated carbon, graphene oxide, zeolites, and clay minerals. Activated carbon is a popular adsorbent due to its high surface area and porosity, which allows it to effectively capture TBBPA molecules. Graphene oxide is another effective adsorbent that can be used for TBBPA removal due to its unique properties, such as high adsorption capacity and stability. Zeolites are a group of crystalline aluminosilicates that have been widely used as adsorbents for various pollutants, including TBBPA. These materials have a porous structure that can trap TBBPA molecules through adsorption and ion exchange. Clay minerals such as montmorillonite and kaolinite have also been shown to be effective adsorbents for TBBPA removal. These materials have a layered structure that can trap TBBPA molecules between the layers through a process known as intercalation.4−10 In summary, there are several adsorbents that have been used for TBBPA removal, including activated carbon, graphene oxide, zeolites, and clay minerals. These materials have different properties and mechanisms for capturing TBBPA, but they have all been shown to be effective in removing the chemical from water.

Adsorption is a common method used to remove pollutants from water. Adsorption has many advantages over other methods, such as ion exchange and chemical precipitation.11 Because it is a surface phenomenon, it takes up less space than other techniques and is more efficient. Additionally, adsorption is often more cost-effective than other methods as it only requires relatively small amounts of material, making it ideal for tackling large-scale water pollution problems. Adsorption also has a faster uptake rate when compared to most other methods, providing effective removal of dyes from water in a relatively short time frame.12,13

In this paper, we aimed to use the functionalized mesoporous silica nanotubes (FMSNTs) nanoadsorbent for the adsorption of the potentially hazardous compound tetrabromobisphenol A from contaminated water. We think that FMSNT nanoadsorbent which has a large surface area with pores will be able to adsorb TBBPA molecules in their wall and within the pores. This allows for a high adsorption rate, along with the ability to be recycled and reused for additional adsorption cycles without degradation. Additionally, due to the high porosity of the nanotubes, quick equilibration times are observed, making them an effective means of adsorption of TBBPA from water.

2. Experimental Section

2.1. Materials and Instruments

All chemicals and instruments are accompanied by detailed illustrations in the Supporting Information.

2.2. Preparation of the FMSNT Nanoadsorbent

The Supporting Information provides a detailed description of the synthesis process of the mesoporous silica nanotubes. The FMSNT nanoadsorbent was synthesized by immobilization of 3-aminopropyltriethoxysilane onto the synthesized mesoporous silica nanotubes. Briefly, a round-bottomed flask was filled with 1 g of the grinded mesoporous silica nanotubes. After that, anhydrous toluene (50 mL) was added followed by addition of 3.0 mL 3-aminopropyltriethoxysilane and refluxed overnight. The solid product of FMSNT nanoadsorbent was filtered off and after that washed with toluene and diethyl ether. Finally, it was dried at 80 °C for 6 h.

2.3. Investigating the FMSNT Nanoadsorbent through Removal and Batch Studies

Different concentrations of TBBPA were studied using adsorption kinetics and adsorption isotherms to assess the adsorption performance of the FMSNT nanoadsorbent for TBBPA. 30 mg of FMSNT nanoadsorbent was included in 50 mL water that had various TBBPA concentrations (2, 5, 10, 20, 50, 100 mg mL–1). 10 min of stirring were given to the solutions, which were then centrifuged. The liquid on top was removed and blended with methanol. Following that, 10 mL of the solution underwent HPLC analysis. An evaluation of the static binding capacity of the FMSNT nanoadsorbent for TBBPA adsorption was performed.14

2.4. Experimental Design

The use of response surface methodology facilitates the use of complex experimental designs to pinpoint levels of variables to maximize the response.14 Response surface designs, such as the Box–Behnken design (BBD), can predict first- and second-order coefficients. Rotatable quadratic designs of this kind are based on four-level incomplete factorial designs. This work maps out the BBD to illustrate the effect of time, concentration, pH, and dose as four independent variables to guess the maximum adsorption capacity as the response. Three levels of experiments were set up −1, 0, and +1.

3. Results and Discussion

3.1. Characterization of the FMSNT Nanoadsorbent

In order to make the FMSNT nanoadsorbent, multiwall carbon nanotubes were used as a hard template to create the first mesoporous silica nanotubes. After removing the multiwall carbon nanotubes template from the core of silica, it was functionalized by immobilization of 3-aminopropyltriethoxysilane. To confirm the structure of the new composite, some analysis has been done. In the low-angle XRD patterns of the mesoporous silica nanotubes and the FMSNTs before and after five times of recycling, a small shoulder peak at 2θ ≈ 1.35° indicated a mesostructured arrangement, as seen in Figure 1A.15−17 The similarities between the three patterns in terms of the same structure and order revealed that the immobilization process had not disrupted the mesostructured pattern of the mesoporous silica nanotubes.16 In addition, the FMSNTs retained their structure even after five times of recycling. In a similar way, a broad peak at 17–39° was detected by the wide-angle XRD in the patterns of the mesoporous silica nanotubes and the FMSNTs before and after five times of recycling as shown in Figure 2B. This indicates that showed that the nanotubes of the three materials were made of amorphous silica.15−17

Figure 1.

Figure 1

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(A) Low-angle XRD patterns and (B) wide-angle X-ray diffraction patterns of the mesoporous silica nanotube, FMSNT nanoadsorbent, and FMSNT nanoadsorbent after five times of recycling samples.

Figure 2.

Figure 2

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Nitrogen isotherms of the mesoporous silica nanotubes, FMSNT nanoadsorbent, and FMSNT nanoadsorbent after five times of recycling samples (A) and pore size distributions of the mesoporous silica nanotubes, FMSNT nanoadsorbent, and FMSNT nanoadsorbent after five times of recycling samples (B).

Using a nitrogen adsorption–desorption analysis at −196 °C, the surface area and pore size of the mesoporous silica nanotubes, FMSNT nanoadsorbent, and FMSNT nanoadsorbent after five times of recycling samples were evaluated. The results indicated type IV, with a tight hysteresis loop at p/po = 0.39–0.9 and a tricky adsorption step at p/po = 0.29 relative pressure in the three samples. A considerable decrease in the surface area of the FMSNT nanoadsorbent was observed after the immobilization of 3-aminopropyltriethoxysilane.15 The surface areas of the mesoporous silica nanotubes, FMSNT nanoadsorbent, and FMSNT nanoadsorbent after five times of recycling samples were 1076.84, 834.07, and 708.95 m2 g–1, respectively. This indicated that 3-aminopropyltriethoxysilane was preserved both inside and outside the mesoporous silica nanotubes. Also, the decrease in surface area of the FMSNT nanoadsorbent after five times of recycling may be due to the retention of some of the TBBPA in and outside the pores. The same happened in the pore volumes of the mesoporous silica nanotubes, FMSNT nanoadsorbent, and FMSNT nanoadsorbent after five times of recycling samples.17 The pore volumes of the three samples were 1.355, 1.084, and 0.921 cm3/g, respectively.

Figure 3 reveals the structural morphology of the mesoporous silica nanotubes, FMSNT nanoadsorbent, and FMSNT nanoadsorbent after five times of recycling samples under FESEM and HRTEM. According to findings, the three samples have a nanotube morphology with different diameters. In addition, the structure of the mesoporous silica nanotubes maintained their shape after 3-aminopropyltriethoxysilane was loaded. Furthermore, the FMSNT nanoadsorbent retained its structural morphology even after five times of reuse.17

Figure 3.

Figure 3

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(A) Representative FESEM images of the mesoporous silica nanotubes, (B) FMSNT nanoadsorbent, and (C) FMSNT nanoadsorbent after five times of recycling samples. (D) HRTEM images of the mesoporous silica nanotubes, (E) FMSNT nanoadsorbent, and (F) FMSNT nanoadsorbent after five times of recycling samples.

3.2. Batch Experiments

3.2.1. Effect of pH

The pH of the solution had a significant influence on the adsorption of TBBPA. The acidity of the solution changes the nonionized and dissociated forms of TBBPA. Figure 4a shows the adsorption of TBBPA due to a solution that varies in pH from 2.0 to 12.0. When the pH was 6 or lower, the TBBPA adsorption onto the FMSNT nanoadsorbent was relatively high and decreased as the pH was increased from 6 to 12. There might be a connection between the pH-induced effects on the adsorption of TBBPA by the FMSNT nanoadsorbent and the distribution of TBBPA. TBBPA is a hydrophobic organic substance which can acquire ions and has an acidity degree (pKa1 and pKa2) of 7.5 and 8.5, respectively. When the pH is greater than 7.0, the TBBPA species with a negative charge appear to be less hydrophobic than when it is in its molecular form. The zeta potential of the FMSNT nanoadsorbent was found to be negative when measuring the pH values from 6.3 to 12.0, as shown in Figure 4b.18 Therefore, the surface charge of the FMSNT nanoadsorbent was positive at pH below 6.3 and at pH higher than this value will be negative charge and, in this case, there will be a repulsion force between the adsorbent and adsorbate. Therefore, it is beneficial that there be adsorption at pH below 6.3 to avoid the repulsion force. To guarantee full adsorption of TBBPA, a pH of 6.0 was chosen to reject electrostatic repulsion while continuing to probe TBBPA removal.

Figure 4.

Figure 4

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(a) Effect of pH of the solution on adsorption of TBBPA and (b) pHpzc of the FMSNT nanoadsorbent (temp 25 °C, time 100 min, dose 0.02 g, and volume 25 mL).

3.2.2. Effect of the Dose

The data in Figure 5 shows the correlation between the FMSNT nanoadsorbent dosage and TBBPA uptake. As the dosage of the adsorbent moved from 0.02 to 0.1 g/25 mL, the adsorption capacity (qe) of TBBPA on the FMSNT nanoadsorbent decreased from 1.32 to 0.38 mmol/g. As a result, increasing the dosage of FMSNT nanoadsorbent from 0.02 to 0.1 g/25 mL led to an improvement in the removal efficiency of TBBPA from 56.8 to 98.4%.19,20 Though when the FMSNT nanoadsorbent was administered in higher doses, the uptake capacity of TBBPA decreased because of agglomeration, aggregation, and overlapping of the adsorbent particles. This consequently caused a decrease in the overall contact between the sorbent and the sorbate. The removal efficiency is affected positively by the dosage of the adsorbent because it increases the number of active sites available in the solution, allowing for more adsorbate to be removed from the solution than was originally present. A closer analysis of Figure 5 shows that the best uptake of adsorbate is attained when the adsorbent dosage is 0.02 g/25 mL, which corresponds to a maximum adsorbate capacity of 1.32 mmol/g. No further improvement in TBBPA uptake was observed beyond this dosage. Thus, this dosage was chosen for further experiments.

Figure 5.

Figure 5

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Effect of the FMSNT nanoadsorbent’s dose on adsorption of TBBPA (pH = 6, temp 25 °C, time 100 min, and volume 25 mL).

3.2.3. Effect of Contact Time

Over the course of time, TBBPA uptake progressed rapidly at first and then leveled off until it reached a balanced point after 100 min. Initially, the uptake rate was high due to the abundance of active sites on the FMSNT nanoadsorbent. However, these active sites gradually became occupied with TBBPA molecules, resulting in a decrease in the rate of TBBPA uptake over time. Additionally, the high initial concentration of TBBPA during adsorption creates a stronger mass transfer driving force, resulting in a faster adsorption rate. This driving force helps to overcome any diffusion limitations within the liquid film surrounding the adsorbent. The maximum uptake of TBBPA was observed to be 98.8% after 100 min, which is consistent with other studies that have observed the impact of contact time on other dyes using carbon nanotube adsorbents. Subsequent experiments were chosen to have a contact time of 100 min, as dictated by these results.21,22

According to the Langmuir model, adsorption happens on a saturated surface and proceeds as a first-order process.23 The Freundlich model is used to describe multicomponent systems and assumes that each site binds adsorbing molecules with varying strength.24 The Temkin model implies a process in which atoms or molecules strongly interact with other, resulting in a multilayer adsorption.25 The Dubinin–Radushkevich model is frequently employed to explain the interactions of large molecules with a surface or system with a large number of adsorbing sites.26 All these isotherm models are useful for predicting how various molecules interact with surfaces in different systems.

The maximal adsorptive capacity is constrained and finite, according to the Langmuir adsorption isotherm model, adsorption takes place on a homogeneous surface. This isotherm shows how the adsorption of molecules onto the surface varies with pressure. It assumes that molecules follow a monolayer adsorption and that molecules have uniform bonding sites onto the surface, as well as an unlimited number of binding sites.23 The Langmuir isotherm is also used to calculate the binding energy between the molecules being adsorbed and the surface. As seen in Figure 6, this may then be utilized to establish the ideal conditions for a certain adsorption procedure.

Figure 6.

Figure 6

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Adsorption isotherm models of adsorption TBBPA onto the FMSNT nanoadsorbent (pH = 6, temp 25 °C, time 100 min, dose 0.02 g, and volume 25 mL).

The Freundlich model of adsorption isotherms is a popular model used to describe adsorption behavior from liquid or gas solutions onto solid surfaces. The model was formulated by Fritz Freundlich in 1906 and is based on the idea that adsorption is a nonuniform process. The isotherm obtained by the Freundlich model shows the correlation between the amount of gas or liquid adsorbed on a solid surface and the equilibrium concentration in the surrounding solution. The Freundlich model assumes that adsorption is a multilayer process and as more molecules are adsorbed onto the surface, the energy required for further adsorption increases exponentially.24 As can be seen in Table 1, there is a nonlinear connection between the concentration of the adsorbate and the quantity that is adsorbed onto the surface.

Table 1. Adsorption Isotherm Parameters of Adsorption of TBBPA onto the FMSNT Nanoadsorbent.
Isotherm value of parameters
Langmuir qm exp (mmol g–1) 1.54
  qm (mmol g–1) 1.538
  KL (L mmol–1) 733997.78
  R2 0.996
Freundlich N 2.345
  KF (mmol g–1) (L mmol–1)1/n 0.45
  R2 0.37
Dubinin–Radushkevich QDR 1.82
  KDR (J2 mol–2) –8.14 × 10–10
  Ea (kJ mol–1) 24.8
  R2 0.971
Temkin bT (L mol–1) 20.28
  AT (kJ mol–1) 18399.08
  R2 0.998
Jovanovic qm 1.503
  kj –312351
  R2 0.855
Khan qm 1.85
  N 1.047
  K 403214.5
  R2 0.974
Hill qm 1.517
  N 1.39
  K 1.034 × 10–8
  R2 0.978
Sips qm 1.517
  K 540733.5
  N 1.39
  R2 0.996
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The Temkin adsorption isotherm model is a mathematical expression of the correlation between the adsorbent concentration and surface coverage of an adsorbate on an adsorbent. The Temkin model describes the behavior of an adsorbed molecule as a function of pressure, temperature, and concentration. The model includes a parameter, β, which describes the strength of binding between the two molecules, as well as an adsorption equilibrium constant, Ka. When data are graphed, the resulting curve appears to reach a “plateau” at high concentrations, indicating that adsorption can no longer occur at this point. The shape of this curve is important in determining the energy of interaction between molecules and their ability to form strong adsorption complexes.25

The Dubinin–Radushkevich isotherm model is a mathematical equation employed to illustrate adsorption of gases or vapors on a solid surface. It explains the equilibrium between the concentration of adsorbed molecules on the surface of the adsorbent material and the concentration of adsorbates in the gas phase. The equation takes into account factors such as the desorption and adsorption rate constants, temperature, enthalpy and entropic forces, and the binding energies of adsorbate and adsorbent molecules. It can be used to predict adsorbed amounts under different process conditions and serves as an important tool for analyzing heterogeneous processes related to adsorption.26

The Jovanovic adsorption isotherm model is an important tool in the field of thermodynamics, which investigates the relationship between a substance and its environment at a constant temperature. The model can be used to calculate the amount of a substance adsorbed onto a surface, as well as its equilibrium concentration, and can be used to evaluate vapor–liquid and vapor–solid adsorption. It can also be used to predict physical and chemical properties of the adsorbed substance. The Jovanovic adsorption isotherm model is widely used in industries such as medical diagnostics and semiconductor manufacturing, providing valuable insights into adsorption processes. Khan adsorption isotherm models are important for understanding the adsorption rate of a certain substance on a given surface. The model, which is based on classical thermodynamics, accurately describes the behavior of a material when adsorbed onto a surface. It can be used to determine values such as capacity, affinity, selectivity, and pressure of adsorption. The model is also important in predicting the performance of adsorbent materials. Knowledge of these models can be used to optimize the design of materials and processes that involve adsorption and provide valuable insights into the interaction between surfaces and molecules.

When studying adsorption processes at a surface or an interface, it is important to have a model that accurately describes the adsorption energies of molecules at different energy levels. The Hill adsorption isotherm model is one such model which provides a graphical representation of the adsorption energy distribution. It is based on the idea that ternary complexes form between the adsorbate, the surface, and the solvent and that the energy associated with such formation increases upon increasing surface–adsorbate contact. This model is widely used in industries to determine how various molecules interact with different surfaces and how those surfaces behave in response to different conditions. Moreover, it can help scientists to identify optimal choices for surface treatment in applications such as catalysis and separations. This model is also useful for predicting the adsorption behavior of pollutants, which can help governments to regulate sources of pollutants in the environment. Sips adsorption isotherm models are important because they allow researchers to develop a better understanding of the adsorption behaviors of various materials. These models help explain the forces that drive the process of adsorption, namely, van der Waals forces, ion exchange, and hydrogen bonding. These models provide valuable insights into the capacity and selectivity of adsorbents and can be used to study systems such as mixed gases, biogas cleaning, and biomass utilization. Ultimately, Sips adsorption isotherm models are an incredibly useful tool for the development of more efficient and cost-effective adsorbent materials as presented in Table 1.

3.2.4. Adsorption Kinetics

The adsorption kinetics model is a complex mathematical process used to study the rate at which molecules are adsorbed onto a surface. Four models are used to interpret the data from this process: the pseudo-first-order,27 pseudo-second-order,28 Weber–Morris intraparticle diffusion,29 and Elovich models.30 The pseudo-first-order model assumes that the adsorption rate is limited by the slowest step in the process, while the pseudo-second-order model assumes that the reaction rate is dependent on adsorbate concentration. The Weber–Morris intraparticle diffusion model considers the diffusion of molecules within a particle, and the Elovich model takes into account both diffusion and reaction rates. Understanding these models can help us analyze data more accurately and understand how different parameters affect adsorption rates.

The pseudo-first-order kinetics model is a widely used approach for understanding the behavior of chemical reactions on a theoretical level. It examines the interplay between reactants and the rate at which reactions occur over time. The model allows scientists to accurately predict the rate and amount of reaction that will occur when certain conditions are met, such as temperature, catalysts, and concentrations. By understanding this model, chemists can design more efficient and effective reactions, as well as better process designs and more efficient chemical processes. This enhanced understanding of reaction kinetics can also lead to better product quality and improved safety and environmental performance.27

The pseudo-second-order kinetics model is an important tool for researchers in the study of chemical reaction mechanisms and kinetics. This model offers a way to predict the rate of a reaction where the rate is limited by the slow reactant or substrate. This model utilizes two parameters, the pseudo-second-order rate constant and the pseudo-second-order reaction rate. These parameters enable researchers to better understand how a reaction can be affected by varying parameters, such as temperature or the reactant concentration.28 Additionally, the use of this model provides researchers with valuable insights into mechanism selection and can help pinpoint rate parameters of the reaction. By utilizing the pseudo-second-order kinetics model, researchers can analyze reaction kinetics with greater accuracy and confidence, as shown in Figure 7.

Figure 7.

Figure 7

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TBBPA adsorption kinetic models using the FMSNT nanoadsorbent (pH = 6, temp 25 °C, time 100 min, dose 0.02 g, and volume 25 mL).

The Weber–Morris intraparticle diffusion model kinetics model is an important tool for understanding the transport of molecules from small pores in a material. It describes how molecules are able to pass through the material due to differences in temperature, pressure, or the concentration.29 This model is helpful in describing the motion of molecules within granular matter, powders, or porous materials and can be used to develop strategies to optimize processes such as absorption, adsorption, and extraction. Additionally, the model can help to identify the role of additives in the diffusion of molecules and develop new formulations of catalysts and adsorbents to improve their performance, as depicted in Table 2.

Table 2. Adsorption of TBBPA onto the FMSNT Nanoadsorbent: Kinetic Parameters.
model value of parameters
pseudo-first-orderkinetic k1 (min–1) 0.703
  qe (mmol g–1) 1.488
  R2 0.999
pseudo-second-orderkinetic k2 (g mg–1 min–1) 1.0428
  qe (mmol g–1) 1.517
  R2 0.923
intraparticle diffusion ki (mg g–1 min1/2) 0.0438
  X (mg g–1) 1.17
  R2 0.828
Elovich β (g/mg) 10.06
  α (mg g–1 min–1) 7233.8
  R2 0.739
Avrami K 0.067
  N 0.072
  R2 0.826
experimental data qe (exp) (mmol g–1) 1.49
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The Elovich model is an empirical kinetic model employed to illustrate the rate of degradation of a contaminant in aqueous systems. It is a three-parameter model that has been widely utilized in the field of groundwater remediation and wastewater treatment to estimate the parameters associated with the degradation process. The model has been used to quantify the effects of different water quality parameters, for example pH and temperature, on the rate of the degradation reaction. It is also commonly used to predict the future concentrations of a contaminant in an aquifer after a certain amount of time.30

The Avrami adsorption kinetic model is an important tool for understanding the process of physical adsorption. These models are used to study the rate and extent of adsorption and help to better understand the underlying mechanisms and parameters that drive the process. The results of these models can be used to guide the development of efficient adsorption methods and new materials with optimized properties. Avrami kinetics can also be used to model the reaction rates of surface reactions, as well as to help gain insights into the surface and interfacial properties of adsorbed materials.

3.2.5. Adsorption Thermodynamics

Thermodynamics is the study of energy, entropy, and enthalpy, and the ways in which these quantities interact and change. Energy is the potential to do work, entropy is a measure of the amount of disorder within a system, and enthalpy is the total energy of a system, including both its energy due to its pressure and volume and internal energy. Understanding thermodynamics is essential to understand the behavior of matter and systems, as well as to predict how energy flows through them as shown in Figure 8.

Figure 8.

Figure 8

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Temperature’s influence on the adsorption of TBBPA onto the FMSNT nanoadsorbent (a) Van’t Hoff, (b) effect of change in temperature on ΔGo (pH = 6, time 100 min, dose 0.02 g, and volume 25 mL).

The thermodynamic study of Van’t Hoff equation tells us about the behavior of a substance when it is dissolved in a solution. It helps in finding out the energy changes that occur during a dissolution process. The equation states that the chemical potential, or the change in energy, of a dissolved substance is equal to the number of moles of the substance multiplied by the absolute temperature and then multiplied by the natural logarithm of the mole fraction of the dissolved substance. This equation is useful in predicting the thermodynamic properties of solutions, such as vapor pressure and osmotic pressure as presented in Table 3.

Table 3. Thermodynamic Parameters of Adsorption of TBBPA onto the FMSNT Nanoadsorbent.
adsorbate T (K) ΔHo (kJ mol–1) ΔSo (J mol–1 K–1) T0 (K) –ΔGo (kJ mol–1)
TBBPA 298 26.82 117.42 226.84 8.36
  303       9.16
  308       10.01
  313       10.82
  318       11.62
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3.3. Statistical Analysis

The BBD was utilized to statistically optimize the adsorption of TBBPA through the FMSNT nanoadsorbent. This enabled an examination of how different parameters impact the adsorption process and how they interact with one another. The BBD approach involves placing experimental points in various locations throughout the experimental cubic space. By estimating the parameters of a quadratic model and using sequential designs with blocks, the BBD design proves to be an efficient and effective method. For the design, the selected influential factors on the adsorption process were pH, the concentration of TBBPA, dose of the FMSNT nanoadsorbent, and time. A statistical summary of various models is presented in Table 4, which was generated using Design Expert Software 7.0. The R2 values indicated that linear and 2FI models were not adequate models, as their values were lower. Despite the aliasing of the cubic model, where the effects of individual variables on signals were identical, the quadratic model was still suggested.

Table 4. Statistical Summary of Different Models.

source sequential p-value lack of fit p-value adjusted R2 predicted R2 remark
linear <0.0001 <0.0001 0.6542 0.5437  
2FI 1.0000 <0.0001 0.5408 0.0568  
quadratic <0.0001 <0.0001 0.9944 0.9840 suggested
cubic 0.2571 <0.0001 0.9961 0.8796 aliased
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3.3.1. Box–Behnken Model and Analysis of Variance (ANOVA)

To design the obtained experimental data, the Box–Behnken design was utilized. This involved considering four factors that interacted to produce 24 sets of experiments, which are shown in Table 5. The statistical model was utilized to optimize the variables that replicated the impact of the adsorption capacity. To investigate the impact of various independent variables (time, dose, pH, and concentration) on the adsorption capacity, statistical analysis techniques such as a sum of degrees of freedom (df), squares, mean sum of squares, F value, and p-value were used to evaluate the experimental data. By utilizing coded factors, an empirical relationship was established through a second-order polynomial equation as given below

3.3.1.

where Y designates adsorption capacity. (A) dose, (B) time, (C) concentration, and (D) pH have a significant effect on the response variable, as depicted in Table 5.

Table 5. Three Separate Process Variables are Represented by a Design Matrix.
        removal capacity qe (mmol/g)
dose g/25 mL time (min.) pH concentration (mol/L) actual predicted
0.1 52.5 0.0014 7 1.412 1.40
0.02 100 0.001035 7 1.432 1.44
0.06 100 0.001035 12 1.398 1.40
0.06 52.5 0.00067 2 1.277 1.28
0.1 5 0.001035 7 0.76656 0.7709
0.06 52.5 0.001035 7 1.31383 1.31
0.06 100 0.0014 7 1.45 1.46
0.06 5 0.001035 2 0.71955 0.6932
0.06 5 0.0014 7 0.79002 0.8092
0.1 100 0.001035 7 1.415 1.41
0.02 52.5 0.00067 7 1.3787 1.37
0.1 52.5 0.00067 7 1.3625 1.38
0.02 52.5 0.0014 7 1.509 1.47
0.06 52.5 0.0014 12 1.3946 1.41
0.06 52.5 0.001035 7 1.3138 1.31
0.06 5 0.00067 7 0.76203 0.7631
0.06 100 0.001035 2 1.32075 1.31
0.1 52.5 0.001035 12 1.36191 1.36
0.06 52.5 0.001035 7 1.31383 1.31
0.02 5 0.001035 7 0.78047 0.7984
0.06 52.5 0.0014 2 1.31618 1.33
0.06 52.5 0.00067 12 1.34612 1.34
0.02 52.5 0.001035 12 1.37808 1.39
0.06 100 0.00067 7 1.39873 1.39
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By using the equation with coded factors, the response can be predicted based on the levels of each factor. The high levels of factors are coded as +1, while the low levels are coded as −1, as per default. Assessing the relative impact of the factors is made easier by comparing the factor coefficients in the coded equation. The equation expressed in terms of the actual factors is provided below

3.3.1.

It is possible to use the equation with the real factors to evaluate the reaction if the levels of each factor are given in their original units. Nevertheless, this equation should not be used to measure the effect of each element, since the coefficients are modified to fit the units of each element, and the intercept is not located in the middle of the design space. Figure 9a displays an exemplary linear correlation between the predicted and actual conversion, suggesting a well-fitted model. Additionally, Figure 9b shows a normal probability plot of residuals, indicating that the response follows a normal distribution with no deviation in variance. Figure 9c illustrates externally studentized residuals as a function of experimental runs, with all data points falling within the limits, signifying that the model is a good fit.

Figure 9.

Figure 9

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Normal probability and perturbation plot; (a) predicted vs actual with predicted, (b) normal plot of residuals, and (c) predicted vs run.

To assess a range of responses, Table 6 presents an ANOVA that confirms the statistical significance of the process variables and their interactions. The model F-value of 707.01 and 358.65 indicates the model’s significance, with a minimal 0.01% probability of such a large F-value arising due to noise.

Table 6. ANOVA Results for Response Surface Methodology and Parameters.
source sum of squares df mean square F-value p-value
model 1.77 14 0.1268 358.65 <0.0001 significant
A-dose 0.0025 1 0.0025 7.20 0.0179  
B-time 1.23 1 1.23 3465.96 <0.0001  
C-conc 0.0100 1 0.0100 28.34 0.0001  
D-pH 0.0146 1 0.0146 41.29 <0.0001  
AB 2.387 × 10–6 1 2.387 × 10–6 0.0068 0.9357  
AC 0.0016 1 0.0016 4.62 0.0496  
AD 7.569 × 10–7 1 7.569 × 10–7 0.0021 0.9637  
BC 0.0001 1 0.0001 0.3833 0.5458  
BD 0.0003 1 0.0003 0.8719 0.3663  
CD 0.0000 1 0.0000 0.0612 0.8082  
A2 0.0129 1 0.0129 36.39 <0.0001  
B2 0.4191 1 0.4191 1185.83 <0.0001  
C2 0.0138 1 0.0138 38.93 <0.0001  
D2 0.0028 1 0.0028 7.91 0.0138  
residual 0.0049 14 0.0004      
lack of fit 0.0049 10 0.0005 2.749 × 106 <0.0001 significant
pure error 7.200 × 1010 4 1.800 × 1010      
cor total 1.78 28        
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Significant model terms are those with p-values below 0.0500, including A, B, C, D, AC, A2, B2, C2, and D2. Model terms with p-values above 0.1000 are deemed insignificant. When there are a lot of meaningless model terms, leaving out the ones needed to support hierarchy can boost the model’s performance. A significant lack of fit F-value of 2749107.47 indicates an undesirable lack of fit, with a minimal 0.01% likelihood of such a large F-value resulting from noise. It is preferable for the model to fit well and for lack of fit to be insignificant as can be seen from Table 6.

The predicted R2 value of 0.9840 is in harmony with the adjusted R2 value of 0.9944, with a divergence of less than 0.2. Adeq precision measures the signal-to-noise ratio, and an amount higher than 4 is thought of as desirable. The obtained ratio of 57.304 indicates a good signal, which means this model is suitable for exploring the design space.

3.3.2. Effect of Process Variables and Their Interactions

Adsorption capacity was used as the response, and the interaction of process factors was assessed using two- and three-dimensional contour plots, as shown in Figure 10a, which depicts the interaction impact of dosage, time, and pH. As seen in the figure, the adsorption capacity was to increase by increasing the time and decreasing the adsorbent dose. Figure 10b represents the interaction between time, pH, and the concentration of TBBPA, which shows that as the time and concentration increase the adsorption capacity increases. Figure 10c represents the interaction between pH, time, and dose and shows that the adsorption capacity increased as the time increased at the pH of 6.

Figure 10.

Figure 10

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3D and 2D response surface plots: (a) dose, time, conc., (b) conc., time, pH, and (c) pH, time, dose.

3.4. Mechanism of Interaction

The adsorption of TBBPA is at its maximum when the pH is lower than 6.3, which corresponds to the pHpzc of the FMSNT nanoadsorbent. The reason for this could be the formation of surface hydrogen bonding between the NH2 groups on the surface of the FMSNT nanoadsorbent and the OH groups of TBBPA. In contrast, the adsorption of TBBPA follows a pseudo-second-order kinetic model that is based on intraparticle diffusion. The surface chemistry of the FMSNT nanoadsorbent and the ionization characteristics of the adsorbate molecule in the sorption solution can be explained by the initial pH. The interaction energy between the charged TBBPA molecule and the charged surface of the FMSNT nanoadsorbent could be the primary mechanism. Experimentally determining the surface charge of the FMSNT nanoadsorbent’s pH to be 6.3, we found the pH at which it becomes zero (pHpzc). As the pH of the system decreases, the number of positively charged positions increases. This leads to an electrostatic interaction between TBBPA and the FMSNT nanoadsorbent. The total adsorption process of TBBPA on the FMSNT nanoadsorbent could be impacted by the electrostatic forces between the quaternary amine on TBBPA and the positive sites on the adsorbent, along with the hydrogen bonds between TBBPA and the NH2 groups of the FMSNT nanoadsorbent particles. The exchange of ions between the positive surface of the FMSNT nanoadsorbent and TBBPA molecules and pore filling may also contribute to the dye’s adsorption onto the FMSNT nanoadsorbent.31

3.5. Impact of Electrolytes on the Effectiveness of Adsorption

Industries that package water can lead to TBBPA contamination, often using significant amounts of salts, such as SO42– and Cl–, which are frequently found alongside dyes in wastewater. Within this system, the adsorbent surface and adsorbate ions interact through attractive electrostatic forces. Theory states that the adsorption capacity will go down as the ionic strength increases. On the other hand, if the electrostatic attraction is repellent, an increase in ionic strength will lead to an increase in adsorption. Figure 11 displays the impact of SO42– and Cl–on TBBPA elimination by the FMSNT nanoadsorbent. As shown, the presence of NaCl and Na2SO4 in the dye solution resulted in decreased uptake of TBBPA. The adsorption of TBBPA decreased as the salt concentration increased. Even when there was very little Na2SO4 in the TBBPA mixture, it was very visible that SO42– was more negatively charged than chloride. The underlying cause of this phenomenon is the result of the interaction between the surface and the additional solutes which can obstruct some of the spots available for adsorption of the TBBPA molecules. The existence of Cl ions in NaCl or SO42– ions in Na2SO4 may obstruct the electrostatic adhesion between SO3– groups on TBBPA and the positive charge on the FMSNT nanoadsorbent.32

Figure 11.

Figure 11

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Examining the effect of ionic strength on the adsorption of TBBPA on the FMSNT nanoadsorbent (pH 6, temp 25 °C, time 100 min, dose 0.02 g, and volume 25 mL).

3.6. Application

The main source of TBBPA contamination is the discharge of wastewater into water bodies, and traditional bioprocessing methods have been found to be inefficient in removing TBBPA. FMSNTs, which demonstrate impressive TBBPA adsorption properties, are therefore of significant importance in removing TBBPA from real water samples. In April 2023, a nearby wastewater treatment plant that uses an anaerobic–anoxic–oxic biotreatment process provided wastewater samples that were collected, filtered through 0.45 μm membrane filters, and stored at 4 °C until use. FMSNTs were then used to remove TBBPA from the WWTP effluent, and the sorption behavior of TBBPA was studied in both pure water and WWTP effluent matrices by adding TBBPA at a concentration of 900 μg/L. The adsorption capacity of TBBPA on FMSNTs was found to be lower in WWTP effluent (462.7 mg/g) than in pure water (548.26 mg/g). This suggests that other materials present in the WWTP effluent compete with TBBPA for adsorption sites on FMSNTs. Moreover, the breakthrough time for TBBPA in the WWTP effluent was 2 days, while in pure water, it was 12 days. This finding indicates that TBBPA leakage occurs much faster in WWTP effluent than in pure water. Despite this, FMSNTs were still effective in removing very low concentrations of TBBPA from real water matrices, demonstrating its potential for practical applications.33

3.7. Reusability

The capacity of adsorbents to regenerate significantly improves the efficiency of pollutant removal. The FMSNT nanoadsorbent used for TBBPA adsorption again after going through regeneration (desorption method) showed excellent removal efficiencies of 98.6 in the first cycle. The initial cycle of TBBPA removal using the FMSNT nanoadsorbent was found to be similar in effectiveness to the fresh nanoadsorbent. Subsequent testing over three additional cycles revealed a gradual decrease in TBBPA removal efficiency, ranging from 96.6 to 91.4%. Despite this decline, the results still demonstrated acceptable performance in removing pharmaceutical pollutants from aqueous solutions. Figure 12 illustrates how the FMSNT nanoadsorbent medication clearance efficacies decreased to 86.4% after four regeneration cycles. The observed decline in TBBPA removal efficiency during subsequent adsorption and desorption cycles using the FMSNT nanoadsorbent could be attributed to the deterioration of the nanoadsorbent’s surface properties. It is likely that the functional groups on the FMSNT nanoadsorbent were depleted as a result of washing with distilled water. We use water and ethanol in a variety of desorption procedures, as well as mechanical forces.34

Figure 12.

Figure 12

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Regeneration efficiency of the FMSNT nanoadsorbent.

4. Conclusions

This study involved the synthesis and characterization of the FMSNT nanoadsorbent, which displayed excellent water stability and was used as an adsorbent. The nanoadsorbent of FMSNTs presented a fair portion of both positive and negative surface charges, and its design was able to create multiple cavities with a high adsorption ability for TBBPA. The adsorption of TBBPA onto the FMSNT nanoadsorbent was accurately depicted by the Langmuir isotherm and the pseudo-second-order kinetics model. The adsorption process was chemisorption process, spontaneous, and endothermic. The adsorption results were fitted using the BBD. The adsorption capacity of the FMSNT nanoadsorbent toward TBBPA has high efficiency as it was 815.85 mg g–1. The reusability of the FMSNT nanoadsorbent was checked, and results indicated that it can be used with high efficiently until five cycles.

Acknowledgments

Princess Nourah bint Abdulrahman University Researchers Supporting project number (PNURSP2023R185), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.3c02788.

  • Detailed information about the materials and instruments, preparation of the mesoporous silica nanotubes, and HPLC analysis (PDF)

The authors declare no competing financial interest.

Supplementary Material

ao3c02788_si_001.pdf (153.4KB, pdf)

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Supplementary Materials

ao3c02788_si_001.pdf (153.4KB, pdf)

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