3.1 Characterization of the Photocatalyst
3.1.1 Scanning electron microscope (SEM): The SEM technique was used to study the morphology of the pure ZnO and green nitrogen doped ZnO. As shown in Figure I, the SEM micrographs for pure ZnO and phyto-enhanced nitrogen doped ZnO were taken at 200nm. The SEM micrograph of pure ZnO show a network configuration of agglomerated elongated rod-like shapes while the SEM micrograph of phyto-enhanced nitrogen doped ZnO revealed a loosed structure of uniformly distributed particles. The increase in surface area in green nitrogen doped ZnO affirms green synthesis had significant effect on the photocatalyst and also suggest that it improves the efficiency of the entire photocatalytic process. (Ali and Amita 2020).
3.1.2 Energy dispersive X-ray (EDX): The elemental composition of pure ZnO and phyto-enhanced nitrogen doped and their respective weights are presented in the Figure II. Figure II presents two spectra for pure ZnO (A) and green nitrogen doped ZnO (B). It can be observed that sample B has nitrogen presented in minute amount, and this resulted from the low doping ratio during the synthesis of the photocatalyst. The observed peaks of Potassium (K), Sulphur(S), magnesium (Mg) and phosphorus (P) in traces is attributed to surface contamination during the analysis. In addition, the carbon peaks observed are due to the carbon composition of the storage material and the carbon from the precursor for nitrogen (urea).
3.1.3 Brunauer-Emmett-Teller (BET): The surface area, pore size and pore volume were determined through the BJH Adsorption method of the BET analysis. As presented in Table 1, the surface area, pore size and pore volume of pure ZnO was observed to be lower than that of phyto-enhanced nitrogen doped ZnO. This is attributed to the effects of green synthesis on pure zinc oxide. The high surface area, pore diameter and pore volume of phyto-enhanced nitrogen doped ZnO led to an increase in percentage degradation of methylene blue dye compounds.
Table i: BET Summary
Photocatalyst
|
Pure ZnO
|
Green N-ZnO
|
Surface area (m2/g)
|
22.27
|
113.3
|
Pore diameter (nm)
|
1.453
|
2.118
|
Pore volume (cm3/g)
|
0.010
|
0.055
|
3.1.4 Fourier-transform infrared spectroscopy (FTIR): The functional groups of pure ZnO and phyto-enhanced nitrogen doped ZnO were investigated with the FTIR spectroscopy. The functional groups are revealed in a wave number range frequency range of 4000 – 500 cm-1. As seen on Figure III below, eight peaks were present in the pure ZnO and green nitrogen doped zinc oxide spectra. In Tables 2 and 3, the peaks and their corresponding groups and classes are clearly stated. The third peak (1773.91 cm-1) belongs to N-O stretching confirms the presence of nitrogen arising from doping on the photocatalyst which agrees with a study carried out by Prabakaran (2019).
Table ii: Frequency Table for Pure ZnO
Peaks (cm-1)
|
Group
|
Class
|
3484.70
|
O-H stretching
|
Alcohols
|
2763.80
|
C-H stretching
|
Aldehydes
|
2426.52
|
C=O stretching
|
Carbon dioxide
|
1788.45
|
C-H bending
|
Aromatic
|
1342.61
|
S=stretching
|
Sulphone
|
834.27
|
C=H
|
Alkene
|
Tableii: Frequency Table for Pure G.S N-ZnO
Peaks (cm-1)
|
Group
|
Class
|
2490
|
D-H stretching
|
Carbon dioxide
|
1773.91
|
C=O stretching
|
Acid halide
|
1524.31
|
N-O stretching
|
Nitro
|
1155.73
|
C-F stretching
|
Floro
|
1069.698
|
S=O stretching
|
Sulphoxide
|
881.33
|
C=C bend
|
Vinyldene
|
3.1.5 X-ray Diffraction (XRD):The phase composition, lattice parameter and crystallite size of pure ZnO and phyto-enhanced N-doped ZnO were determined from the XRD spectra presented in Figure 4. The 2θ values for Pure ZnO corresponding to diffraction peaks are31.82, 34.53, 36.25, 47.57, 56.58, 62.94, 68.00, 69.14o and for nitrogen doped ZnO, 2θ values were31.82, 34.53, 37.25, 47.57, 56.58, 62.94, 68.00, 69.14o.These peaks correspond to the miller index (100), (002), (101), (102), (110), (103) and (112) in that order. This affirms that the hexagonal wurtzite structure of N-doped ZnO (Prabakaran et al., 2019), which agrees with the JCPDS card no 36-1451. On careful examination of the XRD pattern, one can notice a minute shift in the third peak between pure ZnO and Nitrogen doped ZnO. This confirms the presence of nitrogen resulting from doping of urea on pure ZnO. The crystallite sizes of pure ZnO and N-ZnO nanoparticles were calculated with the Bragg’s formula as shown in Equation 2 (Rowshon)
----------------------------- 2
Where D is the crystallite size of the photocatalyst, λ is the wavelength of the X-ray beam operating system, is full width half maximum and θ is the angle of diffraction. The crystallite size of pure ZnO and green N-doped ZnO were estimated as 34.7nm and 24.8nm.
3.2 Effects of process parameters on degradation of MB dye with green N-ZnO
3.2.1 Effect of solution pH on degradation of MB dye: The pH of the solution plays a significant role in Photocatalysis because the pH of wastewater varies.Therefore, it was imperative to study the effects of solution pH on the degradation of methylene blue dye. The pH of MB dye solution was adjusted with sodium hydroxide (NaOH) and hydrochloric acid (HCl). The effect of solution pH was studied at constant initial concentration of 10 mg/L and constant photocatalyst dosage of 100mg. FromFigure5, degradation of MB dye was observed to be lower at pH of 3 and 5 but higher at pH of 9 and 11. The low degradation of MB dye at pH 3 and 5 is attributed to the force of repulsion between MB and the photocatalyst surface. At p< 6, MB exists in a cationic form and the point zero charge of the photocatalyst which was estimated to be 9.3 causes a force of repulsion between both surfaces, which led to reduced adsorption and consequently low degradation of MB dye. However at pH > 7.7, MB exists in anionic form and this creates an electrostatic attraction between the positively charged photocatalyst surface and MB dye, thus leading to high adsorption of MB on the photocatalyst surface and consequently high degradation (Priya 2015)
3.2.2 Effect of photocatalyst dosage on degradation of MB dye
The effect of photocatalyst dosage plays a significant role in Photocatalysis as it is vital for the optimization of materials in wastewater treatment. In this research, the effect of photocatalyst dosage at constant pH of 9, time of 75 minutes and constant initial concentration of 10 mg/L was studied.
From Figure 6, it is observed that there is a continuous increase in percentage degradation of MB dye as photocatalyst dosage increase until the photocatalyst dosage exceeded 100mg. Further increase of photocatalyst dosage beyond 100 mg resulted in a decrease in percentage degradation. This may be attributed to the agglomeration of photocatalyst particles in the solution, increase in turbidity of the solution, which induce a reduction in light penetration.
3.2.3 Effect of initial concentration of MB dye solution
The changes in effluent concentration from the textile industry made it imperative to study the effect of initial concentration on percentage degradation. The effect of concentrations of MB dye solution at constant photocatalyst dosage of 100mg, solution pH of 9 and time of 90 minutes was studied. From Figure 5,one can notice that percentage ceased to after 90 minutes and initial concentration of 5 mg/L had highest degradation. From this, we can deduce that percentage degradation decreases as the initial concentration of organic pollutants increases. This phenomenon can be attributed to increase in dye sorbate o the surface-active sites of the photocatalyst, thereby limiting adsorption of OH- which leads reduced formation of highly oxidative OH* radicals. Also, higher dye concentration reduces visible light penetration on the surface of active sites of the photocatalyst thus reducing the activity of the photocatalyst. Vasiljevic (2020).
3.2.4 Data fitting and Model Validation
Python Programming Language (Scipy, Numpy, Pandas and Matplotlib libraries) was used to perform data fittings and model validation for this research. The lines of codes/scripts below describes steps followed. The experimental data (concentration vs time) was fitted on the first, second and third order kinetic equations. First order kinetic equations had the best fittings as shown in Figure 8, 9, and 10 below, hence it was adopted for the kinetic studies.
a. Data fitting and model validation (1ST, 2ND& 3RDorder kinetics)
# Importing Python Libraries; Numpy, Scipy, Matplotlib
import numpy as np
from scipy.optimize import minimize
import matplotlib.pyplot as plt
# Creating variables; Ca is experimental concentration and T is time in minutes
Cao = 5.0
Ca = np.array([5,2.85,1.85,1.05,0.55,0.3,0.125])
T = np.array([0,15,30,45,60,75,90])
# Defining a function
def kine(k,t,c):
Ca_cal = np.zeros(len(Ca))
for i in range(0, len(Ca)):
Ca_cal[i] = Cao*(np.exp(-k*T[i]))
res = Ca_cal - Ca
SSE = sum(res**2)
return SSE
#Minimize function
OPM = minimize(kine,0,,args=(T,Ca))
k = OPM.x
# Reevaluation of parameters
Ca_cal = np.zeros(len(Ca))
for i in range(0, len(Ca)):
Ca_cal[i] = Cao*(np.exp(-k*T[i]))
#Creating Plots
plt.plot(T,Ca_cal,linewidth=2,color='b')
plt.scatter(T,Ca,color="r")
plt.xlabel("Time (Min)")
plt.ylabel("Concentration (mg/L")
plt.title("Data Fitting: 1st order Kinetics")
plt.legend(["Estimated Ca from 1st order kinetics", "Experimental Ca"])
plt.show()
Similarly, the lines of code are repeated for 2NDand 3RDorder kinetic equations by replacing Ca_cal[i] = Cao*(np.exp(-k*T[i])) with Ca_cal[i] = Cao/(1+Cao*(k)*(T[i])) and Ca_cal[i] = Cao*(np.exp(-k*T[i])) with Ca_cal[i] = (Cao**2/(1+2*(Cao**2)*(k)*(T[i])))**(1/2) respectively.
3.2.5 Reaction kinetics
The rate of degradation of MB dye was studied with the first order kinetics using the kinetic expression below;
ln(Cao/Ca) = K* t. (1)
Where KC = rate constant and Cao= initial concentration and Ca = concentration at time t.
Figure 11 presents a linear plot of ln (Cao/Ca) against time. The slope of each linear represent the rate constants (Kc) initial concentrations. The Kc values for 5, 10, 15 and 20 mg/L initial concentrations are0.04min-1,0.0325 min-1,0.0282min-1and 0.0246min-1respectively.
Table iii: Rate constant (K) and correlation coefficients (R²)
Ca (mg/L)
|
Linear equation
|
(KC)
|
R²
|
5
|
y = 0.04x – 0.1086
|
0.04
|
0.9906
|
10
|
y = 0.0325x - 0.084
|
0.0325
|
0.9888
|
15
|
y = 0.0282x - 0.0011
|
0.0282
|
0.9976
|
20
|
y = 0.0246x - 0.0595
|
0.0246
|
0.9955
|
a. Sum of square error (SSE) analysis
res = c_cal - c
SSE = sum(res**2)
Print(c_cal)
print(res)
print(SSE)
Where Ca_cal = estimated Ca, Ca = experimental Ca, t = time, SSE = sum of square errors and Cao = Initial concentration
Table iv: Sum of Square Error (SSE) Analysis
Time (min)
|
Experimental Ca
|
Estimated Ca
|
Absolute Error |E|
|
E2
|
0
|
5.00
|
5.00
|
0
|
0
|
15
|
2.85
|
2.94
|
0.09
|
0.0081
|
30
|
1.85
|
1.72
|
0.13
|
0.0169
|
45
|
1.05
|
1.01
|
0.04
|
0.0016
|
60
|
0.55
|
0.59
|
0.04
|
0.0016
|
75
|
0.3
|
0.35
|
0.05
|
0.0025
|
90
|
0.125
|
0.21
|
0.085
|
0.007225
|
|
|
|
|
|
|
|
|
|
Ʃ of E2= 0.037925
|
3.2.6 Proposed reaction mechanism
The following reactions represents the proposed reaction mechanism for the photocatalytic degradation of methylene blue dye with green synthesized nitrogen doped zinc oxide.