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The performance of the Nano-magnetite Fe 3 O 4 impregnated onto walnut shell (Fe 3 O 4 -WNS), which possessed the adsorption features of walnut shell and the magnetic property of Fe 3 O 4 , was investigated for the elimination of the methyl violet and Rhodamine 6G from contaminated aqueous solutions. The effects of different experimental variables on the removal efficiency of the cited dyes were examined. Then these variables were used as the inputs to generate linear and non-linear models such as the multiple linear regression, random forest, and artificial neural network to predict the removal efficiency of these dye species at different experimental conditions. The validation studies of these models were performed using the test set, which was not present in the modeling procedure. It was found that ANN had a higher ability to predict the adsorption process under different experimental conditions, and could be applied for the development of an automated dye wastewater removal plant. Also the maximum adsorption capacity (q max ) indicated that the q max value for Fe 3 O 4 -WNS for removal of cationic dyes was comparable or better than that for some reported adsorbents. Also it should be cited that exhausted Fe 3 O 4 -WNS was regenerated using dishwashing liquid, and reused for removal of the cited dye species from aqueous solutions. [ABSTRACT FROM AUTHOR]

Achieving specific selectivity and high sensitivity for the colorimetric recognition of copper(II) ions in aqueous media over a complex background of potentially competing metal ions is inherently challenging in sensor development. Thus, a novel azo–azomethine receptor ( L ) based on the combination of 2-amino-3-(5-bromo-2-hydroxybenzylamino)maleonitrile and azo-coupled salicylaldehyde scaffold has been designed and synthesized for the naked-eye and rapid detection of Cu 2+ ion at trace level in a wide pH range. Accordingly, the devised chemosensor distinguished Cu 2+ from other metal ions by distinct color change from light yellow to light brown without any expensive equipment. The binding stoichiometry between Cu 2+ and L has been investigated using Job’s plot and MALDI-TOF mass analysis. Remarkably, the current sensor can detect Cu 2+ ions even at 1.07 μM level, which is lower than the World Health Organization (WHO) permissible level (30 μM) in drinking water. Furthermore, sensor L was successfully utilized in the preparation of test strips for the detection of copper(II) ions from aqueous environment. [ABSTRACT FROM AUTHOR]

[Display omitted] • The use of Gypsophila aretioides stems as a bio-sorbent to remove the crystal violet (CV) dye. • The bio-sorbent is cheap, simple, minimize excess sludge and eco-friendly. • The column breakthrough curves were fitted by the Thomas and Yoon-Nelson models. • The batch and fixed-bed adsorptions were evaluated by the random forest model. • After five times of regeneration, the removal percentage was high with both methods. In this work, the Gypsophila aretioides (GYP-A) stem is used as a biosorbent to remove crystal violet (CV) by the static and dynamic systems from aqueous solutions; the biosorbent is interesting in green chemistry and, on the other hand, cheaper than activated carbon and does not have the limitation of industrialization. The effects of different operating parameters such as pH(3–9), biosorbent dosage(0.4–1.8 mg/L), and initial concentration of CV(100–250 mg/L) and time for the batch method and the bed height, inlet CV concentration(75–250 mg/L), and flow rate(3–8) on the breakthrough curves for the continuous method is investigated. The result of CV adsorption onto GYP-A using the batch method indicates that the model fits Freundlich > Temkin > Langmuir > R-D, and R2 equal 0.9953, 0.9847, 0.9161, 0.7909 were obtained for isotherm model, respectively. A pseudo-second-order model (R2 = 0.9995–0.9997) is recommended to describe the adsorption kinetics. The Thomas and Yoon-Nelson models were analyzed to study the adsorption kinetics. The random forest model shows an excellent ability to predict the parameters involved in the CV adsorption process with appropriate accuracy and useable for large data, robust against noise; it can be very effective in selecting important variables. [ABSTRACT FROM AUTHOR]