A new achievement in green degradation of aqueous organic pollutants under visible-light irradiation

The global attention has been focused on the removal of environmental aqueous organic pollutants using several methods, including biological oxidation systems, chemical and adsorption technology. However, the mentioned methods are limited by a few drawbacks; for instance, the biological methods are affected in longer retention time, the adsorption method is unable to mineralize the adsorbed pollutants from the environment and the chemical methods generate new toxic intermediates (Takeda & Teranishi 1988; Martino et al. 1995; Martino & Savage 1997; Aviam et al. 2004). On the other hand, advanced oxidation processes (AOPs) are able to mineralize the organics to harmless final products such as H₂O and CO₂ using a stable and non-toxic photocatalyst, suitable reaction time, appropriate light irradiation at ordinary temperature and atmospheric pressure. The photocatalysts included TiO₂, ZrO₂, SnO₂ and ZnO, which have been well-known for ultraviolet (UV) irradiation due to their wide band gap energy (Kansal et al. 2008). On the other side, sunlight is preferred in large-scale operations as it is more economical and safer than UV irradiation (Thuillier et al. 2003). Moreover, sunlight energy is available at more than 1.36 kW per square meter on the Earth's surface, consisting of the visible spectrum (47%), UV (7%) and other spectra (46%) (Fröhlich 2002). The visible spectrum wavelength is between 400 nm and 700 nm, and represents insignificant energy (1.77 to 2.76 eV) for electron excitation through the large band gap of the photocatalyst (3.2 to 3.5 eV). Therefore, several methods have been devised to harness excitation from the lower band gap, including doping coloured transition metal ions into the wide band gap structure of the photocatalyst to improve photocatalytic activity, coupling different semiconductors to enhance recombination time and synthesise the new short band gap and high surface area photocatalyst (Abdollahi et al. 2011b). As a result, the doped photocatalysts were more efficient than their original form under visible light. However, the photocatalyst yield performance is insufficient still, due to the relatively short time recombination of charge carriers. In other methods, coupling two photocatalysts with different band gaps has been used to enhance the separation time, which provides a significant opportunity to trap photo-generated charge carriers (Devi & Kavitha 2013). In this case, several works have been reported as follows: TiO₂ was successfully coupled with SiO₂, MoO₃, CdS, MgO, WO₃, SnO₂, Cu₂O, In₂O₃ and ZnO (Wu et al. 2006). Although the coupling method has promoted the efficiency of the photocatalyst, it is still insufficient for visible-light irradiation. On the other hand, the theoretical and experimental study of new photocatalysts such as CaCu₃Ti₄O₁₂ (CCTO) perovskite has been carried out to achieve materials that possess high activity in the visible range (Chen & Zhang 2010; Clark et al. 2010). Thus, CCTO is an ABO₃ materials with cubic double-perovskite structure with Ca²⁺ and Cu²⁺ on the A site and Ti⁴⁺ on the B site (Subramanian & Sleight 2002). The complex oxide of CCTO facilitates visible-light photocatalytic activity without requiring doping, which arises from charge transfer of Cu²⁺ and Ti⁴⁺ valence band (VB) to Cu³⁺ and Ti³⁺ conduction band (CB). The synthesized CCTO, which process a suitable band gap energy (1.93 and 2.21 eV), was used as photocatalyst to aid degradation of organic water pollutant by AOPs, under visible-light irradiation (700 nm < ʎ > 400 nm) (Clark et al. 2010). However, the average surface area of the photocatalyst was quite low (8.33 m³/g), while the average particle size was in the nano-scale region, 70.15 nm. Consequently, the photodegradation efficiency was insufficient under visible-light irradiation. On the other hand, reduced graphene oxide (RGO) has presented excellent specific surface area, which enhances the surface of the photocatalyst (Sun et al. 2014).

In this work, CCTO was simultaneously synthesized and decorated in different percentages of RGO to prepare CCTO-RGO nano-composites. Thereafter, the final photocatalyst with optimized properties was selected to apply for degradation of methyl orange (MO) as a model of organic environmental pollutants under artificial day time sunlight. To optimize and scale up the degradation, the application was modeled by multivariable methods such as response surface methodology (RSM) and then artificial neural networks. Such multivariate methods are preferred to the single variable approach due to the increase in the dimensionality of the system and production of more generalized results (Abdollahi et al. 2013a).

The CCTO was simultaneously synthesized and decorated in graphene oxide (GO) by the chemical sol-gel method. In this method, equal amounts of Titanium(IV) bis (ammonium lactato) dihydroxide solution (50 wt. % in H₂O), calcium acetate (99%) and copper acetate (99%) were dissolved in five containers of deionized (DI) water as five blank solutions and magnetically stirred for 45 min. Then the appropriate volume of GO (4 mg/mL, water dispersion 1,000 mL) was slowly added to the solutions over half an hour. The effect of GO was included at 0, 4, 6, 8 and 10 weight (wt%) for five samples that were called CCTO-GO (0%), CCTO-GO (4%), CCTO-GO (6%), CCTO-GO (8%), and CCTO-GO (10%). Thereafter, the appropriate amount of porcine skin gelatine powder was dissolved in the samples. The solutions were stirred for 30 min at 70 °C and then evaporated at 90 °C with continuous stirring. The produced paste-like mixture was dried in a hot oven for 8 hours at 150 °C. The dried paste was ground and sieved and the fine powder was calcined at two steps, 350 °C then at 400 °C for 3 hours in the air to burn the gelatine, and generate nanoparticles of CCTO and RGO. The heating and cooling rate were performed at 5 °C/min according to recording of Thermal Gravimetric Analysis (TGA) in a box furnace (cable modem termination system (CMTS) model high-temperature stage (HTS) 1400). The calcination was confirmed by Fourier-transform infrared spectroscopy (FTIR) spectra, which were obtained by Fourier Transform Infrared Spectrometer Nicolet 6,700 (Thermo Nicolet Corp., Madison, WI) equipped with a deuterated triglycine sulfate detector. The samples were characterized by X-ray diffraction (XRD) (PANanalytica, Philips-X'pert Pro PW3040/60). Their electrical properties were determined by UV-visible absorption and diffuse reflectance (Ref %). The Ref % data of the powder samples was provided by Perkin Elmer Lambda 35 UV-Vis-NIR equipped with a Labsphere integrating sphere over the spectral range of 200 to 900 nm. The spectra were analyzed by using the Kubellka-Munk function (Ye & Ohmori 2002). The transmittance electron microscopy (TEM) images were obtained by using a Hitachi H-7100 electron microscope. The field emission scanning electron microscopy (FESEM) morphology and energy dispersive X-ray analysis (EDX) analysis were obtained from JEOL JSM-6400 instruments.

Regarding the characterization, a CCTO-RGO (8%) sample has been selected as the photocatalyst, and methyl orange (System brand) was used as model of the environmental aqueous organic pollutant. The performance has been started according to the experimental design (Table 1) by mixing various concentrations of MO with appropriate amount of the photocatalyst in 300 ml DI water. The mixture solutions (suspension) were irradiated by a Philips lamp (23 W) as the light source in a designed batch photoreactor that was used in our previous work (Abdollahi et al. 2012a). The suspensions were magnetically stirred during the irradiation at 200 rpm. To increase solution fluidization, access oxygen, volatilize the produced gas and kept the temperature at around 25 °C, air was blown into the solution by using an air pump. During the operation, samples were withdrawn from the bulk solution at specific time intervals, centrifuged at 4,000 rpm for 30 min and then filtered through 0.2 μm polytetrafluoroethylene (PTFE) membrane to measure the remaining concentration of the MO.

Figure 1 shows the UV-visible absorption of the CCTO-RGO samples, including CCTO-RGO (0%), CCTO-RGO (4%), CCTO-RGO (6%), CCTO-RGO (8%) and CCTO-RGO (10%). As observed, all of the samples presented absorption in the visible area of wavelength (400 to 600 nm); however, the CCTO-RGO (8%) has illustrated a sharp slope of absorption in the region (curve (b) in Figure 1). In addition, Table 2 shows the calculated band gap of the samples. The band gaps were calculated by the Kubelka-Munk function, which used diffuse reflectance spectra of the powder (Ye & Ohmori 2002). As indicated, samples CCTO-RGO (0%) and CCTO-RGO (8%) have presented two distinct band gaps that are well comparable with reported values (Rakhshani & Barakat 1987). As reported, mostly CaCu₂Ti₄O₁₂ has two band gaps due to transition metals compounds such as Cu and Ti (Clark et al. 2010). The reported band gap was 2.0 eV, which is in the middle of the visible-light energy (λ = 400 to 500 nm). In addition to the electrical properties, as Table 2 shows, the Brunauer–Emmett–Teller (BET) surface area of CCTO-RGO (8%) samples was at maximum value in comparison with the other sample's BETs surface area, which confirmed its effectiveness under visible-light irradiation. Therefore, the CCTO-RGO (8%) sample was selected for further characterization such as XRD, EDX, FTIR, TEM and FESEM.

Figure 2 shows the XRD patterns phase determination analysis of the CCTO-RGO (8%) sample. The phases were analysed according to the corresponding 2θ characteristic values of their diffraction peaks. Therefore, CaCu₃Ti₄O₁₂ was identified by reference 01-070-0609 (Propach 1977), while the small amounts of CuO and TiO₂ were also identified by 01-089-5895 and 00-065-1118 references respectively. As indicated, CaCu₃Ti₄O₁₂ is the main product, while a small amount of CuO and TiO₂ was identified as impurity contents.

Moreover, the FTIR spectra of CCTO-RGO (8%) before and after calcination have been compared and are in agreement with a previous report (Titelman et al. 2005). As observed, there is no peak for epoxy functional groups (C-O-C) on the calcinated sample. The epoxy groups peak of single layer graphene oxide is to be reported between 800 to 850 cm⁻¹ and also 1,200 to 1,250 cm⁻¹ (Acik et al. 2010). Therefore, it can be concluded that the GO was reduced to RGO during the calcination process.

Figure 3 shows the FESEM morphology and EDX of the CCTO-RGO (8%) sample. As Figure 3(a) indicates, CCTO particles have been homogenized and dispersed inside the sheets of RGO. Moreover, Figure 3(b) depicts a piece of agglomerated CCTO over GO sheet. Therefore, the CCTO particles were decorated both inside and outside (over) of the RGO sheets. In addition, EDX element analysis of the sample is presented in Figure 3(c), which confirmed the initial elements used, including Ca, Cu, Ti and O in a large area of CCTO-RGO (8%). Table 3 presents carbon of the GO sheets. As observed, the percentage of calcium is less than the stoichiometry of CCTO, which may be due to the presence of TiO₂ and CuO respectively on XRD investigation (Figure 2).

Figure 4 shows TEM images of the synthesized mesoporous CCTO-GO nano-composite matrix structure that presents an excellent uniform particle size. The TEM images are in very good agreement with the morphology that is presented in FESEM to confirm dispersion of CCTO particles in the sheets of GO (Figure 3). Figure 4(a) shows the sheets of GO that were decorated by CCTO particles, while Figure 4(b) shows both decorated sheet (I) and CCTO particles without GO sheets (II). The average particle size of CCTO-RGO, which was calculated from the TEM images, was 5.3 nm.

The mentioned characterization result of CCTO-RGO (8%) has confirmed its merit as a visible active photocatalyst; therefore, it was selected for degradation of MO as a model of organic pollutants under artificial visible-light irradiation. For this purpose, the preliminary experiments for the photodegradation, including the initial photocatalyst absorption and photolysis, were investigated in the dark and the absence of the photocatalyst at pH 6. The obtained results were considered in the final efficiencies calculation (Abdollahi et al. 2014b). The maximum interaction between the surface of the photocatalyst and MO was determined to be around pH 6 (Clark et al. 2010).

RSM has been used to obtain the optimized level of the effective input variables. Initially, RSM used the actual efficiencies as output responses for the fitting process to suggest a suitable model for the photodegradation process. The methodology of the fitting process has been performed similarly to our previous work (Dorraj et al. 2015). As a result, the quadratic model was suggested by the software. Therefore, the authors have followed the software's suggestion and selected the quadratic model as the provisional model for more validation.

An ANN has been used to determine the importance of the degradation input effective variables. As observed, the Root Mean Squared Error (RMSE) from the learning process versus node number of the hidden layer of the ANN structures for quadratic programming (QP), batch back-propagation (BBP), incremental back-propagation (IBP) and Levenberg-Marquardt (LM) algorithms were compared (Abdollahi et al. 2014a). The node numbers are the number of the nodes in the hidden layer of the architectures. For each algorithm, there are 20 architectures where one of them has presented the lowest value of RMSE (topology). The topologies included were QP with 14 nodes (QP-4-14-1), BBP with 12 nodes (BBP-4-12-1), IBP with 13 nodes (IBP-4-13-1) and LM with 10 nodes (LM-4-10-1). Among them, QP-4-14-1 topology illustrated the minimum RMSE (2.661); however, they were compared by calculated percentage of absolute average deviation (AAD) and R² to select the final model for the photodegradation (Table 4). Table 4 shows the results of AAD calculation for QP-4-14-1, BBP-4-13-1, IBP-4-12-1 and LM-4-10-1 and QP-4-14-1 topologies in training, testing and validation datasets. Among the topologies, QP-4-14-1 has presented the lowest AAD in the all three datasets that exhibited a reasonable AAD.

To compare the R² of the selected topologies, the actual efficiencies of the experiments were plotted versus the training predicted efficiencies of QP-4-14-1, BBP-4-13-1, IBP-4-12-1 and LM-4-10-1 topologies (Figure 6). As shown, all obtained values for the R²s in the training dataset were acceptable; however, the predicted values of QP-4-14-1 topology were 100% fitted to the actual values (R² = 1).

According to the similar calculations for testing the dataset, QP-4-14-1 topology has presented the highest value of R², which confirmed the training dataset. Moreover, R² was also calculated for the topologies in the validation dataset. The results show that QP-4-14-1 topology depicted the highest value of the R² (0.975).

Therefore, the QP-4-14-1 topology was selected as an efficient final model for the photodegradation due to having the lowest RMSE and AADs (Table 4) as well as the highest R²s (Figure 6).

Figure 7 presents the structure of the selected topology as the final model (QP-4-14-1), which consists of four nodes in the input layer including irradiation time, pH, photocatalyst amount and MO concentration, while the efficiency of the photodegradation is only the node in its output layer. As observed, the determined nodes in the hidden layer content of 14 nodes, which connected to the nodes of the input and output layers by multilayer normal feed-forward, and the bias shifts the space of the efficiency (Ghaffari et al. 2006). As a result, the model was used to obtain the importance of the input variables. Moreover, the model predicted the optimum condition of photodegradation with maximum efficiency.

Figure 8 shows the 3D plots of the input variables' simultaneous effect on the efficiency as the only output of the photodegradation process.

In the 3D plots, the red color is the desirable area, the blue color shows the area with lowest values and yellow color is minimum applicable value of the efficiency which is based on determined optimized level of the variables. Figure 8(a) shows the simultaneous effect of photocatalyst amount (g/L) and time of irradiation (min) on the efficiency while the value of the pH (= 5) and concentration of MO (40 mg/L) were kept constant. As the bordering line (yellow color) shows, the level of the photocatalyst amount was 0.95 to 1.7 g/L while the level of irradiation time was 250 to 300 min. Figure 8(b) illustrates the effect of MO concentration (mg/L) and irradiation time on the efficiency, while the value of the pH and photocatalyst amount was kept constant at 5.00 and 1.25 g/L respectively. According to the bordering line, the level of the MO concentration was less than 40 mg/L and the level of the irradiation time was the same as mentioned in level in Figure 8(a). Figure 8(c) depicts the simultaneous effect of pH and irradiation time as variables while the concentration of MO (40 mg/L) and photocatalyst (1.25 g/L) were kept constant. As shown, the level of the pH was 5 to 5.8 and the level of the irradiation time in the previous plots was confirmed. Figure 8(d) shows the effect of the photocatalyst amount and pH on the photodegradation efficiency in the presence of the constant amount of MO (40 mg/L) and at 240 min of irradiation time. The determined level for photocatalyst and pH were 0.95 to 1.7 and 5.0 to 5.8 respectively. Figure 8(e) presents the effect of MO and photocatalyst on the efficiency in pH 5 and at 240 min of irradiation time. The observed level for MO and photocatalyst concentration confirmed the reported levels in the previous plots. Figure 8(f) exhibits the effect of MO and pH on the efficiency, which shows the level of the pH was larger than the reported values in Figure 8(c) and 8(d). The high value of the efficiency at the lower pH could be an advantage for low concentrations and pH of acidic wastewater.

The relative importance of the input effective variables of the photodegradation indicates the relative effect of them on the efficiency. As observed, all the variables are not ignorable and the order of importance of the variables has been included as MO >photocatalyst > pH > irradiation time. This order proved that the concentration of MO is the most important factor in the photodegradation. Moreover, the model (QP-4-14-1) predicted efficiency for five runs of the optimum condition at different irradiation times (Table 5). Then the runs were validated by further experiment. As shown, the obtained efficiencies were very close to the predicted efficiency. Particularly, the obtained result of the second run has confirmed the RSM's prediction as mentioned in the previous sections.

Table 6 shows the compared level of the input variables, which were modeled by RSM and ANN processing. As observed, the order of calculated input variable levels is ‘one variable at a time’ >RSM > ANN. The narrowest level is more accurate than the wider level. The other achievement of the multivariate methods is the determination of the effective input variables' optimum amount. Therefore, the optimum values of the RSM calculation are included as irradiation time (288 min), pH (5.8), photocatalyst amount (0.96 g/L) and MO concentration (45 mg/L), which were experimentally validated. As Table 6 shows, the optimum values have been confirmed by ANN model (QP-4-14-1), which indicates the methods confirm each other. Moreover, the importance of the input variables that can be useful for industrial scaling up were determined by ANN. The ANN importance order is MO >photocatalyst > pH> irradiation time, while the RSM has reported two kinds of importance order, including linear pH >photocatalyst > MO> irradiation time and for the quadratic as photocatalyst >pH > MO> irradiation time. As observed, the factor of irradiation time is was not relatively important in either calculation, while other factors were very important as input effective variables.

To enhance visible-light photocatalytic activities, CaCu₃Ti₄O₁₂ nanocomposite with a high surface area was simultaneously synthesized and decorated in different percentages of GO. The result of characterization showed the sample with 8% RGO presented the optimum properties, such as visible-absorption, low band gap energy (2.1 eV) and a great surface area of as high as 130 m²/g. Thereafter, the photocatalyst (CCTO – RGO (8%)) was applied to aid photodegradation of MO as a model of aqueous organic pollutants under visible-light irradiation. To optimize the efficiency, the photodegradation was modeled using multivariate semi-empirical methods such as RSM and ANN. The models optimized the effective level of the photodegradation factors including pH, photocatalyst amount, pollutant concentration and time of irradiation as well as their order of importance, which is crucial to industrial application scale-up. The result of optimized photodegradation indicated that 0.96 g/L of the photocatalyst removed 45 mg/L of the methyl-orange at pH 5.8 during 288 min of irradiation with 89% efficiency. This satisfactory performance along with the optimized method of photodegradation represents the potential of this photocatalyst as a powerful candidate toward scaling up for industrial applications. Moreover, the photodegradation has been scaled up for industrial applications by determination of the importance of the input effective variables according to the following order: organics > photocatalyst > pH > irradiation time.