Competitive adsorption of methylene blue and brilliant green onto graphite oxide nano particle following: Derivative spectrophotometric and principal component-artificial neural network model methods for their simultaneous determination

Adsorption kinetics of ciprofloxacin and ofloxacin by green-modified carbon nanotubes

This paper investigated the uptake of CIP and OFL in single and multicomponent adsorptive systems using modified carbon nanotubes (CNTs) as adsorbent material. The characterization analyses of the pre- and post-process material by XPS, TG/DTG, FT-IR, SEM/EDS, and XRD helped in the elucidation of the mechanisms, indicating greater involvement of n-n and π -π interactions. In the kinetic studies, the simple systems with CIP and OFL were similar, both showed equilibrium time around 20/30 min and increased adsorptive capacity with increasing initial drug concentration. In the multicomponent system, different fractions of CIP and OFL were tested and the time to reach equilibrium also varied between 20 and 30 min. In general, the adsorption capacity of CIP is slightly lower than that of OFL under the conditions tested. The selectivity analysis of the system showed that the selectivity's of the two drugs are identical in equimolar fractions. The mathematical modeling of the kinetic data indicated that in monocomponent systems, the model of pseudo-second order (PSO) adequately described both CIP and OFL kinetics. Furthermore, with the implementation of Artificial Neural Networks (ANN), it was possible to obtain a more assertive prediction of the behavior of single and binary systems.

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

Prediction of methyl orange removal by iron decorated activated carbon using an artificial neural network

Date Stones were used as a bio-degradable waste source for preparing iron impregnated activated carbon. The prepared activated carbon-containing oxides of iron were characterized using SEM, XRD, FTIR, and BET. The specific surface area of the iron decorated activated carbon was 738.65 m²/g. The XRD confirmed the presence of magnetite and hematite while the SEM images assured the presence of pores. The prepared activated carbon was used to remove methyl orange from wastewater. Genetic Algorithm was used to develop a model which could predict the removal efficiency of the dye. The ANN model was validated and the effect of different parameters like adsorbent dosage (0.1-1 g/L), initial dye concentration (2-20 mg/L), pH (2-11), time (10-55 min) and temperature (30-75°C) was estimated both experimentally and predicted using the model. The adsorption process follows the Freundlich isotherm and pseudo-second-order kinetic model. The values of 1/n and K_F obtained from the Freundlich isotherm designate good adsorption capacity. Both experimental and model-predicted data agrees with the kinetic model. The adsorption rate is proportionate to the square of the number of vacant adsorption sites. From the thermodynamic study, the positive worth of ΔH° indicates the energy-absorbing nature of the surface assimilation method and the process is endothermic in nature. The low values of each ΔG° (-200 to 0 kJ/mol) and ΔH° correspond to physical surface assimilation. A positive worth of ΔS° reflects the inflated randomness at the solid-aqueous interface with some structural changes in adsorbate and adsorbent.

Development of Ultra-Performance Liquid Chromatography-Mass Spectrometry Method for Simultaneous Determination of Three Cationic Dyes in Environmental Samples

Lower dye concentrations and the presence of several dyes along with other matrices in environmental samples restrict their determination. Herein, a highly sensitive and rapid ultra-performance tandem mass spectrometric method was developed for simultaneous determination of cationic dyes, namely methylene blue (MB), rhodamine B (RB) and crystal violet (CV), in environmental samples. To preconcentrate environmental samples, solid-phase extraction cartridges were developed by using hydrogen peroxide modified pistachio shell biomass (MPSB). The surface morphological and chemical functionalities of MPSB were well characterized. The developed method was validated considering different validation parameters. In terms of accuracy and precision, the %RSD for all three dyes at all four concentration points was found to be between 1.26 and 2.76, while the accuracy reported in terms of the recovery was found to be 98.02%-101.70%. The recovery was found to be in the range of 98.11% to 99.55%. The real sample analysis shows that MB, RB, and CV were found in the ranges of 0.39-5.56, 0.32-1.92 and 0.27-4.36 μg/mL, respectively.

A comparison between digital camera and spectrophotometer for sensitive and selective kinetic determination of brilliant green in wastewaters

In this study, a simple and novel kinetic spectrophotometric method has been proposed for the sensitive and highly selective determination of Brilliant Green. The method is based on the interaction of Brilliant Green with Triton X-100 in micellar media at room temperature. As a result of this interaction, the peak wavelength (625 nm) is gradually shifted toward longer wavelength region (634 nm) and more intensive hyper chromic effect has been seen. As well as, variations in the red, blue and green (RGB) components of the images as a function of time were observed. The kinetic interaction of Brilliant Green with Triton X-100 was recorded, using UV-Vis Spectrophotometer-diode array detector and a digital camera. The fixed-time method was used for the construction of a calibration curves. Brilliant Green can be measured in the range of 1.0 to 12.0 mg L^-1 and 1.0 to 10.0 mg L^-1with the detection limit of 0.047 mg L^-1 and 0.037 mg L^-1 using spectrophotometer and digital camera, respectively. The proposed method has been successfully used to determine Brilliant Green in some wastewaters such as textile dye effluent and goldfish farming water in the presence of some triphenylmethan dyes as the interferences.

Sustainable competitive adsorption of methylene blue and acid red 88 from synthetic wastewater using NiO and/or MgO silicate based nanosorbcats: experimental and computational modeling studies

The competitive adsorption of cationic and anionic model molecules; methylene blue (MB) and acid red 88 (AR88), respectively, in aqueous solutions onto NiO and/or MgO SBNs was studied. Adsorption isotherms, kinetics and pH effect were investigated in batch modes. Computational modeling was conducted on Acclerys Material Studio for MB and AR88 adsorption. pH study showed that the adsorption is strongly pH dependent, increases for MB while decreases for AR88 with increasing the pH from 4 to 11. Isotherm studies revealed that the Sips model was the best fit for both molecules in single cases, and thus the Extended-Sips model for the binary systems. The kinetics for the binary systems were well-described by the external mass transfer model; thus, film diffusion is the most dominant in the adsorption of both organic onto the SBNs. The adsorption uptakes in binary systems exceed 130 mg g⁻¹ for AR88 (167.7 MgO-SBNs, 132.93 NiO-SBNs, and 178.5 mg g⁻¹ NiO-MgO-SBN), while it reached an uptake of 76.2 MgO-SBNs, 81.5 NiO-SBNs, and 94.7 mg g⁻¹ NiO-MgO-SBNs for MB within the time needed to reach equilibrium (10 min). The adsorption of these two molecules in binary systems showed a synergistic effect onto the three types of SBNs, that enhanced the adsorption uptakes. Computational modeling confirmed the synergistic effect, the adsorption energy of binary systems was lower than that in single systems. Regeneration study was conducted over four adsorption cycles to confirm the sustainability of SBNs. They were stable under thermal oxidation at 400 °C, without any impact on the adsorption capacity.

Silica-based NiO and MgO nanosorbcats (SBNs) for competitive adsorption of methylene blue and acid red 88.

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.

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.

Removal of synthetic dyes from multicomponent industrial wastewaters

Colored effluents containing dyes from various industries pollute the environment and pose problems in municipal wastewater treatment systems. Industrial effluents consist of a mixture of dyes and require study of the simultaneous removal of dyes. Simultaneous quantification of dyes in the solution is a common problem while using a spectrophotometric method due to overlapping of their absorption spectra. Derivative spectroscopy and chemometric methods in spectrophotometric analysis facilitate simultaneous quantification of dyes. Adsorption is a widely used treatment method for the removal of a mixture of recalcitrant dyes in industrial wastewaters. Confirming the assertion, this paper presents a state-of-the-art review on methods used for simultaneous quantification of dyes and the effects of various parameters on their adsorptive removal. This paper also reviews the adsorption equilibrium, modeling, mechanisms of dyes adsorption, and adsorbent regeneration techniques in multicomponent dye systems. It has been observed that chemometric techniques provide accuracy, repeatability, and high speed in processing and helps in better operability in real wastewater treatment plants. The conclusions include the need for the development of thermodynamic models that can predict simultaneous physisorption and chemisorption exhibited by different dyes and to develop isotherm models that can describe chemisorption of a mixture of dyes. The paper delves into inadequately researched gray areas of adsorption of a mixture of dyes which require the development of modified adsorption methods that serves process intensification for complete degradation/mineralization.

Ultrasonic enhancement of the simultaneous removal of quaternary toxic organic dyes by CuO nanoparticles loaded on activated carbon: Central composite design, kinetic and isotherm study

Copper oxide nanoparticles loaded on activated carbon (CuO-NPs-AC) were prepared and fully analyzed and characterized with FE-SEM, XRD and FT-IR. Subsequently, this novel material was used for simultaneous ultrasound-assisted adsorption of brilliant green (BG), auramine O (AO), methylene blue (MB) and eosin yellow (EY) dyes. Problems regard to dyes spectra overlap in quaternary solution of this dyes were omitted by derivative spectrophotometric method. The best pH in quaternary system was studied by using one at a time method to achieved maximum dyes removal percentage. Subsequently, sonication time, adsorbent dosage and initial dyes concentrations influence on dyes removal was optimized by central composite design (CCD) combined with desirability function approach (DFA). Desirability score of 0.978 show optimum conditions set at sonication time (4.2 min), adsorbent mass (0.029 g), initial dyes concentration (4.5 mg L(-1)). Under this optimum condition the removal percentage for MB, AO, EY and BG dyes 97.58, 94.66, 96.22 and 94.93, respectively. The adsorption rate well fitted by pseudo second-order while adsorption capacity according to the Langmuir model as best equilibrium isotherm model for BG, MB, AO and EY was 20.48, 21.26, 22.34 and 21.29 mg g(-1), respectively.

Solid-phase extraction of iridium from soil and water samples by using activated carbon cloth prior to its spectrophotometric determination

A solid-phase extraction method for separation and preconcentration of Ir(IV) ion by using activated carbon cloth (ACC) has been presented. Ir(IV) as their 1-(2-pyridylazo) 2-naphtol (PAN) chelate was adsorbed on ACC at pH 2.0 and was eluted from ACC with acidic dimethylformamide (DMF). The Ir(IV) concentration was determined at 536 nm as Ir(IV)-PAN complex by using UV-vis spectrophotometer. The analytical parameters including pH, sample and eluent flow rates, amount of PAN, eluent type, concentration, and sample volume were optimized. The effects of foreign ions on the recoveries of iridium were also investigated. The preconcentration factor was calculated as 60. The limit of detection (LOD) and the limit of quantification (LOQ) of the method were found as 0.039 and 0.129 μg L(-1), respectively. The method was applied to soil and water samples for iridium determination.

Simultaneous ultrasound-assisted ternary adsorption of dyes onto copper-doped zinc sulfide nanoparticles loaded on activated carbon: optimization by response surface methodology

The simultaneous and competitive ultrasound-assisted removal of Auramine-O (AO), Erythrosine (Er) and Methylene Blue (MB) from aqueous solutions were rapidly performed onto copper-doped zinc sulfide nanoparticles loaded on activated carbon (ZnS:Cu-NP-AC). ZnS:Cu nanoparticles were studied by FESEM, XRD and TEM. First, the effect of pH was optimized in a one-at-a-time procedure. Then the dependency of dyes removal percentage in their ternary solution on the level and magnitude of variables such as sonication time, initial dyes concentrations and adsorbent dosage was fully investigated and optimized by central composite design (CCD) under response surface methodology (RSM) as well as by regarding desirability function (DF) as a good and general criterion. The good agreement found between experimental and predicted values supports and confirms the suitability of the present model to predict adsorption state. The applied ultrasound strongly enhanced mass transfer process and subsequently performance. Hence, a small amount of the adsorbent (0.04 g) was capable to remove high percentage of dyes, i.e. 100%, 99.6% and 100% for MB, AO and Er, respectively, in very short time (2.5 min). The experimental equilibrium data fitting to Langmuir, Freundlich, Temkin and Dubinin-Radushkevich models showed that the Langmuir model applies well for the evaluation and description of the actual behavior of adsorption. The small amount of proposed adsorbent (0.015 g) was applicable for successful removal of dyes (RE>99.0%) in short time (2.5 min) with high adsorption capacity in single component system (123.5 mg g(-1) for MB, 123 mg g(-1) for AO and 84.5 mg g(-1) for Er). Kinetics evaluation of experiments at various time intervals reveals that adsorption processes can be well predicated and fitted by pseudo-second-order and Elovich models.

Ternary dye adsorption onto MnO2 nanoparticle-loaded activated carbon: derivative spectrophotometry and modeling

MnO₂ nanoparticle-loaded activated carbon (MnO₂-NP-AC) as an efficient, environmental friendly and cost-effective adsorbent was synthesized and characterized using different techniques such as FE-SEM, EDX, XRD, BET and FTIR.

Experimental design for simultaneous analysis of malachite green and methylene blue; derivative spectrophotometry and principal component-artificial neural network

In this study, oxidized multiwalled carbon nanotubes (MWCNT) with sizes in the range of 10–30 nm were efficiently applied for simultaneous and competitive adsorption of malachite green (MG) and methylene blue (MB).