| Literature DB >> 30229073 |
Samira Taherkhani1,2, Mohammad Darvishmotevalli1,2, Kamaleddin Karimyan3,4, Bijan Bina1, Adibeh Fallahi5, Hossein Karimi1,2.
Abstract
Removal of pharmaceutical ingredients such as tetracycline from aqueous solution has a great importance. The aim of the current study was to investigate the degradation of tetracycline antibiotic in the presence of a triode semiconductor oxide as well as modeling of the photocatalytic degradation process in order to determine optimal condition Zinc stannate nanoflower (Zn2SnO4) was synthesized by hydrothermal process and characterized by X-ray diffraction (XRD), Fourier transform infrared (FT-IR), and scanning electron microscopy (SEM) techniques. Response surface methodology (RSM) was used to model and optimize four key independent variables, including photocatalyst dosage, initial concentration of tetracycline antibiotic (TC) as model pollutant, pH and reaction time of photocatalytic degradation. The proposed quadratic model was in accordance with the experimental results with a correlation coefficient of 98%. The obtained optimal experimental conditions for the photodegradation process were the following: zinc stannate (ZTO) dosage=300 mg L-1, initial concentration of TC= 10 mg L-1, reaction time= 100 min and pH=4.5. Under the optimal conditions, the predicted degradation efficiency was 95.45% determined by the proposed model. In order to evaluate the accuracy of the optimization procedure, the confirmatory experiment was carried out under the optimal conditions and the degradation efficiency of 93.54% was observed, which closely agreed with the predicted value.Entities:
Keywords: Antibiotic; Modeling; Nanoflower; Photodegradation; RSM; Water treatment; ZTO
Year: 2018 PMID: 30229073 PMCID: PMC6141147 DOI: 10.1016/j.dib.2018.06.030
Source DB: PubMed Journal: Data Brief ISSN: 2352-3409
Experimental design matrix and the value of responses based on experiment run.
| 1 | 6 | 150 | 25 | 40 | 42.8 | 42.91 |
| 2 | 9 | 250 | 25 | 40 | 13 | 15.25 |
| 3 | 7.5 | 200 | 20 | 70 | 51 | 52.57 |
| 4 | 7.5 | 200 | 20 | 130 | 64.7 | 64.22 |
| 5 | 7.5 | 300 | 20 | 70 | 52.1 | 51.02 |
| 6 | 6 | 150 | 15 | 40 | 48.6 | 49.32 |
| 7 | 6 | 250 | 25 | 100 | 77.12 | 79.24 |
| 8 | 7.5 | 200 | 30 | 70 | 52 | 50.86 |
| 9 | 9 | 250 | 25 | 100 | 43.6 | 41.16 |
| 10 | 9 | 150 | 15 | 40 | 27 | 25.9 |
| 11 | 9 | 200 | 20 | 70 | 11.8 | 13.88 |
| 12 | 6 | 250 | 15 | 40 | 62.1 | 62.13 |
| 13 | 4.5 | 200 | 20 | 70 | 76.8 | 75.09 |
| 14 | 9 | 150 | 25 | 40 | 19 | 16.94 |
| 15 | 6 | 250 | 25 | 40 | 54.4 | 54.36 |
| 16 | 7.5 | 100 | 20 | 70 | 38.6 | 40.01 |
| 17 | 9 | 250 | 15 | 100 | 41.3 | 21.42 |
| 18 | 7.5 | 200 | 20 | 10 | 21.7 | 22.55 |
| 19 | 7.5 | 200 | 20 | 70 | 51.8 | 22.55 |
| 20 | 9 | 150 | 25 | 100 | 42.3 | 43.29 |
| 21 | 6 | 150 | 25 | 100 | 67.3 | 67.93 |
| 22 | 7.5 | 200 | 20 | 70 | 50.5 | 52.57 |
| 23 | 9 | 250 | 15 | 40 | 27.6 | 25.5 |
| 24 | 9 | 150 | 15 | 100 | 44.06 | 42.68 |
| 25 | 7.5 | 200 | 20 | 70 | 51.7 | 52.57 |
| 26 | 7.5 | 200 | 20 | 70 | 50.3 | 52.57 |
| 27 | 7.5 | 200 | 10 | 70 | 56.5 | 58.01 |
| 28 | 6 | 200 | 15 | 100 | 76.8 | 77.44 |
| 29 | 7.5 | 200 | 20 | 70 | 60.1 | 52.57 |
| 30 | 6 | 150 | 15 | 100 | 66 | 64.77 |
| 31 | 7.5 | 200 | 20 | 70 | 52.6 | 52.57 |
Fig. 1The relationship between the predicted and actual responses.
Analysis of variance (ANOVA) for the selected quadratic model.
| Source | DOF | Adj SS | Adj MS | F-value | P-value |
|---|---|---|---|---|---|
| Regression | 14 | 9079.61 | 648.54 | 89.96 | 0.000 |
| Residual | 16 | 115.34 | 7.21 | – | – |
| Total | 30 | 9194.96 | – | – | – |
SS: Sum of squares.
MS: Mean squares.
The ANOVA results for the coefficients of variables of quadratic model.
| A | −30.605 | 0.000 |
| B | 5.4883 | 0.000 |
| C | −3.5783 | 0.005 |
| D | 20.8317 | 0.000 |
| A2 | −8.0806 | 0.001 |
| B2 | −7.0306 | 0.001 |
| C2 | 1.8694 | 0.336 |
| D2 | −9.1806 | 0.000 |
| A×B | −13.145 | 0.000 |
| A×C | −2.545 | 0.000 |
| A×D | 1.335 | 0.626 |
| B×C | −1.355 | 0.621 |
| B×D | −0.135 | 0.961 |
| C×D | 9.565 | 0.003 |
Optimized values of parameters effective on the tetracycline degradation.
| ZTO (mg/L) | 300 |
| pH | 4.5 |
| mg/L) )TC | 10 |
| Time(min) | 100 |
| Removal Percent | 93.54 |
Fig. 2FT-IR spectrum of prepared ZTO.
Fig. 3XRD pattern spectrum of prepared ZTO.
Fig. 4SME images of prepared ZTO.
Fig. 5Surface and counter plots of the photocatalytic degradation of tetracycline.
Fig. 6The schematic of UV photoreactor.
The properties of TCA.
| Molecular formula | C22H24O8N2HCl |
| Molecular weight (g/mol) | 480.9 |
| Solubility (mol/L) | 0.041 |
| λ max (nm) | 359 |
| Chemical structure |
Factors and levels of designing experiments via the CCD method.
| (X1) pH | 4.5 | 6 | 7.5 | 9 | 10.5 |
| (X2) ZTO | 100 | 150 | 200 | 250 | 300 |
| (X3) TC | 10 | 15 | 20 | 25 | 30 |
| (X4) Time | 10 | 40 | 70 | 100 | 130 |
Designing of experiment via the CCD method based on the real values of the variables.
| 1 | 6 | 150 | 25 | 40 |
| 2 | 9 | 250 | 25 | 40 |
| 3 | 7.5 | 200 | 20 | 70 |
| 4 | 7.5 | 200 | 20 | 130 |
| 5 | 7.5 | 300 | 20 | 70 |
| 6 | 6 | 150 | 15 | 40 |
| 7 | 6 | 250 | 25 | 100 |
| 8 | 7.5 | 200 | 30 | 70 |
| 9 | 9 | 250 | 25 | 100 |
| 10 | 9 | 150 | 15 | 40 |
| 11 | 10.5 | 200 | 20 | 70 |
| 12 | 6 | 250 | 15 | 40 |
| 13 | 4.5 | 200 | 25 | 70 |
| 14 | 9 | 150 | 25 | 40 |
| 15 | 6 | 250 | 20 | 40 |
| 16 | 7.5 | 100 | 15 | 70 |
| 17 | 9 | 250 | 20 | 100 |
| 18 | 7.5 | 200 | 20 | 10 |
| 19 | 7.5 | 200 | 25 | 70 |
| 20 | 9 | 150 | 25 | 100 |
| 21 | 6 | 150 | 25 | 100 |
| 22 | 7.5 | 200 | 25 | 70 |
| 23 | 9 | 250 | 25 | 40 |
| 24 | 9 | 150 | 15 | 100 |
| 25 | 7.5 | 200 | 20 | 70 |
| 26 | 7.5 | 200 | 20 | 70 |
| 27 | 7.5 | 200 | 10 | 70 |
| 28 | 6 | 200 | 15 | 100 |
| 29 | 7.5 | 200 | 20 | 70 |
| 30 | 6 | 150 | 15 | 100 |
| 31 | 7.5 | 200 | 20 | 70 |
| Subject area | Environmental sciences |
| More specific subject area | Environmental chemistry |
| Type of data | Tables and figures |
| How data was acquired | In this study, Firstly, Zn2SnO4 was synthesized and investigated for TC removal in aqueous solution. After that, it characterized by XRD, FT-IR, and SEM techniques. Response surface methodology (RSM) was used to model and optimize four independent variables, including photocatalyst dosage, initial concentration of TC, pH and reaction time of photocatalytic degradation |
| Data format | Raw, analyzed |
| Experimental factors | Zinc stannate nanoflower (Zn2SnO4) was synthesized by hydrothermal process. |
| Experimental features | The samples preparation and analysis of them were performed according to standard method that provided invalid and similar references. |
| Data source location | Isfahan city, Iran |
| Data accessibility | Data are included in this article |