# Optimization Studies on Methyl Orange (MO) Dye Adsorption using Activated Carbon Nanoadsorbent ofOcimum basilicum Linn Leaves

Authors: V. Aruna Janani; D. Gokul; N. Dhivya; A. Nesarani; K. Mukilan; A. Suresh Kumar; M. Vignesh Kumar
Journal: Journal of Nanomaterials
 (2023)

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## Abstract

This study is focused on the application of activated carbon nanoadsorbent derived fromOcimum basilicum Linn (sweet basil) leaves for the removal of methyl orange dye from an aqueous solution. The Ocimum basilicum Linn leaves are dried, powdered, cured with H2SO4, and thermally treated to form an activated carbon biosorbent. Sorbent characterization studies like scanning electron microscope (SEM) and Fourier transform infrared (FTIR) spectroscopy have revealed the adsorption of the methyl orange dye from their aqueous solution in the batch mode process. The biosorbent has shown a maximum adsorption capacity of 1.54 mg g−1 at 10 mg l−1 concentration, 1.2 g sorbent dosage, pH of 3, contact time of 180 min, and pHpzc at 3.9. Experimental results are analyzed using equilibrium models and it is found that the Langmuir isotherm model and kinetic model fit well and also the results have corresponded well with pseudo-first order. The intraparticle diffusion (IPD) mechanism has shown that pore diffusion occurs at a slower rate. The Elovich model has displayed that adsorption is affected by film diffusion. From the statistical optimization studies, it is demonstrated that Box–Behnken model can correlate the good agreement between experimental and predicted values. The highest adsorption capacity for the nanoadsorbent was found using quadrate models and optimizing the variables at a time of 237 min, initial dye concentration of 5.31 mg l−1, adsorbent dose of 1.22 g, and pH of 4.23.


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## Body

## 1. Introduction

Dyes are one of the specialty chemicals employed in various industries such as textiles, pharmaceutical, food, paper, and leather industries [1]. The dyeing segment is responsible for 15%–20% of overall wastewater flow. Also, World Bank states that 17%–20% of industrial water pollution originates from textile fabric dyeing activities [2]. Textile dyes are hazardous chemicals that are highly harmful to the environment and humans. Statistics cite that textile dyes are the top-ranked pollutant responsible for water pollution [3]. Bioremediation, electrochemical treatment, oxidation, adsorption, and solvent extraction [4–8] are some of the approaches that are being used to eliminate dye from aqueous solutions. An adsorption is a prominent approach among those methods. Adsorption offers several advantages, including reduced cost, modest plan, low investment, low reagent usage, and toxic insensitivity [9–13].In this work, we have chosen methyl orange (MO) dye as an adsorbate due to its high usage in textile, paper, food, and pharma productions. Because of the existence of the mono azo group in MO and its limited biodegradability, it is an issue for ecological concerns and public health hazards, and it must be removed from the water because of its toxicity. Dye pollutants have been removed from wastewaters using a variety of adsorbents. Because of its large internal surface area, porosity, pollutant removal efficiency, chemical composition, and eco-friendliness, activated carbons have captivated researchers’ attention [13, 14]. Despite activated carbon is the most effective adsorbent for dye adsorption, its expensive nature and regeneration issues have necessitated the quest for a less expensive, biodegradable, and efficient adsorbent. Several studies have recently been published on the cost-effective removal of dyes utilizing adsorbents including cassava peels [15], coconut tree sawdust [16], cocoa shells [17], hen feathers [18], lemon peel [19], jute fiber [20], pineapple stem [21], raw Posidonia oceanica fibers [22], and leaf-based adsorbents [23].Nanomaterials, due to their renowned chemical and physical properties and promising approach in usage as an adsorbent in waste-water treatment, have been a benchmark in recent trends as they show a gradual increase in effective removal of pollutants from waste water as surface area of particles is very high in nanoscale. In perticular, nanoadsorbents have made a significant contribution in removal of dye from their aqueous media than the other forms of adsorbents studied. Also it is noticeable that the nanoadsorbents show a very high chemical activity with dyes. Thus, this study is performed with nanobased adsorbents in order to improve the effectiveness of dye removal.In this work, we have employed the leaves ofOcimum basilicum Linn (sweet basil) as the precursor for an adsorbent material is collected from Pappampatti Pirivu located at the district of Coimbatore in Tamil Nadu, India. Sweet basil is a native plant of Asia, a short-lived annual plant. The taxonomical classification of the leaf is as follows: Kingdom—Plantae; Subkingdom—Tracheobionta; Superdivision—Spermatophyta; Division—Magnoliophyta; Class—Magnoliopsida; Subclass—Asteridae; Order—Lamiales; Family—Lamiaceae; Genus—Ocimum; Species—basilicum; Binomial name—Ocimum basilicum [24]. It can grow in a wide spectrum of soils at tropic- and moderate-tropic conditions without human supervision. The leaves of Linn are used as a traditional medicine in respiratory and skin disorders. Few reports exhibit the usage of leaf as a flavoring agent. Due to its resilient growing conditions and availability, we are drawn to use the leaves as the adsorbent material, and also there is no report of usage of sweet basil as an adsorbent for the removal of MO dye. Physicochemical examination of Linn leaves has shown extractive values of 3.7%, 6.24%, and 4% (w/w) of ether, water, and alcohol, respectively. 0.1% foreign matter is present in the leaves and the plant has 8.7% total ash value. Also the leaves’ powder exhibit light green color in day light and greenish-yellow in UV light. Phytochemical analysis exhibits the presence of triterpenoids steroids, sterols, tannins, phenolic compound, saponins, proteins, gums, mucilage, amino acids, flavonoid, glycoside, and flavones in the leaves [24]. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) methods were utilized to analyze all of the adsorbents employed. Freundlich and Langmuir’s adsorption isotherms were also evaluated to show the most suited isotherm data. The adsorption process resulting equilibrium, kinetics, and thermodynamics were also investigated. The fit of the adsorption isotherms and kinetic models to the experimental findings was assessed using the reduced chi-square test, residual root means square error, and average relative error.The process variables were adjusted using the response surface methodology (RSM) to get the best dye adsorption conditions. RSM is widely regarded as a crucial approach for refining particular responses that are impacted by factors. It delivers the greatest quantity of information with the least amount of effort [25–27]. As a result, we offer a complete investigation of leaves from sweet basil for adsorptive removal of MO in an aqueous media in this paper. Adsorption performance and theoretical calculations were used to determine its suitability.


## 2. Experimental Details

### 2.1. Adsorbent Preparation

The leaves of sweet basil, as shown in Figure1(a), are collected, rinsed with clean water, and dried under sunlight for 2 days. Later, it is soaked in a solution of sulfuric acid (30%) for 24 hr. After that, the leaves were washed several times with deionized water followed by the final alcohol wash as chlorophyll pigment present in leaves is completely removed by alcohol wash and does not affect water treatment. The washed leaves were subjected to oven drying at 150°C overnight. Then the leaves are crushed using mortar and heat-treated in a muffle furnace for 1 hr at 500°C under a nitrogen atmosphere. The black powder derived as shown in Figure 1(b) was reduced to a nanometre size, as shown in Figure 1(c), using the dry milling technique operated at 3 : 1 zirconium balls to feed ratio for about 4 hr and sieved. The particles are collected at a time interval of 30 min and analyzed in a particle size analyzer to identify the particle size in which the optimum size of 85 nm is obtained in a time of 4 hr dry milling [28]. There are some difficulties faced in separation of activated carbon nanoparticles after adsorption through centrifugation technique. For the same, magnetic separation technique was found to be the best method. The prepared activated carbon nanoparticles was magnetized using chemical precipitation technique. For this, 3.9 g of ferrous sulfate (FeSO4·7H2O) was mixed with 7.8 g of ferric chloride (Fe2Cl3) and was heated at 40°C in a hot air oven for 30 min. To this mixture, 3.3 g of prepared carbon was added, followed by dropwise addition of 100 ml of 5 M sodium hydroxide (NaOH) solution with continuous stirring. The resulting precipitate of ferric oxide (Fe2O3) was separated by filtration and taken for reuse after washing and drying at 60°C [29].Figure 1
(a)Ocimum basilicum plant; (b) activated Ocimum basilicum leaf; (c) nanoadsorbent.
(a)(b)(c)


### 2.2. Adsorbate Preparation

One gram of MO dye was dissolved in 1 L of deionized water to make the 1,000 ppm stock solution. The dilution method was used to create additional necessary concentrations. Using UV/vis spectrophotometer (Perkin–Elmer Lambda 25), the dye concentration was determined at 464 nm.


### 2.3. Adsorbent Characterizations

The activated carbon nanoadsorbent sample derived from sweet basil leaves was analyzed using Fourier transform infrared spectrophotometer (IR Tracer 100) to identify the available functional groups. The morphological behavior of the sample was studied by scanning electron microscope EVO18 (CARL ZEISS). The prepared dye solutions were investigated using a UV–vis spectrophotometer (JASCO). Textural characteristics like Brunauer–Emmett–Teller (BET) theory of surface area, microporosity area, total porosity area, microporosity volume, and external surface area were also evaluated.


### 2.4. Batch Adsorption and Desorption Studies

The dye aqueous solutions of different concentrations were prepared by dilution method using the stock solution. Batch adsorption experiments were carried out in 100 ml of dye solution in a 250 ml conical flask. The activated carbon derived from sweet basil leaves in nano size was used as an adsorbent. The following parameters were varied to conduct the batch experiment: initial dye concentration (5–25 mg l−1), sorbent dosage (1.2–2 g), pH (1–6), and contact time (0–240 min). The mechanism of adsorption is shown in Figure 2.Figure 2
Mechanism of adsorption.The experiments were carried out at room temperature of 35°C. For analysis, 2 ml of sample was withdrawn, separated using magnetic separation, and absorbance was measured in UV–vis spectrophotometer atλmax of 464 nm. The adsorbed quantity of MO dye was calculated by Equation (1) [30].(1)q=C0−Ceq×VW,where W is dosage amount (g), V is the volume of dye solution (l), q is adsorption capacity (mg g−1), and C0 and Ceq are the initial and equilibrium concentrations (mg l−1), respectively. Also, the percentage removal of dye was computed by Equation (2) [29].(2)q=C0−Ceq×100C0.Batch desorption studies were performed for MO dye removal using prepared nanoactivated carbonOcimum basilicum leaves regeneration. One gram dye contaminated nano-AC was mixed with 100 ml of 0.1 M NaOH eluent solution for 60 min at 500 rpm. The material undergo centrifugation after elution subjected to determine the MO concentration using spectrophotometer. Amount of MO desorbed is evaluated using the relation: ratio of desorption to adsorption concentration. The number of cycles for AC reusability was determined for optimum eluent solution with maximum MO desorption. Separation of dye from AC on treatment with eluent and subjected to distilled water wash is proceeded. It was soaked with distilled water overnight, solid AC was dried overnight after centrifugation. The process cycle was repeated for 10 cycles and MO removal dye efficiency was computed [31].


### 2.5. Experiment Design and Statistical Examination

The elimination of dye was investigated using RSM, a statistical modeling approach that uses data obtained from experiments to find the equation of regression model and optimum conditions [32]. Box–Behnken (BB) is a standard model used in this evaluation to identify optimum conditions [33]. The number of experimental runs is calculated by Equation (3).(3)N=2KK−1+N0,where N0 is the number of central points and K is the number of factors. There are 29 total runs as per the BB model. The analysis is performed in Design Expert 7.0.0. The adsorption capacity was taken as a dependent variable and dosage, initial dye concentration, pH, and time were taken as independent variables. The range of variables and experimental run data are given in Tables 1 and 2, respectively.Table 1
Experiment level and ranges of independent variables.
Independent variablesRange and level−10+1IDC51525SD1.21.62pH13.56Time0120240Table 2
Box–Behnken of uncoded values matrix design and their response.
Time (min)SD (g)IDC (mg l−1)pHExp.qt (mg g−1)12021510.3901.6151001.653.502401.61510.751201.6510.391201.6153.50.5401.2153.502401.6253.50.8201.615602402153.50.911201.6153.50.56


## 2.1. Adsorbent Preparation

The leaves of sweet basil, as shown in Figure1(a), are collected, rinsed with clean water, and dried under sunlight for 2 days. Later, it is soaked in a solution of sulfuric acid (30%) for 24 hr. After that, the leaves were washed several times with deionized water followed by the final alcohol wash as chlorophyll pigment present in leaves is completely removed by alcohol wash and does not affect water treatment. The washed leaves were subjected to oven drying at 150°C overnight. Then the leaves are crushed using mortar and heat-treated in a muffle furnace for 1 hr at 500°C under a nitrogen atmosphere. The black powder derived as shown in Figure 1(b) was reduced to a nanometre size, as shown in Figure 1(c), using the dry milling technique operated at 3 : 1 zirconium balls to feed ratio for about 4 hr and sieved. The particles are collected at a time interval of 30 min and analyzed in a particle size analyzer to identify the particle size in which the optimum size of 85 nm is obtained in a time of 4 hr dry milling [28]. There are some difficulties faced in separation of activated carbon nanoparticles after adsorption through centrifugation technique. For the same, magnetic separation technique was found to be the best method. The prepared activated carbon nanoparticles was magnetized using chemical precipitation technique. For this, 3.9 g of ferrous sulfate (FeSO4·7H2O) was mixed with 7.8 g of ferric chloride (Fe2Cl3) and was heated at 40°C in a hot air oven for 30 min. To this mixture, 3.3 g of prepared carbon was added, followed by dropwise addition of 100 ml of 5 M sodium hydroxide (NaOH) solution with continuous stirring. The resulting precipitate of ferric oxide (Fe2O3) was separated by filtration and taken for reuse after washing and drying at 60°C [29].Figure 1
(a)Ocimum basilicum plant; (b) activated Ocimum basilicum leaf; (c) nanoadsorbent.
(a)(b)(c)


## 2.2. Adsorbate Preparation

One gram of MO dye was dissolved in 1 L of deionized water to make the 1,000 ppm stock solution. The dilution method was used to create additional necessary concentrations. Using UV/vis spectrophotometer (Perkin–Elmer Lambda 25), the dye concentration was determined at 464 nm.


## 2.3. Adsorbent Characterizations

The activated carbon nanoadsorbent sample derived from sweet basil leaves was analyzed using Fourier transform infrared spectrophotometer (IR Tracer 100) to identify the available functional groups. The morphological behavior of the sample was studied by scanning electron microscope EVO18 (CARL ZEISS). The prepared dye solutions were investigated using a UV–vis spectrophotometer (JASCO). Textural characteristics like Brunauer–Emmett–Teller (BET) theory of surface area, microporosity area, total porosity area, microporosity volume, and external surface area were also evaluated.


## 2.4. Batch Adsorption and Desorption Studies

The dye aqueous solutions of different concentrations were prepared by dilution method using the stock solution. Batch adsorption experiments were carried out in 100 ml of dye solution in a 250 ml conical flask. The activated carbon derived from sweet basil leaves in nano size was used as an adsorbent. The following parameters were varied to conduct the batch experiment: initial dye concentration (5–25 mg l−1), sorbent dosage (1.2–2 g), pH (1–6), and contact time (0–240 min). The mechanism of adsorption is shown in Figure 2.Figure 2
Mechanism of adsorption.The experiments were carried out at room temperature of 35°C. For analysis, 2 ml of sample was withdrawn, separated using magnetic separation, and absorbance was measured in UV–vis spectrophotometer atλmax of 464 nm. The adsorbed quantity of MO dye was calculated by Equation (1) [30].(1)q=C0−Ceq×VW,where W is dosage amount (g), V is the volume of dye solution (l), q is adsorption capacity (mg g−1), and C0 and Ceq are the initial and equilibrium concentrations (mg l−1), respectively. Also, the percentage removal of dye was computed by Equation (2) [29].(2)q=C0−Ceq×100C0.Batch desorption studies were performed for MO dye removal using prepared nanoactivated carbonOcimum basilicum leaves regeneration. One gram dye contaminated nano-AC was mixed with 100 ml of 0.1 M NaOH eluent solution for 60 min at 500 rpm. The material undergo centrifugation after elution subjected to determine the MO concentration using spectrophotometer. Amount of MO desorbed is evaluated using the relation: ratio of desorption to adsorption concentration. The number of cycles for AC reusability was determined for optimum eluent solution with maximum MO desorption. Separation of dye from AC on treatment with eluent and subjected to distilled water wash is proceeded. It was soaked with distilled water overnight, solid AC was dried overnight after centrifugation. The process cycle was repeated for 10 cycles and MO removal dye efficiency was computed [31].


## 2.5. Experiment Design and Statistical Examination

The elimination of dye was investigated using RSM, a statistical modeling approach that uses data obtained from experiments to find the equation of regression model and optimum conditions [32]. Box–Behnken (BB) is a standard model used in this evaluation to identify optimum conditions [33]. The number of experimental runs is calculated by Equation (3).(3)N=2KK−1+N0,where N0 is the number of central points and K is the number of factors. There are 29 total runs as per the BB model. The analysis is performed in Design Expert 7.0.0. The adsorption capacity was taken as a dependent variable and dosage, initial dye concentration, pH, and time were taken as independent variables. The range of variables and experimental run data are given in Tables 1 and 2, respectively.Table 1
Experiment level and ranges of independent variables.
Independent variablesRange and level−10+1IDC51525SD1.21.62pH13.56Time0120240Table 2
Box–Behnken of uncoded values matrix design and their response.
Time (min)SD (g)IDC (mg l−1)pHExp.qt (mg g−1)12021510.3901.6151001.653.502401.61510.751201.6510.391201.6153.50.5401.2153.502401.6253.50.8201.615602402153.50.911201.6153.50.56


## 3. Results and Discussion

Before the adsorption process, the surface morphology of nanoadsorbent was studied using a SEM. The SEM image is shown in Figure3(a), and it revealed that the surface of the particle is uneven, coarse, peaked with lumps/pores, and surface dejections which facilitates the access between MO dye and sorbent as well improved adsorption. After adsorption, the surface of the adsorbent turns as smooth and which clearly depicts the deposition of dye particles on the surface as shown in Figure 3(b).Figure 3
SEM images ofOcimum basilicum Linn leaves-derived activated carbon nanoadsorbent of size 85 nm: (a) before adsorption; (b) after adsorption.
(a)(b)The textural characteristics of the prepared nanoadsorbent is calculated and the values of BET theory of surface area (m2/g), microporosity area (m2/g), total porosity area (cm2/g), microporosity volume (cm3/g), and external surface area (m2/g) are identified as 986.2, 473.4, 273.78, 0.38, and 0.43, respectively.The existence of functional groups of nanoadsorbent was determined using FTIR spectrum analysis and the results of before and after adsorption are displayed in Figures4(a) and 4(b), respectively. Adsorption is enhanced by the presence of oxygen groups at the surface. The presence of bonds as indicated by the peaks available at 3,290.56 (–OH stretch), 1,391.31 (–OH bend), 1,056.99 (–CO stretch), and 1,249.8 cm−1 (–OH in plane bend). Also the –OH vibration stretches have inferred the occurrence of adsorption as described in various literature [34–36]. FTIR peaks at 3,212 cm−1 for C–H stretching, O=S=O stretching at 1,170, 1,617 cm−1 corresponds C=O stretching, 816 cm−1 corresponds distributed para aromatic ring clearly depicts that the MO dye has been adsorbed on the surface of the adsorbent.Figure 4
FTIR spectra ofOcimum basilicum Linn leaves-derived activated carbon nanoadsorbent: (a) before adsorption; (b) after adsorption.
(a)(b)By employing the drift method, as shown in Figure5, the value of the zero-point charge is determined and it is found to be 3.9 (pHpzc) [37]. At pH above the value of pHpzc, the carbon surface possesses the net negative charge; on the other hand, at pH below pHpzc, the carbon surface shows the net positive charge. It is observed that the pH values between 1 and 3.9 lead to an increase in the qm (biosorbent uptake capacity) and above 3.9 pH show a decrease in qm. This behavior is possible due to the presence of an attractive electrostatic force between the negatively charged dye molecules and positively charged biosorbent surfaces [36]. So it is understood that the adsorption of MO anionic dye takes place at the lower pH values.Figure 5
The pHpzc of Ocimum basilicum Linn leaves-derived activated carbon nanoadsorbent determined by the pH drift method.As the initial dye concentration (IDC) is an important parameter used to define the adsorption phenomenon of dyes, from Figure6(a), the resulting influence of IDC on the adsorption of MO dye is exhibited. During this test, the IDC is varied from 5 to 25 mg l−1 which resulted in the qm value of adsorbent increased from 1.21 to 1.54 mg g−1, correspondingly. It is found that the IDC impacts greatly with adsorption characteristics such as sorbent dosage and maximum uptake capacity. From the test results, it is established that at the IDC concentration of 10 mg l−1, the biomass has shown a maximum uptake capacity of 1.54 mg g−1.Figure 6
Influence of different parameters on the removal of MO dye by activated carbon nanoadsorbent fromOcimum basilicum Linn leaves: (a) initial dye concentration effect; (b) sorbent dosage effect; (c) pH effect; (d) contact time effect.
(a)(b)(c)(d)The influence of sorbent dosage (SD) on the removal of MO dye with the biomass sample is studied and the findings are displayed in Figure6(b). The sorbent dosage is varied from 0.8 to 2 g and the qm value of adsorbent is raised from 0.829 to 1.357 mg g−1, respectively. Improved dye penetration is facilitated by the available large surface area of sorbent and accessibility of adsorption sites [38]. At qm = 1.2 g, the maximum uptake capacity reaches an equilibrium state. However, after 1.2 g, it is observed that the equilibrium time qt starts to decline due to the higher sorbent dosage level. Besides, equilibrium time qt of the biomass adsorbent is not completely utilized while testing the influence of lower SD values which can be ascribed to the saturation of sites by adsorption and agglomeration of biomass. From the above results and explanation, it is understandable that the equilibrium time qt and SD have an inverse relationship with each other.The results derived from the effect of pH on the removal of MO dye by activated carbon from sweet basil leaves are presented in Figure6(c). The equilibrium time qt has shown a gradual increase with pH until it reaches the value of 3, and above the pH value of 3, the qt value starts to reduce. This explains that electrostatic force is strong between the anionic dye and cationic sorbent in the range of pH ≤ 3. However, when the pH > 3, the establishment of nonionic functional groups on the adsorbent surface reduces the equilibrium time of dye. Therefore, we have chosen the pH = 3 as an optimum value for equilibrium and kinetics investigations. Also, these results are in agreement with the results derived by the drift method in zero-point charge analysis.In the construction of the adsorption process, the relationship between the contact time (CT), SD, and dye concentration plays a vital role. In this regard, the impact of contact time on the removal of MO dye by activated carbon is depicted in Figure6(d). From the results, it is inferred the equilibrium time and CT are in a direct relationship, i.e., adsorption increases with increase in time and attains equilibrium at the maximum adsorption capacity of adsorbent, which is attributed to the availability of adequate surface active sites on the sorbent surface for the adsorption of dye [38]. However, after reaching 180 min of CT, the equilibrium state has been reached due to the reduction of available active sites followed by the lowered concentration gradient of MO dye. As a result, the qt vs. time curve becomes stable and constant with increasing CT.To understand the interaction between the dye, i.e., adsorbate and biomass-derived activated carbon (adsorbent), the adsorption isotherms are employed. Equation (4) represents the linear version of the Langmuir isotherm [39].(4)1qe=1qm+1qmKL1Ce,where KL is the Langmuir adsorption constant (l mg−1) and qm is the maximum concentration of dye (mg g−1) that gets adsorbed on the adsorbent surface during monolayer development. The process of adsorption on a heterogeneous surface is mirrored by the Freundlich adsorption isotherm, which has consistent energy and allows the development of many layers. Freundlich’s isotherm has a linear form as given in Equation (5) [40](5)logqe=logKF+1nlogXe,where KF is the adsorption constant of Freundlich isotherm and n is the adsorption intensity. The smaller the value of n, the greater the adsorption capacity and the number of active sites available for adsorption. Table 3 correspondingly shows the Langmuir and Freundlich parameters derived from Figures 7(a) and 7(b). From the Langmuir isotherm calculation, the values of KL and qm were found to be 0.55 l mg−1 and 1.19 mg g−1, respectively. Additionally, from the Freundlich isotherm, the constant value KF is calculated as 1.563 l g−1. Langmuir isotherm has shown a higher degree of fitting of experimental values than Freundlich isotherm which suggests that the adsorption is highly oriented toward the monolayer phenomena.Figure 7
Adsorption isotherm plots of MO dye by activated carbon nanoadsorbent fromOcimum basilicum Linn leaves: (a) Langmuir isotherm; (b) Freundlich isotherm.
(a)(b)Table 3
Langmuir and Freundlich isotherm constants.
Langmuir adsorption isothermFreundlich adsorption isothermqm (mg g−1)KL (l mg−1)R2Kf (l g−1)n−1R21.190.550.9741.56321.8340.083The maximum adsorption capacity reported by various literature of researchers using activated carbon is compared and presented in Table4. Though the values of reported literature is high compared with our adsorbent, our adsorbent stands unique in terms of key considerable parameters for opting an adsorbent like raw-material availability, cost-efficient adsorbent, requires less process on utilizing as an adsorbent, etc.Table 4
Comparision of maximum adsorption capacity of our adsorbent with other reported adsorbent in recent literatures.
Name of the adsorbent usedAdsorption capacity reported (mg/g)ReferencesOcimum basilicum Linn1.54This studyBoiler residue77.101[41]Waste orange and lemon peel33[42]Activated carbon-reinforced conducting polyaniline192.52[43]Polyaniline-multiwalled carbon nanotubes149.25[44]Chitosan-coated sodium zeolite composite films287[45]By testing samples every 10 min, a kinetic analysis reflecting the quantity of dye adsorbed at different intervals of time was carried out. Pseudo-first-order and pseudo-second-order results are derived using the kinetic Equations (6) and (7) [46, 47](6)logQe−Qt=logQe+k12.303t,(7)log1Qt=1k2Qe2+tQe,where k1 and k2 are the pseudo-first-order and pseudo-second-order adsorption rate constants, respectively. The kinetics details of adsorption of MO dye on the biosorbent surface derived by pseudo-first-order and pseudo-second-order models are depicted in Figures 8(a) and 8(b) correspondingly. Also, the respective data sets are given in Table 5. It is visible from the results that the pseudo-first-order model is showing a better fit than the pseudo-second order model which is further justified by the values of coefficient of determination (R2) as shown in Table 5.Figure 8
Adsorption kinetics study graphs of MO dye adsorption by activated carbon nanoadsorbent fromOcimum basilicum Linn leaves: (a) pseudo-first-order model; (b) pseudo-second-order model; (c) Elovich model; (d) intraparticle diffusion model.
(a)(b)(c)(d)Table 5
Pseudo-first-order kinetics.
IDCqeq, exp (mg g−1)Pseudo-first orderPseudo-second orderk1 (l min−1)qeq, cal (mg g−1)R2K2 (g mg−1 min−1)qeq, cal (mg g−1)R251.320.00851.070.90880.00751.660.7641101.540.00981.190.9860.00701.910.8443151.470.01701.320.9490.00432.180.6223201.430.01171.320.85390.00451.850.1547251.210.01081.180.9550.02371.220.6246For the pseudo-first-order model, the determination coefficients vary from 0.854 to 0.986. Furthermore, the actual and predicted uptake capacity (qe) values were nearly identical. The coefficients of determination for a pseudo-second model, on the other hand, ranges between 0.155 and 0.844. In the pseudo-first-order model, R2 values are also closer to unity. As a result, it is clear that the pseudo-first-order equation is the best fit for our system and it indicates that physical adsorption is the governing adsorption mechanism.The disadvantage of pseudo-first and pseudo-second-order kinetic models is that they do not specify the process of diffusion. Due to this, the intraparticle diffusion model [48] and Elovich model [49] were employed by using Equations (8) and (9), respectively(8)qt=kidt0.5+Cid,(9)qt=1βlnαβ+1βlnt,where kid is the intraparticle diffusion rate constant (mg g min0.5), Cid is the intraparticle diffusion constant, α is the initial adsorption rate (mg g−1 min−1), and β is the desorption constant (g mg−1). It has been identified that either film diffusion or intraparticle diffusion controls the adsorption process, implying that one of the processes is the rate-limiting step. So the adsorption mechanism entails several phases, it stands to reason that the slowest of these steps would limit the total pace of the process. A plot between qt and √t should be a straight line with slope k and intercept C, as per the intraparticle diffusion model. If the lines pass through the origin, the only rate-controlling step is intraparticle diffusion.In this investigation, the plots present multidistant linear portions (Figure8(c)) indicating adsorption was not controlled only by intraparticle diffusion. The first linear portion was due to the external surface adsorption in which uptake rate was high due to the film diffusion of the solution onto the external surface of activated carbon. The later gradual various linear portions refer to slow intraparticle pore diffusion. This indicates that both external film and intraparticle pore diffusion mechanism were involved in the adsorption onto activated carbon.Using the Elovich model, the chemisorption nature of adsorption may be better understood. This model aids in the prediction of mass and surface diffusion, as well as activation and deactivation energy. The model implies that as the amount of dye adsorbed increases, the rate of dye adsorption falls exponentially. According to this model, a plot betweenqt vs. ln t is made and the diffusion mechanism of the process is examined, as shown in Figure 8(d). Elovich model graph reveals that there is a decrease in adsorption rate as the quantity of sorbate increases. Multilinear plots with very nearer values convey that rate decreases consequently as sorbate concentration on sorbent increases.To maintain process economic feasibility, it is important to proceed with desorption and regeneration studies. The maximum desorption of MO dye using 0.1 M NaOH was found to be 72.34%. During first regeneration cycle, the adsorption capacity decreases nearly 20% and this drastic drop in adsorption capacity is due to the surface damage caused by NaOH used. Adsorption capacity drops nearly 4% next two consecutive cycles and it remains constant for next two cycles without any further loss in adsorption capacity. After the fifth cycle, adsorption capacity was too low and thus the adsorbent cannot be used again after five cycles which is depicted in Figure9.Figure 9
Regeneration studies plot of adsorbent.Using the RSM technique, the BB model is employed with four factors at one level to examine the influence of independent variables on adsorption capacity. Also, experimental data model adequacy is examined for misleading or lack of results where, the linear, two-factor interaction, cubic, quadratic, and interactive degree polynomials are evaluated [50]. Model summary statistics and sequential model sum of squares were studied and results are displayed in Table 6. The results confirm that the linear model is significant and R2 values are maximum and the p-value is low which is further solidified with analysis of variance (ANOVA).Table 6
Model summary statistics and sequential sum of squares.
SourceSum of squaredfMean squareF valueProb >FR2AdjustedR2PredictedR2PressRemarksMean7.87317.873Linear3.05640.76425.817<0.00010.8110.7800.7051.111Suggested2FI0.17860.0301.0050.45240.8590.7800.5331.759Quadratic0.18340.0461.8310.17900.9070.8150.4662.010AliasedCubic0.27880.0352.9280.10370.9810.912−1.71110.210Residual0.07160.012Total11.639290.401The development of mathematical models is important to evaluate this study and comprehend the relationship between dependent and independent variables of this study. The relationship is a second-order polynomial equation developed by Box–Behnken matrix developed using the variables specified [51] and the final Equation (10) with coded factors is as follows:(10)Adsorptioncapacity=0.54+0.49×A−0.084×B−0.089×C+0.044×D−0.13×A×B−0.025×A×C+0.068×A×D+0.14×B×C−0.043×B×D−0.033×C×D−0.057×A2+0.090×B2+0.018×C2−0.11×D2.The models with the higherR2 (0.9073) and adjusted R2 (0.8145) values are the best fit. Further, the difference between R2 and adjusted R2 values of the regression equations is lower, which signifies that the closer the agreement between calculated and observed adsorption capacity. Model significance is examined by ANOVA results and it represents the correlation between responses and variables. A higher F value of 114.31 and associated p-value lower than 0.05 for the model indicates that the model is significant statistically surmise the precise and accuracy of the developed model [52]. It is desirable if the signal to noise ratio is greater than 4 and this model exhibits the ratio of 12 which conveys that model is highly desirable. CV% values also infer the reliability of experimental data. ANOVA results are given in Tables 7 and 8.Table 7
ANOVA for the model.
SourceRCSEp-valueModel3.4170.071<0.0001A-time2.8520.046<0.0001B-SD0.0850.0460.0862C-IDC0.0950.0460.0708D-pH0.0230.0460.3492AB0.0680.0790.122AC0.0020.0790.7562AD0.0180.0790.4071BC0.0780.0790.098BD0.0070.0790.5989CD0.0040.0790.6869A20.0210.0620.3736B20.0530.0620.1666C20.0020.0620.7759D20.0740.0620.1065RC: regression coefficient; SE: standard error.Table 8
ANOVA significance data.
CV %30.314Adeq. precision12.412R-squared0.90725381Adj.R-squared0.814507619Variation coefficient (CV) is defined as the ratio of estimated standard deviation to observed response mean value. The reproducibility and repeatability of the models are measured by this factor. The CV value of 3.314% was determined by the computations, indicating that the model is reasonably repeatable (because its CV was not greater than 10%).In 3D response plots, software-assisted techniques were employed. The adsorption capacity was shown as a function of any two factors (time (A), sorbent dosage (B), initial dye concentration (C), and pH (D)) while maintaining the other two constants in these plots. Figure10 shows the results of MO dye adsorption onto the adsorbent in terms of the relationship between AB, AC, AD, BC, BD, and CD.Figure 10
Response surface plots of: (a) AB, i.e., IDC vs. sorbent dosage; (b) AC, i.e., IDC vs. pH; (c) AD, i.e., IDC vs. time; (d) BC, i.e., sorbent dosage vs. pH; (e) BD, i.e., sorbent dosage vs. time; (f) CD, i.e., pH vs. time.
(a)(b)(c)(d)(e)(f)Based on available interaction sites, the dye removal capability of the adsorbent improved with an increase in the dose of the adsorbent. With a rise in starting dye concentration and adsorbent dose, the adsorption capacity of the adsorbent increases. This is because increasing the initial dye concentration enhanced the rate of bulk dye diffusion toward the adsorbent surface. With a rise in initial dye concentration and in lower pH value, the interaction between starting dye concentration and pH of the dye solution at a constant dosage of the adsorbent enhanced the adsorption capacity. As a result, it can be inferred that increasing the pH value has an inhibitory activity on all forms of adsorption interactions.In this study, the optimum conditions were obtained using second-order polynomial models which are evaluated using Derringer’s desirability function method [33]. Desired function method helps to obtain the following optimized conditions with a time of 237 min, sorbent dosage of 1.22 g, IDC of 5.31 mg l−1, and pH of 4.24, and it delivers the adsorption capacity of 1.56 mg g−1 with a desirability function of 1.000. All the values are presented in the desirability ramp as shown in Figure 11.Figure 11
Desirability ramp for optimization of MO dye removal by activated carbon nanoadsorbent fromOcimum basilicum Linn leaves.All the optimum values obtained through BB RSM studies are highly concurrent with the data achieved through several experimental studies. Isotherm, kinetic, and diffusion mechanisms are also valid due to their best fit of experimental data inferring the occurrence of adsorption.


## 4. Conclusion

The purpose of this study was to employ activated carbon derived fromOcimum basilicum Linn leaves in nano-size as an adsorbent to remove MO dye from an aqueous solution. Various approaches were used to characterize the adsorbent. Adsorbent demonstrated excellent adsorption efficiency for MO dye with a maximum adsorption capacity of 1.54 mg g−1 at 10 mg l−1 concentration, 1.2 g sorbent dosage, pH of 3, contact time of 180 min, and pHpzc of 3.9. The Langmuir isotherm model had the best match to the experimental data with a high coefficient of determination, according to the adsorption equilibrium. The kinetics of adsorption followed a pseudo-first-order model, indicating physisorption. The rate was not only controlled by intraparticle diffusion but also mathematical models for optimizing time, starting dye concentration, adsorbent dose, and pH were created. By using BB-RSM statistical analysis with a regression model for regression, the difference between adsorption capacity of predicted data (1.56 mg g−1) and experimental data (1.54 mg g−1) was found minimal and showed the best agreement. Finally, Ocimum basilicum Linn leaves, which have yet to be investigated for this purpose, appear to be a great option for removing MO dye from an aqueous solution due to their inexpensive cost.



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