Removal of caffeine from aqueous solution by green approach using Ficus Benjamina zero-valent iron/copper nanoparticles

Abdelaziz, Hossam M.; Farag, R. S.; Abdel‐Gawad, Soha A.

doi:10.1177/0263617420947495

Cited by 22 publications

(5 citation statements)

References 41 publications

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“…The studies conducted by Abdel-Aziz, Farag and Abdel-Gawad [36], Abdel-Aziz, Farag and Abdel-Gawad [37], and Abdel-Aziz, Farag and Abdel-Gawad [38] reported the use of nano FeCu in caffeine (CAF), metformin hydrochloride (MF) and carbamazepine (CBZ) removal, respectively. From the studies of Abdel-Aziz, Farag and Abdel-Gawad [36] and Abdel-Aziz, Farag and Abdel-Gawad [37], the efficiency of nanoFeCu removed CAF and MF applying different CAF and MF concentrations after reusing the nanoFeCu five times was still over 68% and 50%, respectively. After reusing the nanoFeCu in the 1st, 2nd, 3rd, 4th, and 5th recycles, the removal efficiency of CAF was 82, 78, 83, 70, and 69% while the removal efficiency of MF was 67, 61, 57, 54, and 52%, respectively.…”

Section: Organic Matter Removal In Wastewater Treatmentmentioning

confidence: 99%

The application of machine learning in nanoparticle treated water: A review

et al. 2023

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Pollution from industrial effluents and domestic waste are two of the most common sources of environmental pollutants. Due to the rising population and manufacturing industries, large amounts of pollutants were produced daily. Therefore, enhancements in wastewater treatment to render treated wastewater and provide effective solutions are essential to return clean and safe water to be reused in the industrial, agricultural, and domestic sectors. Nanotechnology has been proven as an alternative approach to overcoming the existing water pollution issue. Nanoparticles exhibit high aspect ratios, large pore volumes, electrostatic properties, and high specific surfaces, which explains their efficiency in removing pollutants such as dyes, pesticides, heavy metals, oxygen-demanding wastes, and synthetic organic chemicals. Machine learning (ML) is a powerful tool to conduct the model and prediction of the adverse biological and environmental effects of nanoparticles in wastewater treatment. In this review, the application of ML in nanoparticle-treated water on different pollutants has been studied and it was discovered that the removal of the pollutants could be predicted through the mathematical approach which included ML. Further comparison of ML method can be carried out to assess the prediction performance of ML methods on pollutants removal. Moreover, future studies regarding the nanotoxicity, synthesis process, and reusability of nanoparticles are also necessary to take into consideration to safeguard the environment.

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Section: Organic Matter Removal In Wastewater Treatmentmentioning

confidence: 99%

The application of machine learning in nanoparticle treated water: A review

et al. 2023

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“…First improvement involves replacing the chemically prepared Fe/Cu nanoparticles by green synthesized nanomaterials in order to reduce the treatment cost. For example, Abdel-Aziz et al 66 have synthesized Ficus Benjamina zero-valent Fe/Cu nanoparticles and used it for caffeine removal from aqueous solution. In another study, Farag et al 67 have synthesized green nano-iron carbide and used it for phosphate removal from aqueous solutions.…”

Section: Future Perspectivesmentioning

confidence: 99%

A prototype of textile wastewater treatment using coagulation and adsorption by Fe/Cu nanoparticles: Techno-economic and scaling-up studies

Mahmoud

Mostafa

Peters

2021

Nanomaterials and Nanotechnology

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This study aims to investigate the efficiency of a pilot prototype system comprising coagulation/flocculation, filtration, and nano-bimetallic iron/copper (Fe/Cu) degradation and adsorption units for the removal of chemical oxygen demand (COD), biological oxygen demand (BOD), color, total nitrogen (TN), total phosphorus (TP), and TSS from real textile wastewater. The total removal efficiencies of the system were 96, 98, 82, 69, 88, and 97%, respectively, using 0.5 g/L ferric chlorides as a coagulant under an optimum adsorption condition of pH 6.0, nano-dosage 1.4 g/L, contact time 80 min, and stirring rate 250 r/min at room temperature. Adsorption isotherms indicated that the removal of COD and TP obeys both Koble–Corrigan and Freundlich adsorption models, removal of color obeys both Koble–Corrigan and Hill adsorption models, and removal of TN and TSS obeys Koble–Corrigan and Khan models, respectively. Avrami kinetic models adequately describe the adsorption data for COD, BOD, TN, and TSS, while pseudo-second-order and intraparticle models described the removal mechanism of color and TSS, respectively. An artificial neural network (ANN) with r2-value exceeding 0.98 is accurate and can be used with confidence in predicting removal efficiencies of the targeted parameters. Sensitivity analysis results showed that the initial concentration was the most influential parameter for TSS removal with relative importance greater than 25%, while the bimetallic Fe/Cu dosage was the most influential factor for all other studied parameters with relative importance greater than 40%. The total treatment cost of the proposed system per m3 after scaling up was found to be US$4.5 for reuse of the treated water for the irrigation of forest trees.

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“…Abdel-Aziz et al [156] synthesized bimetallic zero-valent iron/copper nanoparticles (FB-nZVFe/Cu), having used this material for caffeine adsorption. FB-nZVFe/Cu was used during five successive times for the adsorption of caffeine (5 mg/L).…”

Section: Regeneration and Reuse Of Adsorbentsmentioning

confidence: 99%

A Review of Caffeine Adsorption Studies onto Various Types of Adsorbents

Quintero-Jaramillo

Carrero-Mantilla

Sanabria‐González

2021

The Scientific World Journal

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A systematic literature review of publications from 2000 to 2020 was carried out to identify research trends on adsorbent materials for the removal of caffeine from aqueous solutions. Publications were retrieved from three databases (Scopus, Web of Science, and Google Scholar). Words “adsorption AND caffeine” were examined into titles, abstracts, and keywords. A brief bibliometric analysis was performed with emphasis on the type of publication and of most cited articles. Materials for the removal of caffeine were classified according to the type of material into three main groups: organic, inorganic, and composites, each of them subdivided into different subgroups consistent with their origin or production. Tables resume for each subgroup of adsorbents the key information: specific surface area, dose, pH, maximum adsorption capacity, and isotherm models for the removal of caffeine. The highest adsorption capacities were achieved by organic adsorbents, specifically those with granular activated carbon (1961.3 mg/g) and grape stalk activated carbon (916.7 mg/g). Phenyl-phosphate-based porous organic polymer (301 mg/g), natural sandy loam sediment (221.2 mg/g), composites of MCM-48 encapsulated graphene oxide (153.8 mg/g), and organically modified clay (143.7 mg/g) showed adsorption capacities lower than those of activated carbons. In some activated carbons, a relation between the specific surface area (SSA) and the maximum adsorption capacity (Qmax) was found.

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Removal of caffeine from aqueous solution by green approach using Ficus Benjamina zero-valent iron/copper nanoparticles

Cited by 22 publications

References 41 publications

The application of machine learning in nanoparticle treated water: A review

The application of machine learning in nanoparticle treated water: A review

A prototype of textile wastewater treatment using coagulation and adsorption by Fe/Cu nanoparticles: Techno-economic and scaling-up studies

A Review of Caffeine Adsorption Studies onto Various Types of Adsorbents

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