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- Ahsan, Hajra, et al.
(författare)
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Photocatalysis and adsorption kinetics of azo dyes by nanoparticles of nickel oxide and copper oxide and their nanocomposite in an aqueous medium
- 2022
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Ingår i: PeerJ. - : PeerJ. - 2167-8359. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Azo dyes are recalcitrant organic pollutants present in textile industry effluents. Conventional treatment methods to remove them come with a range of disadvantages. Nanoparticles and their nanocomposites offer more efficient, less expensive and easy to handle wastewater treatment alternative. Methods. In this study, nanoparticles of nickel oxide (NiO-NPs), copper oxide (CuO-NPs) and their nanocomposite (NiO/CuO-NC) were synthesized using co- precipitation method. The functional groups present on the surface of synthesized nanomaterials were verified using Fourier-transform infrared spectroscopy (FTIR). Surface morphology was assessed using scanning electron microscopy (SEM) whereas purity, shape and size of the crystallite were determined using X-ray diffraction (XRD) technique. The potential of these nanomaterials to degrade three dyes i.e., Reactive Red-2 (RR-2), Reactive Black-5 (RB-5) and Orange II sodium salt (OII) azo dyes, was determined in an aqueous medium under visible light (photocatalysis). The photodegradation effectiveness of all nanomaterials was evaluated under different factors like nanomaterial dose (0.02-0.1 g 10 mL-1), concentration of dyes (20-100 mg L-1), and irradiation time (60-120 min). They were also assessed for their potential to adsorb RR-2 and OII dyes. Results. Results revealed that at optimum concentration (60 mgL-1) of RR-2, RB-5, and OII dyes, NiO-NPs degraded 90, 82 and 83%, CuO-NPs degraded 49, 34, and 44%, whereas the nanocomposite NiO/CuO-NC degraded 92, 93, and 96% of the said dyes respectively. The nanomaterials were categorized as the efficient degraders of the dyes in the order: NiO/CuO-NC > NiO-NPs > CuO-NPs. The highest degradation potential shown by the nanocomposite was attributed to its large surface area, small particles size, and quick reactions which were proved by advance analytical techniques. The equilibrium and kinetic adsorption of RR-2 and OII on NiO-NPs, CuO-NPs, and NiO/CuO-NC were well explained with Langmuir and Pseudo second order model, respectively (R2 ≥0.96). The maximum RR-2 adsorption (103 mg/g) was obtained with NiO/CuO-NC. It is concluded that nanocomposites are more efficient and promising for the dyes degradation from industrial wastewater as compared with dyes adsorption onto individual NPs. Thus, the nanocomposite NiO/CuO-NC can be an excellent candidate for photodegradation as well as the adsorption of the dyes in aqueous media.
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| 2. |
- Naqvi, Salman Raza, et al.
(författare)
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Pyrolysis of high-ash sewage sludge : Thermo-kinetic study using TGA and artificial neural networks
- 2018
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Ingår i: Fuel. - Oxon, UK : Elsevier Ltd. - 0016-2361 .- 1873-7153. ; 233, s. 529-538
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Tidskriftsartikel (refereegranskat)abstract
- Pyrolysis of high-ash sewage sludge (HASS) is a considered as an effective method and a promising way for energy production from solid waste of wastewater treatment facilities. The main purpose of this work is to build knowledge on pyrolysis mechanisms, kinetics, thermos-gravimetric analysis of high-ash (44.6%) sewage sludge using model-free methods & results validation with artificial neural network (ANN). TG-DTG curves at 5,10 and 20 °C/min showed the pyrolysis zone was divided into three zone. In kinetics, E values of models ranges are; Friedman (10.6–306.2 kJ/mol), FWO (45.6–231.7 kJ/mol), KAS (41.4–232.1 kJ/mol) and Popescu (44.1–241.1 kJ/mol) respectively. ΔH and ΔG values predicted by OFW, KAS and Popescu method are in good agreement and ranged from (41–236 kJ/mol) and 53–304 kJ/mol, respectively. Negative value of ΔS showed the non-spontaneity of the process. An artificial neural network (ANN) model of 2 * 5 * 1 architecture was employed to predict the thermal decomposition of high-ash sewage sludge, showed a good agreement between the experimental values and predicted values (R2 ⩾ 0.999) are much closer to 1. Overall, the study reflected the significance of ANN model that could be used as an effective fit model to the thermogravimetric experimental data. © 2018 Elsevier Ltd
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