Fabrication of TiO2@ITO-grown nanocatalyst as efficient applicant for catalytic reduction of Eosin Y from aqueous media

Pato, Abdul Hameed; Balouch, Aamna; Talpur, Farah Naz; Abdullah, .; Panah, Pirah; Mahar, Ali Muhammad; Jagirani, Muhammad Saqaf; Kumar, Sagar; Sanam, Safia

doi:10.1007/s11356-020-10548-y

Cited by 16 publications

(5 citation statements)

References 50 publications

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“…According to World Health Organization (WHO), each year more than 700,000 tons of manufactured dyes are made across the world and the disposing of dyes is for the most part through industrial effluents (Bhatia et al 2017). A number of researchers have focused to degrade/remove the dyes from wastewater before their discharge into water bodies (Dang et al 2016;Mahar et al 2020;Mittal 2014;Pato et al 2020b;Samal et al 2017;Sharma et al 2017;Tan et al 2008). Various strategies have been utilized to induce the water free from poisons, i.e., adsorption, electroflotation, ozonation and nanofiltration (Ghernaout et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous kinetics of CuO nanoflakes in simultaneous decolorization of Eosin Y and Rhodamine B in aqueous media

et al. 2021

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This study describes the heterogeneous photocatalysis of CuO nanoflakes for the effective degradation of hazardous dyes. The successful synthesis of CuO nanoflakes was carried out by exploiting aqueous chemical growth method. The synthesized material was characterized by versatile analytical tools including XRD, FE-SEM, EDX, AFM, ZP and FT-IR. The engineered CuO reveals nanoflakes texture having average size of 18.3 nm and (− 36.5 ± 3 mV) surface charge with excellent phase purity and crystalline nature. The catalytic kinetics exposed that CuO nanocatalyst exhibits an exceptional degradation over 97% for Eosin Y (EY) and Rhodamine B (RB) dyes in the aqueous medium using minimum catalyst dose of 120 and 140 μg individually and simultaneously, within 3 min of time period under sunlight. Due to ultrafine processing, cost-effective synthetic outline, supreme reduction percentage in short time and intense recyclability with an insignificant decrease in degradation abilities, the proposed nanocatalyst is extremely useful. Consequentially, the nanocatalyst presented here is more useful and realistic, hence can be suggested as an effective candidate for the successful degradation of complex dyes at the commercial level.

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Section: Introductionmentioning

confidence: 99%

Heterogeneous kinetics of CuO nanoflakes in simultaneous decolorization of Eosin Y and Rhodamine B in aqueous media

et al. 2021

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“…The Artificial Neural Network (ANN) stands as a mathematical framework extracted from interdependent process variables, constituting a pivotal modeling instrument in domains like predictive analytics, regression analysis, and pattern recognition. Moreover, it exhibits distinctive traits encompassing reliability, resilience, and adeptness in grasping intricate and non-linear phenomena [29]. In this study, an ANN method was employed for the removal of MB dye.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of Response Surface Methodology and Artificial Neural Network in Removal of Methylene Blue with Olive Pits Activated Carbon

Ozcelik,

Altintig,

Cetinkaya

et al. 2024

Preprint

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Activated carbon produced from olive pits (OPAC) is a low-cost adsorbent that removes methylene blue (MB) from aqueous solutions. OPAC was characterized using FTIR and SEM analysis. The response surface methodology (RSM) and artificial neural network (ANN) approaches have been combined to optimize and model the adsorption MB. To assess the optimal conditions for MB adsorption, RSM was initially applied using four controllable operating parameters. Throughout the optimization process, varying levels of independent variables were employed, including initial dye concentration ranging from 25 to 125 mg/L, adsorbent dosage ranging from 0.1 to 0.9 g/L, pH values spanning from 1 to 9, and contact times ranging from 15 to 75 min. Moreover, the R2 value (R2 = 0.9804) indicates that the regression can effectively forecast the response within the examined range of the adsorption process. This research showcases the capability of optimizing and predicting the colour removal process through the combined RSM-ANN approach. It highlights the effectiveness of adsorption on OPAC as a viable primary treatment method for the removal of colour from wastewater containing dyes.

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“…Nonetheless, the solution for the certain drawbacks of electrochemical methods could easily be achieved by modifying the sensing probes with several catalytic and conductive materials which not only improve the detection process but effectively enhance the electron transfer kinetics (Arduini et al 2016;Buledi et al 2020b;Buledi et al 2020c;Chen and Chatterjee 2013;Hashemi et al 2019;Viswanathan and Manisankar 2015). For making the sensing probes more sensitive, reliable and to enhance the electrocatalytic proficiencies of electrodes, different metal oxide nanoparticles are being utilized for the modification of electrodes (Bakhsh et al 2021;Buledi et al 2020a;Khairy et al 2018;Khand et al 2021;Lavanya et al 2018;Memon et al 2020;Pato et al 2020;Sanghavi and Srivastava 2013). Amongst the different metal oxide nanoparticles CuO nanostructures have been widely exploited in different applications such as supercapacitors, electrochemical sensor, adsorption and degradation (Cheng et al 2014;Wang et al 2018;Yang et al 2019;Zhang et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Exploring electrocatalytic proficiencies of CuO nanostructure for simultaneous determination of bentazone and mexacarbate pesticides

Buledi

Batool

et al. 2021

Appl Nanosci

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Pesticides are the most perilous organic compounds that are of major human health concern. The hazardous pesticides such as bentazone (BTZN) and mexacarbate (MCBT) which badly cause the environmental pollution and pose lethal impacts on human health. In an effort to develop a highly efficient, reliable and sensitive electrochemical sensor, the novel CuO nanostructures were synthesized through easy and green aqueous chemical growth procedure and used as sensitive probe for the simultaneous determination of bentazone and mexacarbate pesticides. The prepared material was used as conductive and catalytic tool for the modification of glassy carbon electrode (GCE). The exquisite CuO nanostructures were characterized by FTIR, FE-SEM, XRD, EDS, zeta sizer and zeta potential to reveal the functionalities, morphological texture, crystallinity, size and existing charge on the surface of nanostructures. The conductive nature and charge transfer kinetics of CuO/GCE was explored through cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Under the optimized parameters, the sensitive and reliable simultaneous determination of two pesticides was carried out via CV and DPV that exhibited fluent determination process. The Ipa response was linearly proportional to the concentration of pesticides with low LOD and LOQ observed as (0.008 and 0.026 µM) for bentazone and (0.0015 and 0.004 µM) for mexacarbate, which is lower than the permissible limit set by US Health Advisory Level. Moreover, the developed sensor manifested tunable reusability, stability, and selectivity for both analytes. The proposed method is a reliable step towards the on-site detection of pesticides in various resources.

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Fabrication of TiO2@ITO-grown nanocatalyst as efficient applicant for catalytic reduction of Eosin Y from aqueous media

Cited by 16 publications

References 50 publications

Heterogeneous kinetics of CuO nanoflakes in simultaneous decolorization of Eosin Y and Rhodamine B in aqueous media

Heterogeneous kinetics of CuO nanoflakes in simultaneous decolorization of Eosin Y and Rhodamine B in aqueous media

Application of Response Surface Methodology and Artificial Neural Network in Removal of Methylene Blue with Olive Pits Activated Carbon

Exploring electrocatalytic proficiencies of CuO nanostructure for simultaneous determination of bentazone and mexacarbate pesticides

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