Rapid extraction of copper ions in water, tea, milk and apple juice by solvent-terminated dispersive liquid-liquid microextraction using p-sulfonatocalix (4) arene: optimization by artificial neural networks coupled bat inspired algorithm and response surface methodology

Determination of Ultra-Trace Amounts of Copper in Environmental Water Samples by Dispersive Liquid-Liquid Microextraction Combined with Graphite Furnace Atomic Absorption Spectrometry

A new method of dispersive liquid-liquid microextraction (DLLME) combined with graphite furnace atomic absorption spectrometry (GFAAS) was proposed for the determination of ultra-trace copper. It was based on the reaction of Cu(II) with the laboratory-prepared chelating agent 2-(5-bromo-2-pyridylazo)-5-dimethylaminoaniline (5-Br-PADMA) in a HAc-NaAc buffer solution at pH 5.0 to form stable hydrophobic chelates, which were separated and enriched by DLLME with chlorobenzene (C6H5Cl) and acetonitrile (CH3CN) as extraction and disperser solvents, respectively. The sedimented phase containing the chelates was then determined with GFAAS. Various operating variables that may be affected by the extraction process such as the pH of the solution, the concentration of the chelating agent 5-Br-PADMA, the types and volumes of extraction and disperser solvents, the extraction time, and the centrifugation time were investigated. Under optimum conditions, the calibration curve was linear in the range from 0.02 ng/mL to 0.16 ng/mL of copper with a correlation coefficient of r = 0.9961, and the detection limit was 0.01 ng/mL based on 3Sb. The relative standard deviation for six replicate measurements of 0.05 ng /mL of copper was 3.9%. An enrichment factor (EF) of 110 was obtained. The method has the advantages of low detection limit, high sensitivity, simple operation, less consumption of organic solvents, higher enrichment factor, and environmental friendliness and was applied to the determination of trace copper in environmental water samples with satisfactory results.

Recent advances of bat-inspired algorithm, its versions and applications

Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.

Modeling of malachite green adsorption onto novel polyurethane/SrFe12O19/clinoptilolite nanocomposite using response surface methodology and biogeography-based optimization-assisted multilayer neural network

This research studied the modeling of malachite green (MG) adsorption onto novel polyurethane/SrFe₁₂O₁₉/clinoptilolite (PU/SrM/CLP) nanocomposite from aqueous solutions by the application of biogeography-based optimization (BBO) algorithm-assisted multilayer neural networks (MNN-BBO) as a new evolutionary algorithm in environmental science. The PU/SrM/CLP nanocomposite was successfully fabricated and characterized by some spectroscopic analyses. Four variables influencing the removal efficiency were modeled by MNN-BBO and response surface methodology (RSM). The MNN-BBO model gave higher percentage removal (99.6%) about 7.6% compared to the RSM technique. Under optimal conditions obtained by MNN-BBO, the four independent variables including pH, shaking rate, initial concentration, and adsorbent dosage were 6.5, 255 rpm, 50 mg.L^-1, and 0.08 g, respectively. Under these conditions, the results were fitted well to the Langmuir isotherm with a monolayer maximum amount of sorbate uptake (q_max) of 68.49 mg.g^-1 and the pseudo-first-order kinetic pattern with the rate constant (K₁) of 0.01 min^-1 with the R² values of 0.9248 and 0.9980, respectively. The results of thermodynamics demonstrated that the MG uptake was not spontaneous due to the positive value of the adsorption ΔG. In addition, the positive values of ΔS (0.079 kJ/mol K) and ΔH (30.816 kJ/mol) indicated the feasible operation and endothermic approach, respectively. Besides, the wastewater investigations showed that the nanocomposite could be used as a new promising sorbent for efficient removal of MG (R% > 72) and magnetically separable from the real samples.