Au-O-MWCNTs and TiO<sub>2</sub>-O-MWCNTs as Efficient Nanocarriers for Dexamethasone: Adsorption Isotherms and Kinetic Studies

Haghighi, Maryam; Khoshfetrat, Azadeh

doi:https://doi.org/10.1155/2021/2040363

International Journal of Chemical Engineering

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Abstract Introduction Materials and Methods Results and Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2021 | Article ID 2040363 | https://doi.org/10.1155/2021/2040363

Au-O-MWCNTs and TiO₂-O-MWCNTs as Efficient Nanocarriers for Dexamethasone: Adsorption Isotherms and Kinetic Studies

Maryam Haghighi¹and Azadeh Khoshfetrat¹

Academic Editor: Prem Kumar Seelam

Received08 May 2021

Revised07 Aug 2021

Accepted24 Aug 2021

Published21 Sept 2021

Abstract

In this research, the fabrication of drug delivery systems based on oxidized multiwall carbon nanotubes (O-MWCNTs) was studied. Herein, TiO₂ and Au were conjugated with O-MWCNTs to prepare efficient nanocarriers for dexamethasone (dex). The samples were characterized by Fourier transform infrared (FTIR), scanning electron microscopy (SEM), and X-ray diffraction (XRD). In addition, dex loading was studied using adsorption isotherms including Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich. The results show that dex adsorption agreed well with the Freundlich isotherm. Increasing the TiO₂ to O-MWCNT ratio from (1 : 4) to (1 : 2) can improve the adsorption capacity from to 320 . The increasing Au amount increases the adsorption capacity from (SA1) to maximum (SA6). The maximum equilibrium binding energy was obtained for SA2, and SA7 shows high binding strength between dex and the nanoadsorbent. Carbon nanotubes (CNTs) show good affinity with high loading capabilities for dexamethasone adsorption. The synthesized TiO₂-O-MWCNTs:1/2 with the maximum removal percent (80%) was proposed as an appropriate nanocarrier for dexamethasone. Pseudo-first order, pseudo-second order, Elovich, and intraparticle diffusion models were investigated for all synthesized drug nanocarriers. According to regression coefficients, experimental data are in good agreement with the pseudo-second order model for all adsorbents except O-MWCNT/CTAB. Experimental results revealed that the Elovich model could account for the O-MWCNT/CTAB adsorbent.

1. Introduction

Dexamethasone is a type of steroid hormone and a strong combinatorial derived of the glucocorticoid hydrocortisone [1]. The chemical name of dex is 9-fluoro-11,17,21-trihydroxy-16-methylpregna-1,4-diene-3,20-dione with molecular formula C₂₂H₂₉FO₅. Dex is extensively used to treat many diseases such as allergy, inflammation, osteoporosis, and some skin diseases [2, 3]. The prolonged use of dex sometimes shows some harmful side effects, particularly during pregnancy. The dosage injection or oral administration of dex is a key factor in treatment [4].

Targeted drug delivery has been employed to overcome the adverse effects using different nanostructures. Nanomaterials can be served as the commercially invented structures to improve drug efficacy [4, 5]. Over the past decade, several types of nanomaterials have been successfully synthesized for the use against pathogens, and a variety of nanoplatforms, including nanofibers, nanosized micelles, nanomagnetic particles, and liposomes, have been emerged to promote the efficiency of drug delivery systems (DDSs) [6–10]. These nanomaterials have new opportunities and prospects for targeted and adjusted drug delivery and controlled therapy [11]. In addition, using nanoDDSs can prevent the destruction of healthy tissues and drug resistance [12].

The nanocarriers can be classified to organic, inorganic, and hybrid inorganic/organic categories. Biomolecules such as dendrimers and liposomes have been used for targeted drug delivery to cancer cells or tissues [13, 14]. Biopolymers such as collagen, polylactic acid, albumin, gelatin, cellulose, starch, and hyaluronic acid have been employed as exciting alternatives. Chitosan is a well-known and biodegradable polymer and can serve as a high potency carrier for loading, delivery, and release of paclitaxel, betamethasone, and tetracycline into human bodies [15, 16]. One important issue in the design of the bioderived polymers is achieving uniform and highly pure structures with similar molecular weight, chain length, viscosity, and number of functional groups. One strategy to enhance the drug efficacy is to use synthetic polymers in the design of DDSs with predictable properties. Moreover, more nanoplatforms have been formulated using inorganic nanocarriers including metal oxides [17], MOFs [18, 19], and carbon-based materials [20, 21]. These inorganic nanomaterials showed distinct properties, and are often cheap, highly stable, biocompatible, controllable, and adjustable.

Mesoporous silica nanoparticles have gained more attention in metal ion removal such as Pb and Cu in waste water treatment [22, 23]. Its large surface area, specific shape of pores, and tunable pore size offer excellent storage capacity. The mesoporous silica can be combined with different ligands to prepare innovative nanocomposites. The potency of these mesoporous silica to physical and chemical adsorption is affected by operating conditions. The targeted delivery of poor water-soluble drugs can be possible by using mesoporous materials such as mesoporous silica and mesoporous carbon. Scattered size distribution and difficulty in stable-colloidal suspension are the remarkable drawbacks of silica-based DDSs [24]. According to literature survey, some modifications were carried out to enhance the biocompatibility and targeted drug delivery of silica nanocomposites. Recently, mesoporous silica has been combined with carbon nanotubes (CNTs) to improve the drug delivery capabilities [25].

Among numerous nanovehicles with the aim of targeted drug delivery, carbon nanotubes were extensively studied [26–29]. The extraordinary potencies of CNTs, including high elasticity [30], high thermal and electrical conductivity [31, 32], remarkable optical properties [33], and extreme aspect ratio, made them suitable for a wide range of applications such as energy storage systems, composites, sensors [34, 35], molecule transportation and protein carriers [36], tissue engineering, and photothermal therapy [37–41]. In light of the enormous advantages of CNTs in biological applications, the carbon-based nanomedicine has been considered by many multidisciplinary researchers.

The CNTs were reported in 1991 for the first time by the Japanese physicist Sumio Iijima [42]. He synthesized CNTs using an arc-burned graphite probe (CVD method) that showed enormous characteristics and were used in many industrial and scientific applications. Carbon nanotubes can form a single layer of graphene, which is called single-wall carbon nanotubes (SWCNTs), or concentric graphene sheets, which is called multiwall carbon nanotubes (MWCNT). CNTs can be synthesized in different sizes (diameter is variable from 10 to 200 nm and different lengths) and different grades of purity, which is dependent on the synthesis method and operating conditions such as temperature and pressure.

MWCNTs show encouraging biological features compared to other nanostructures that have been studied over the past decade. The lower price, higher efficiency, the ability to load more cargo of drug, high chemical resistance, physical steadiness, and less cytotoxic properties of MWCNTs compared to the SWCNTs made them more appropriate for DDS systems. Moreover, the remarkable aspect ratio of carbon nanotubes provides the physical or chemical adsorption of biomolecules onto CNTs. The special needle-shape of CNTs allows the efficient intramembrane permeability during endocytosis and improves enhanced permeability and retention (EPR) effect [26]. They also show the antioxidant nature that can protect drug molecules. The covalent/noncovalent functionalization of CNTs makes them more efficient for drug loading and controlled release. The blood-brain barrier (BBB) is the main obstacle in brain tumor treatment. In addition, CNTs equipped with chemotherapy drugs can overcome the blood-brain barrier and facilitate the drug delivery to brain tissue [43]. Some earlier studies represent the use of MWCNTs for targeted delivery and controlled release of anticancer drugs such as doxorubicin (DOX) and paclitaxel [44, 45]. The folic acid grafted MWCNTs were employed successfully for targeted delivery of doxorubicin [46]. The CNTs have been also employed to prepare a wide range of biological scaffoldings in tissue engineering. Moreover, CNT is a suitable candidate for the adsorption of ciprofloxacin hydrochloride from aqueous solutions. The results show that ciprofloxacin hydrochloride can be adsorbed by MWCNTs successfully. Moreover, the kinetic study and isotherm models were investigated comprehensively [47].

However, apart from these distinguished properties, insolubility of MWCNTs is an important challenge in DDS development (led to a slight cytotoxicity). Due to the intratublar van der Waals forces, the roll sheets of CNTs tend to aggregate together, which shows their amphiphobic nature. The difficulty is enhanced due to the poor dispersibility and solubility of MWCNTs in most solvents. On the other hand, the intrinsic poor solubility of MWCNTs is a serious obstacle in the commercialization of these nanocarriers [48–50]. Therefore, MWCNTs as a drug carrier tend to deposit in the blood circulation system or different organs. Different strategies have been proposed to overcome the hydrophobic nature of CNTs and increase the stability of the solutions [51]. The tuning of MWCNT structure by functionalization using a hydrophilic group improves dispersibility and reduces cytotoxicity. For instance, carbon nanotubes are simply functionalized by carboxylic groups of strong acids, which increases drug adsorption. It was found that carboxylic groups show hydrophilic property and can create defects on the CNT side walls and get attached to these generated defects. It has been postulated that the COOH groups can be coupled with the open ends of CNTs and functionalize them; they can cut and shorten the tubes chemically and make them appropriate for intratube drug loading without deposition and toxicological side effects to the human body. Moreover, the biocompatibility of carbon nanotubes escalates and, therefore, a large diversity of molecules such as proteins, nucleic acids, and drug molecules can be attached to the oxidized MWCNTs [17]. Drug molecules attach to the MWCNT surface via covalent or noncovalent bonds and can be delivered to the intended organ, and are released and absorbed into the human body. The functionalized MWCNTs can be degraded enzymatically and excreted via the kidney at the end of the drug delivery process. Therefore, the CNTs functionalized with COOH groups render many active sites for drug loading. The drug molecules can be trapped in the O-MWCNTs for providing an efficient drug delivery system.

The main goal of this work is to prepare and study the novel nanocomposites based on multiwall carbon nanotubes as suitable carriers for dexamethasone. Both covalent functionalization of sidewalls and intrinsic defects were considered in this research. Metal oxides show great potential in the design of the DDSs due to their inherent mechanical and chemical stability. In order to increase the adsorption capacity and overcome the limitations of using of MWCNTs, metal oxides/MWCNT nanocomposites have been constructed and studied in this research. This could be a solution to increase the drug uptake on the surface of MWCNTs to provide targeted delivery. Different nanoparticles were dispersed and immobilized on the surface of O-MWCNTs. In this endeavor, TiO₂ and Au were applied as good alternatives for drug nanocarrier fabrication due to their high adsorption capacity. Drug release characteristics are dependent on the method of drug loading onto or into a nanocarrier. Moreover, the drug loading and pharmacokinetic behavior of these nanodrug carriers were further investigated comprehensively in order to control drug adsorption and drug delivery to target cells. Meanwhile, the adsorption isotherms and kinetic models were studied in this research. The results show that the prepared nanocomposites with high surface area can adsorb dex molecules effectively.

2. Materials and Methods

2.1. Materials

All chemicals used are commercially available as received. MWCNTs (95%, 10 μm length, and 10–30 nm diameter) and the colloidal mixture of gold nanoparticles (1000 ppm) were purchased from Nanosav Company. Dex as an anti-inflammatory drug was obtained from Osvah Company. Titanium dioxide was purchased from Nanosany Corporation. Cetyltrimethylammonium bromide (CTAB) and polyethylene glycol with MW = 4000 (PEG 4000) were of analytical-reagent grade and prepared by Merck Company. The solutions in this study were all prepared with double distilled water.

2.2. Instruments

A Bruker FTIR model Tensor 27 spectrometer (KBr disks, 500–4000 cm⁻¹) was applied for recording the IR spectra. SEM images were determined using a scanning electron microscope (model EM 3200, kyky), and an X-ray diffractometer (cobalt anode, wavelength of 1.7889°A, Cu K1 radiation, 40 kV, 40 mA) was used for obtaining XRD patterns. UV-vis spectra were obtained using a UV spectrophotometer (model Perkin Elmer lambda 35, 190 nm–1100 nm).

2.3. Functionalization of MWCNTs

0.2 g of MWCNTs were added to 100 ml of H₂SO₄ (98%) and HNO₃ (69%) mixture (V/V = 3 : 1) and stirred with a magnetic stirrer (rpm = 250) for 2 h at room temperature, then mixed using an ultrasonic device for 1 h. The prepared solution was centrifuged and washed with purified water several times until the pH of the mixture was adjusted to neutral. The precipitate was dried in an oven at for 24 h. The pretreated MWCNTs, which were the so-called O-MWCNTs, were used for adsorbent fabrication.

2.4. Adsorbent Preparation

In this study, the effect of surfactants (CTAB and PEG 4000) on the dex loading efficiency was investigated. In this regard, O-MWCNTs (0.006 g), CTAB (0.006 g), and 100 mL double distilled water were stirred in an ultrasonic bath for 45 min. The prepared nanotubes were centrifuged and washed several times with deionized water and absolute ethanol. The produced precipitate was dried at in an oven for 4 h to obtain SA1 nanocomposite. In a next attempt, O-MWCNTs (0.006 g) and PEG 4000 (0.006 g) were taken, and the aforementioned procedure was carried out to prepare SA2.

Metals have been routinely applied in the structure of different adsorbents, which can improve the adsorption performance. Herein, Au and TiO₂ were added to the adsorbent structure. First, different amounts of Au were immobilized on the surface of the O-MWCNTs to determine the optimum value. 0.05 g of O-MWCNTs and 0.5 mL of colloidal Au solution (1000 ppm) were dispersed in 20 mL of purified water using a magnetic stirrer for 1 h, followed by redispersion in an ultrasonic bath for 1 h again. The mixture was centrifuged and washed with double distilled water and absolute ethanol several times and then dried at for 4 h again (SA3 sample). The experiment was utilized for different values of the Au solution (1000 ppm) as 1 mL, 1.5 mL, and 2 mL. The samples were called SA4, SA5, and SA6, respectively. In the next step, titanium dioxide was used to improve the adsorption capacity of carbon nanotubes. 0.05 g of O-MWCNTs and 0.0125 g of TiO₂ (anatase phase, with average size 10–25 nm) were dispersed in 20 ml double distilled water to produce SA7. The same procedure was applied to 0.01 g of TiO₂ and 0.02 g of O-MWCNTs to prepare SA8 sample. The specifications of materials and obtained adsorbents are summarized in Tables 1 and 2.

2.5. Analysis of Dex Samples

New nanoplatforms were designed to maximize drug concentration into targeted sites with minimum drug loading dosage. In order to overcome the drug toxicity problems, its dosage should be controlled by taking advantages of drug delivery nanosystems. Dex loading on the samples, which were reported in Table 2, were compared together in this research. The oxidized samples were subjected to the dex adsorption process under a similar procedure to measure the drug uptake. Dex (2 mg/8 mL) was immersed in double distilled water. Specific amounts of different nanocarriers were added to this solution under ultrasonic stirring. Finally, the suspension was centrifuged, and the precipitate was washed several times with distilled water and absolute ethanol. Dex content in upper clear solution was quantitatively analyzed by UV-vis spectrophotometry at 238 nm to calculate the drug uptake. The UV spectrum of dex solution is shown in Figure 1 with maximum absorptivity at .

All experiments were performed in diluted solution so that the Beer–Lambert law is valid. The linear relationship between the concentration of adsorbed pieces and absorbance can be written as follows:where is the molar absorptivity coefficient , b is the path length (width of cell is 1 cm), C is the molar concentration, and is the slope of the calibration curve. Four solutions with a low molar concentration of dex were prepared (4e-6, 8e-6, 12e-5, and 16e-5), and the absorbance versus molar concentration of these samples is depicted in Figure 2. The experimental data were fitted to a straight line, and the concentrations of unknown samples were calculated using this calibration curve.

2.6. Batch Experiments for Optimization, Kinetic and Isotherm Studies

2.6.1. Batch Studies for Optimum Temperature

According to the literature, adsorption is a function of various parameters such as temperature, pH, and contact time [52]. Since acidic or basic pH may affect drug structure, dex loading was studied at neutral pH to avoid drug destruction or any other side effects. To define the proper adsorption temperature, 0.024 g of O-MWCNTs and 0.024 g of CTAB were added to 100 ml double distilled water in a 500 mL beaker and mixed together in an ultrasonic bath for 45 min. Furthermore, 400 ml of dex solution (6.2e-5 M) was slowly added to the beaker and mixed for 30 min under sonication. Adsorption experiments were conducted at different temperatures; 293 K, 298 K, 303 K, and 308 K for 30 min at neutral pH. After dex loading, the adsorbent was separated from the suspension via the centrifugal method. The dex concentration in effluent solution was analyzed using the UV-vis adsorption spectrum according to a prepared calibration curve. The removal percent of dex and adsorption capacity at the equilibrium state were calculated by equations (2) and (3), respectively [23],where and are the initial and equilibrium concentrations of dex, respectively; V (L) is the volume of the drug solution; and m (g) is the weight of the adsorbent. The desired temperature can be identified with regard to the maximum adsorption capacity, although dex thermal stability ought to be considered. Similar experiments conducted on other nanoadsorbents showed the same results for optimum temperature. This may be due to the fact that the O-MWCNTs were applied as the main part of all synthesized nanoadsorbents. On the other hand, the temperature adjusted for O-MWCNTs (as the support of nanocarriers) can be generalized to all samples.

2.6.2. Batch Studies for Surfactant Selection

Coupling the functional groups with adsorbents during drug loading or nanocarrier formulation are widely used to improve the solubility and nanocarrier stabilization. As a part of this work, the effectiveness of PEG and CTAB on dex loading was examined. 0.006 g of SA1 was added to 100 mL of 2.32e-5 M of dex solution (pH = 7.3) and mixed in an ultrasonic bath at a defined temperature of 303 K. The amount of dex concentration in the remaining solution was measured at different time intervals (5, 15, 25, 35, 50, 65, 80, 95, and 110 min). Dex adsorption capacity at any given time was plotted as a function of contact time by the following equation:where is the concentration of adsorbed dex at any given time. All experiments were conducted several times, and the average values were applied for this research. Furthermore, the same adsorption experiments were conducted for SA2 in the presence of an appropriate surfactant (which was identified regarding higher drug loading values).

2.6.3. Batch Studies for Contact Time, Kinetic and Isotherm Models

The kinetic studies were performed in a 250 mL beaker containing 0.006 g of SA3 or other adsorbents (SA4, SA5, SA6, SA7, and SA8), 0.006 g of desired surfactant, and 100 mL of double distilled water at optimum temperature and neutral pH. After mixing in an ultrasonic bath for 45 min, 100 mL of 2.32e-5 M of dex solution was dissolved in this solution. Mixing was continued, and drug adsorption capacity at defined time intervals were analyzed for each case. In this regard, the optimum contact time was further investigated with equilibrium adsorption capacity. It is necessary to mention that, since the surfactant can help in drug encapsulation or conjugation, the proposed surfactant should be added to the synthesized nanoplatforms during the drug loading process simultaneously.

Furthermore, in order to investigate the isotherms, batch experiments were examined for different dosages of dex at equilibrium contact time, neutral pH, and optimum temperature. 0.006 g of SA1 was dissolved in drug solutions of different concentrations 3.1e-5, 2.71e-5, 1.94e-5, and 1.55e-5 at laboratory temperature and at pH = 7.3 and agitated during the optimum contact time. After reaction completion, the adsorbent was separated by the centrifugal process, and the dex concentration was quantitatively analyzed using UV. Similar experiments were conducted for other adsorbents at proper contact time.

2.7. Kinetic Models

Kinetic studies provide novel methods to design selective adsorbents for other drugs with innovative sorption properties. Moreover, the experimental results were applied to understand the optimum contact time and maximum adsorption capacity, aiming at controlling the complexity of the adsorption systems. The kinetics of dex loading were studied by four well-known models, pseudo-first order, pseudo-second order, Elovich, and intraparticle diffusion models, to obtain new insights into dex adsorption [52–57].

In the pseudo-first-order model, the difference in equilibrium uptake and time dependent uptake is expressed as a linear function of the adsorption time. The pseudo-first-order kinetic model is given as follows [58–60]:where is the adsorption rate constant of the pseudo-first-order model.

The pseudo-second-order model can be applied in adsorption processes, whereas chemisorption is the rate-controlling step. The linear equation of the pseudo-second-order kinetic model evaluates the adsorption rate as follows [61]:where is the rate constant of the pseudo-second-order kinetic model.

The Elovich model is usually used to predict the adsorption rate where the chemisorption is carried out in heterogeneous systems. The linear form of the Elovich kinetic model can be expressed as follows [62]:where is the initial adsorption rate, and is the Elovich adsorption constant.

The kinetic model of adsorption depends on the physical properties of the adsorbent and chemical properties of the sorption process. In general, diffusion was carried out at different stages, comprising of mass transfer from the aqueous solution to the surface of the solid adsorbent through a diffusion boundary layer, penetration to the tortuous path, and subsequently the intraparticle diffusion of species. The process continued to achieve the equilibrium state between absorption and desorption rates. It is important to identify the controlling step of adsorption kinetics aimed toward understanding the effective parameters of the adsorption rate. In this regard, the intraparticle kinetic model was taken into consideration to investigate the diffusion mechanism instead of the pseudo-first and second order equations. Weber–Morris model was applied to intraparticle diffusion of dex in this research to define the rate determining and slowest step of the diffusion mechanism [63, 64]. The Weber–Morris kinetic model was illustrated as follows:where is the rate constant of diffusion and C is the intercept of the plot.

2.8. Isotherm Models

The amount of the adsorbed drug on the solid surface is a function of its concentration in the bulk of the solution at the equilibrium state. Multiple isotherms have been developed to describe the adsorption behavior on the adsorbent. Insights obtained from the isotherms have been used to predict the equilibrium adsorption capacity, which plays a key role in the design of new adsorbents for industrial applications. In this research, various isotherms have been employed for the O-MWCNT-based adsorbents. The isotherms have been developed with regard to the complexity of the interactions between the atoms and molecules in the sorptive-sorbent system. All experimental data have been measured under well-defined conditions at the equilibrium state to indicate the mechanism of physisorption or chemisorption. In most cases, the theoretical results are in line with experimental data without significant deviation.

In this investigation, the adsorption system was analyzed using four most proposed isotherms, Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich. The Langmuir model describes the monolayer adsorption by a linear equation as follows [22, 65]:where is the amount of adsorbate at the equilibrium state, is the maximum adsorption capacity, is the adsorbate concentration at the equilibrium state, and is the Langmuir constant. The is plotted versus as a straight line with intercept . The separation factor is a main criterion for adsorption evaluation that can be calculated as follows:

The separation factor at all examined initial concentrations should be determined using the Langmuir constant. Whether the values are lower than unity, the adsorption process is favorable and valid [58]. The Freundlich isotherm is normally represented by the nonuniform adsorption of the species on heterogeneous systems. Equation (11) represents the Freundlich isotherm as follows [66, 67]:where and n are the Freundlich constants, which are calculated using the Freundlich isotherm plot. is depicted versus , and the slope and intercept of this straight line are calculated. Furthermore, the linear Temkin isotherm can be expressed by the following formula [68]:where b (J/mol) and are the equilibrium binding constants, b is a function of heat of sorption, is the Temkin equilibrium isotherm constant, whereas R is the gas constant, and T is the absolute temperature. is plotted against as a straight line, and the constants of Temkin isotherm are measured, which can be used for result prediction. If the heat of adsorption decreases with an increment of the surface coverage, Temkin isotherm governs the adsorption system.

The Dubinin–Radushkevich isotherm was developed with regard to the Guassian energy distribution onto the heterogeneous surface of the adsorbent with a linear formula as follows [62]:where describes the theoretical saturation capacity, is the Dubinin–Radushkevich constant related to the mean free energy of adsorption, and is the Polanyi potential, which is expressed according to the equilibrium data as follows:

The mean energy of adsorption can be calculated by the following equation:

3. Results and Discussion

3.1. Characterization of Prepared Nanocomposites

The synthesized samples were thoroughly characterized via FTIR, SEM images, and XRD patterns. In order to identify the mechanism of dex adsorption, the FTIR spectra of various nanocarriers after dex loading were recorded, and the results were compared to the FTIR spectrum of O-MWCNTs before dex loading. Figure 3 shows the FTIR spectra of O-MWCNTs before dex loading and O-MWCNT/CTAB (SA1), O-MWCNT/PEG (SA2), Au-O-MWCNT (SA6), and TiO₂-O-MWCNT (SA7) after dex adsorption onto these nanocarriers. The recorded spectrum in Figure 3(a) corresponds to the O-MWCNT before dex adsorption. The observed bands at 3449 cm⁻¹ and 1429 cm⁻¹ are mainly assigned to the O-H stretching vibration and O-H bending vibration of the carboxylic groups. The 1633 cm⁻¹ band can be attributed to the stretching vibration. The weak band at 1383 cm⁻¹ is ascribed to the symmetric stretching vibration of the carboxyl group that validates that the MWCNTs were functionalized well.

Dex was loaded on SA1, and the FTIR spectrum of this sample is shown in Figure 3(b) to indicate specific chemical bands at different wavenumbers. Detected peaks around 3735 cm⁻¹ and 1664 cm⁻¹ were concerned with –NH stretching and bending vibration modes, respectively, which are due to the CTAB molecules. The broad peaks around 3420 cm⁻¹ and 1491 cm⁻¹ are attributed to stretching and bending vibration of OH groups, respectively, that are due to drug loading on the surface of the adsorbent. The symmetric and unsymmetrical stretching vibration of the C-H groups were observed at 2849 cm⁻¹ and 2918 cm⁻¹, respectively. The adsorption band at 1110 cm⁻¹ was assigned to the P-O stretching vibration due to sodium phosphate groups in the dex structure. The peak around 1594 cm⁻¹ is due to the C-N stretching vibration. Furthermore, the FTIR spectrum of O-MWCNT/PEG4000/dex is displayed in Figure 3(c). The adsorption peak at 1383 cm⁻¹ is concerned with C-O groups of PEG and symmetric stretching vibration of the carboxyl group. The symmetric and asymmetric stretching vibrations of C-H groups were observed around 2852 cm⁻¹ and 2912 cm⁻¹ of dex and PEG, respectively. The weak peak at 1629 cm⁻¹describes the stretching vibration of the group. In addition, the stretching vibration of the P-O group can be observed at 1083 cm⁻¹. In addition, the stretching vibration bands of and P-O groups were detected at 1633 cm⁻¹ and 1068 cm⁻¹, respectively, which is shown in Figure 3(d). The FTIR spectrum of TiO₂-O-MWCNT/dex is presented in Figure 3(e), and the presence of different functional groups and metal oxides in the synthesized samples is revealed. The observed peaks around 1458 cm⁻¹, 2919 cm⁻¹, and 2850 cm⁻¹ indicate the bending vibration, symmetric stretching vibration, and asymmetric stretching vibration of the C-H group of the adsorbed dex, respectively. The carboxylate group can be observed at 1383 cm⁻¹ again. The IR band at 1036 cm⁻¹ presented the stretching vibration of P-O of drug molecules. In addition, the very strong peaks in the range of 521 cm⁻¹–669 cm⁻¹ are assigned to the Ti-O stretching vibration of the group.

The morphology and size of the synthesized catalysts were investigated using the SEM technique, which is shown in Figure 4. Figure 4(a) shows the SEM image of the O-MWCNT/CTAB/dex sample with an average diameter of 30 nm. This is higher than the purchased MWCNTs (15 nm) due to the CTAB coverage. Figure 4(b) shows the SEM images of O-MWCNT/PEG4000/dex. It can be seen that carbon nanotubes were adhered together, which may be due to the presence of PEG. The shape and size of the MWCNTs are uniform, and the average diameter of the nanotubes is in the range of 30–40 nm. Figure 4(c) shows the SEM image of Au-O-MWCNT nanocomposites before dex adsorption. The average size of Au colloidal nanoparticles is about 30 nm. In addition, the images show that the Au nanoparticles were immobilized on the surface of carbon nanotubes uniformly with an average diameter of 25 nm. The SEM image of SA7 is shown in Figure 4(d). TiO₂ was dispersed uniformly on the MWCNT surface, and the average size of TiO₂ is about 40 nm.

(a)

(b)

(c)

(d)

In addition, the XRD pattern of the O-MWCNT sample is depicted in Figure 5(a). The noise of the intensity signals is due to the amorphous behavior and the low degree of crystallinity of the O-MWCNT nanocarrier. The fairly broad peaks observed at the angles (2θ) , , and match with those of crystalline planes 002, 100, and 004, respectively, which corresponds to JCPDS card No. 41–1487. Furthermore, the addition of small amounts of PEG and dex to O-MWCNTs influenced the initial peaks to produce the sharp peaks. Furthermore, the XRD spectrum of O-MWCNT/PEG4000/dex can be observed in Figure 5(b). All main peaks of O-MWCNTs appeared at a similar position with higher intensity; moreover, some new peaks were detected over the O-MWCNT/PEG4000/dex sample. Overall, six diffraction peaks were observed (using cobalt anode with λ = 1.78) at angles (2θ), 19.8, 26.6, 37.3, 39.6, 48.6, and , which correspond to 002, 060, 072, 452, 580, and 801 crystalline planes, respectively. The new recorded peaks of the XRD pattern correspond to the JCPDS card No. 39–1691 of dexamethasone.

(a)

(b)

3.2. Optimum Operating Conditions for Adsorption Batch Studies

3.2.1. Optimum Adsorption Temperature

To elucidate the optimum temperature for the adsorption of dex, the removal percent of dex versus different temperatures for SA1 is depicted in Figure 6, and the data are reported in Table 3. It is necessary to mention that the surfactant and dex were added to the O-MWCNTs, respectively; and only post-treatment processes, such as purification and drying steps, were carried out for the characterization of SA1 and was omitted in dex loading.

As it is expected, the amount of the removal percent of dex was highly enhanced with temperature increment that maximum removal percent was gained at 308 K. Since increasing the adsorption temperature can destruct the drug, 303 K is proposed as the optimum temperature for dex loading onto SA1. Similar experiments were conducted on other nanoadsorbents that showed the same results for temperature. This may be due to the fact that the O-MWCNTs were applied as the main part of all synthesized nanoadsorbents. In other words, the temperature adjusted for O-MWCNTs (as the support of nanocarriers) can be generalized to all samples.

3.2.2. Optimum Contact Time

The effect of adsorption time was analyzed at different time intervals for all synthesized adsorbents (Table 3). The adsorption capacity at different times was monitored and compared in order to understand the equilibrium contact time for each adsorbent. All experiments were conducted under similar conditions. Dex adsorption amounts should be calculated according to equation (4) (Table 4).

The amounts of are depicted versus time in Figure 7 for all samples to achieve the optimum adsorption time. It can be seen that the amount of dex adsorbed onto SA1 was increased even after a short time with a steep slope. The maximum adsorption capacity on SA1 was achieved in 80 min, that is, about 57.59% and remains constant after this time. Since there was no considerable increase in drug loading after 80 min, the optimum adsorption time of 80 min was considered for SA1. This is due to the occupation of active sites by drug molecules, whereby the system reaches the equilibrium state. In the next step, the effects of surfactant on the adsorption potency of O-MWCNTs were examined. Therefore, SA2 was applied to further studies, and the concentration of remained dex was measured in the range 5–125 min. Interestingly, the amount of adsorbed dex for the SA2 sample is markedly greater than SA1 that depends on the surfactant. In other words, PEG 4000 can drastically enhance the adsorption rate of dex due to the active sites of the surface area. As a result, all samples that possess PEG 4000 were applied to drug loading in this study. The results show that the adsorption curve of SA2 reached a plateau at 85 min (adsorption capacity = 305.14 mg g⁻¹), and the removal percent is above 76%.

Metals have been routinely introduced to the structure of different adsorbents, which improves the adsorption performance. Herein, Au and TiO₂ were used in adsorbent synthesis. First, different amounts of Au were immobilized on the surface of the O-MWCNTs to determine the optimum value. Plots of adsorption capacity vs. contact time for different adsorbents are depicted in Figure 7. As it was expected, the adsorption amounts were increased for all mentioned samples, and then became constant. It can be observed that the ascending trend of adsorption performance was more or less similar for all the Au adsorbents in the order: SA3 > SA4 > SA5 > SA6. Quite remarkable is the increase in Au-concentration results in the lowering of the adsorption capacity at the onset of an adsorption period (45 min) relative to SA2. Although the experimental results show that the adsorption capacity of dex for Au-based samples surpassed that of the SA2 sample gradually. Thus, the ultimate adsorption uptakes for all Au supported adsorbents are higher than SA2. Considering the experimental data, it can be observed that the optimum adsorption time is 115 min, and the Au increment did not show a significant and enormous effect on the equilibrium state.

Furthermore, two designated samples of TiO₂ were examined for dex loading in order to check their adsorption potency. The remaining concentration of dex in the solution was measured using UV spectra (at 238 nm) continuously for the entire adsorption duration. It can be seen in Figure 7 that the adsorption capacity and removal percent of the drug were increased with a steep slope, and became constant after 85 min. It can be observed that increasing the TiO₂ to O-MWCNT ratio from (1 : 4) to (1 : 2) rises the adsorption capacity from to . In addition, the removal percent increases about 7%, which is considerable in industrial applications. The comparison of the results shows that the most efficient synthesized drug nanocarrier is TiO₂-O-MWCNTs:1/2(SA7) with a maximum removal percent of 80%.

3.2.3. Kinetic Models

Different kinetic models were used to demonstrate the adsorption activity of functionalized MWCNTs loaded with metal oxides. The kinetic plots were investigated. The results (Figure 7) show a high adsorption efficiency for all synthesized samples. It was found that metal immobilization on the surface of adsorbents improved seriously the adsorption efficiency of dex. It was speculated that this effect emerges due to offering new active sites with high affinity compared to O-MWCNTs. The kinetic data were fitted with pseudo-first order, pseudo-second order, Elovich, and intraparticle models in Figures 8–10 and 11, respectively, and the models were compared together. All parameters and regression coefficients of aforementioned kinetic models are presented in Table 5. It was found that the pseudo-first order model shows a high degree of fitness for SA5, SA6, and SA7 with correlation coefficients all above 0.93. The correlation coefficients for other samples were all lower than 0.9, which demonstrated that the pseudo-first order kinetic model is not consistent with the experimental data. Figure 8 shows, in most cases, that the pseudo-first order kinetic model did not fit well over the whole range of contact time.

(a)

(b)

(c)

(d)

The and the first order kinetic constant were calculated from the slope and intercept of the pseudo-first order model. Since the adsorption of dex upon other solid adsorbents did not follow the pseudo-first model, the experimental data were examined with the pseudo-second order model to understand the kinetic dynamics of adsorption. This model describes the chemisorption on adsorbents. In the next attempt, the experimental data were analyzed with the pseudo-second order kinetic model, and the results were compared together. The plot of versus t gives a linear relationship (Figure 9) in which and can be determined from the slope and intercept of the linear kinetic model.

The results show that the correlation coefficients of the pseudo-second order kinetic model for all samples (except SA1) were more than 0.99, indicating that the adsorption of dex on O-MWCNT-based adsorbents (except SA1) was followed by chemical mechanism. Since pseudo-first and second order kinetic models did not show good agreement with the experimental data of SA1, the absorption properties of dex on SA1 were investigated using the Elovich kinetic model in Figure 10.

It was found that the correlation coefficient of the Elovich model for SA1 was above 0.92, indicating a good agreement between theoretical predictions with experimental data. In other words, the correlation coefficient of the Elovich model was higher than that of the pseudo-first and second order models. Therefore, the Elovich model described the kinetics of dex adsorption on the O-MWCNTs accurately.

To identify the diffusion mechanism, experimental data were analyzed by using the intraparticle diffusion model proposed by Weber and Morris. The rate-controlling step in diffusion can be determined using the intraparticle model. The proposed adsorption mechanism occurs in two stages. In the first stage, dex molecules were diffused through a boundary layer surrounding the surface of adsorbents, and in the next stage, dex molecules diffuse toward the tortuous pores with an interaction between adsorbate species and adsorbent (intraparticle diffusion). For the regression of Weber and Morris diffusion model, a plot of versus was depicted, and the diffusion rate constants and C values were obtained from the slope and intercept of distinct linear portions of the diffusion plot. The parameters of the Weber–Morris model are reported in Table 5 in detail. The intraparticle multistage linear fit for the adsorption of dex upon SA1 adsorbent is depicted in Figure 11(a), and the coefficients calculated from it are shown in Table 5. It can be seen that the plot consists of two distinct linear parts, indicating a two-stage diffusion mechanism in adsorption. The high values of regression coefficients and indicate that the intraparticle mechanism well illustrates the adsorption of dex in the case O-MWCNT/CTAB.

Conspicuously, the first stage line with the intercept −12.613 mg g⁻¹ does not pass through the origin (shows low deviation), which suggests that both boundary layer diffusion and intraparticle diffusion contributed to the overall adsorption process. Although, the first portion (stage I) was only attributed to the dex diffusion through the bulk to the exterior surface of the adsorbents. The comparison between the two stages shows that the linear slope of the first portion is significantly larger than the slope of the second portion , which refers to the faster adsorption rate of bulk diffusion in comparison to the intraparticle diffusion. In other words, the intraparticle diffusion is the rate controlling step in the adsorption process by SA1.

The Weber and Morris diffusion model for dex diffusion through SA2 was investigated, and the plot is depicted in Figure 11(b). The results show significant deviation from the origin, which was represented by the intercept of the first stage linear plot. This can be accounted by the fact that both film diffusion and intraparticle diffusion participated in the adsorption process. First, dex molecules quickly diffused through the film with a steep slope. The second portion (stage II) followed by the intraparticle diffusion of dex molecules within the CNTs. It was speculated that the second part is the rate-limiting step due to the gradual diffusion of species. Figure 11(c) shows the adsorption of dex on the Au-O-MWCNT samples (SA3, SA4, SA5, and SA6). According to the experimental results, the adsorption process can be divided into two phases for these Au-based adsorbents. It was found that both boundary layer diffusion and intraparticle diffusion control the diffusion mechanism of all four mentioned cases.

The adsorption kinetic model of TiO₂-O-MWCNT samples are depicted in Figure 11(d). It was worthy to mention that the solute diffused through the film and the pores slowly relative to other adsorbents. Comparing the rate constants of stage I and stage II for the SA7 adsorbent, it can be concluded that both mechanisms (film diffusion and intraparticle diffusion) control the dex adsorption onto the TiO₂-based samples.

3.3. Isotherm Models

To determine the best model to better understand the system behavior, different isotherms were applied and compared together. Molecular distribution between aqueous solution and the solid phase at the equilibrium state can be defined by adsorption isotherms. Moreover, equilibrium capacity and adsorbent efficiency can be calculated according to isotherm studies. The combination of different adsorption mechanisms due to the surface structure may result in more complexity in defining the system. Batch scale systems with different initial concentrations of dex on O-MWCNT-based samples (SA1, SA2, SA6, and SA7) were prepared to measure the equilibrium adsorption capacity. Adsorption experiments were carried out in triplicate, and the average data for each case were reported in this investigation. The measured data of adsorption are listed in Table 6, comprehensively.

In the present work, Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich isotherms were applied to the experimental data and studied in detail on different adsorbents. The regression coefficients r² and the parameters of adsorption isotherms for all isotherms are compared in Table 7 to select the best model for dexamethasone adsorption.

In the first attempt, Langmuir isotherm was developed based on a uniform and monolayer distribution on a homogeneous adsorbent, which is an ideal assumption. versus is plotted linearly in Figure 12 for all samples, and the constants of Langmuir were calculated.

The experimental data of versus of dex for SA1 at the equilibrium state were fitted to the Langmuir isotherm with a linear relationship with regression coefficient r² = 0.958. and for SA1 were derived from the slope and the intercept of the Langmuir linear isotherm 0.1655 and 0.9583. The results show that increasing the Au amount improves the adsorption capacity from (SA1) to (SA6) slightly. This indicates the stronger interaction between dex and SA6 rather than that with SA1. In the cases of SA1 and SA7 adsorbents, the values of the Langmuir adsorption isotherm close to unity demonstrate the good agreement between the experimental data and theoretical results. This indicates that, in these adsorbents composed of a homogeneous surface, all adsorption sites are energetically equivalent. Therefore, the physical binding of dex molecules with the active sites is monolayer adsorption. The adsorption rate constant of SA1 was measured to be 0.165, which is lower than SA7 (0.2), which implies a lower adsorption rate. It can be seen that Langmuir shows a significant deviation from the straight line for SA2 and SA6 samples. This means that Langmuir cannot describe the adsorption behavior in the absence of experimental data for these two cases. In addition, the separation factor for all cases was, demonstrating that the adsorption process was favorable under the examined condition.

Furthermore, values are plotted against in Figure 13, and were determined from the slope and the intercept of the linear Freundlich relationship for all adsorbents.

It is clear that the efficiency of SA1 for drug adsorption is higher than the other samples due to the highest value of . shows the Freundlich constant that was calculated to be for the SA1 sample. The separation factor indicates that the surface uniformity was (0.5151, 0.4243, 0.5645, and 0.4308) for SA1, S2, SA6, and SA7, respectively. Since , the adsorption process belongs to a favorable range. This means that multilayer adsorption took place at the heterogeneous sites of the adsorbent surface with nonuniform adsorption heat. This adsorption is reversible. Since, n is greater than unity, the system in this study underwent physisorption mechanism and chemical adsorption did not happen. Furthermore, all equilibrium data obtained for samples were fitted to Temkin and Dubinin–Radushkevich isotherms and are reported in Table 7 in detail. Figure 14 illustrates the variation in the equilibrium capacity with to determine Temkin constants.

The maximum heat of adsorption and minimum equilibrium binding energy for SA6 were obtained as and , respectively, with regard to the Temkin isotherm. The highest value of was obtained for SA2, and SA7 shows the binding strength between dex molecule and the active site of the carrier. Furthermore, the Dubinin–Rudeshkevich model was applied to experimental data that can be seen in Figure 15.

The theoretical saturation capacity for the Dubinin–Radushkevich isotherm increased as , which indicates that adsorption is facilitated by adding the metal oxides and using PEG as a surfactant. The adsorption energy is , illustrating that the physical adsorption of dex occurred on all adsorbents.

Comparing the results shows that the Freundlich isotherm has the lowest deviation from the experimental data (highest value of ) and is the most appropriate model for predicting the results for all cases. On the other hand, the Freundlich isotherm shows the best fitting with experimental data for various nanoadsorbents. This confirms the multilayer adsorption of dex molecules onto the surface of adsorbents. Each molecule of the drug has two Na⁺ that can bond to COOH groups to form NaCOOH. This forms the first layer of adsorbates that can bind with other adsorbed layers, and a multilayer adsorption occurs. Regarding the highest value of obtained from the Freundlich model, it was suggested that this model is the best fit to the experimental data among the tested models. This implies that adsorption of dex on adsorbents takes place at the heterogeneous sites on the surface.

In recent years, the anticancer drug paclitaxel has been adsorbed onto Sylopute and Diaion HP-20. The isotherm models were analyzed based on experimental data [58, 64]. CNTs were primarily applied in cancer treatment. It is worthy to mention that a few studies have been devoted to MWCNTs in dex-targeted delivery. Among the relevant studies, a limited number have demonstrated the uptake analysis, including kinetic studies and isotherm models. Some of the studies conducted in this regard in recent years are reported in Table 8.

4. Conclusion

The long time use of dexamethasone shows some therapy failure. To overcome the side effects and drug inefficiency, CNTs were used as a drug carrier. In recent years, an increasing number of studies have been published analyzing the MWCNT role in developing the drug delivery systems due to their extraordinary properties. This research is devoted to the fabrication and analysis of the novel nanocomposites based on O-MWCNTs for dex-targeted delivery. In order to further take advantage, MWCNTs can be combined with Au and TiO₂ to form hybrid composites, including Au-O-MWCNTs and TiO₂-O-MWCNTs. The functionalization of MWCNTs by COOH as a hydrophilic group can improve the dex adsorption and reduce its cytotoxicity. Moreover, the organic surfactants (CTAB and PEG) were used for drug encapsulation or conjugation.

According to the experimental results, 303 K was defined as the optimum adsorption temperature for dex loading. Furthermore, the kinetic data were fitted with pseudo-first order, pseudo-second order, Elovich, and intraparticle models. Regarding the kinetic studies, within the first 85 min, good linearity with a steeper slope can be observed; however, after 85 min, another linearity with a smoother slope was observed (optimum adsorption time is 85 min).

Experimental results show that the pseudo-second order model is an appropriate model for interpreting the adsorption process on SA2, SA3, SA4, SA5, SA6, SA7, and SA8. However, the adsorption of dex onto SA1 did not correspond with the pseudo-second order model. The diffusion mechanism was investigated via Weber and Morris intraparticle diffusion model. The of the first portion for all cases was higher than 0.95, indicating that the adsorption of dex followed the intraparticle diffusion model. However, the straight lines did not pass through the origin, which shows that the intraparticle step is the rate limiting step, and bulk diffusion may contributed to the diffusion control. The is greater than for all samples, which is assigned to the importance of intraparticle diffusion (stage II). Similar phenomena were observed for all adsorbents, which suggests that both film diffusion and intraparticle diffusion contributed to the adsorption process.

The adsorption equilibrium data were applied to the Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich isotherms. The maximum adsorption capacity for SA1 was measured to be and is larger than of SA7. This means that SA1 is a more appropriate nanocarrier for dex loading. The adsorption capacity was improved from (SA1) to (SA6) by increasing Au. According to the experimental data, the favorability of isotherms for SA1 and SA6 decreased in the order: Freundlich > Temkin > Langmuir > Dubinin–Radushkevich. However, for SA2 and SA7 adsorbents, Langmuir can predict the results more accurately than Temkin and favorability decreased in the order: Freundlich > Langmuir > Temkin > Dubinin–Radushkevich. For Temkin and Dubinin–Radushkevich models, the best fitting and highest values were obtained for SA1. The maximum heat of adsorption and minimum equilibrium binding energy for SA6 were obtained as and with regard to the Temkin isotherm.

Data Availability

The data are available on request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors thank the Alzahra Research Council for its financial support.

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Copyright

Copyright © 2021 Maryam Haghighi and Azadeh Khoshfetrat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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International Journal of Chemical Engineering

Au-O-MWCNTs and TiO2-O-MWCNTs as Efficient Nanocarriers for Dexamethasone: Adsorption Isotherms and Kinetic Studies

Abstract

1. Introduction

2. Materials and Methods

2.1. Materials

2.2. Instruments

2.3. Functionalization of MWCNTs

2.4. Adsorbent Preparation

2.5. Analysis of Dex Samples

2.6. Batch Experiments for Optimization, Kinetic and Isotherm Studies

2.6.1. Batch Studies for Optimum Temperature

2.6.2. Batch Studies for Surfactant Selection

2.6.3. Batch Studies for Contact Time, Kinetic and Isotherm Models

2.7. Kinetic Models

2.8. Isotherm Models

3. Results and Discussion

3.1. Characterization of Prepared Nanocomposites

3.2. Optimum Operating Conditions for Adsorption Batch Studies

3.2.1. Optimum Adsorption Temperature

3.2.2. Optimum Contact Time

3.2.3. Kinetic Models

3.3. Isotherm Models

4. Conclusion

Data Availability

Conflicts of Interest

Acknowledgments

References

Copyright

Au-O-MWCNTs and TiO₂-O-MWCNTs as Efficient Nanocarriers for Dexamethasone: Adsorption Isotherms and Kinetic Studies