Development of a magnetic nanocomposite sorbent (NiCoMn/Fe3O4@C) for efficient extraction of methylene blue and Auramine O

A rapid and efficient method for the simultaneous monitoring and recovery of Auramine O (AO) and Methylene Blue (MB) dyes from water samples is presented. This method, named ultrasound-assisted dispersive-magnetic nanocomposites-solid-phase microextraction (UA-DMN-μSPE), utilizes NiCoMn/Fe3O4@C composite sorbents. Response surface methodology (RSM) combined with artificial neural networks (ANN) and generalized regression artificial neural network (GRNN) under central composite design (CCD) was employed to optimize various parameters for efficient extraction, followed by further refinement using desirability function analysis (DFA) and genetic algorithms (GA). Under optimized conditions, the method achieved exceptional recovery rates (99.5 ± 1.2% for AO and 99.8 ± 1.1% for MB) with acetone as the eluent. Additionally, a high preconcentration factor of 45.50 and 47.30 for AO and MB, respectively, was obtained. Low detection limits of 0.45 ng mL⁻1 (AO) and 1.80 ng mL⁻1 (MB) were achieved with wide linear response ranges (5-1000 and 5-2000 ng mL⁻1 for AO and MB, respectively). The method exhibited good stability with RSDs below 3% for five recycling runs, and minimal interference from various ions was observed. This UA-DMN-μSPE-UV/Vis method offers significant advantages in terms of efficiency, preconcentration, and detection limits, making it a valuable tool for the analysis of AO and MB in water samples.

Efficient removal of ciprofloxacin and ofloxacin from aqueous solutions using a novel nano-scale adsorbent: Modeling, optimization, and characterization

In the fascinating realm of water purification, our study unveils the remarkable potential of a cutting-edge nano-scale adsorbent-combining graphene oxide (GO), chitosan (CS), and polydopamine (PDA)-in efficiently remove ciprofloxacin (CPF) and ofloxacin (OFL) from aqueous solutions. Our exploration delves deep into the adsorbent's character, utilizing a range of analytical techniques including SEM, RAMAN, FTIR, TGA, BET, XRD, and Zeta potential analyses provided insights into the adsorbent's properties. Modeling the adsorption process with Response Surface Methodology (RSM), Artificial Neural Network (ANN) and General Regression Neural Network (GRNN) indicated excellent predictions by GRNN, with RMSE = 0.0200 and 0.0166, MAE = 0.0082 and 0.0092, as well as AAD = 0.0002 and 0.0006, highlighting its modeling power. Optimization using genetic algorithm (GA) revealed maximum CPF removal efficiency of approximately 95.20% under pH = 6.3, sonication time = 9.0 min, adsorbent dosage = 2.10 g L⁻1, temperature = 45 °C and initial CPF concentration = 90.0 mg L⁻1. Similarly, OFL removal reached about 95.50% under pH = 6.30, sonication time = 8.0 min, adsorbent dosage = 2.0 g L⁻1, temperature = 45 °C and OFL concentration = 115.0 mg L⁻1. RSM optimization closely aligned with GA results. Pseudo-second-order (PSO) kinetic model and Langmuir isotherm model best fitted the experimental data for both antibiotics. Thermodynamic analysis indicated a favorable and spontaneous adsorption process for CPF and OFL. The study concludes that the proposed adsorbents show effectiveness in removing CPF and OFL at lower doses and shorter sonication times compared to various reported adsorbents.

Graphitic carbon nitride-adorned PDMS self-cleaning floating photocatalyst for simultaneous removal of Rhodamine B, Crystal Violet and Malachite Green from a ternary dye mixture

Graphitic carbon nitride-adorned polydimethylsiloxane (PDMS) floating catalyst was prepared by a simple procedure. The prepared catalyst was utilized for the simultaneous mitigation of recalcitrant organic pollutants such as Rhodamine B, Crystal Violet and Malachite Green from their ternary mixture for the first time. Derivative spectroscopic method was used to calculate the degradation efficiencies of individual dyes in the mixture. The prepared catalyst showed a consistent degradation performance up to 4 cycles inducing a degradation of 94.02%, 92.1% and 97.13% of Rhodamine B, Crystal Violet and Malachite Green, respectively, in a dye(s) solution with a catalytic loading of 0.5 g L-1. A kinetic analysis of the dye(s) degradation under visible light was carried out during the course of this work up to 120 min. A detailed characterization of the surface of this novel catalyst was carried out in this study by SEM, EDX, XRD, DRS, DTG, FTIR, Raman spectroscopy and UV-Vis spectroscopy and provided the experimental proof for the catalyst presenting high hydrophobicity, self-cleaning ability, good recyclability and high chemical stability.

Synthesis, characterization, and application of graphene oxide/layered double hydroxide /poly acrylic acid nanocomposite (LDH-rGO-PAA NC) for tetracycline removal: A comprehensive chemometric study

Tetracycline (TC), as the second produced and used antibiotic worldwide, is difficult to be entirely metabolized not only in the body, but also in the treatment processes of water and/or wastewater. Therefore, special attention needs to be paid on defining or developing new options for removing such contaminant. Herein, a reduced graphene oxide (GO) was integrated with Ni-Al layered double hydroxide (LDH) as well poly acrylic acid (LDH-rGO-PAA) and examined to reduce TC -as a model antibiotic-in water media under different operational parameters of TC initial concentration, pH, NC dose, and time. The governed behaviour in the adsorption process was investigated using three model methods of response surface methodology (RSM), artificial neural networks (ANN), and general regression neural network (GRNN) after confirming the physico-chemical properties of LDH-rGO-PAA nanocomposite (NC) using different techniques. The LDH-rGO-PAA NC displayed a good performance as either removal efficiency (R = 94.87 ± 0.25%) or adsorption capacity (q_e = 887.5 mg/g) with the respective values of 110 mg/L, 6.3, 20 mg, and 18.50 min for the mentioned factors (TC initial concentration, pH, NC dose, and time, respectively), which was higher than that of reported for the similar adsorbents until now.

High-performance and selective adsorption of ZIF-8/MIL-100 hybrids towards organic pollutants

Environmental contamination by organic pollutants has become a pressing concern. In this study, metal-organic framework composites with a core-shell structure of MIL-100 wrapped around ZIF-8 (ZIF-MIL hybrids) were synthesized and characterized for their effectiveness to remove organic pollutants. First, a sequence of routine characterizations will examine the ZIF-MIL series samples' physicochemical properties and morphological characteristics. Then, the adsorption capacities of ZIF-MIL towards organic pollutants, including cationic dyes (methylene blue (MB), and rhodamine B (RHB)), anionic dyes (methyl orange (MO)), neutral pollutants (Sudan III (SD-III), tetracycline (TC) and amoxicillin (AMX)), were investigated. Among the ZIF-MIL series, ZIF-MIL-4 has an excellent specific surface area with high uptake of TC (1288 mg g^-1) and RHB (1181 mg g^-1). Based on the adsorption data from kinetic and dynamic studies, the adsorption process was closest to the pseudo-second-order kinetic model and Freundlich isotherm. In terms of thermodynamic parameter values, the adsorption of TC is an endothermic and spontaneous process, while the adsorption of RHB is an exothermic and spontaneous process. Furthermore, the reusability and selectivity studies of ZIF-MIL-4 towards TC and RHB exhibited significant regeneration ability and high selectivity. The effects of ionic strength and pH on pollutant removal efficiency were also tested. The experimental results showed that the main interactions between ZIF-MIL-4 and RHB or TC were weak coordination, electrostatic, hydrogen bonding, and π-π stacking interactions. Thus, the proposed MOF hybrid, by forming mixtures with other MOFs, can be a potential purifier with improved adsorption capacity and selectivity for organic pollutants as well as self-reusability.

A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes

Biomass ashes like rice husk ash (RHA), bagasse fly ash (BFA), were used for aqueous phase removal of a pesticide, diuron. Response surface methodology (RSM) and artificial neural network (ANN) were successfully applied to estimate and optimize the conditions for the maximum diuron adsorption using biomass ashes. The effect of operational parameters such as initial concentration (10–30 mg/L); contact time (0.93–16.07 h) and adsorbent dosage (20–308 mg) on adsorption were studied using central composite design (CCD) matrix. Same design was also employed to gain a training set for ANN. The maximum diuron removal of 88.95 and 99.78% was obtained at initial concentration of 15 mg/L, time of 12 h, RHA dosage of 250 mg and at initial concentration of 14 mg/L, time of 13 h, BFA dosage of 60 mg respectively. Estimation of coefficient of determination (R 2) and mean errors obtained for ANN and RSM (R 2 RHA = 0.976, R 2 BFA = 0.943) proved ANN (R 2 RHA = 0.997, R 2 BFA = 0.982) fits better. By employing RSM coupled with ANN model, the qualitative and quantitative activity relationship of experimental data was visualized in three dimensional spaces. The current approach will be instrumental in providing quick preliminary estimations in process and product development.

Cooperativity between Dimerization and Binding Equilibria in the Ternary System Laponite-Indocyanine Green-Water

Laponite is an artificial nanoclay available in large quantities and at low cost. This marterial represents an efficient and suitable way of delivering hydrophobic vital dyes without the need for chemical functionalization. Laponite is available in large quantities and at low cost, then it would be an efficient way of delivering hydrophobic vital dyes without the need for chemical functionalization. The hydrodynamic diameter of laponite extrapolated to infinite dilution indicates that this clay is completely exfoliated. Furthermore, the hydrodynamic diameter in the laponite-Indocyanine green-water ternary system, at a fixed laponite concentration (2% (m/m)) exhibits a saturation curve. It was found that the extrapolated diameter at dye zero concentration is smaller than in pure water. Absorption spectra with fixed concentration of dye exhibit a red shift of 10–13 nm. On the contrary, the spectra acquired at a constant concentration of laponite do not undergo any displacement. The deconvolution of the spectra with two Gaussian peaks allows to calculate the concentration of the monomeric and dimeric species. The results were interpreted as a synergy between the dye dimerization balance and the dye-laponite binding.

Bio-mass derived functionalized graphene aerogel: a sustainable approach for the removal of multiple organic dyes and their mixtures

Herein, fabrication of a functionalized graphene aerogel (f-GA) from a biomass (pear fruit)-derived graphene aerogel (GA) is described. f-GA is showing better adsorption capacity towards CV, MB and RhB dyes than GA and activated charcoal.

Modeling azo dye removal by sono-fenton processes using response surface methodology and artificial neural network approaches

Textile industry wastewaters, which cause serious problems in the environment and human health, include synthetic dyes, complex organic pollutants, surfactants, and other toxic chemicals and therefore must be removed by advanced treatment methods. Determination of appropriate treatment conditions for efficient use of advanced treatment methods is an important and necessary step. In the last thirty years, the Artificial Neural Network-Genetic Algorithm (ANN-GA) and Response Surface Methodology (RSM) have emerged as the most effective empirical modeling and optimization methods especially for nonlinear systems. Reactive Red 195 azo dyestuff was chosen as the target pollutant. The color removal efficiency was modeled and optimized as a function of Sono-Fenton conditions such as H₂O₂ dosage, Fe²⁺ dosage, initial pH value, ultrasound power, and ultrasound frequency, using ANN-GA and RSM. The generalization and predictive ability of these methods were compared using the results of the 46 experimental sets generated by the Box-Behnken design. The mean square errors for these models are 3.01612 and 0.00295, and the regression coefficients showing the superiority of ANN in determining nonlinear behavior are 0.9856 and 0.9164, respectively. In optimal conditions, the prediction errors with hybrid ANN-GA and RSM models are 0.002% and 3.225%, respectively.

Comprehensive analysis on sorptive uptake of enrofloxacin by activated carbon derived from industrial paper sludge

The current investigation deals with how chemically activated carbon derived from industrial paper sludge (ACPS) performs on sorptive removal of enrofloxacin (ENF), an antibacterial drug from its water solution. Thermogravimetric (TGA) and proximate analysis of raw paper sludge (RPS) were conducted. ACPS was characterized with proximate analysis, XRD, FT-IR, SEM and BET. The influence of five operational parameters viz. adsorbate concentration (initial), dose of adsorbent, pH, temperature, and contact time on the adsorption of ENF onto ACPS has been conducted using batch experiments. The process of adsorption was optimized through ANN (artificial neural network) in addition to RSM (response surface methodology). The maximum percentage removal (95.85%) was achieved at initial ENF concentration 12 mg/g, adsorbent dose 1.2 g/L, contact duration of 18 h and temperature 20 °C. Kinetic data were best fitted into pseudo-second order kinetic model and adsorption equilibrium study indicates that the adsorption process follows Langmuir isotherm model. Adsorption capacity was noted to have a highest value of 44.44 mg/g. A study on thermodynamics of the adsorption process suggests that it exhibits spontaneity, being essentially exothermic. Cost analysis and reusability study confirm that adsorbent produced from industrial paper sludge is cost-effective and reusable. Therefore, ACPS as adsorbent has potency for removing ENF from aqueous solution.

Growth profiling, kinetics and substrate utilization of low-cost dairy waste for production of β-cryptoxanthin by Kocuria marina DAGII

The dairy industry produces enormous amount of cheese whey containing the major milk nutrients, but this remains unutilized all over the globe. The present study investigates the production of β-cryptoxanthin (β-CRX) by Kocuria marina DAGII using cheese whey as substrate. Response surface methodology (RSM) and an artificial neural network (ANN) approach were implemented to obtain the maximum β-CRX yield. Significant factors, i.e. yeast extract, peptone, cheese whey and initial pH, were the input variables in both the optimizing studies, and β-CRX yield and biomass were taken as output variables. The ANN topology of 4-9-2 was found to be optimum when trained with a feed-forward back-propagation algorithm. Experimental values of β-CRX yield (17.14 mg l-1) and biomass (5.35 g l-1) were compared and ANN predicted values (16.99 mg l-1 and 5.33 g l-1, respectively) were found to be more accurate compared with RSM predicted values (16.95 mg l-1 and 5.23 g l-1, respectively). Detailed kinetic analysis of cellular growth, substrate consumption and product formation revealed that growth inhibition took place at substrate concentrations higher than 12% (v/v) of cheese whey. The Han and Levenspiel model was the best fitted substrate inhibition model that described the cell growth in cheese whey with an R2 and MSE of 0.9982% and 0.00477%, respectively. The potential importance of this study lies in the development, optimization and modelling of a suitable cheese whey supplemented medium for increased β-CRX production.

Application of novel nanocomposite-modified electrodes for identifying rice wines of different brands

In this paper, poly(acid chrome blue K) (PACBK)/AuNP/glassy carbon electrode (GCE), polysulfanilic acid (PABSA)/AuNP/GCE and polyglutamic acid (PGA)/CuNP/GCE were self-fabricated for the identification of rice wines of different brands. The physical and chemical characterization of the modified electrodes were obtained using scanning electron microscopy and cyclic voltammetry, respectively. The rice wine samples were detected by the modified electrodes based on multi-frequency large amplitude pulse voltammetry. Chronoamperometry was applied to record the response values, and the feature data correlating with wine brands were extracted from the original responses using the 'area method'. Principal component analysis, locality preserving projections and linear discriminant analysis were applied for the classification of different wines, and all three methods presented similarly good results. Extreme learning machine (ELM), the library for support vector machines (LIB-SVM) and the backpropagation neural network (BPNN) were applied for predicting wine brands, and BPNN worked best for prediction based on the testing dataset (R 2 = 0.9737 and MSE = 0.2673). The fabricated modified electrodes can therefore be applied to identify rice wines of different brands with pattern recognition methods, and the application also showed potential for the detection aspects of food quality analysis.

Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods

The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

Mild synthesis of a Zn(II) metal organic polymer and its hybrid with activated carbon: Application as antibacterial agent and in water treatment by using sonochemistry: Optimization, kinetic and isotherm study

In this work, a room temperature and short method (30min) for synthesis of nanosized rod-like metal organic polymer (MOP) has been described. Reaction of 1,4-phenylenedioxy diacetic acid with zinc salt leads to the formation of [Zn(C₁₀H₈O₆)(H₂O)₄]_n and subsequently was loaded on activated carbon following sonication and structurally characterized by FTIR, SEM, EDX and XRD analysis. The combination of this new composite with sonication was applied for rapid and efficient adsorption of Bromocresol Purple (BCP). Effects of initial BCP concentration, mass of adsorbent and sonication time on response were investigated and optimized by central composite design (CCD). Analysis of variation (ANOVA) was adapted to experimental data to find best optimum conditions which was set at 15.22mgL^-1, 2.41min, 0.02g and 0.009mg for initial BCP concentration, sonication time and adsorbent mass, respectively. Conduction of similar experiments at specified condition permit achievement of 98.69% removal percentage. 1,4-phenylenedioxy diacetic acid and Zn(NO3)₂.4H₂O which have applied for preparation of MOP are interesting antibacterial properties and accordingly MOP was screened in vitro for their antibacterial actively against Proteus vulgaris bacteria and experimental results reveal this MOP was able to inhibit growth of the tested bacteria. The experimental data were best fitted by pseudo-second order and Langmuir for kinetic model and the adsorption equilibrium isotherm, respectively.

Synthesis of nanocomposites of iron oxide/gold (Fe3O4/Au) loaded on activated carbon and their application in water treatment by using sonochemistry: Optimization study

This paper focuses on the finding best operational conditions using response surface methodology (RSM) for Rhodamine123 (R123) and Disulfine blue (DSB) dyes removal by ultrasound assisted adsorption onto Au-Fe₃O₄ nanoparticles loaded on activated carbon (Au-Fe₃O₄ NPs-AC). The influences of variables such as initial R123 (X₁) and DSB concentration (X₂), pH (X₃), adsorbent mass (X₄) and sonication time (X₅) on their removal were investigated by small central composite design (CCD) under response surface methodology. The significant variables and the possible interactions among variables were investigated and estimated accordingly. The best conditions were set as: 4min, 4.0, 0.025g, 13.5 and 26.5mgL^-1 for sonication time, pH, adsorbent weight, initial R123 and DSB concentration, respectively. At above conditions, the adsorption equilibrium and kinetic follow the Langmuir isotherm and pseudo-second-order kinetic model, respectively. The maximum monolayer capacity (Q_max) of 71.46 and 76.38mgg^-1 for R123 and DSB show sufficiency of model for well presentation of experimental data.

Sonochemistry: a good, fast and clean method to promote the removal of Cu(ii) and Cr(vi) by MWCNT/CoFe2O4@PEI nanocomposites: optimization study

In this study, branched polyethylenimine (PEI) loaded on magnetic multiwalled carbon nanotubes (MWCNT/CoFe₂O₄) was synthesized and characterized by transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) analysis and Fourier transform infrared spectroscopy (FTIR).

Ultrasound assisted extraction of Maxilon Red GRL dye from water samples using cobalt ferrite nanoparticles loaded on activated carbon as sorbent: Optimization and modeling

In this research, a selective, simple and rapid ultrasound assisted dispersive solid-phase micro-microextraction (UA-DSPME) was developed using cobalt ferrite nanoparticles loaded on activated carbon (CoFe₂O₄-NPs-AC) as an efficient sorbent for the preconcentration and determination of Maxilon Red GRL (MR-GRL) dye. The properties of sorbent are characterized by X-ray diffraction (XRD), Transmission Electron Microscopy (TEM), Vibrating sample magnetometers (VSM), Fourier transform infrared spectroscopy (FTIR), Particle size distribution (PSD) and Scanning Electron Microscope (SEM) techniques. The factors affecting on the determination of MR-GRL dye were investigated and optimized by central composite design (CCD) and artificial neural networks based on genetic algorithm (ANN-GA). CCD and ANN-GA were used for optimization. Using ANN-GA, optimum conditions were set at 6.70, 1.2mg, 5.5min and 174μL for pH, sorbent amount, sonication time and volume of eluent, respectively. Under the optimized conditions obtained from ANN-GA, the method exhibited a linear dynamic range of 30-3000ngmL^-1 with a detection limit of 5.70ngmL^-1. The preconcentration factor and enrichment factor were 57.47 and 93.54, respectively with relative standard deviations (RSDs) less than 4.0% (N=6). The interference effect of some ions and dyes was also investigated and the results show a good selectivity for this method. Finally, the method was successfully applied to the preconcentration and determination of Maxilon Red GRL in water and wastewater samples.

ICA and ANN Modeling for Photocatalytic Removal of Pollution in Wastewater

This paper discusses the elimination of Colour Index Acid Yellow 23 (C.I. AY23) using the ultraviolet (UV)/Ag-TiO2 process. To anticipate the photocatalytic elimination of AY23 with the existence of Ag-TiO2 nanoparticles processed under desired circumstances, two computational techniques, namely artificial neural network (ANN) and imperialist competitive algorithm (ICA) modeling are developed. A sum of 100 datasets are used to establish the models, wherein the introductory concentration of dye, UV light intensity, initial dosage of nano Ag-TiO2, and irradiation time are the four parameters expressed in the form of input variables. Additionally, the elimination of AY23 is considered in the form of the output variable. Out of the 100 datasets, 80 are utilized in order to train the models. The remaining 20 that were not included in the training are used in order to test the models. The comparison of the predicted outcomes extracted from the suggested models and the data obtained from the experimental analysis validates that the performance of the ANN scheme is comparatively sophisticated when compared with the ICA scheme.

Synthesis of ZnO-nanorod-based materials for antibacterial, antifungal activities, DNA cleavage and efficient ultrasound-assisted dyes adsorption

Undoped and Au-doped ZnO-nanorods were synthesized in the presence of ultrasound and loaded on activated carbon following characterization by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmittance electron microscopy (TEM), UV-vis spectrophotometry and Fourier transform infrared spectroscopy (FTIR). The Au-doped ZnO-nanorod-loaded activated carbon (Au-ZnO-NRs-AC) was used for the simultaneous removal of methylene blue (MB) and auramine O (AO) from aqueous solutions. Central composite design (CCD) under response surface methodology (RSM) was applied to model and optimize the dyes removal versus adsorbent mass, pH, and initial dyes concentration and sonication time as well as to investigate the possible interaction between these variables. The optimum values of the initial MB and AO dyes concentration, adsorbent mass, pH and sonication time were found to be 12 and 10mgL^-1, 0.0124g, 6.4, and 4min respectively. The rapid adsorption process at neutral pH using very small amount of the adsorbent makes it promising for the wastewater treatment applications. More than 99.5% of both dyes was removed with maximum adsorption capacities in binary-component system (107.5 and 95.7mgg^-1 for MB and AO, respectively). The kinetics and isotherm studies showed that the second-order and Langmuir models apply for the kinetics and isotherm of the adsorption of MB and AO on the adsorbent used here. Moreover, the wastewater treatment by using an antibacterial/antifungal adsorbent makes the process much more valuable. Therefore, additional studies were performed which showed efficient antibacterial/antifungal activities and DNA cleavage of undoped and Au-doped ZnO nanorods as constituent of the adsorbent applied here.

Ultrasonically assisted removal of Congo Red, Phloxine B and Fast green FCF in ternary mixture using novel nanocomposite following their simultaneous analysis by derivative spectrophotometry

In this study dependency of simultaneous adsorption of Congo Red (CR), Phloxine B (BP) and Fast green FCF (FG) onto CuS/ZnS nanocomposites loaded on activated carbon (CuS/ZnS-NCs-AC) to pH, adsorbent mass, sonication time and initial dyes concentration were modeled and optimized, while CuS/ZnS-NCs-AC was identified by XRD, FESEM and EDS analysis. CR, PB and FG concentration determination were undertaken by first and second order derivative spectrophotometry in ternary mixture. According to central composite design (CCD) based on desirability function (DF), the best experimental conditions was set as pH 6.0, 0.02g CuS/ZnS-NCs-AC, 5min sonication time, 15mgL^-1 for PB and 10mgL^-1 for other dyes. Conduction of experiments to above conditions lead to highest dyes removal efficiency of 99.72, 98.8 and 98.17 for CR, PB and FG, respectively. The adsorption data efficiently fitted by Langmuir isotherm model, while the order of maximum adsorption capacity (Q_max) for PB (128.21mgg^-1)>CR (88.57mgg^-1)>FG (73.40mgg^-1) is related to their different structure and charges. Kinetics of process was efficiently explained according to pseudo-second-order kinetic in cooperation of Weber and Morris based on intraparticle diffusion.

Improved adsorption performance of nanostructured composite by ultrasonic wave: Optimization through response surface methodology, isotherm and kinetic studies

In this work, ultrasound-assisted adsorption of an anionic dye, sunset yellow (SY) and cationic dyes, malachite green (MG), methylene blue (MB) and their ternary dye solutions onto Cu@ Mn-ZnS-NPs-AC from water aqueous was optimized by response surface methodology (RSM) using the central composite design (CCD). The adsorbent was characterized using Field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), Energy-dispersive X-ray spectroscopy (EDX) and EDX mapping images. The effects of various parameters such as pH, sonication time, adsorbent mass and initial concentrations of SY, MG and MB were examined. A total 33 experiments were conducted to establish a quadratic model. Cu@ Mn-ZnS-NPs-AC has the maximum adsorption efficiency (>99.5%) when the pH, sonication time, adsorbent mass and initial concentrations of SY, MG and MB were optimally set as 6.0, 5min, 0.02g, 9, 12 and 12mgL^-1, respectively. Sonication time has a statistically significant effect on the selected responses. Langmuir isotherm model was found to be best fitted to adsorption and adsorption capacities were 67.5mgg^-1 for SY, 74.6mgg^-1 for MG and 72.9mgg^-1 for MB. Four kinetic models (pseudo-first order, pseudo-second order, Weber-Morris intraparticle diffusion rate and Elovich) were tested to correlate the experimental data and the sorption was fitted well with the pseudo-second order kinetic model.

Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: A review

Artificial neural networks (ANNs) have been widely applied for the prediction of dye adsorption during the last decade. In this paper, the applications of ANN methods, namely multilayer feedforward neural networks (MLFNN), support vector machine (SVM), and adaptive neuro fuzzy inference system (ANFIS) for adsorption of dyes are reviewed. The reported researches on adsorption of dyes are classified into four major categories, such as (i) MLFNN, (ii) ANFIS, (iii) SVM and (iv) hybrid with genetic algorithm (GA) and particle swarm optimization (PSO). Most of these papers are discussed. The further research needs in this field are suggested. These ANNs models are obtaining popularity as approaches, which can be successfully employed for the adsorption of dyes with acceptable accuracy.

Optimization of simultaneous ultrasound assisted toxic dyes adsorption conditions from single and multi-components using central composite design: Application of derivative spectrophotometry and evaluation of the kinetics and isotherms

Present study is devoted on the efficient application of Sn (O, S)-NPs -AC for simultaneous sonicated accelerated adsorption of some dyes from single and multi-components systems. Sn (O, S) nanoparticles characterization by FESEM, EDX, EDX mapping and XRD revel its nano size structure with high purity of good crystallinity. Present adsorbent due to its nano spherical shape particles with approximate diameter of 40-60nm seems to be highly effective in this regard. The effects of five variables viz. pH (3.5-9.5), 0.010-0.028g of adsorbent and 0.5-6.5min mixing by sonication is good and practical conditions for well and expected adsorption of MB and CV over concentration range of 3-15mgL^-1. Combination of response surface methodology (RSM) based on central composite design (CCD) and subsequent of analysis of variance (ANOVA) and t-test statistics were used to test the significance of the independent variables and their interactions. Regression analysis reveal that experimental data with high repeatability and efficiency well represented by second-order polynomial model with coefficient of determination value of 0.9988 and 0.9976 for MB and CV, respectively following conditions like pH 8.0, 0.016g adsorbent, 15mgL^-1 of both dyes 4min sonication time is proportional with achievement of experimental removal percentage of 99.80% of MB and 99.87% of CV in batch experiment. Evaluation and estimation of adsorption data with Langmuir and Freundlich isotherm well justify the results based on their correlation coefficient and error analysis confirm that Langmuir model is good model with adsorption capacity of 109.17 and 115.34mgg^-1 in single system and 95.69 and 102.99mgg^-1 in binary system for MB and CV, respectively. MB and CV kinetic and rate of adsorption well fitted by pseudo-second order equation both in single and binary systems and experimental results denote more and favorable adsorption of CV than respective value in single system. The pseudo-second-order rate constant k₂ in binary system larger than single system.

Multi-responses optimization of simultaneous biosorption of cationic dyes by live yeast Yarrowia lipolytica 70562 from binary solution: Application of first order derivative spectrophotometry

Present study is based on application of live yeast Yarrowia lipolytica 70562 as new biosorbent was investigated for the simultaneous biosorption of Crystal Violet (CV) and Brilliant Green (BG) from wastewater. The effect of operating parameters such as initial dye concentrations (6-14mgL^-1), solution pH (4.0-8.0) and contact time (4-20h) was investigated by response surface methodology (RSM) for modeling and optimization of biosorption process and accordingly the best operational conditions was set as: initial CV and BG concentration of 8.0, and 10mgL^-1, pH of 7.0 and contact time of 16h. Above specified conditions lead to achievement of maximum biosorption of 98.823% and 99.927% for CV and BG dyes, respectively. The experimental equilibrium data well explained according to Langmuir isotherm model with maximum biosorption capacity of 65.359 and 56.497mgg^-1 for BG and CV, respectively. The second order and intraparticle diffusion models as cooperative mechanism has high efficiency and performance for interpretation of real data.

Application of experimental design and derivative spectrophotometry methods in optimization and analysis of biosorption of binary mixtures of basic dyes from aqueous solutions

Simultaneous biosorption of malachite green (MG) and crystal violet (CV) on biosorbent Yarrowia lipolytica ISF7 was studied. An appropriate derivative spectrophotometry technique was used to evaluate the concentration of each dye in binary solutions, despite significant interferences in visible light absorbances. The effects of pH, temperature, growth time, initial MG and CV concentration in batch experiments were assessed using Design of Experiment (DOE) according to central composite second order response surface methodology (RSM). The analysis showed that the greatest biosorption efficiency (>99% for both dyes) can be obtained at pH 7.0, T=28°C, 24h mixing and 20mgL^-1 initial concentrations for both MG and CV dyes. The quadratic constructed equation ability for fitting experimental data is judged based on criterions like R² values, significant p and lack-of-fit value strongly confirm its high adequacy and applicability for prediction of revel behavior of the system under study. The proposed model showed very high correlation coefficients (R²=0.9997 for CV and R²=0.9989 for MG), while supported by closeness of predicted and experimental value. A kinetic analysis was carried out, showing that for both dyes a pseudo-second order kinetic model adequately describes the available data. The Langmuir isotherm model in single and binary components has better performance for description of dyes biosorption with maximum monolayer biosorption capacity of 59.4 and 62.7mgg^-1 in single component and 46.4 and 50.0mgg^-1 for CV and MB in binary components, respectively. The surface structure of biosorbents and the possible biosorbents-dyes interactions between were also evaluated by Fourier transform infrared (FT-IR) spectroscopy and scanning electron microscopy (SEM). The values of thermodynamic parameters including ΔG° and ΔH° strongly confirm which method is spontaneous and endothermic.

Application of modificated magnetic nanomaterial for optimization of ultrasound-enhanced removal of Pb2+ ions from aqueous solution under experimental design: Investigation of kinetic and isotherm

Magnetic γ-Fe₂O₃ nanoparticles modificated by bis(5-bromosalicylidene)-1,3-propandiamine (M-γ-Fe₂O₃-NPs-BBSPN) and characterized by field emission scanning electron microscopy (FE-SEM), Fourier transforms infrared spectroscopy (FT-IR) and X-ray diffraction (XRD). This modified compound as novel adsorbent was applied for the ultrasound-assisted removal of Pb²⁺ ion in combination with flame atomic absorption spectroscopy (FAAS). The influences of the effective parameters including initial Pb²⁺ ion concentration, pH, adsorbent mass and ultrasound time were optimized by central composite design (CCD). Maximum removal percentage of Pb²⁺ ion which obtained at 25mgL^-1 of Pb²⁺, 25mg of adsorbent and 4min mixing with sonication at pH 6.0. The precision of the equation obtained by CCD was confirmed by the analysis of variance and calculation of correlation coefficient relating the predicted and the experimental values of removal percentage of Pb²⁺ ion. The kinetic and isotherm of ultrasound-assisted removal of Pb²⁺ ion was well described by second-order kinetic and Langmuir isotherm model with maximum adsorption capacity of 163.57mgg^-1.