Kinetic and thermodynamic studies on methylene blue biosorption using corn-husk

Oana Maria Paşkaa, Cornelia Păcurariua and Simona Gabriela Muntean *b
aPolitehnica University of Timişoara, Faculty of Industrial Chemistry and Environmental Engineering, P-ţa Victoriei No. 2, 300006, Timişoara, Romania
bInstitute of Chemistry Timisoara of the Romanian Academy, 24 Mihai Viteazul, 300223, Timisoara, Romania. E-mail: sgmuntean@acad-icht.tm.edu.ro; Fax: +40-256-491824; Tel: +40-256-491818

Received 15th September 2014 , Accepted 10th November 2014

First published on 12th November 2014


Abstract

A low-cost waste biomass derived from corn plant (husk) was tested as an alternative to other expensive treatment options, for the removal of methylene blue (MB), from aqueous solutions. The effects of different experimental parameters, such as biosorbent dosage, dye concentration, contact time, and temperature, on the adsorption process were investigated. An optimum value of discoloration was observed at pH 6.0 and 2 g L−1 biomass dosage. The amount of dye removed per adsorbent unit decreased with increasing adsorbent dosage, temperature, and increased with increasing contact time, and concentration. Experimental data were modeled using first-order, pseudo-second-order, Elovich, and intraparticle diffusion kinetics models. The adsorption kinetics of MB could be described by the pseudo-second order reaction model. The experimental data were fitted to: Langmuir, Freundlich, Temkin, Redlich–Peterson, Toth, and Sips isotherm models and the best fitting was obtained with the Sips model. The thermodynamic parameters (ΔH°, ΔS° and ΔG°) obtained revealed that MB adsorption is a spontaneous, exothermic and physical process. The obtained results indicated that corn husk as a low-cost biomaterial is an attractive candidate for the removal of basic dye MB from aqueous solutions.


Simona Gabriela Muntean graduated in 1994 as chemical engineer at Simona University of Timisoara, Romania. In 1997 she obtained the Master of Science degree in chemistry at Politehnica University of Timişoara, In Organic Synthesis. From 1995 to 1999 she worked as Assistant Research, from 1999 to 2008 as Research Scientist and since 2008 as Senior Research Scientist, at the Institute of Chemistry Timisoara of Romanian Academy. From 2000 to 2008 she was Lecture at The “Victor Babes” University of Medicine and Pharmacy Timisoara, Faculty of Pharmacy. Simona Gabriela Muntean took her PhD in the field of Chemistry at The Romanian Academy, in 2009. The research interest of Dr Simona Gabriela Muntean is Synthesis, Characterization and Application of Dyes, in the topics of Dye Aggregation and Dyes Removal. Now she has published more than 45 papers in national and international journals, and one book. During the past 15 years she successfully completed many research projects and developed various processes. She was also supervisor of two Diploma (undergraduate) theses. Since 2000 is member of the Romanian Society of Chemistry. Dr Simona Gabriela Muntean is Editor-in-Chief of New Trends and Strategies in the Chemistry of Advances Materials (Timisoara). She was appointed as member in national committees and reviewer for several chemistry journals.


Introduction

Contamination of water, air, and soil with pollutants is a consequence of the development of industry, which is necessary for the satisfaction of modern human needs. Because of that, one of the most serious problems that scientists have faced in the past two decades is the development of methods for decreasing environmental pollution levels.1 Synthetic dyes represent one of the most important groups of organic pollutants with an estimated annual production of 7 × 108 kg per year and over 100[thin space (1/6-em)]000 commercially available dyes.2,3 These compounds have a wide application in many fields of industry, such as: textile, paper, printing, rubber, plastics, leather, cosmetics, food and pharmaceutical industries.

Dyeing industry effluents present a high degree of coloration and the release into the environment without treatment may affect the ecosystem due to the toxicological impact and mutagenic character of dyes,4,5 and by reduction of sunlight penetration and photosynthetic activity.3 Also, dyes can be accumulating in sediment and soil, causing different problems to the ecological balance of the environment.6

Synthetic dyes present a complex aromatic structure with high stability to aerobic digestion, light, temperature, detergent and microbial attack,1,5 which makes them stable and resistant to biodegradation methods present in the environment, and to different chemical treatments.2,7

The conventional methods of dye removal involve the combination of physical and chemical processes such as adsorption, precipitation, sedimentation, ultrafiltration, oxidation processes, ozonation, coagulation/flocculation, ion exchange and reverse osmosis, and also biological degradation methods.2,5,8 However, these methods often have the disadvantage of a high operating cost, formation of hazardous by-products or intensive energy requirements.1,5,8 From these methods, adsorption has proved to be an effective alternative process, being an economic method that presents a simple operating design and highly efficiency for the removal of dyes from wastewater.2,7 For wastewater treatment, commercial activated carbon is currently the most widely used adsorbent due to its high adsorption capacity, surface area and degree of surface reactivity as well as a microporous structure. However, its use is limited by the operating cost and regeneration issues.1,2,9 Therefore, non-conventional low-cost alternatives have been searched in the last years, easy available, renewable and environmentally friendly materials that can successfully replace the classical adsorbents. For this purpose, different agricultural biomasses were examined for the removal of different classes of dyes: bean,10 kohlrabi peel,11 capsicum seeds,12 olive pomace,13,14 wheat waste,15,16 rice hull,17 sugar beet pulp,18 ginger,19 silk cotton hull,20 orange,1,21 pineapple leaf,22 coconut bunch,9 cones,3,5 sawdust;4,23,24 with or without chemically modifying.16,17,23 These materials are abundant in nature, by-products or waste from different industries, which involve a very low acquisition cost. Utilization of different by-products for the wastewater treatment could be helpful not only economically, but also to the environment by solving the solid waste disposal problem.25

The biggest problem when vegetable origin biosorbents are used is that they are only locally available and their transport over long distances would make the process economically unprofitable. Therefore, the aim of this study is to investigate the adsorbent properties of corn husk; corn is widely cultivated throughout the world, according to FAOSTAT organization (Food and Agriculture Organization of the United Nations), worldwide production was 844 × 106 tones in 2010, more than any other grain, adapting easily to different climate conditions, from which 9 × 106 tones represents the production on the Romanian territory.

Bioremoval of a pollutant using biosorbents is affected by several factors like the chemical nature of pollutant, specific surface properties of the biosorbent and environmental conditions.18

Methylene blue (MB) was selected as a basic dye model in order to evaluate the adsorption capacity from aqueous solutions of corn husk, an agro-waste renewable and without economic value. MB does not belong to the class of the most dangerous pollutants, but acute exposure causes difficult breathing, increased heart rate, nausea, vomiting, diarrhea, shock, jaundice, quadriplegia, and tissue necrosis in humans.4,13,15 Methylene blue can be used in different fields, including coloring paper, temporary hair colorant, dyeing cottons and wools.4 The investigations were performed also using activated carbon, in order to compare the adsorption capacity of corn husk with those of activated carbon. The effects of contact time, adsorbent dose, initial dye concentration and temperature on MB adsorption were evaluated. The kinetics, thermodynamic parameters and the factors controlling the adsorption process were also calculated and discussed.

Materials and methods

Materials

Corn husk was obtained from a small farm near Timişoara, Romania. The waste material was abundantly washed with tap water in order to remove the dust and in the end with distilled water and dried in oven to 353 K until constant weight. The dried biomaterial was crushed using a domestic grinder and sieved. It were selected particles smaller than 630 μm. Powdered biosorbent was stored for further use without any other chemical or physical modification. Corn husk was characterized by means of FT-IR spectroscopy. FT-IR spectrum was recorded using a Shimadzu Prestige-21 spectrometer in the range 400–4000 cm−1, using KBr pellets and resolution of 4 cm−1.

Activated carbon was purchased from UTCHIM-ROMANIA. Like the corn husk, it was crushed and sieved; particles smaller than 630 μm were selected.

Methylene blue (basic dye, chemical formula: C16H18ClN3S; molecular weight 319.86 g mol−1) was supplied by CHEMICAL – ROMANIA and it was not purified prior to use. The chemical structure of MB is presented in Fig. 1.


image file: c4ra10504d-f1.tif
Fig. 1 Molecular structure of the MB dye.

Stock dye solution (1000 mg L−1) was prepared by dissolving in distilled water; desired concentrations were prepared by dilution of the stock solution.

Batch biosorption experiments

Biosorption studies were performed in Erlenmeyer flask containing 50 mL of dye solution, and stirring at 200 rpm. In order to determine the optimum biosorption conditions, the experiments were conducted using different biosorbent mass, in the range of 1.0 to 3.0 g L−1, and different dye concentrations in the range 20 to 100 mg L−1. The effect of temperature on the biosorption process was studied at three different temperatures: 298, 313 and 333 K. Studies were carried out using an UVmini-1240 SHIMADZU spectrophotometer by monitoring the absorbance changes at maximum absorbance wavelength (665 nm).

The assessment of biosorption capacity was done by calculating the adsorption capacity (1), and the removal percentage (2) of the MB dye:

 
image file: c4ra10504d-t1.tif(1)
 
image file: c4ra10504d-t2.tif(2)
where: qt is the amount of dye adsorbed per unit of bisorbent (mg g−1); η the percentage of dye removal (%); C0 initial concentration of dye solution (mg L−1); Ct dye concentration at different periods of time (mg L−1); Ce dye concentration at equilibrium (mg L−1); V volume of solution (L); W mass of biosorbent (g).

Results and discussion

Biosorbent characterization

The chemical composition of the corn husk is: 82.7% carbohydrates and 6.6% lignin26 which determines the existence of numerous hydroxyl groups and aromatic rings. The FT-IR spectrum of corn husk is presented in Fig. 2.
image file: c4ra10504d-f2.tif
Fig. 2 The FT-IR spectrum of corn husk.

The broadband at 3200–3500 cm−1 is attributed to O–H stretching vibration in phenolic and aliphatic structures.27,28 The band at 2916.37 cm−1 is assigned to C–H stretching vibration in aromatic methoxyl groups and in methyl and methylene groups of side chains.28 The band at 1732.08 cm−1 indicates the presence of C[double bond, length as m-dash]O stretching of carbonyl group. The bands at 1633.71 cm−1, 1517.97 cm−1 and 1429.25 cm−1 are characteristic for the aromatic skeleton vibrations. The band at 1373.32 cm−1 can be assigned to aliphatic C–H bending vibrations and phenolic OH.27 The band at 1249.87 cm−1 can be assigned to C–O–C antisymmetric stretching vibration and the band at 1055.99 cm−1 can be attributed to C–O–C symmetric vibration plus C–OH in primary alcohols.27,29,30 The band at 1161.15 cm−1 and 1105.25 cm−1 can be assigned to C–OH stretching vibrations in phenol and respectively in secondary alcohols.27,30

This result is in accordance with the composition of lignocellulosic materials. The FTIR spectrum indicates that the corn husk presents different functional groups which may be potential biosorption sites for MB dye.

Effect of biosorbent mass

Biosorbent mass is an important factor, influencing the adsorption capacity for a given initial concentration of the dye solution. The effect of biosorbent mass in the adsorption of MB was followed using an initial dye concentration of 20 mg L−1 and varying the corn husk mass in the range 1–3 g L−1. Fig. 3 shows the variation in time of the amount of the dye adsorbed for three different biosorbent quantities (1, 2 and 3 g L−1).
image file: c4ra10504d-f3.tif
Fig. 3 The influence of the biosorbent mass (20 mg L−1, 298 K, pH 6) on the MB dye removal.

The results (Table 1) indicate that the quantity of dye adsorbed per unit of dry biosorbent decreased, and the uptake of the dye increase from 90.3% to 93.6% with increasing the biosorbent mass.

Table 1 Influence of different parameters for the adsorption for the MB on corn husk
  Initial dye concentration (mg L−1) Mass of biosorbent (g L−1) Temperature (K)
20 30 50 70 100 1 2 3 298 313 333
qe (mg g−1) 9.34 13.86 22.44 30.14 41.55 18.06 9.34 6.24 9.34 9.26 8.85
η (%) 93.40 92.39 89.74 86.12 83.10 90.30 93.40 93.60 93.40 92.55 88.54
te (min) 34 42 53 65 85 60 34 26 34 28 16


By increasing the biosorbent dosage the number of active sites available for adsorption increased, facilitating the adsorption of dyes, which explain the increase of removal percentage. The decrease of adsorption capacity by increasing the biosorbent dosage (higher weight of biosorbent per dye ratio) is maybe due to the unsaturation of biosorbent sites, during the adsorption process. Similar results have been reported by other authors.15,31,32,35 The further studies were carried out using 2 g L−1 biosorbent mass.

Effect of initial dye concentration and contact time

The evolution of the amount of the uptake dye as function of the contact time for different initial dye concentrations is presented in Fig. 4.
image file: c4ra10504d-f4.tif
Fig. 4 Influence of the initial concentration and contact time (2 g L−1, 298 K, pH 6) onto the MB dye removal.

The obtained data presented in Table 1 show the increase of the amount of MB adsorbed at equilibrium, and the decrease of the percentage of dye removal with increasing the initial dye concentration.33,34,36 It can be also noted the increase of equilibrium time with increasing the initial dye concentration,37 due to the fact that adsorption can occur both at the surface and in the pores of the biosorbent, and the diffusion to the internal adsorption sites requires a longer time.

It is evident that the increase of the amount of MB adsorbed is fast in the first hour of the process, and becomes much slower near the equilibrium. This can be explained by the large number of vacant sites available at the initial stage which gradually are occupied in time as a result of sorption process. These results indicating that the dye removal is concentration dependent.

Corn husk presents a good adsorption capacity of 41.55 mg g−1, indicating that it could be considered a promising material for the removal of MB dye from aqueous solution.

Effect of temperature

The influence of temperature on the adsorption process was investigated at 298, 313, and 333 K, using a MB solution with 20 mg L−1 initial concentration, and pH 6. The results are presented in Fig. 5.
image file: c4ra10504d-f5.tif
Fig. 5 The influence of the temperature (20 mg L−1, 2 g L−1, pH 6) onto the MB dye removal.

Watching the process in time, it can be observed that the necessary time for reaching equilibrium decreases from 80 minutes to 45 minutes with the increase of temperature, most of the dye being adsorbed in the first hour of the process. The adsorption capacity and the percentage of MB removal decreases with the increase of temperature suggesting that biosorption of MB is an exothermic process (Table 1). Similar results have been obtained for MB removal by other researchers.38–40,62

These results are economically advantageous because the MB removal can be conducted at environmental temperature (25 °C) without additional costs for power generation.

Comparison between the adsorption capacity of corn husk and activated carbon

The efficiency of the corn husk as adsorbent for the removal of MB was compared with those of activated carbon. Fig. 6 shows comparatively, the amount of MB removed as a function of contact time using corn husk, and respectively activated carbon as adsorbents.
image file: c4ra10504d-f6.tif
Fig. 6 The amount of MB removed using corn husk, respectively activated carbon as adsorbents (C0 20 mg L−1, 298 K, pH 6, biosorbent mass 2 g L−1).

It can be noticed that the amount of MB removed by corn husk (9.34 mg g−1) is comparable to those removed by activated carbon (9.83 mg g−1). The very close removal capacity for the two adsorbents and the notable economic advantages of using corn husk, recommend it as a viable alternative for the MB removal.

Biosorption kinetics

Kinetic studies provide information regarding the kinetic parameters and the mechanism of MB biosorption onto corn husk. The biosorption kinetics of MB was investigated using four kinetic models, namely pseudo-first-order (3), pseudo-second-order (4), Elovich equation (5), and intraparticle diffusion models (6).41–43
 
ln(qeqt) = ln[thin space (1/6-em)]qek1t (3)
 
image file: c4ra10504d-t3.tif(4)
 
image file: c4ra10504d-t4.tif(5)
 
qt = kit0.5 + l (6)
where, qe is the amount of solute adsorbed at equilibrium per unit weight of adsorbent (mg g−1); qt is the amount of solute adsorbed at any time (mg g−1); k is the adsorption rate constant, α is the initial sorption rate (mg g−1 min−1), β is the desorption constant (g mg−1), and l is the effect of boundary layer thickness.

The initial biosorption rate, h (mg g−1 min−1), can be defined as:

 
h = k2qe2 (7)

The correlation coefficients were used to determine the best fitting kinetic model. The comparison of the experimental adsorption capacity values (obtained at different temperatures) with the computed results estimated from eqn (3)–(6), are presented in Table 2.

Table 2 Kinetic parameters for biosorption of MB onto corn husk
Kinetic model Kinetic parameters Temperature
298 313 333
qe,exp (mg g−1) 9.34 9.26 8.85
First-order kinetic model qe,calc (mg g−1) 12.17 3.01 0.31
k1 × 102 (min−1) 11.98 9.73 7.36
R2 0.9853 0.9367 0.6525
SD 0.1909 0.3302 0.5516
Pseudo-second-order kinetic model qe,calc (mg g−1) 9.48 9.19 8.78
k2 × 102 (g mg−1min−1) 3.93 6.87 15.24
h (mg g−1min−1) 3.53 5.79 11.75
R2 0.9998 0.9999 0.9999
SD 0.0175 0.0240 0.0405
Elovich model Α 180.61 18.23 × 103 30.38 × 1011
Β 1.03 1.61 3.67
R2 0.9320 0.9016 0.8824
SD 0.2967 0.2322 0.1121
Intrapart diffusion ki 0.88 0.67 0.30
c 4.43 5.66 7.20
R2 0.9651 0.9826 0.9873
SD 0.2656 0.1421 0.0544


The correlation coefficient close to unity, low standard deviation, and experimental values for qe similar to the calculated ones (Table 2) indicate that MB biosorption process is described by the pseudo-second-order model (Fig. 7).


image file: c4ra10504d-f7.tif
Fig. 7 Pseudo-second order kinetic model fitting for the adsorption of MB dye on corn husk.

Similar results were reported for MB adsorption on meranti sawdust,4 olive pomace and charcoal,13 scolymus hispanicus,35 and banana stalk waste.44 As the temperature increases, the k2 constant increases indicating that the necessary time for reaching the equilibrium decreases with increasing temperature, and that the adsorption of MB on corn husk is an exothermic process.

In order to explain the diffusion mechanism the intraparticle diffusion model was used. The dye adsorption process involves several steps: dye diffusion through solution to the outer surface of the adsorbent (film diffusion), dye adsorption on the outer surface of the adsorbent, dye diffusion from the surface into the adsorbent interior (intraparticle diffusion) and, dye adsorption onto the active centres in the interior surface of the adsorbent. From the plot of qt versus t0.5 the values of intraparticle diffusion rate constant (ki) and the effect of boundary layer thickness (l) were calculated (Table 2). As can be seen from Fig. 8, the plots are not linear over the whole time range which means that the intraparticle diffusion is not the rate determining step of the biosorption mechanism of MB onto corn husk. The boundary layer diffusion was also significant.16 Maximum is the intercept length (l), adsorption is more boundary layer controlled.


image file: c4ra10504d-f8.tif
Fig. 8 The intraparticle diffusion of MB by corn husk.

The second-order rate constants listed in Table 2 were used to calculate the activation energy for MB biosorption on corn husk, using Arrhenius equation:

 
image file: c4ra10504d-t5.tif(8)
where: E is the activation energy (J mol−1), k2 is the rate constant of biosorption (g mg−1 min−1), A is the pre-exponential factor (g mg−1 min−1), R is the general gas constant (J mol−1 K−1) and T is the temperature (K).

The activation energy was calculated from the slope of linear fitted function of ln(k2) versus 1/T and was found to be 32.02 kJ mol−1, which indicates that the MB biosorption is a physical process. This value is of the same order of magnitude with the value in the literature.15,23,34,45

Biosorption isotherms

The study of the equilibrium biosorption isotherms is the most appropriate way in designing and assessing the performance of the biosorption process. Biosorption data, obtained at equilibrium, plotted as qe, function of Ce, were fitted to five different sorption isotherm models: Langmuir, Freundlich, Temkin, Redlich–Peterson, Toth and Sips.46,47 Two two-parameter and three three-parameter equilibrium isotherm models were used to fit the experimental data as follows:

Langmuir isotherm model represented by the eqn (9) is based on the assumptions:5,48 finite number of identical sites, homogeneously distributed over the adsorbent surface; monolayer coverage of adsorbate over the adsorbent surface; no interaction between the adsorbent molecules and the heat of adsorption is independent of the coverage of adsorbent surface.

 
image file: c4ra10504d-t6.tif(9)
where qe is the amount of dye adsorbed at equilibrium (mg g−1); Ce is the equilibrium dye concentration in the solution (mg L−1); qm is the maximum biosorption capacity of the biosorbent (mg g−1); and KL is the Langmuir constant (L mg−1) and is related to the free energy of biosorption.

The essential characteristic of Langmuir isotherm can be expressed using a dimensionless constant, the separation factor RL:5,49

 
image file: c4ra10504d-t7.tif(10)
where C0 is the initial concentration of the dye (mg L−1). The value of this coefficient indicates whether the isotherm is or not favourable: for 0 < RL < 1 the model is favourable; for RL > 1 the model is unfavorable and for value RL = 0 the model is irreversible.

The Freundlich empirical isotherm is applicable to the adsorption on heterogeneous surfaces with interaction between adsorbed molecules. In this case, the heat of adsorption exponentially decreases with the coverage of adsorbent surface.5 The Freundlich isotherm is expressed as:48

 
qe = KFC1/ne (11)
where KF is the Freundlich constant (mg1−1/n L1/n g−1), indicating the adsorption capacity of the adsorbent and 1/n (dimensionless) is a constants related to the intensity of adsorption.

The Temkin isotherm assumes the linear decrease of the heat of adsorption with the coverage of the adsorbent surface due to some adsorbate–adsorbent interaction.6,48 The Temkin isotherm is expressed as:35,49

 
image file: c4ra10504d-t8.tif(12)
where: KT is the Temkin isotherm equilibrium binding constant (L g−1), b is the Temkin isotherm constant B = RT/b is a constant related to the heat of adsorption (J mol−1).

The Redlich–Peterson isotherm is an empirical with three parameters equation. This isotherm is valid over a wide range of concentrations and combines elements from Langmuir and Freundlich isotherms. The Redlich–Peterson isotherm is expressed as:50,51

 
image file: c4ra10504d-t9.tif(13)
where: KR is the Redlich–Peterson constant (L g−1), αR is also a constant ((L g−1)β) and β is an exponent varying between 0 and 1.

Toth isotherm is derived from Langmuir equation with the purpose of reducing errors arising from experimental data. This model presumes a quasi-Gaussian distribution of energy. This is a three parameters isotherm, fits multilayer adsorption processes and is expressed by the equation:35,51

 
image file: c4ra10504d-t10.tif(14)
where: qm the Toth maximum adsorption capacity (mg g−1), KT the Toth equilibrium constant, and t is the Toth isotherm constants.

The Sips isotherm is a three-parameter empirical equation, a combination of the Langmuir and Freundlich models. This equation is based on the Freundlich equation assumption, where the amount of adsorbed dye increases with the increase of initial concentration, but Sips equation presumes that the adsorption capacity has a finite limit when the concentration is sufficiently high. This model is represented as:35,51

 
image file: c4ra10504d-t11.tif(15)
where: KS is the Sips constant ((L mg−1)n) and n is the Sips model exponent. For n = 1, eqn (17) reduces to Langmuir isotherm (eqn (9)). Alternatively, for low equilibrium concentration, close to 0, the Sips isotherm reduces to the Freundlich isotherm (eqn (11)).

Analysis of adsorption isotherms

The experimental equilibrium data for MB biosorption onto corn husk were fitted to the six isotherms models by plotting qe versus Ce.

The analysis of the experimental data and determination of isotherms parameters were performed using the non-linear regression analysis from ORIGIN 6.1 software package. The main statistical criteria were the squared multiple regression coefficient (R2), and the chi-square analysis (χ2) (16).

 
image file: c4ra10504d-t12.tif(16)
where: qe is the equilibrium capacity (mg g−1) obtained from experimental data, and qe,m is the equilibrium capacity obtained by calculating from the model (mg g−1).

The data obtained for the fitted models are presented in Table 3, and the comparison between experimental data and the best fitted sorption isotherm curves is presented in Fig. 9.

Table 3 The isotherm parameters for the biosorption of MB on corn husk
Parameters Isotherm
Langmuir Freundlich Temkin Redlich–Peterson Toth Sips
K 0.19 14.58 3.11 8.24 1.12 0.16
qm 47.95 78.05 50.69
RL 0.025–0.173
n 3.76 1.19
b 294.14
αR 0.16
β 1.01
t 0.35
R2 0.9944 0.9565 0.9815 0.9929 0.9841 0.9956
χ2 1.75 13.85 4.22 2.57 5.79 1.58



image file: c4ra10504d-f9.tif
Fig. 9 Correlations between experimental data and sips adsorption isotherm for MB biosorption on corn husk.

Comparing the correlation coefficients of the analyzed isotherms, it follows that the Sips model yields a better fit for the experimental equilibrium data than the other isotherms. These results suggest that the adsorption process of the MB dye is following a combined Freundlich and Langmuir model: diffused adsorption at low dye concentration, and a monomolecular adsorption with a saturation value – at high dye concentrations. Similar results were reported for the adsorption of MB on yellow passion fruit waste.50,52

The maximum adsorption capacity of the corn husk determined from the Sips sorption isotherm curves was 50.69 mg g−1, and the values are higher or comparable with the data reported before (Table 4). This data indicates that corn husk can be considered a promising material for the removal of MB dye from aqueous solution.

Table 4 Comparison of adsorption capacity of various biosorbents for MB dye
Adsorbent Adsorption capacity (mg g−1) Ref.
Neem leaf 8.76–19.61 54
Palm-trees waste 8.4 30
Data stones 8.8 30
Fly ash 13.42 55
Yellow passion fruit 14 28
Wheat shells 16.56–21.50 14
Oak sawdust 29.94 56
Cherry sawdust 39.84 56
Indian rosewood sawdust 11.8–51.4 57
Rice husk 40.58 58
Corn husk 18.06–41.55 This work
Coconut bunch 30.42–65.55 9


In order to establish if the biosorption process is favorable or not, the RL factor was determined from the Langmuir isotherm model.

The obtained values for RL parameter were in the range of 0.025–0.173 (Table 3), quite close to zero, indicating that the MB biosorption process on corn husk is favorable, and it is a relatively irreversible reaction.9,44,49,53

Biosorption thermodynamics

The thermodynamic parameters of biosorption process, including free energy change (ΔG°), enthalpy change (ΔH°) and entropy change (ΔS°) were calculated using the following equations:
 
ΔG° = −RT[thin space (1/6-em)]ln[thin space (1/6-em)]K (17)
 
image file: c4ra10504d-t13.tif(18)
 
image file: c4ra10504d-t14.tif(19)
where K is the equilibrium constant, R is the universal gas constant (J mol−1 K−1), T is temperature (K), qe is the amount of MB biosorbed on the corn husk per liter of the solution at equilibrium (mg L−1), Ce is the equilibrium concentration of the MB in the solution (mg L−1).

The enthalpy ΔH° and entropy ΔS° of biosorption process were estimated from the slope and intercept of the plot of ln[thin space (1/6-em)]K versus 1/T (figure not show).

The negative values of ΔG° (Table 5), calculated using K, indicate that the biosorption of MB onto corn husk is thermodynamically possible and it is a spontaneous process.

Table 5 Thermodynamic parameters for the biosorption of MB on corn husk
T (K) K ΔG° (kJ mol−1) ΔH° (kJ mol−1) ΔS° (J mol−1 K−1)
298 14.1515 −6.5651    
313 12.5135 −6.5755 −4.3792 15.9408
333 7.6957 −5.6497    


The negative value of ΔH° confirms the exothermic nature of biosorption process. The value of ΔH° for the present study is less than 20 kJ mol−1 (Table 5), indicating that MB biosorption on corn husk is likely a physical process, which is in agreement with the results obtained from activation energy.34,59,60

The positive value of ΔS° reflects the increased randomness at the solid-solution interface during the biosorption of MB on corn husk.49,61

Conclusions

The present study demonstrated that corn husk, a low-cost agro-waste biomass, can be successfully used as adsorbent for the removal of MB dye from aqueous solutions. The amount of dye removed increase with increasing the contact time, and initial dye concentration, and the percentage of dye removal increase with increasing the adsorbent dosage. The rate of adsorption was found to comply with pseudo-second-order kinetics with a correlation higher than 0.99. Equilibrium data fitted with the Sips isotherm equation, gave a maximum adsorption capacity of 50.69 mg g−1. The obtained results at different temperatures indicate that MB adsorption onto corn husk is an exothermic and spontaneous process. The very close removal capacity for the corn husk compared with activated carbon and the notable economic advantages of using corn husk, recommend it as a viable alternative for the MB removal. The sorption process can be conducted at ambient temperatures with best results.

Taking into consideration the presented results, it can be concluded that corn husk can be an alternative adsorbent reported to other more expensive adsorbents used for coloured wastewater treatment.

Acknowledgements

This work was partially supported by the strategic grant POSDRU 107/1.5/S/77265, inside POSDRU Romania 2007–2013 co-financed by the European Social Fund – Investing in People. This work was supported by Program 2 of the Institute of Chemistry Timisoara of Romanian Academy (Research Project 2.3.).

Notes and references

  1. M. Asgher and H. N. Bhatti, Ecol. Eng., 2010, 36, 1660 CrossRef PubMed.
  2. G. Crini, Bioresour. Technol., 2006, 97, 1061 CrossRef CAS PubMed.
  3. F. Deniz, S. Karaman and S. D. Saygideger, Desalination, 2011, 270, 199 CrossRef CAS PubMed.
  4. A. Ahmad, M. Rafatullah, O. Sulaiman, M. H. Ibrahim and R. Hashimb, J. Hazard. Mater., 2009, 170, 357 CrossRef CAS PubMed.
  5. T. Akar, A. S. Ozcan, S. Tunali and A. Ozcan, Bioresour. Technol., 2008, 99, 3057 CrossRef CAS PubMed.
  6. A. Khaled, A. El Nemr, A. El-Sikaily and O. Abdelwahab, Desalination, 2009, 238, 210 CrossRef CAS PubMed.
  7. N. A. Oladoja, C. O. Aboluwoye, Y. B. Oladimeji, A. O. Ashogbon and I. O. Otemuyiwa, Desalination, 2008, 227, 190 CrossRef CAS PubMed.
  8. S. Kara, C. Aydiner, E. Demirbas, M. Kobya and N. Dizge, Desalination, 2007, 212, 282 CrossRef CAS PubMed.
  9. B. H. Hameed, D. K. Mahmoud and A. L. Ahmad, J. Hazard. Mater., 2008, 158, 65 CrossRef CAS PubMed.
  10. S. T. Akar, A. S. Özcan, T. Akar, A. Özcan and Z. Kaynak, Desalination, 2009, 249, 757 CrossRef CAS PubMed.
  11. R. Gong, X. Zhang, H. Liu, Y. Sun and B. Liu, Bioresour. Technol., 2007, 98, 1319 CrossRef CAS PubMed.
  12. S. T. Akar, A. Gorgulu, T. Akar and S. Celik, Chem. Eng. J., 2011, 168, 125 CrossRef PubMed.
  13. F. Banat, S. Al-Asheh, R. Al-Ahmad and F. Bni-Khalid, Bioresour. Technol., 2007, 98, 3017 CrossRef CAS PubMed.
  14. T. Akar, I. Tosun, Z. Kaynak, E. Ozkara, O. Yeni, E. N. Sahin and S. T. Akar, J. Hazard. Mater., 2009, 166, 1217 CrossRef CAS PubMed.
  15. Y. Bulut and H. Aydın, Desalination, 2006, 194, 259 CrossRef CAS PubMed.
  16. R. Gong, S. Zhu, D. Zhang, J. Chen, S. Ni and R. Guan, Desalination, 2008, 230, 220 CrossRef CAS PubMed.
  17. S. T. Ong, C. K. Lee and Z. Zainal, Bioresour. Technol., 2007, 98, 2792 CrossRef CAS PubMed.
  18. Z. Aksu and I. A. Isoglu, Chem. Eng. J., 2007, 127, 177 CrossRef CAS PubMed.
  19. R. Kumar and R. Ahmad, Desalination, 2011, 265, 112 CrossRef CAS PubMed.
  20. K. S. Thangamani, M. Sathishkumar, Y. Sameena, N. Vennilamani, K. Kadirvelu, S. Pattabhi and S. E. Yun, Bioresour. Technol., 2007, 98, 1265 CrossRef CAS PubMed.
  21. L. D. Fiorentin, D. E. G. Trigueros, A. N. Modenes, F. R. Espinoza-Quiñones, N. C. Pereira, S. T. D. Barros and O. A. A. Santos, Chem. Eng. J., 2010, 163, 68 CrossRef CAS PubMed.
  22. S. Chowdhury, S. Chakraborty and P. Saha, Colloids Surf., B, 2011, 84, 520 CrossRef CAS PubMed.
  23. F. A. Batzias and D. K. Sidiras, J. Hazard. Mater., 2007, 149, 8 CrossRef CAS PubMed.
  24. A. E. Ofomaja and Y. S. Hob, Bioresour. Technol., 2008, 99, 5411 CrossRef CAS PubMed.
  25. F. Delval, G. Crini and J. Vebrel, Bioresour. Technol., 2006, 97, 2173 CrossRef CAS PubMed.
  26. B. Barl, C. G. Biliaderis, E. D. Murray and A. W. Macgregor, J. Sci. Food Agric., 1991, 56, 195 CrossRef CAS.
  27. K. Nakanishi and P. H. Solomon, Infrared Absorption Spectroscopy, 2nd edn, Holden-Day, Inc., 1977 Search PubMed.
  28. C. G. Boeriu, D. Bravo, R. J. A. Gosselink and J. E. G. van Dam, Ind. Crops Prod., 2004, 20, 205 CrossRef CAS PubMed.
  29. H. Yang, R. Yan, H. Chen, D. Ho Lee and C. Zheng, Fuel, 2007, 86, 1781 CrossRef CAS.
  30. R. Singh, S. Singh, K. D. Trimukhe, K. V. Pandare, K. B. Bastawade, D. V. Gokhale and A. J. Varma, Carbohydr. Polym., 2005, 62, 57 CrossRef CAS PubMed.
  31. I. Osasona, O. L. Faboya and A. O. Oso, Br. J. Appl. Sci. Technol., 2013, 3, 1006 CrossRef PubMed.
  32. M. C. Somasekhara Reddy, L. Sivaramakrishnab and A. Varada Reddy, J. Hazard. Mater., 2012, 203–204, 118 CrossRef CAS PubMed.
  33. S. Patil, S. Renukdas and N. Patel, Int. J. Environ. Sci., 2011, 1, 711 CAS.
  34. T. Yan and L. Wang, BioResources, 2013, 8, 4722 Search PubMed.
  35. N. Barka, M. Abdennouri and M. EL Makhfouk, J. Taiwan Inst. Chem. Eng., 2011, 42, 320 CrossRef CAS PubMed.
  36. A. S. Alzaydien, Am. J. Environ. Sci., 2009, 5, 197 CrossRef CAS.
  37. T. Khan, S. R. M. Kutty and M. Chaudhuri, Adsorpt. Sci. Technol., 2010, 28, 657 CrossRef CAS PubMed.
  38. Y. Liu, Y. Zheng and A. Wang, J. Environ. Sci., 2010, 22, 486 CrossRef CAS.
  39. A. Gürses, Ç. Doğar, M. Yalçın, M. Açıkyıldız, R. Bayrak and S. Karaca, J. Hazard. Mater., 2006, 131, 217 CrossRef PubMed.
  40. D. B. Jirekar, A. A. Pathan and M. Farooqui, Orient. J. Chem., 2014, 30, 1263 CrossRef CAS.
  41. Y. S. Ho and G. McKay, Trans IChemE, 1998, 76, 183 CrossRef CAS.
  42. W. J. Weber Jr and J. C. Morriss, J. Sanit. Eng. Div., Am. Soc. Civ. Eng., 1963, 89, 31 Search PubMed.
  43. J. Crank, The Mathematics of Diffusion, Clarendon Press, Oxford, 1979 Search PubMed.
  44. B. H. Hameed, D. K. Mahmoud and A. L. Ahmad, J. Hazard. Mater., 2008, 158, 499 CrossRef CAS PubMed.
  45. M. Dogan, Y. Ozdemir and M. Alkan, Dyes Pigm., 2007, 75, 701 CrossRef CAS PubMed.
  46. K. Vijayaraghavan, T. V. N. Padmesh, K. Palanivelu and M. Velan, J. Hazard. Mater., 2006, 133, 304 CrossRef CAS PubMed.
  47. T. V. N. Padmesh, K. Vijayaraghavan, G. Sekaran and M. Velan, Biorem. J., 2006, 10, 37 CrossRef CAS.
  48. O. Hamdaouia and E. Naffrechoux, J. Hazard. Mater., 2007, 147, 381 CrossRef PubMed.
  49. Z. Belala, M. Jeguirim, M. Belhachemi, F. Addoun and G. Trouvé, Desalination, 2011, 271, 80 CrossRef CAS PubMed.
  50. F. A. Pavan, E. C. Lima, S. L. P. Dias and A. C. Mazzocato, J. Hazard. Mater., 2008, 150, 703 CrossRef CAS PubMed.
  51. O. Hamdaouia and E. Naffrechoux, J. Hazard. Mater., 2007, 147, 401 CrossRef PubMed.
  52. B. Royer, N. F. Cardoso, E. C. Lima, J. C. P. Vaghetti, N. M. Simon, T. Calvete and R. C. Veses, J. Hazard. Mater., 2009, 164, 1213 CrossRef CAS PubMed.
  53. R. Mahmood and A. Naseer, Afr. J. Pure Appl. Chem., 2013, 7(4), 173–178 CAS.
  54. K. G. Bhattacharyya and A. Sharma, Dyes Pigm., 2005, 65, 51 CrossRef CAS PubMed.
  55. S. Wang, Y. Boyjoo and A. Choueib, Chemosphere, 2005, 60, 1401 CrossRef CAS PubMed.
  56. F. Ferrero, J. Hazard. Mater., 2007, 142, 144 CrossRef CAS PubMed.
  57. V. K. Garg, M. Amita, R. Kumar and R. Gupta, Dyes Pigm., 2004, 63, 243 CrossRef CAS PubMed.
  58. V. Vadivelan and K. V. Kumar, J. Colloid Interface Sci., 2005, 286, 90 CrossRef CAS PubMed.
  59. Z. Hu, H. Chen, F. Ji and S. Yuan, J. Hazard. Mater., 2010, 173, 292 CrossRef CAS PubMed.
  60. M. A. Zulfikar and H. Setiyanto, Int. J. ChemTech Res., 2013, 5, 1671 CAS.
  61. S. Nethaji, A. Sivasamy and A. B. Mandal, Int. J. Environ. Sci. Technol., 2013, 10, 231 CrossRef CAS PubMed.
  62. P. Monash and G. Pugazhenthi, Korean J. Chem. Eng., 2010, 27, 1184 CrossRef CAS PubMed.

Footnote

Electronic supplementary information (ESI) available. See DOI: 10.1039/c4ra10504d

This journal is © The Royal Society of Chemistry 2014
Click here to see how this site uses Cookies. View our privacy policy here.