Photocatalytic Degradation of Acid Blue 25 Dye in Wastewater by Zinc Oxide Nanoparticles
 
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1
Department of Chemical Sciences, Faculty of Pure and Applied Sciences, Federal University Wukari, PMB 1020, Wukari, Nigeria
 
2
Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University, PMB 1045, Zaria, Nigeria
 
 
Submission date: 2024-02-26
 
 
Acceptance date: 2024-03-28
 
 
Publication date: 2024-03-30
 
 
Corresponding author
James Dama Habila
yerimaemmanuel@yahoo.com
 
 
Trends in Ecological and Indoor Environmental Engineering, 2024;2(1):50-55
 
KEYWORDS
ABSTRACT
This study investigates the photocatalytic degradation of Acid Blue Dye using zinc oxide nanoparticles synthesized from Senna siamea flower extracts (ZnO-S.S.). The synthesized nanoparticles were investigated using Transmission Emission Spectroscopy (TEM) and Scanning Electron Microscope (SEM), which made it possible to reveal the spherical shape of ZnO-S.S. nanoparticles. Themogravimetric Analysis – Differential Thermal Analysis (TGA-DTA) revealed high thermal stability of ZnO-S.S. with an insignificant weight loss as temperature increases from 600 °C to 846.9 °C. While Energy Dispersive X-ray analysis (EDX) reveals the elemental composition and atomic weight percentage as C (4.8%), O (11%), S (6%) and Zn (78.2%). The synthesized nanoparticles ZnO-S.S. used to degrade Acid Blue dye contaminated waste water under visible light irradiation. Exhibits an optimum degradation efficiency of 99% achieved in 150 minutes using a catalyst dosage of 150 mg at an initial acid blue concentration of 10 mg/L. The degradation process best conformed to the pseudo first order kinetics pathway. In conclusion the study established that the synthesized catalyst exhibits good efficiency and utility in degrading dye contaminated wastewater. However, further studies are recommended on the influence of other indicators such as light intensity, temperature and pH on the degradation process.
REFERENCES (24)
1.
Afzal, M., Farooq, M. S., Ahmad, H. K., Begum, I., & Quddus, M. A., (2010). Relationship between school education and economic growth in Pakistan: ARDL bounds testing approach to cointegration. Pakistan Economic and Social Review, 48(1), 39–60. https://doi.org/10.2307/417624....
 
2.
Akaike, H., (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.19....
 
3.
Attavanich, W., & McCarl, B. A., (2014). How is CO2 affecting yields and technological progress? A statistical analysis. Climatic Change, 124, 747–762. https://doi.org/10.1007/s10584....
 
4.
Bakır, H., Ağbulut, Ü., Gürel, A. E., Yıldız, G., Güvenç, U., Soudagar, M. E. M., Hoang, A. T., Deepanraj, B., Saini, G., & Afzal, A., (2022). Forecasting of future greenhouse gas emission trajectory for India using energy and economic indexes with various metaheuristic algorithms. Journal of Cleaner Production, 360, 131946. https://doi.org/10.1016/j.jcle....
 
5.
Brown, M. E., Carr, E. R., Grace, K. L., Wiebe, K., Funk, C. C., Attavanich, W., Backlund, P., & Buja, L., (2017). Do markets and trade help or hurt the global food system adapt to climate change? Food Policy, 68, 154–159. https://doi.org/10.1016/j.food....
 
6.
Ehrlich, P. R., & Holdren, J. P., (1971). Impact of Population Growth: Complacency concerning this component of man's predicament is unjustified and counterproductive. Science, 171(3977), 1212–1217.
 
7.
Granger, C. W. J., (1988). Some recent development in a concept of causality. Journal of Econometrics, 39(1–2), 199–211. https://doi.org/10.1016/0304-4....
 
8.
Iwata, H., & Okada, K., (2014). Greenhouse gas emissions and the role of the Kyoto Protocol. Environmental Economics and Policy Studies, 16, 325–342. https://doi.org/10.1007/s10018....
 
9.
Johansen, S., & Juselius, K., (1990). Maximum likelihood estimation and inference on cointegration—with appucations to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169–210. https://doi.org/10.1111/j.1468....
 
10.
Lott, M. C., Pye, S., & Dodds, P. E., (2017). Quantifying the co-impacts of energy sector decarbonisation on outdoor air pollution in the United Kingdom. Energy Policy, 101, 42–51. https://doi.org/10.1016/j.enpo....
 
11.
Nkoro, E., & Uko, A. K., (2016). Autoregressive Distributed Lag (ARDL) cointegration technique: application and interpretation. Journal of Statistical and Econometric Methods, 5(4), 63–91.
 
12.
Pesaran, M. H., & Pesaran, B., (1997). Working with Microfit 4.0: Interactive econometric analysis. Oxford University Press.
 
13.
Pesaran, M. H., Shin, Y., & Smith, R. J., (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.61....
 
14.
Resilience, B., (2017). The State of food security and nutrition in the world. Rome: Building resilience for peace and food security.
 
15.
Salam, M., Alam, F., Dezhi, S., Nabi, G., Shahzadi, A., Hassan, S. U., Ali, M., Saeed, M. A., Hassan, J., & Ali, N., (2021). Exploring the role of Black Soldier Fly Larva technology for sustainable management of municipal solid waste in developing countries. Environmental Technology & Innovation, 24, 101934. https://doi.org/10.1016/j.eti.....
 
16.
Salam, M., Shahzadi, A., Zheng, H., Alam, F., Nabi, G., Dezhi, S., Ullah, W., Ammara, S., Ali, N., & Bilal, M., (2022). Effect of different environmental conditions on the growth and development of Black Soldier Fly Larvae and its utilization in solid waste management and pollution mitigation. Environmental Technology & Innovation, 28, 102649. https://doi.org/10.1016/j.eti.....
 
17.
Salam, M., Zheng, L., Shi, D., Huaili, Z., Vambol, V., Chia, S. Y., Hossain, M. N., Mansour, A., Eliw, M., & Dong, M., (2023). Exploring Insect-based technology for waste management and livestock feeding in selected South and East Asian countries. Environmental Technology & Innovation, 32, 103260.
 
18.
Schwarz, G., (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/11....
 
19.
Wang, F., Shackman, J., & Liu, X., (2017). Carbon emission flow in the power industry and provincial CO2 emissions: Evidence from cross-provincial secondary energy trading in China. Journal of Cleaner Production, 159, 397–409. https://doi.org/10.1016/j.jcle....
 
20.
Yang, H., Zheng, H., Liu, H., & Wu, Q., (2019). NonLinear effects of environmental regulation on eco-efficiency under the constraint of land use carbon emissions: Evidence based on a Bootstrapping approach and Panel Threshold Model. International Journal of Environmental Research and Public Health, 16(10), 1679. https://doi.org/10.3390/ijerph....
 
21.
York, R., Rosa, E. A., & Dietz, T., (2003a). A rift in modernity? Assessing the anthropogenic sources of global climate change with the STIRPAT model. International Journal of Sociology and Social Policy, 23(10), 31–51. https://doi.org/10.1108/014433....
 
22.
York, R., Rosa, E. A., & Dietz, T., (2003b). STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics, 46(3), 351–365. https://doi.org/10.1016/S0921-....
 
23.
Zhang, S., Ma, M., Xiang, X., Cai, W., Feng, W., & Ma, Z., (2022). Potential to decarbonize the commercial building operation of the top two emitters by 2060. Resources, Conservation and Recycling, 185, 106481. https://doi.org/10.1016/j.resc....
 
24.
Zhu, E., Deng, J., Zhou, M., Gan, M., Jiang, R., Wang, K., & Shahtahmassebi, A. (2019). Carbon emissions induced by land-use and land-cover change from 1970 to 2010 in Zhejiang, China. Science of the Total Environment, 646, 930–939. https://doi.org/10.1016/j.scit....
 
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