Egusi seed shells (ESS) were used as precursor for the preparation of two activated carbons (ACs) following H 3PO 4 and ZnCl 2 activation. The effect of factors controlling the preparation of ACs such as chemical activating agent concentration (2 - 10 M), activation temperature (400°C - 700°C) and residence time (30 - 120 min) were optimized using the Box-Behnken Design (BBD). The optimized activated carbons based H 3PO 4 (ACP) and ZnCl 2 (ACZ) were characterized by N2 adsorption, elemental analysis, atomic force microscopy (AFM), Boehm titration and Fourier transformed infrared (FTIR) techniques. The specific surface area was found to be 1053.91 and 1009.89 m 2·g -1 for ACP and ACZ respectively. The adsorbents had similar surface functionalities and were both microporous. The effect of various parameters such as initial pH, concentration, and contact time on the adsorption of nitrate ions on ACP and ACZ in aqueous solution was studied. ACZ demonstrated better adsorption capacity (8.26 mg·g -1) compared to ACP (5.65 mg·g -1) at the same equilibrium time of 20 min. The adsorption process was governed by a “physical interactions” phenomenon for both adsorbents.
The main source of contamination of surface and ground water by nitrate ions is the excessive use of nitrogen fertilizers in agriculture. And the untreated waste water, released from industrial and municipal sites. This increases in disposal of nitrates in the environmental causes, eutrophication of water bodies which stimulates the rapid growth of algae and aquatic plants and consequently, affects fish and other aquatic life negatively [
Among the methods used for the removal of nitrate ions from waste and drinking water, adsorption has been shown to be the better economical and efficient alternative [
Physical, chemical and physicochemical activation are the three common methods used for production of ACs. Physical activation involves two steps: carbonization and activation at high temperature (600˚C - 1200˚C) in presence of activating agents such as steam and/or CO2 [
Egusi (Cucumeropsis mannii Naudin) is an herbaceous annual plant belonging to the large family of Cucurbitaceae. It is cultivated for seed which is commonly used in food as condiment and thickener (in soup) in Nigeria, Cameroon, Ghana, Middle East, Uganda and other African countries [
In the preparation of ACs by chemical activation, several factors including the carbonization temperature, residential time, impregnation ratio, heating rate influences the properties of the obtained AC. Such multivariate systems, require numerous trials to thoroughly investigate the factors which control the system [
In present work, we use the Box-Behnken Design (BBD) to optimize the preparation conditions of ACs from ESS by chemical activation using two activating agents (ZnCl2 and H3PO4). The optimized ACs were characterized to determine their specific surface area (N2 adsorption), surface morphology (Atomic Force Microscopy), elemental analysis, major functional groups (FT-IR spectroscopy and Boehm titration), pH of point of zero charge (pHPZC), and used as adsorbent for the removal of nitrate ions from aqueous solution.
The Egusi Seed Shells (ESS) were collected from Mokolo, a local market in the Centre Region of Cameroon. They were washed with deionized water and dried for 24 h at 105˚C. The dried ESS were ground and sieved to 1 - 1.25 mm sizes. Impregnation was carried out by adding 50 mL of ZnCl2 or H3PO4 (2 - 10 M) to 10.0 g of ESS and stirred constantly for 12 h at room temperature (27˚C), to ensure completion of reaction between activating agent and ESS particles. This mixture was filtered and the residue was dried in an oven at 105˚C for 24 h [
The iodine adsorption test is employed to determine the adsorption capacity of AC, and IN value is used to determine if an AC is microporous (0 - 2 nm). IN value is obtained as the quantity of Iodine (I2) adsorbed per gram of AC on a milligram scale. The IN for all samples were obtained following ASTM D4907 − 94 method [
The MBN is a measure of the mesoporosity (2 - 50 nm) of an AC, and is obtained as the amount of dye adsorbed on 1 g of adsorbent [
The activated carbon yield was calculated as the dry weight of obtained AC to raw material according to Equation (1),
Yield ( % ) = m A C m 0 × 100 (1)
where mAC and m0 are dried mass of AC and the dried mass of raw material respectively.
The ACs preparation was studied using the Box-Behnken Design (BBD). BBD reduces the number of experiments with no loss of accuracy and estimates complex response functions more effectively, compared to other design [
N = k 2 + k + C p = 3 2 + 3 + 3 = 15 (2)
where k and Cp are the number of variables studied and the number of central points (replicates) respectively [
The three variables studied are the concentration of activating agent (H3PO4 or ZnCl2) (x1), carbonization temperature (x2) and residence time (x3). These variables were chosen based on preliminary studies. The effect of the variables (i.e. x1, x2, x3) on IN, MBN values and product yield was evaluated using a second order polynomial equation as given by Equation (3) [
Y = a 0 + ∑ i = 1 k a i x i + ∑ i = 1 k a i i x i 2 + ∑ i = 1 k ∑ j = 1 k a i j x i x j + ε (3)
where, Y is the response obtained, a0 is a constant, ai slope or linear effect of the input factor xi, aij, defines a linear interaction between factors xi and xj, aii is the quadratic effect of factor xi and ε is the random error or represent uncertainties between predicted and measured values.
Minitab16 statistical software (Minitab 16 Inc.) was used for regression analysis of experimental data, to fit the second order polynomials equations and for the evaluation of the statistical significance of the developed equations (Equation (3)). The response surface plots were generated using SigmaPlot 11 software (Systat. Software, Inc.) to study the relationship between the factors and the responses.
The characterization of the raw material and optimized ACs was done following several analytical techniques. N2 adsorption experiments were carried out at −196˚C using a NovaWin Quantachrome instrument and the surface area and porosity determined respectively via Multipoint BET and Dubinin-Astakhov (DA) method. An Atomic Force Microscope (Agilent 5500 Technologies, Germany) was used to study the surface morphology. Elemental analysis (C, H, and N) of ACs and ESS was performed on a Perkin Elmer Series II 2400 analyzer. The surface functional groups of ACs was determined by both Boehm titration method [
Adsorption studies were conducted in order to investigate the effects of pH, adsorbate concentration and contact time on the adsorption of NO 3 − on ACP and ACZ. All experiments were performed repeatedly to get statistical value.
For equilibrium adsorption studies, a set of Erlenmeyer flasks containing 100 mL NO 3 − solution in the concentration range of 20 to 200 mg∙L−1 and 0.1 g of adsorbent (ACP or ACZ), were shaken at fixed speed of 150 rpm for 1 hour at room temperature (27˚C ± 2˚C). The solution was filtered through a Whatman N˚ 4 filter paper and the amount of nitrate was estimated according to rapid colorimetric determination by the nitration of salicylic acid as reported by Cataldo et al. (1975) [
q e = C 0 − C e m × V (4)
where, C0 and Ce (mg∙L−1) are the concentration of NO 3 − at initial and equilibrium state respectively; V (L) is the volume of solution; and m (g) the mass of the dry adsorbent. Three isotherm models, Freundlich, Langmuir and Dubinin-Radushkevich (D-R) were used to study the nitrate ions adsorption on ACP and ACZ. The non-linear form of these isotherm models is given in
The effect of pH (3 - 11) on the adsorption of NO 3 − by both ACP and ACZ was conducted using pH meter (Insmark, model IS 128). pH of solution was adjusted by adding 0.1 M NaOH or 0.1 M HCl solutions. The concentration of NO 3 − and adsorbent (ACP and ACZ) dose used were 50 mg∙L−1 and 0.1 g, respectively.
For kinetics studies, batch adsorption experiments were carried out by stirring 0.1 g of adsorbent (ACP and ACP) and 100 ml of NO 3 − solution (50 mg∙L−1). The residual concentration of NO 3 − in solution was determined at different time interval in range of 5 - 90 min. The quantity of NO 3 − adsorbed was calculated by the following Equation (5)
q t = C 0 − C t m × V (5)
where, C0 and Ct (mg∙L−1) are the initial and at time t concentration of NO 3 − . The kinetic models: pseudo-first-order, pseudo-second-order and intraparticle diffusion is given in
Non-linear equation | Parameters | |
---|---|---|
Freundlich | q e = K F C e 1 / n | KF (mg∙g−1)(L∙mg−1)1/n: Freundlich adsorption constant n (dimensionless): empirical parameter representing the energetic heterogeneity of surface qe (mg∙g−1): equilibrium adsorbed quantity |
Langmuir | q e = q m K L C e 1 + K L C e | qm (mg∙g−1): monolayer adsorption capacity Ce (mg∙L−1): equilibrium concentration KL (L∙mg−1): equilibium adsorption constant |
Dubinin-Radushkevich | q e = q s exp ( − k a d [ R T ln ( 1 + 1 C e ) ] 2 ) | qs: maximum adsorbed amount R (8.314 J∙mol−1K−1): gas constant T (K): temperature |
Pseudo-first-order | q t = q e [ 1 − exp ( − k 1 t ) ] | qt (mg∙g−1): adsorbed quantity at time t t (min): contact time k1 (min−1): pseudo-fist order rate constant |
Pseudo-second-order | q t = q e 2 k 2 t 1 + q e k 2 t | k2 (g.mg−1∙min−1): pseudo-fist order rate constant |
Intraparticular diffusion | q t = k i p t 2 + C i | Kip (mg∙g−1∙min1/2): intraparticle diffusion rate constant Ci: constant value depicting the boundary layer effect |
In this study, non-linear regression was applied using Microsoft Excel Solver function. The best fit for experimental data was determined from the correlation coefficient (R2), residual root mean square error (RMSE) and Chi-square test (χ2), which are defined by Equations (6)-(8) respectively [
R 2 = 1 − ∑ n = 1 n ( q e . e x p , n − q e . p r e , n ) 2 ∑ n = 1 n ( q e . e x p , n − q e . e x p , n ¯ ) 2 (6)
R M S E = ∑ n = 1 n ( q e . e x p , n − q e . p r e , n ) 2 n − 1 (7)
χ 2 = ∑ n = 1 n ( q e . e x p , n − q e . p r e , n ) 2 q e . e x p , n (8)
where, qe.exp and qe.pre are experimental and predicted equilibrium adsorption capacities.
The data collected for the proximate and elemental analyses of Egusi seed shell (ESS) are given in
The carbonization temperature of ACs production depends on the thermal behavior of precursor (ESS), therefore, ESS was subjected to thermogravimetric analysis (TGA), TGA profile is given in
Property | Percentage (wt%) | ASTM test Standard | |||
---|---|---|---|---|---|
Egusi seed shells (Present study) | Sherry stones shells [ | Fox nuts shells [ | Coconut shells [ | ||
Proximate analysis | |||||
Moisture | 6.32 | 2.67 | 4.0 | 5.62 | D 1762-84 |
Volatiles | 69.53 | 78.5 | 70.1 | 71.4 | D 5832-98 |
Ash | 4.13 | 0.17 | 5 | 1.11 | D 2866-11 |
Fixed carbona | 20.02 | 21.33 | 20.9 | 23.3 | |
Elemental analysis | |||||
Carbon | 47.02 | 48.72 | 42.3 | 48.7 | |
Hydrogen | 5.46 | 6.41 | 4.3 | 6.34 | |
Nitrogen | 3.16 | 1.85 | 0.82 | 1.52 | |
Sulphur | n.db | n.da | 0.07 | 0.038 | |
Oxygena | 44.36 | 43.02 | 52.51 | 43.4 |
bno detection; aby difference.
and few lignin [
The FTIR spectrum of ESS is presented in
The Iodine Number (IN), Methylene Blue Number (MBN) and ACs yield were chosen as responses for the Box-Behnken design. The experimental matrix, together with the experimental and predicted values of the responses are given in
The results obtained from Iodine adsorption are given in
H3PO4 activation (Model 1) | ACP preparation parameters | IN (mg/g) - Y1 | MBN (mg/g) - Y2 | Yield (%) -Y3 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Run order | H3PO4 Concentration (x1) | Carbonization temperature (x2) | Carbonization time (x3) | Exp. value | Pre. value | Exp. value | Pre. value | Exp. value | Pre. value | |
1 | 2(−1) | 400(−1) | 75(0) | 342.27 | 341.43 | 107.05 | 128.68 | 52.53 | 51.84 | |
2 | 10(1) | 400(−1) | 75(0) | 347.67 | 369.88 | 249.79 | 278.67 | 46.36 | 45.53 | |
3 | 2(−1) | 700(1) | 75(0) | 455.01 | 432.10 | 195.76 | 166.89 | 33.98 | 34.81 | |
4 | 10(1) | 700(1) | 75(0) | 451.31 | 452.15 | 369.82 | 348.19 | 38.31 | 38.99 | |
5 | 2(−1) | 550(0) | 30(−1) | 373.32 | 389.30 | 166.19 | 175.17 | 48.26 | 49.58 | |
6 | 10(1) | 550(0) | 30(−1) | 441.57 | 434.50 | 364.68 | 366.42 | 40.97 | 42.41 | |
7 | 2(−1) | 550(0) | 120(1) | 392.92 | 399.99 | 191.68 | 189.94 | 38.68 | 37.23 | |
8 | 10(1) | 550(0) | 120(1) | 418.58 | 402.60 | 338.95 | 329.97 | 43.58 | 42.27 | |
9 | 6(0) | 400(−1) | 30(−1) | 446.84 | 431.71 | 346.00 | 315.39 | 46.57 | 45.95 | |
10 | 6(0) | 700(1) | 30(−1) | 497.49 | 503.73 | 374.14 | 394.03 | 39.91 | 37.78 | |
11 | 6(0) | 400(−1) | 120(1) | 412.53 | 406.30 | 349.23 | 329.33 | 41.19 | 43.32 | |
12 | 6(0) | 700(1) | 120(1) | 492.79 | 507.93 | 327.81 | 358.42 | 27.31 | 27.93 | |
13 | 6(0) | 550(0) | 75(0) | 464.84 | 466.04 | 265.17 | 265.79 | 44.66 | 43.36 | |
14 | 6(0) | 550(0) | 75(0) | 465.01 | 466.04 | 266.11 | 265.79 | 43.73 | 43.36 | |
15 | 6(0) | 550(0) | 75(0) | 468.28 | 466.04 | 266.11 | 265.79 | 41.70 | 43.36 | |
ZnCl2 activation (Model 2) | ACZ preparation parameters | IN (mg/g) - Y4 | MBN (mg/g) - Y5 | Yield (%) -Y6 | ||||||
Run order | ZnCl2 Concentration (x1) | Carbonization temperature (x2) | Carbonization time (x3) | Exp. value | Pre. value | Exp. value | Pre. value | Exp. value | Pre. value | |
1 | 2(−1) | 400(−1) | 75(0) | 350.44 | 359.12 | 149.88 | 137.30 | 49.93 | 51.62 | |
2 | 10(1) | 400(−1) | 75(0) | 431.83 | 456.77 | 203.91 | 225.85 | 45.36 | 43.38 | |
3 | 2(−1) | 700(1) | 75(0) | 452.79 | 427.85 | 158.03 | 136.10 | 31.65 | 33.63 | |
4 | 10(1) | 700(1) | 75(0) | 516.25 | 507.57 | 374.25 | 386.83 | 38.71 | 37.03 | |
5 | 2(−1) | 550(0) | 30(−1) | 357.96 | 378.21 | 148.86 | 182.28 | 48.26 | 46.74 | |
6 | 10(1) | 550(0) | 30(−1) | 503.27 | 507.27 | 351.14 | 350.05 | 39.23 | 41.38 | |
7 | 2(−1) | 550(0) | 120(1) | 441.94 | 437.94 | 150.90 | 151.99 | 40.71 | 38.56 | |
8 | 10(1) | 550(0) | 120(1) | 506.51 | 486.25 | 356.93 | 323.51 | 37.56 | 39.08 | |
9 | 6(0) | 400(−1) | 30(−1) | 492.60 | 463.66 | 336.17 | 315.33 | 44.34 | 44.17 | |
10 | 6(0) | 700(1) | 30(−1) | 539.98 | 544.66 | 359.67 | 348.18 | 36.78 | 36.32 | |
11 | 6(0) | 400(−1) | 120(1) | 508.93 | 504.25 | 228.38 | 239.87 | 42.78 | 43.24 | |
12 | 6(0) | 700(1) | 120(1) | 513.84 | 542.78 | 345.96 | 366.81 | 26.60 | 26.77 | |
13 | 6(0) | 550(0) | 75(0) | 507.30 | 502.39 | 235.52 | 272.23 | 41.91 | 40.71 | |
14 | 6(0) | 550(0) | 75(0) | 499.12 | 502.39 | 281.40 | 272.23 | 39.20 | 40.71 | |
15 | 6(0) | 550(0) | 75(0) | 500.76 | 502.39 | 299.76 | 272.23 | 41.01 | 40.71 |
respectively. Statistical analysis was carried out to determine the significant variables in IN values (
Y 1 = 466.044 + 11.951 x 1 + 43.412 x 2 − 5.301 x 3 − 61.397 x 1 2 − 5.581 x 2 2 + 1.953 x 3 2 − 2.274 x 1 x 2 − 10.648 x 1 x 3 + 7.402 x 2 x 3 (10)
Y 4 = 502.395 + 44.342 x 1 + 29.882 x 2 + 9.678 x 3 − 62.992 x 1 2 − 1.575 x 2 2 + 13.016 x 3 2 − 4.481 x 1 x 2 − 20.184 x 1 x 3 − 10.62 x 2 x 3 (11)
Source | DF | IN | MBN | Yield | ||||||
---|---|---|---|---|---|---|---|---|---|---|
MS | F-value | P-value | MS | F-value | P-value | MS | F-value | P-value | ||
Model 1 | 9 | 3469.8 | 8.10 | 0.017** | 10,313.5 | 9.49 | 0.012** | 54.306 | 11.15 | 0.008** |
x 1 | 1 | 1142.7 | 2.67 | 0.163 | 54,873.4 | 50.48 | 0.001** | 2.237 | 0.46 | 0.528 |
x 2 | 1 | 15,076.7 | 35.18 | 0.002** | 5802.5 | 5.34 | 0.069* | 277.772 | 57.01 | 0.001** |
x 3 | 1 | 224.8 | 0.52 | 0.501 | 234.9 | 0.22 | 0.662 | 77.813 | 15.97 | 0.010** |
x 1 2 | 1 | 13,918.6 | 32.48 | 0.002** | 13,095.7 | 12.05 | 0.018** | 11.693 | 2.40 | 0.182 |
x 2 2 | 1 | 115.0 | 0.27 | 0.627 | 2192.1 | 2.02 | 0.215 | 20.355 | 4.18 | 0.096* |
x 2 2 | 1 | 14.1 | 0.03 | 0.863 | 12,911.1 | 11.88 | 0.018** | 19.033 | 3.91 | 0.105 |
x 1 x 2 | 1 | 20.7 | 0.05 | 0.835 | 245.3 | 0.23 | 0.655 | 27.563 | 5.66 | 0.063* |
x 1 x 3 | 1 | 453.5 | 1.06 | 0.351 | 656.0 | 0.60 | 0.472 | 37.149 | 7.62 | 0.040** |
x 2 x 3 | 1 | 219.1 | 0.51 | 0.507 | 613.8 | 0.56 | 0.486 | 13.032 | 2.67 | 0.163 |
Residual Error | 5 | 428.5 | 1087.1 | 4.873 | ||||||
Lack-of-Fit | 3 | 711.7 | 189.05 | 0.005** | 1811.7 | 6177.11 | 0.000** | 6.594 | 2.88 | 0.268 |
R2 93.58% | Adj R2 82.02% | R2 94.47% | Adj R2 84.51% | R2 95.25% | Adj R2 86.71% | |||||
Source | DF | Adj MS | F-value | P-value | Adj MS | F-value | P-value | Adj MS | F-value | P-value |
Model 2 | 9 | 4626.0 | 5.78 | 0.034** | 11,130.2 | 8.14 | 0.016** | 51.448 | 8.12 | 0.016** |
x 1 | 1 | 15,729.6 | 19.64 | 0.007** | 57,557.9 | 42.09 | 0.001** | 11.737 | 1.85 | 0.232 |
x 2 | 1 | 7143.6 | 8.92 | 0.031** | 12,764.9 | 9.33 | 0.028** | 296.096 | 46.73 | 0.001** |
x 3 | 1 | 749.3 | 0.94 | 0.378 | 1614.9 | 1.18 | 0.327 | 54.915 | 8.67 | 0.032** |
x 1 2 | 1 | 14,650.9 | 18.30 | 0.008** | 12,484.5 | 9.13 | 0.029** | 18.866 | 2.98 | 0.145 |
x 2 2 | 1 | 9.2 | 0.01 | 0.919 | 204.4 | 0.15 | 0.715 | 8.923 | 1.41 | 0.289 |
x 2 2 | 1 | 625.5 | 0.78 | 0.417 | 5297.8 | 3.87 | 0.106 | 8.610 | 1.36 | 0.296 |
x 1 x 2 | 1 | 80.3 | 0.10 | 0.764 | 6576.4 | 4.81 | 0.080* | 33.814 | 5.34 | 0.069* |
x 1 x 3 | 1 | 1629.7 | 2.04 | 0.213 | 3.5 | 0.00 | 0.962 | 8.644 | 1.36 | 0.295 |
x 2 x 3 | 1 | 451.1 | 0.56 | 0.487 | 2213.1 | 1.62 | 0.259 | 18.576 | 2.93 | 0.148 |
Residual Error | 5 | 800.8 | 1549.5 | 6.336 | ||||||
Lack-of-Fit | 3 | 1322.1 | 70.67 | 0.014** | 1549.5 | 1.42 | 0.439 | 9.291 | 4.88 | 0.175 |
R2 91.23% | Adj R2 75.44% | R2 93.61% | Adj R2 82.11% | R2 93.60% | Adj R2 82.07% |
**most significant, *less significant.
By applying the statistical model ANOVA, (see
For the methylene blue adsorption, the values of MBN varied from 107.05 to 374.14 mg∙g−1 for ACP and 149.88 to 374.25 mg∙g−1 for ACZ (see
Y 2 = 265.797 + 82.82 x 1 + 26.932 x 2 − 5.419 x 3 − 59.555 x 1 2 + 24.366 x 2 2 + 59.133 x 3 2 + 7.83 x 1 x 2 − 12.806 x 1 x 3 − 12.387 x 2 x 3 (11)
Y 5 = 272.227 + 84.822 x 1 + 39.945 x 2 − 14.208 x 3 − 58.148 x 1 2 + 7.441 x 2 2 + 37.879 x 3 2 + 40.545 x 1 x 2 + 0.938 x 1 x 3 + 23.522 x 2 x 3 (12)
The correlation coefficients (R2) were found to be 0.94 and 0.93 respectively (see
(above 500˚C), which generate more mesopores in the resultant ACs [
The resultant yield of ACs is a key response factor directed to production. A set of experiment were performed with variable experimental conditions, in order to determine the optimal conditions for obtaining maximum yield of product (i.e. ACP and ACZ). According to the results (
Y 3 = 43.363 − 0.529 x 1 − 5.893 x 2 − 3.119 x 3 + 1.78 x 1 2 − 2.348 x 2 2 − 2.27 x 3 2 + 2.625 x 1 x 2 + 3.048 x 1 x 3 − 1.805 x 2 x 3 (13)
Y 6 = 40.707 − 1.211 x 1 − 6.084 x 2 − 2.62 x 3 + 2.26 x 1 2 − 1.555 x 2 2 − 1.527 x 3 2 + 2.907 x 1 x 2 + 1.47 x 1 x 3 − 2.155 x 2 x 3 (14)
All the terms, x2, x3, x1x2 and x1x3 and x1, x3, x1x2 for ACP and ACZ respectively (see
material. The maximum yield was achieved when the temperature and residence time were maintained at their lowest value and the concentration was kept constant at the central point. The obtained results are in accordance with those from the literature [
The optimization process and method validation are quite essential in setting up the optimum conditions for maximum AC yield, high iodine and methylene blue adsorption capacity from the precursor. However, it is difficult to optimize these three responses under the same conditions because the zone of interest of the factors is different. The desirability function was then applied using Minitab 16 software in order to consolidate the three factors by considering the same weight for all the factors on the three responses [
The surface area of ACs was determinate through multipoint BET method by keeping the relative pressure (P/P0) in the range of 0.02 to 0.3. The pore size parameters were calculated using Dubinin-Astakhov method. The results are summarized in
x1 (mol/L) | x2 (˚C) | x3 (min) | IN (mg/g) | MBN (mg/g) | Yield (%) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Pred. | Exp. | Error | Pred. | Exp. | Error | Pred. | Exp. | Error | ||||
ACP | 7 | 633 | 30 | 492.83 | 496.24 | 0.69 | 382.55 | 378.47 | 1.07 | 40.77 | 43.64 | 6.58 |
ACZ | 10 | 698 | 30 | 541.26 | 535.41 | 1.08 | 413.48 | 410.11 | 0.82 | 38.86 | 36.07 | 7.18 |
Textural properties | Elemental analysis (%) | |||||||
---|---|---|---|---|---|---|---|---|
BET surface area (m2/g) | Pore volume (cc/g) | Pore size (nm) | C | H | N | Oa | ||
ACP | 1058.91 | 0.66 | 1.84 | 57.50 | 0.18 | n.db | 42.35 | |
ACZ | 1008.99 | 0.59 | 2.02 | 71.55 | 0.39 | 0.99 | 27.07 | |
Boehm titration (mmol.g−1) | ||||||||
Carboxylic | Lactonic | Phenolic | Acidic | Basic | pHPZCc | |||
ACP | 0.71 | 0.47 | 0.39 | 1.57 | 0.43 | 4.00 | ||
ACZ | 0.35 | 0.56 | 0.31 | 1.22 | 0.93 | 7.00 |
acalculated by difference; bno detection; cpH of point of zero charge.
The surface morphology of the precursor (
The optimized ACs were subjected to Boehm’s titration and FTIR spectroscopy in order to determine the surface functional groups. The results from Boehm’s titration show that ACP and ACZ contain higher number of acidic groups (
The FTIR spectra of the ACs are noticeably different from that of the precursor
(see
The results of the elemental analysis are provided in
An initial nitrate ions concentration of 50 mg∙L−1 was used to study the effect of pH on removal of nitrate ions in the range of 3 - 11. The maximum quantity of nitrate ion removed occurred at pH 3, and this amount decreases with increase in pH (
The experimental data obtained was analyzed using Freundlich, Langmuir, D-K and Tempkin non-linear isotherm models (see
According to the presented result in
E ( kJ ⋅ mol − 1 ) = 1 2 k a d
From the model, if the magnitude of the energy (E) lies in the range of 8 - 16 kJ∙mol−1, the sorption process is said to take place via ion exchange, whereas, if E < 8 kJ∙mol−1, the sorption process is said to be controlled by physical adsorption. The values of E were found to be 0.021 and 0.027 kJ∙mol−1 for ACP and ACZ, respectively, implying physical adsorption is dominant [
The adsorption process was analyzed using three kinetic models, pseudo-first-order, pseudo-second-order and intraparticle diffusion kinetic model.
Models | Parameters | Adsorbents | |
---|---|---|---|
ACP | ACZ | ||
Freundlich | KF | 0.035 | 0.063 |
1/n | 1.455 | 1.369 | |
R2 | 0.957 | 0.965 | |
RMSE | 3.874 | 3.653 | |
χ2 | 3.355 | 1.815 | |
Langmuir | qm | 2752.29 | 236,754.9 |
KL | 10.6 × 10−6 | 1.52 × 10−6 | |
R2 | 0.878 | 0.915 | |
RMSE | 6.508 | 5.733 | |
χ2 | 9.422 | 5.170 | |
D-K | qs | 63.772 | 62.632 |
kad | 10.9 × 10−4 | 7.60 × 10−4 | |
E | 21.391 | 25.691 | |
R2 | 0.991 | 0.907 | |
RMSE | 1.809 | 5.975 | |
χ2 | 2.574 | 4.932 |
From
The pseudo-first-order kinetic model has a high R2 value and low RMSE and χ2 values among all models as shown in
Models | Parameters | Adsorbents | |
---|---|---|---|
ACP | ACZ | ||
Pseudo-first-order | qe (exp) | 5.65 | 8.26 |
qe (pre) | 5.766 | 8.352 | |
k1 | 0.194 | 0.216 | |
R2 | 0.991 | 0.994 | |
RMSE | 0.185 | 0.219 | |
χ2 | 0.057 | 0.049 | |
Pseudo-second-order | qe (pre) | 6.160 | 8.859 |
K2 | 0.058 | 0.038 | |
R2 | 0.985 | 0.985 | |
RMSE | 0.238 | 0.602 | |
χ2 | 0.086 | 0.208 | |
Intraparticle diffusion | Kip | 0.493 | 0.688 |
Ci | 2.361 | 3.614 | |
R2 | 0.851 | 0.819 | |
RMSE | 2.911 | 1.879 | |
χ2 | 1.255 | 2.146 |
rate-controlling step. In addition, the value of Ci ≠ 0, suggest the adsorption is a complex process and involve more than one diffusive resistance process [
In the present study, Box-Behnken design was used to optimize the preparation conditions of ACs from Egusi seed shells (Cucumeropsis mannii) by chemical activation using two activating agents (H3PO4 and ZnCl2). The ability of the optimized ACs (ACP and ACZ) towards nitrate ions removal was investigated. During optimization, the residence time for maximal responses (IN, MBN, and obtained yield) was 30 minutes for both ACP and ACZ. The concentration of H3PO4 and ZnCl2 and the carbonization temperature was 7 M at 633˚C and 10 M at 698˚C for ACP and ACZ respectively. ACP and ACZ were found to be microporous having surface areas of 1053.91 m2∙g−1 and 1009.89 m2∙g−1 respectively, obtained from BET analysis. FTIR showed that both ACs have approximately same surface chemistry. AFM images show the presence of well-developed pores and cavities on the surface of ACP and ACZ, which were absent in the precursor material. Maximum adsorption occurs at pH 3 and the adsorption equilibrium was reached after 20 minutes for both ACs. ACZ showed a better adsorption capacity of nitrate ion as compared to ACP. The equilibrium and kinetic studies reveal that physical interaction exhibits between adsorbent and adsorbate. This study demonstrates the potential of Egusi seed shell as a good precursor for the preparation of activated carbons having large surface areas, and the ability of these ACs to be used as adsorbents for the removal of nitrate ions from wastewater.
The authors would like to thank The Word Academy of Sciences (TWAS) and International Center for Chemical & Biological Sciences (ICCBS) for financial support of this study under ICCBS-TWAS Postgraduate Fellowship. The authors also thank all the members of the research group “Adsorption and Surface” of Applied Physical and Analytical chemistry laboratory of University of Yaoundé I.
The authors declare no conflict of interest.
Lékéné, R.B.N., Nsami, J.N., Rauf, A., Kouotou, D., Belibi, P.D.B., Bhanger, M.I. and Mbadcam, J.K. (2018) Optimization Conditions of the Preparation of Activated Carbon Based Egusi (Cucumeropsis mannii Naudin) Seed Shells for Nitrate Ions Removal from Wastewater. American Journal of Analytical Chemistry, 9, 439-463. https://doi.org/10.4236/ajac.2018.910034