Abstract
A novel intermittent microwave-assisted carbonization method was developed to prepare bio-char (BWSC) from Durian wood sawdust (BWS). The BWS was further activated using a base-catalytic approach to produce a graphitic form of bio-carbon (BWSAC). A three factor, two-level central composite (CCD) experimental design was used to maximize Pb(II) ion removal from aqueous solution using BWSAC. Three independent variables (initial pH of solution (pH0) ranging from 2 to 8, initial metal ion concentration of Pb(II) cations (C0) ranging from 50 to 100 mg/L, and contact time (Ct) ranging from 10 to 300 min) were consecutively coded as x1, x2, and x3 at three levels (−1, 0 and 1) of the design matrix. The experimental conditions in terms of actual factors were determined to be x1 (pH0) = 5.86, x2 (C0) = 57.77 mg/L, and x3 (temperature) = 53.85 °C, and the resultant Pb(II) ion removal efficiency (y1) obtained was 92.73%, with a model desirability of 0.974. The change in physiochemical properties after carbonization as well as activation was observed by scanning electron microscopy (SEM), X-ray diffraction (XRD), Brunauer, Emmett and Teller surface area analysis (BET), Thermogravimetric analysis (TGA), Ultimate analysis, and Fourier transform infrared spectroscopy (FTIR).
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Application of Graphitic Bio-Carbon using Two-Level Factorial Design for Microwave-assisted Carbonization
Zaira Z. Chowdhury, Wageeh A. Yehye,a,* Nurhidayatullaili Muhd Julkapli,a Mohammed Abdul Hakim Al Saadi,a Mautaz Ali Atieh,b
A novel intermittent microwave-assisted carbonization method was developed to prepare bio-char (BWSC) from Durian wood sawdust (BWS). The BWS was further activated using a base-catalytic approach to produce a graphitic form of bio-carbon (BWSAC). A three factor, two-level central composite (CCD) experimental design was used to maximize Pb(II) ion removal from aqueous solution using BWSAC. Three independent variables (initial pH of solution (pH0) ranging from 2 to 8, initial metal ion concentration of Pb(II) cations (C0) ranging from 50 to 100 mg/L, and contact time (Ct) ranging from 10 to 300 min) were consecutively coded as x1, x2, and x3 at three levels (−1, 0 and 1) of the design matrix. The experimental conditions in terms of actual factors were determined to be x1 (pH0) = 5.86, x2 (C0) = 57.77 mg/L, and x3 (temperature) = 53.85 °C, and the resultant Pb(II) ion removal efficiency (y1) obtained was 92.73%, with a model desirability of 0.974. The change in physiochemical properties after carbonization as well as activation was observed by scanning electron microscopy (SEM), X-ray diffraction (XRD), Brunauer, Emmett and Teller surface area analysis (BET), Thermogravimetric analysis (TGA), Ultimate analysis, and Fourier transform infrared spectroscopy (FTIR).
Keywords: Biomass; Graphitic carbon; Central composite design; Response surface methodology
Contact information: a: Nanotechnology and Catalysis Research Center (NANOCAT), IPS Building, University Malaya, Kuala Lumpur 50603, Malaysia; b: Qatar Environment and Energy Research Institute, Qatar Foundation, PO Box 5825, Doha, Qatar;
* Corresponding author: wdabdoub@um.edu.my
INTRODUCTION
The impending scarcity of non-renewable fossil fuels has led to the consideration of waste lignocellulosic biomass to produce biocarbon for waste water treatment. Thus, biomass can be regarded as one of the most expedient precursors to obtain carbon-rich materials. Preparation of bio-chemicals from lignocellulosic residues is a grand challenge of our time (Kobayashi et al. 2016).
Increasing attention is being given to the imminent health hazards caused by careless discarding of heavy metals into the aquatic environment (Chowdhury et al. 2012a). Mining and metallurgical waste waters are the major sources of heavy metal accumulation in the environment. Thus, the elimination of metal ions from waste streams has resulted in the expansion of new, cost-effective separation technologies.
Conversion of lignocellulosic residues for synthesis of activated char and adsorption of metallic pollutants from aqueous effluents is regarded as a useful substitute compared with conventional separation techniques (such as precipitation, coagulation, oxidation, electrochemical processes, and/or membrane processes) (Veglio and Beolchini 1997). Most of these conventional methods are often ineffective when the heavy metal ion concentration is in the range of 10 to 100 mg/L, and the permissible limit is less than 1 mg/L (Holan and Volesky 1994). In this context, the adsorption of metal ions onto activated char is considered an innovative approach. This method is inexpensive as well as competitive, and it has been shown to be more effective than various alternative measures (Volesky et al. 1992; Ucun et al. 2002). Adsorption is considered an ecofriendly, versatile treatment and is extensively used for the disposal of waste effluent and toxic gas elimination from air in process industries. The effectiveness of the adsorption process lies in the simplicity of process design, economic feasibility, and reuse of potential adsorbents for recycling and long-term applications. Adsorption using biomass-derived char has been ascertained to be the least expensive technique, especially for treating wastewater streams of low metal ion concentrations as well as maintaining the rigorous treatment of metallic pollutants in drinking water (Sahu et al. 2009a).
Among the metallic pollutants, lead is extremely toxic for living organisms, even at low concentrations. Metallic lead is used for storage batteries, fertilizers, pesticides, printing processes, dyes and pigments, fuels, and explosives (Jalali et al. 2002). It can cause damage to the nervous system, gastrointestinal track, kidneys, and reproductive system, predominantly in children (Feng et al.2004). It can cause diseases like anemia, encephalopathy, hepatitis, and nephritic syndrome (Lo et al.1999). Previous studies have reported that moderate poisoning from lead(II) cation exposure can result in neurobehavioral and intelligence discrepancies in humans (Selatnia et al. 2004; Chen et al. 2007).
Graphitic carbon materials with micro- and mesoporous textures and high surface area have attracted attention because of their potential application in super-capacitors, field emission, catalyst support, adsorption, and solar energy conversion (Kumar et al. 2012). With respect to the subtle electronic properties, antifouling properties, and ability to promote electron transfer reactions, graphitic carbon materials also find interesting application in biosensoring devices. In addition, because of their bioselectivity for redox enzymes, these materials have proven to be very useful for the quantification of various organic substances, including glucose (Lu et al. 2007), protein (Avramescu et al. 2002), DNA, pesticides (Daniel et al. 1997), and hydroquinone (Vieira and Fatibello-Filho 2000). Synthesis of graphitic biocarbon materials requires additional high-temperature treatment (Kumar et al. 2012). Microwave-assisted pretreatment offers the apparent advantage of carbonization of biomass at comparatively lower temperature and time, which is energy-efficient. It has been reported that microwave-assisted pre- and post-heating is a very effective method for modifying the surface chemistry of carbon fiber (Sathish-Kumar et al. 2012). Biocarbon is a black, non-toxic, solid carbonaceous material primarily derived from agricultural residues resembling a granular or powder form of charcoal (Chowdhury et al. 2013). With activation, its properties can be easily changed to have large internal surface area and porosity with high mechanical strength (Chowdhury et al. 2012b). Activated biochar- or biocarbon-based systems can remove a large variety of toxic pollutants from the solution phase with very high efficiencies (Liu and Zhang 2009; Han et al. 2013; Singanan and Peters 2013; Zhu et al. 2013; Mohan et al. 2014). Numerous attempts have been made to prepare biocarbon from lignocellulosic waste (Antal et al. 2007; Funke and Ziegler 2010; Jamari and Howse 2012). Recently, tamarind wood (Singh et al. 2008), digested sewage sludge and coconut husk (Tay et al.2001), palm shell (Adinata et al. 2007), rice husk (Sahu et al. 2009b), rubber wood sawdust (Srinivasakannan and Bakar 2004), bamboo waste (Ahmad and Hameed 2010), and rattan sawdust (Ahmad et al. 2009) have been utilized as low-cost feedstocks to obtain biocarbon with unique properties.
In this work, biochar was synthesized from Durian wood based sawdust (BWS) using a two-step microwave-assisted semi-carbonization and physio-chemical activation method. The prepared char sample (BWSC) was further activated to obtain mesoporous-structured graphitic carbon because liquid-phase adsorption is mainly dominated by mesopores. Durian (Durio zibethinus) is a kind of tropical fruit that is abundant in South East Asia. It belongs to the family Bombaceae and genus Durio(Jun et al. 2010). It is well-known as the “King of Fruits”. Malaysia produces Durio zibethinus commercially, and the annual production of durian in Malaysia was approximately 376,273 metric tons in the year 2008 (Department of Agriculture Malaysia 2009). There has been no research on the synthesis of bio-char from Durian wood sawdust using microwave-assisted carbonization and its subsequent activation for waste water treatment. The conventional method for analyzing a synthetic process, keeping other factors at an unstipulated persistent level, does not illustrate the combined effects of all the variables involved. In that case, the optimization process of a multivariable system typically outlines one factor at a time. This method involves a lot of experiments to be performed, and even then, it cannot disclose the interaction effects between the factors involved. This makes the overall process time-consuming, as it requires a large number of experiments to define optimum levels, which are not reliable enough for process design. Recently, several types of statistical investigational design methods have been developed for chemical process optimization. Experimental design method is a very advantageous contrivance for this purpose. It delivers mathematical models, which are helpful in explicating the interactions among the parameters under consideration (Alam et al. 2007). Among these methods, response surface methodology (RSM) is one of the most appropriate methods employed in many fields. The RSM technique is a collection of mathematical and statistical methods that is useful for developing, improving, and optimizing processes and can be employed to appraise the relative importance of numerous affecting factors, even in complex interactions. The purpose of using RSM is to define the optimum operational conditions for the system or to limit a region that contains the operating stipulations (Montgomery 2001). In this work, the influence of several operating parameters, i.e., initial concentration, temperature, and pH, were studied in batch mode using the activated biocarbon sample (BWSAC). The optimum values of the selected variables were determined by resolving the quadratic regression model, as well as by interpretation of the response surface contour plots.
EXPERIMENTAL
Materials
The starting sawdust sample of BWS derived from Durian wood was collected from a local sawmill in Penang, Malaysia. The sawdust was ground and sieved. The average particle size of the sieved biomass sample was kept at approximately 0.2 to 0.4 mm. The ground BWS sample was washed with deionized water and then dried at 110 °C for 24 h. It was stored in a sealed container for preliminary characterization. Hydrochloric acid (HCl) and potassium hydroxide (KOH) pellets were purchased from Sigma Aldrich, Malaysia. The chemicals purchased were of analytical grade.
Methods
Microwave-assisted carbonization of biomass wood sawdust (BWS)
The dried biomass sample was carbonized using deionized water under microwave irradiation (Ethos Plus, Millestone, USA), where the biomass to solvent ratio was kept at 1:25 (based on dry weight of biomass). One gram of biomass sample was added with 25 mL deionized water in 70 mL autoclave tube and placed inside the microwave for char synthesis. During the microwave-assisted carbonization process, the sample was irradiated at 2.45 GHz, and the power was kept at 750 W. The sample was carbonized for 60 min at a temperature of 220 °C under nitrogen flow. The char thus obtained was filtered and labeled BWSC. The BWSC sample was washed several times with hot deionized water. The sample was oven-dried at 50 °C and stored in an air-tight container for further characterization.
Fixed bed pyrolysis of biochar (BWSC)
The prepared biochar (BWSC) was impregnated with solid KOH pellets. The ratio between BWSC and solid KOH was kept at 1:0.5 (based on weight), which means that 100 g of char sample was mixed with 50 gram of KOH pellets. Approximately 500 mL of deionized water was added to the impregnated BWSC sample with constant stirring. The mixture with KOH pellets was then heated at 90 to 100 °C for 6 h to ensure the unclogging of pores as well as disruption of residual lignocellulosic linkages, with subsequent removal of partially burnt debris materials from the surface. The mixture was kept overnight in an oven at 105 °C to dry the sample completely. To enhance the surface area of the final product, it was further pyrolyzed in presence of carbon dioxide gas flow at 700 °C for 2 h and labeled BWSAC. KOH and CO2 gas flow were used as chemical and physical activating agent, respectively.
Multivariable design of experiment
In this work a central composite design (CCD) was applied to study the variables for adsorption of Pb(II) cations from aqueous solution using BWSAC in a batch process. The design CCD is extensively used for fitting an empirical second-order polynomial equation. Usually, CCD is based on 2n factorial runs, with 2n axial runs and nc center runs (six replicates). For two-level factorial designs, each variable is investigated at two levels. However, as the number of factors, n, increases, the number of runs for a complete replicate of the design also increases. The response of removal percentage (y1) and the corresponding factors were modeled and optimized by ANOVA. This helps to estimate the statistical parameters governing the sorption process. Overall, the optimization process contains three major steps: statistically designing and performing the experiments, determining the coefficients in a mathematical model, and analyzing the adequacy of the model according to Eq. 1,
(1)
where Y is the response of the system and Xi is the variable under investigation for the process. The goal is to optimize the response variable (Y). Table 1 summarizes the range of variables in terms of actual and coded factors.
Table 1. Independent Variables for Pb(II) Adsorption with their Actual and Coded Levels
It is expected that the independent variables are continuous and manageable by experiments with insignificant errors. The goal is to find an appropriate estimate for the accurate functional relationship between independent variables and the response surface (Gunaraj and Murugan 1999). The order of experimental runs was randomized to minimize the effects of the uncontrolled factors. The response obtained was used to develop an empirical model that correlated the response of adsorption uptake of lead ions from a single solute system using a second-degree polynomial equation, given by Eq. 2,
(2)
where Y is the predicted response, b0 is the constant coefficient, b1 is the linear coefficient, bij are the interaction coefficients, bii are the quadratic coefficients, and xi, xj are the coded values of the adsorption. For three variables, the recommended number of tests at the center point is six. Hence, the total number of tests (N) required for the three independent variables is:
N = 2n + 2n + nc = 2 
3 + (2 × 4) + 6 = 20 (3)
Once the desired ranges of values of the factors are predetermined, they are coded to lie at ±1 for the factorial points, 0 for the center points, and ± α for the axial points. The complete layout for the design matrix is provided by Table 2.
Batch adsorption study
An aqueous Pb(II) solution having an ion concentration of 1000 mg/L was prepared. A known amount of corresponding lead nitrate salt was dissolved in 1 L of distilled water to obtain stock solution. The stock solution thus obtained was diluted as required to obtain standard solutions of concentrations provided by the basic design matrix (Table 2).
The experiments were performed in a thermal water bath shaker at various temperatures for a predetermined period of time at 150 rpm using 250-mL Erlenmeyer flasks containing (50 
mL of various concentrations of Pb(II) ions solutions with 0.2 g of BWSAC, with the pH, concentration, and temperature levels defined by Table 2. Preliminary screening studies showed that the system reached equilibrium within 6 h; after that, the uptake percentage varied insignificantly. Thus the samples were taken out after 6 h and the residual concentration in the solution was analyzed using atomic absorption spectroscopy (AAS) (Perkin-Elmer-6000, Germany) after filtering the activated BWSAC sample.
Table 2. Central Composite Design and Experimental Responses for Adsorption Percentage onto BWSAC
The percentage removal of Pb(II) ions was calculated according to Eq. 4,
(4)
where C0 and Ce are the initial and equilibrium concentrations (mg/L) of Pb(II) ions in the solution, respectively.
Physicochemical characterization
The changes in the morphologies of BWS, BWSC, and BWSAC were observed using a scanning electron microscope (SEM, Model Leo Supra 50VP Field Emission, UK). The crystalline structure of the samples was analyzed using X-ray diffraction (XRD, Burker AXSD8 Avance, Germany) at 40 kV and 40 mA using Cu-Kα radiation sources. Thermogravimetric analysis coupled with a differential thermal analyzer (DTA) (Mettler Toledo Star SW901, Japan) was carried out to determine the thermal stability of the samples under a 5 mL/min nitrogen flow. In the TGA analysis, 7 mg of each sample was heated under a N2 flow at 1000 °C with a heating rate of 5 °C/min. The BET surface area, pore volume, and pore size distribution of BWS, BWSC, and BWSAC were analyzed with an Autosorb 1, Quantachrome Autosorb automated gas sorption system supplied by Quantachrome, Japan. The samples were outgassed under vacuum at 400 °C for 4 h to remove moisture content before nitrogen gas adsorption. The changes in the chemical functional groups were verified using the infrared spectroscopy technique. The FTIR spectra for BWS, BWSC, and BWSAC were recorded using a Bruker spectrometer model IFS 66V/S (USA). The FT-IR spectra were recorded in the range of 400 to 4000 cm−1.
RESULTS AND DISCUSSION
Experimental Design and Fitting of Mathematical Model
The results obtained for each trial conducted as per the experimental design matrix are given in Table 2. The solicitation of the response surface methodology for the estimated parameters indicated an empirical relationship between the response and input factors, which is illustrated by the following quadratic model (Eq. 5), where y1 represents the removal percentage:
y1 = 87.31 + 5.14x1 – 6.96x2 + 9.10x3 + 1.10x2 + 2.15x1x3 – 0.12x2x3
– 2312x12 – 498x22 – 8.09x32 (5)
The linear coefficients for the variables pH (x1), concentration (x2), and temperature (x3) demonstrate the effect of that individual variable on Pb(II) ion uptake from water by BWSAC. The constants shown as the product of two variables, namely x1x2, x2x3, and x3x1, illustrate the collective impact on those responses. x12, x22, and x32 denote quadratic results. A positive sign in front of the equation is an indication of the synergistic influence, whereas a negative sign indicates an incompatible influence of process factors on responses. The significance of each parameter shown in Eq. 5 was determined by the p-values (Montgomery 2001). The results obtained for the quadratic model for percentage adsorption (y1) in terms of ANOVA are provided in Table 3. The values of R2 and adjusted R2 are near 1.0 (Table 4), which suggests a high correlation between the observed and predicted values. This means that the proposed model delivers an excellent interpretation of the relationship between the independent variables (factors) and the response, y1.
Table 3. ANOVA Analysis and Lack of Fit Test for Quadratic Modeling of Removal Percentage of Pb(II) Ions from Synthetic Waste Water by BWSAC
The value for Prob > F obtained for the model is lower than 0.05 (i.e., a = 0.05, or 95% confidence), which indicated that the model is statistically significant. Investigation of the fit summary output illustrated by Table 3 revealed that the quadratic model is statistically significant for the desired response. Hence, it can used for further analysis. For the response of removal percentage, y1, pH (x1), and concentration (x2) had more moderate influences than temperature (x3), i.e., temperature had the greatest influence on removal percentage. The interaction influences of pH and concentration (x1x2) and pH and temperature (x1x3) were more prominent than the other interaction factor (x2x3) related to the percentage removal, y1. In this case, x1, x2, and x3 and their quadratic terms are significant.
Table 4. Statistical Parameters for Model Verification
From Table 4, it can be observed that the experimental R2 value was in reasonable agreement with the adjusted R2. The values obtained for co-efficient of variation (CV) and standard deviation were small, suggesting the reproducibility of the model. The values of adequate precision were determined based on signal-to-noise ratio. For effective model simulation, the adequate precision should be larger than 4. The adequate precision observed for y1 was 14.02. This revealed that the experimental data obtained here are statistically significant to prepare the design matrix of Table 2.
Figure 1 shows the linear plot of predicted versus experimental percentage removal of Pb(II) cations (y1) by BWSAC. As can be observed from this plot, the predicted values for the percentage removal were legitimately close to their experimental values. The R2 value for Eq. 5 was 0.949, which was near to unity, reflecting the suitability of the model.
Fig. 1. Predicted versus actual percentage removal of Pb (II) cations using BWSAC
Fig. 2. Normal probability plot for percentage removal of Pb (II) cations using BWSAC
Analysis of Residual Graphs
The normality of the experimental points can be checked by plotting the normal probability plot of the residuals for percentage removal for Pb(II) cations onto BWSAC. The normal probability plot is a graphical illustration for quantifying whether or not a data set is approximately normally scattered. The residual reflects the difference between the observed and the predicted value (or the fitted value) from the regression. If the data points on the plot are reasonably close to the straight line, then the data are expected to be normally distributed. Figure 2 shows the normal probability plot of residual values. The experimental points are reasonably associated with the straight line, signifying a normal distribution. Since the adsorption data points on the plot are scattered legitimately adjacent with the straight line, it is expected that the data are normally distributed (Abd Hamid et al. 2014).
Figure 3(a) shows Studentized residuals versus the fitted value whereas Fig. 3(b) shows Studentized residuals versus experimental runs for the percentage removal. The data points for residuals are randomly scattered about zero. This demonstrates that the variance of experimental observations is constant. All of the data points observed are found to fall within the range of +3 to -3. This confirms that no response transformation is needed for the experimental design of this study.
To investigate the interactive effect of two factors on the percentage removal of the Pb(II) ions, three-dimensional (3D) response surfaces with contour plots were drawn. The inferences obtained from those plots are discussed below.
(a)
(b)
Fig. 3. (a) Studentized residuals versus fitted plot and (b) studentized residuals versus run number for removal percentage of Pb(II) cations onto BWSAC
Interpretation of Response Surface Contour Plots
A response surface is the prediction of a contour plot as a 3D plane. To investigate the interactive effect of two factors on the percentage removal of the Pb(II) ions, 3D response surfaces with contour plots were drawn. The inferences thus obtained are discussed below. In this case, two variables were changed, but the third variable was set to its center value. Figure 4a shows a 3D response surface plot of the combined effect of pH and initial Pb(II) ion concentration on removal percentage. It is clear from the figure that the removal percentage increased with increasing pH up to 6.0, after which it decreased. It was also observed that removal percentage decreased with increasing metal ion concentration. At higher concentrations, there are more ions competing to be adsorbed; therefore, less removal efficiency was observed.
Fig. 4. (a) Response surface and (b) contour plots of the combined effects of pH (x1) and initial concentration (x2) when temperature (x3) is at its center point
When the solution pH was lower than 4, the removal percentage was low because of competition between positive hydrogen and metal ions at the binding sites of the prepared carbon (Pavan et al.2008). A relatively strong interaction between initial concentration and pH was reflected by the elliptical nature of the contour curve (Fig. 4b). It showed an increase in Pb(II) ion removal percentage from 53.35 to 82.61 (Fig. 4b). Thus, a combination of higher initial concentration and moderate range of pH can result in the maximum lead removal percentage. The metal solubility seemed to decrease at higher pH, and precipitation started to decrease the removal percentage at pH 8.5 (Yan and Viraraghavan 2003). At higher pH, precipitation of Pb(II) cations can block the pores of carbon, resulting in lower removal efficiency (Yan and Viraraghavan 2003).
Fig. 5. (a) Response surface and (b) contour plots of the combined effects of pH (x1) and temperature (x3) when initial concentration (x2) is at its center point
Figure 5 shows the combined effect of pH and temperature on the removal percentage of lead. The curve reveals that the removal percentage increased with increasing temperature (Fig. 5a). The robust interaction effect of pH and temperature can be illustrated by a circular contour plot (Fig. 5b). The increase in lead uptake with increasing temperature reflects the endothermic nature of sorption.
Alternatively, these effects might be attributed to the higher rate of diffusion of the pollutant species across the external boundary layer as well as inside the pores of the carbon particle with increasing temperature. Increasing temperature might decrease the viscosity of the solution (Wang and Zhu 2007). It has been reported that the active surface sites increase proportionally with increasing temperature to enhance the uptake capacity of the pollutant species (Bulut and Tez 2007).
Process Optimization
For successful adsorption process design, relatively high removal efficiencies are expected for targeted pollutants under optimum condition. As observed from the basic design matrix, when pH and temperature increased, the percentage ion removal increased up to a certain limit, whereas concentration had the opposite effect. To optimize the adsorption process, the targeted goal was set at maximum values for removal percentage, while the values of the three variables (pH, concentration, and temperature) were adjusted in the ranges under study. A weight can be assigned as 1 for each goal to regulate the form of its precise desirability function. The goals were combined into an overall desirability function. Desirability is an impartial function that ranges from zero, outside of the limits, to one, at the goal (Amini et al. 2008). The desirability ramp for optimization is provided by Fig. 6. The error was also evaluated, as shown in Table 5.
Fig. 6. Desirability ramp for numerical optimization of pH, initial concentration, temperature, and Pb(II) ion removal
Table 5. Numerical Optimization for Adsorption Percentage onto BWSAC
Physiochemical Characterization
The surface morphological features of BWS, BWSC, and BWSAC were observed by SEM and are illustrated in Fig. 7. The outer surface of BWS was comparatively smooth, with some minor folding. Woody biomass contains cellulose, hemicellulose, and lignin, with some pectin and wax. Lignin and hemicellulose helps to bind the cellulosic fiber in bundles, which makes their surface even, with less folding and cracks, as shown by Fig. 7(a). After microwave-assisted carbonization, the surface of BWSC was still smooth, with minor amount of circular pores (Fig. 7(b)). Small amount of debris were blocking the pores which might be due to dissolution of hemicellulose and lignin partially (Zhang et al. 2014). However after KOH treatment and activation using carbon dioxide gas flow, a large amount of irregular pores were visible on the surfaces of the activated BWSAC (Fig. 7(c)). This implies that char formation using microwave heating with a supplementary stage of activation with KOH can competently improve the pore volume up to a greater scale, which can subsequently increase the surface area with greater removal efficiency. This observation is further supported by BET analysis.
Fig. 7. SEM images of (a) BWS, (b) BWSC, and (c) BWSAC
The BET surface area and micropore surface areas of BWSC and BWSAC are provided in Table 6. The surface area of the biochar substantially increased after base catalytic treatment and the subsequent activation process. The average pore diameter of the activated char was 21.22 nm.
Table 6. Physico-Chemical Characteristics of BWSC and BWSAC
The surface functional groups, especially oxygen-containing groups, play a vital role in forming surface complexes with pollutant molecules, thereby initiating removal from waste water (Johari et al.2013). All samples were analyzed using FTIR. The FTIR analysis illustrates the changes in the surface functional groups of the sample with carbonization and pyrolysis. The FTIR peaks obtained for BWS, BWSC, and BWSAC are listed in Table 7. In the region of 3400 to 3600 cm−1, the spectra all show broad and strong bands because of the vibration of hydroxyl groups and the stretched vibration of adsorbed water molecules (Chowdhury et al. 2015). This implies that after carbonization and activation, the hydroxyl groups remained in the samples. The spectra also contain an aliphatic C-H band near 2800 to 3000 cm-1, which can be attributed to aliphatic stretching vibrations. The peak near the 1000 to 1400 cm-1 region represents C-O stretching in hydroxyl, ester, or ether, and O-H bending vibrations. The stretching vibration of C=O groups can be identified near 1450 to 1550 cm-1. Minor peaks near 450 to 950 cm-1 can be assigned to C-H out-of plane bending vibration and O-H stretching vibrations of C-O-H bands.
Table 7. List of FTIR Peaks Observed for BWS, BWSC, and BWSAC
X-ray diffraction studies of BWS, BWSC, and BWSAC were conducted to investigate the crystalline behavior of these samples. However, no remarkable variations were observed in the appearance of the XRD patterns for the BWS, BWSC, and BWSAC. This result was expected because the activation temperature used here was below 900 °C (Acharya et al. 2009; Liu and Zhao 2012). A similar phenomenon was reported during the preparation of activated char from tamarind wood and seed biomass (Sahu et al. 2008; Munusamy et al. 2011). Two diffraction peaks, at approximately 23.70 to 25.6o and 41.41o to 43.54o, were observed for BWSC and BWSAC, corresponding to the 002 and 101 planes of the disordered hexagonal graphitic structure of carbon. The broad peaks show the amorphous nature of the prepared carbon sample (Munusamy et al. 2011). This suggests that the crystalline structures of the prepared biochar and activated biochar remain unchanged after treatment with KOH and activation. A similar XRD pattern was observed for cashew nut shell-based activated carbon (Tangjuank et al. 2009). This type of structure is known as turbostratic and can be caused by two-dimensional parallel orientation of carbon layer planes (Azargohar and Dalai 2006). The crystalline behavior of the samples are given in Table 8.
Table 8. Crystalline Structures of BWSC and BWSAC Samples
The degree of graphitization of carbonaceous materials can be estimated from the value of the interlayer spacing (d). A greater stacking height (Lc) represents a thicker graphite-like structural layer. Similarly, a higher average carbon layer (Lc/d) further suggests a denser structure (Su et al. 2012). The values for interlayer spacing (d) were 0.317 nm and 0.338 nm for BWSC and BWSAC, respectively, similar to the ideal graphite spacing of 0.335 nm (Wenjing and Guangjie 2012). After activation at 700 °C, the value for d decreased but the value for crystallinity, Lc, and Lc/d increased. This implies that the aromatic structure of BWSC was changed to a graphitic structure at approximately 700 °C (Liu and Zhao 2012). A similar trend was observed for activated carbon prepared from liquefied wood after steam activation (Liu and Zhao 2012).
The thermal stability of BWS, BWSC, and BWSAC was investigated using the thermogravimetric method. The thermal degradation curve for all the samples shows several stages. The first degradation step at approximately 70 to 130 °C corresponds to the evaporation of adsorbed water. The second degradation step for BWS proceeds in the temperature range of 200 to 300 °C for hemicellulose and 300 to 400 °C for cellulose. It has been reported that the degradation of hemicelluloses partially overlaps with cellulose degradation in the case of biomass, and lignin degradation proceeds within the temperature range of 200 to 800 °C (Gronli et al. 2002; Putun et al. 2007). The maximum weight loss of BWS was observed near 365 °C because of the thermal degradation of cellulose. Compared to BWS, BWSC and BWSAC showed higher thermal stability. As the temperature increases, the sample contains a larger proportion of stable carbon particles, which are more heat-resistant. This phenomenon is evident and can be observed from Table 9 and Fig. 8.
The proportion of fixed carbon and ash was higher in BWSC and BWSAC than in BWS. The amount of volatile matter decreased substantially after the activation process, which released gas and liquid products because of the volatilization of organic compounds. The carbon content of the sample increased from 24.50% to 70.90% after activation of the bio-char. This illustrated that prepared biochar after base catalytic activation method was suitable to produce carbonaceous materials competent for adsorbent preparation. Similarly, ultimate analysis revealed that the elemental carbon content in BWSAC was higher than that in BWSC. The hydrogen content was reduced after the activation process.
Table 9. Proximate and Ultimate Analyses of BWS, BWSC, and BWSAC Samples
Fig. 8. TGA and DTG profile for (a) BWS, (b) BWSC, and (c) BWSAC
CONCLUSIONS
- Graphitic phase biochar (BWSAC) with high carbon content and surface area was successfully synthesized from Durian wood sawdust (BWS) using a two-step method that combined microwave-assisted carbonization with subsequent activation in the presence of a base catalytic medium under carbon dioxide gas flow.
- Microwave- assisted carbonization yielded biochar (BWSC) with low surface area, which was further activated for substantial removal of Pb(II) cations from a single solute system.
- Thermogravimetric analysis of the mesoporous carbon showed higher thermal stability with less ash content compared with BWS, and BWSC.
- The optimum removal efficiency of Pb(II) was 91.22%. The experimental conditions at this optimum point were pH = 5.86, C0 = 57.77 mg/L, and T = 53.85 °C.
- The fit of the model was checked in terms of determination coefficient (R2). In this study, the value of the determination coefficient obtained was R2 = 0.949. Furthermore, the value elucidated for the adjusted determination coefficient (adjusted R2 = 0.904) was also high, reflecting the significance of the predictive model.
ACKNOWLEDGMENTS
The authors would like to thank Grand Challenge (GC001B-14SBS) and BKP (BK054-2015) of University Malaya, Malaysia for their cordial support in completing this work.
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Article submitted: August 24, 2015; Peer review completed: December 18, 2015; Revised version received: February 15, 2016; Accepted: February 17, 2016; Published: March 1, 2016.
DOI: 10.15376/biores.11.2.3637-3659