Comparison of nickel doped Zinc Sulfide and/or palladium nanoparticle loaded on activated carbon as efficient adsorbents for kinetic and equilibrium study of removal of Congo Red dye

A review on activated carbon modifications for the treatment of wastewater containing anionic dyes

Polluted water resources, particularly those polluted with industrial effluents' dyes, are carcinogenic and hence pose a severe threat to sustainable and longstanding worldwide development. Meanwhile, adsorption is a promising process for polluted/wastewater treatment. In particular, activated carbon (AC) is popular among various wastewater treatment adsorbents, especially in the organic contaminants' remediation in wastewater. Hence, the AC's synthesis from degradable and non-degradable resources, the carbon activation involved in the AC synthesis, and the AC's modification to cutting-edge and effective materials have been modern-research targets in recent years. Likewise, the main research focuses worldwide have been the salient AC characteristics, such as its surface chemistry, porosity, and enhanced surface area. Notably, various modified-AC synthesis methods have been employed to enhance the AC's potential for improved contaminants-removal. Hence, we critically analyze the different modified ACs (with enhanced (surface) functional groups and textural properties) of their capacity to remove different-natured anionic dyes in wastewater. We also discuss the corresponding AC modification techniques, the factors affecting the AC properties, and the modifying agents' influence on the AC's morphological/adsorptive properties. Finally, the AC research of future interest has been proposed by identifying the current AC research gaps, especially related to the AC's application in wastewater treatment.

Removal of lead ions from wastewater using lanthanum sulfide nanoparticle decorated over magnetic graphene oxide

In this study, the new lanthanum sulfide nanoparticle (La₂S₃) was synthesized and incorporated onto magnetic graphene oxide (MGO) sheets surface to produce potential adsorbent (MGO@LaS) for efficient removal of lead ions (Pb²⁺) from wastewater. The synthesized MGO@LaS adsorbent was characterized using Fourier transform infrared spectroscopy, field emission scanning electron microscopy and energy-dispersive X-ray spectroscopy. The effective parameters on the adsorption process including solution pH (~5), adsorbent dosage (20 mg), contact time (40 min), initial Pb²⁺ concentration and temperature were studied. The removal efficiency was obtained >95% for lead ions at pH 5 with 20 mg adsorbent. To validate the adsorption rate and mechanism, the kinetic and thermodynamic models were studied based on experimental data. The Langmuir isotherm model was best fitted to initial equilibrium concentration with a maximum adsorption capacity of 123.46 mg/g. This indicated a monolayer adsorption pattern for Pb²⁺ ions over MGO@LaS. The pseudo-second-order as the kinetic model was best fitted to describe the adsorption rate due to high R² > 0.999 as compared first-order. A thermodynamic model suggested a chemisorption and physisorption adsorption mechanism for Pb²⁺ ions uptake into MGO@LaS at different temperatures; ΔG° < -5.99 kJ mol^-1 at 20 °C and ΔG° -18.2 kJ mol^-1 at 45 °C. The obtained results showed that the novel nanocomposite (MGO@LaS) can be used as an alternative adsorbent in wastewater treatment.

On the Suitability of Almond Shells for the Manufacture of a Natural Low-Cost Bioadsorbent to Remove Brilliant Green: Kinetics and Equilibrium Isotherms Study

Almond production generates a large number of coproducts, but the farmer's interest mainly focuses on the nutritional and commercial aspects of the kernel for getting the best return from their harvests. Thus, almond coproducts such as almond shells that represent more than 70% of biomass remain underexplored. In this work, the suitability of almond shell powder (ASP) as a natural low-cost adsorbent was evaluated in the adsorption of brilliant green dye (BG), which is known as a chemical pollutant. Brunauer-Emmett-Teller (BET) method, for the determination of specific surface area, Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) techniques were performed to characterize the ASP adsorbent. The batch adsorption kinetic study for the removal of BG dye was carried out by varying pH, temperature, initial concentration of the dye, bioadsorbent dose, and contact time. It was found that 98% of BG dye is removed under the following optimal experimental conditions: ASP bioadsorbent dose of 1 g/L at T = 25°C, pH = 6.8, and C 0 = 1 g/L, which proves that ASP can be used as an excellent low-cost bioadsorbent for the removal of BG dye from wastewater. The experimental isotherm data were analyzed using Freundlich and Langmuir models. The results show the best correlation with single-layer adsorption, and the adsorption kinetics seems to follow a pseudo-second-order model.

Dye removal from water and wastewater by nanosized metal oxides - modified activated carbon: a review on recent researches

The conventional water and wastewater treatment methods are unable to provide up-to-data organized standards for drinking water and discharging effluents into natural ecosystems. Therefore, developing advanced and cost-effective methods to achieve published standards for water and wastewater and population needs are nowadays necessity. The important parts of this article are providing literature information about dyes and their effects on the environment and human health, adsorption properties and mechanism, adsorbent characteristics, and recent information on various aspects of modified activated carbons with nanosized metal oxides (AC- NMOs) in the removal of dyes. This review also summarized the effect of main environmental and operational parameters such as adsorbent dosage, pH, initial dye concentration, contact time, and temperature on the dye adsorption using AC-NMOs. Furthermore, the applied isotherm and kinetic models have been discussed.

Copper and cerium co-doped cobalt ferrite nanoparticles: structural, morphological, optical, magnetic, and photocatalytic properties

A rapid synthetic technique is investigated for magnetic nanoparticles (Co_1-yCu_yFe_2-xCe_xO₄ (x = 0, y = 0), (x = 0.05, y = 0), (x = 0, y = 0.5), and (x = 0.05, y = 0.5)). The structure, morphology, optical and magnetic performance of prepared nanoparticles are analyzed by powder XRD, XPS, FT-IR, SEM-EDAX, TEM, DRS, and VSM. The photocatalytic activity of the synthesized nanoparticles for the removal of the Congo red (CR) dye and bisphenol A (BPA) from aqueous solution is examined by UV-visible spectrometer. Research indicates that the co-doping of Cu²⁺ and Ce³⁺ showed marked effect on the structural, optical, magnetic, and photocatalytic properties of the CoFe₂O₄ nanoparticles. DRS showed that the Co_0.5Cu_0.5Fe_1.95Ce_0.05O₄ nanoparticles have lower band gap energy (0.78 eV) than other synthesized compounds. High removal percentage of CR and BPA (99.09% and 99.33%) was observed within 30 min and 180 min under visible and UV-light illumination respectively using Co_0.5Cu_0.5Fe_1.95Ce_0.05O₄. The corresponding photocatalytic degradation kinetics and mechanism are analyzed.

Optimization and modeling of simultaneous ultrasound-assisted adsorption of ternary dyes using copper oxide nanoparticles immobilized on activated carbon using response surface methodology and artificial neural network

The present study examines simultaneous adsorption of ternary dyes such as rose bengal (RB), safranin O (SO) and malachite green (MG) from aqueous media on copper oxide nanoparticles immobilized on activated carbon (CuO-NPs-AC) in a batch system. To forecast and optimize the adsorption, artificial neural network (ANN) and response surface methodology (RSM) were utilized. The effect of various factors, e.g. dye concentration, sonication time, adsorbent dosage and pH on the adsorption process were evaluated through five level six factor central composite design (CCD) using RSM. Maximum removal efficiency of MG, SO and RB dyes were seen 94.26%, 71% and 76% under optimal operating conditions. The suggested quadratic models revealed good fit with the actual data. To testing the data, the coefficients of determination (R²) of 0.9976, 0.9971 and 0.9952 and Fisher F-values of 2048.92, 1660.95 and 926.84 were obtained for MG, SO and RB dyes, respectively. The same data were utilized to construct the ANN models. The results revealed that both models yielded high R² values, while the RSM models were slightly more accurate in predictions as compared to ANN models for MG, SO and RB dyes removal. The equilibrium data followed the Langmuir isotherm model, although the rate of the adsorption process well fitted to pseudo-second-order kinetics. The maximum adsorption capacity of the CuO-NPs-AC for MG, SO and RB were found to be 212.79, 149.25 and 172.42 mg/g, respectively.

One step effective removal of Congo Red in chitosan nanoparticles by encapsulation

Chitosan nanoparticles (CNPs) were prepared with ionotropic gelation between chitosan and tripolyphosphate for the removal of Congo Red. The production of chitosan nanoparticles and the dye removal process was carried out in one-step. The removal efficiency of Congo Red by encapsulation within chitosan from the aqueous solution and its storage stability are examined at different pH values. The influence of some parameters such as the initial dye concentration, pH value of the dye solution, electrolyte concentration, tripolyphosphate concentration, mixing time and speed on the encapsulation is examined. Congo Red removal efficiency and encapsulation capacity of chitosan nanoparticles were determined as above 98% and 5107mg Congo Red/g chitosan, respectively.

The performance of nanorods material as adsorbent for removal of azo dyes and heavy metal ions: Application of ultrasound wave, optimization and modeling

The present research is focused on the synthesis and characterization of zinc (II) oxide nanorods loaded on activated carbon (ZnO-NRs-AC) to prepare an outstanding adsorbent for the simultaneous adsorption of heavy metals and dyes as hazardous pollutant using ultrasound energy. The adsorbent was identified by Scanning Electron Microscope (SEM), Transmission Electron Microscopy (TEM), Energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) analysis. The individual effects and possible interactions between the most effective variables including initial metal ions (Cd²⁺ and Co²⁺) and azo dyes (methylene blue (MB) and crystal violet (CV)) concentration, adsorbent dosage and ultrasonic time on the responses were investigated by response surface methodology (RSM) and optimum conditions was fixed at Cd²⁺, Co²⁺, MB and CV concentrations were 25, 24, 18 and 14mgL^-1, respectively, 0.025g of ZnO-NRs-AC and 5.1min sonication to achieve maximum removal percentage (>97.0%) for targets compounds. The artificial neural network (ANN) model was applied for prediction of data with Levenberg-Marquardt algorithm (LMA), a linear transfer function (purelin) at output layer and a tangent sigmoid transfer function (tansig) in the hidden layer with 14 neurons. The minimum mean squared error (MSE) of 0.9646, 0.0402 and 0.0753 with high determination coefficient (R²) of 0.9996, 0.9991 and 0.9999 for train, test and validation, respectively, were able to predict and model the adsorption process. The results of examination of the time on experimental adsorption data and their subsequent fitting reveal applicability of pseudo-second-order and intraparticle diffusion model. The experimental equilibrium data was analyzed by Langmuir, Freundlich, Temkin and D-R isotherm models and explored that the data well presented by Langmuir model with maximum adsorption capacity of 97.1, 92.6, 83.9 and 81.6mgg^-1 for Cd⁺², Co⁺² ions, MB and CV dyes, respectively.

Preparation of nanomaterials for the ultrasound-enhanced removal of Pb2+ ions and malachite green dye: Chemometric optimization and modeling

Copper oxide nanoparticle-loaded activated carbon (CuO-NP-AC) was synthesized and characterized using different techniques such as FE-SEM, XRD and FT-IR. It was successfully applied for the ultrasound-assisted simultaneous removal of Pb²⁺ ions and malachite green (MG) dye in binary system from aqueous solution. The effect of important parameters was modeled and optimized by artificial neural network (ANN) and response surface methodology (RSM). Maximum simultaneous removal percentages (>99.0%) were found at 25mgL^-1, 20mgL^-1, 0.02g, 5min and 6.0 corresponding to initial Pb²⁺ concentration, initial MG concentration, CuO-NP-AC amount, ultrasonication time and pH, respectively. The precision of the equation obtained by RSM was confirmed by the analysis of variance and calculation of correlation coefficient relating the predicted and the experimental values of ultrasound-assisted simultaneous removal of the analytes. A good agreement between experimental and predicted values was observed. A feed-forward neural network with a topology optimized by response surface methodology was successfully applied for the prediction of ultrasound-assisted simultaneous removal of Pb²⁺ ions and MG dye in binary system by CuO-NPs-AC. The number of hidden neurons, MSE, R², number of epochs and error histogram were chosen for ANN modeling. Then, Langmuir, Freundlich, Temkin and D-R isothermal models were applied for fitting the experimental data. It was found that the Langmuir model well describes the isotherm data with a maximum adsorption capacity of 98.328 and 87.719mgg^-1 for Pb²⁺ and MG, respectively. Kinetic studies at optimum condition showed that maximum Pb²⁺ and MG adsorption is achieved within 5min of the start of most experiments. The combination of pseudo-second-order rate equation and intraparticle diffusion model was applicable to explain the experimental data of ultrasound-assisted simultaneous removal of Pb²⁺ and MG at optimum condition obtained from RSM.

Sonochemical assisted hydrothermal synthesis of ZnO: Cr nanoparticles loaded activated carbon for simultaneous ultrasound-assisted adsorption of ternary toxic organic dye: Derivative spectrophotometric, optimization, kinetic and isotherm study

Chromium doped zinc oxide nanoparticles (ZnO: Cr-NPs) was synthesized by ultrasonically assisted hydrothermal method and characterized by FE-SEM, XRD and TEM analysis. Subsequently, this composite ultrasonically assisted was deposited on activated carbon (ZnO: Cr-NPs-AC) and used for simultaneous ultrasound-assisted removal of three toxic organic dye namely of malachite green (MG), eosin yellow (EY) and Auramine O (AO). Dyes spectra overlap in mixture (major problem for simultaneous investigation) of this systems was extensively resolved by derivative spectrophotometric method. The magnitude of variables like initial dyes concentration, adsorbent mass and sonication time influence on dyes removal was optimized using small central composite design (CCD) combined with desirability function (DF) approach, while pH was studied by one-a-time approach. The maximized removal percentages at desirability of 0.9740 was set as follow: pH 6.0, 0.019g ZnO: Cr-NPs-AC, 3.9min sonication at 4.5, 4.8 and 4.7mgL(-1) of MG, EY and AO, respectively. Above optimized points lead to achievement of removal percentage of 98.36%, 97.24%, and 99.26% correspond to MG, EY and AO, respectively. ANOVA for each dyes based p-value less than (<0.0001) suggest highly efficiency of CCD model for prediction of data concern to simultaneous removal of these dyes within 95% confidence interval, while their F-value for MG, EY and AO is 935, 800.2, and 551.3, respectively, that confirm low participation of this them in signal. The value of multiple correlation coefficient R(2), adjusted and predicted R(2) for simultaneous removal of MG is 0.9982, 0.9972 and 0.9940, EY is 0.9979, 0.9967 and 0.9930 and for AO is 0.9970, 0.9952 and 0.9939. The adsorption rate well fitted by pseudo second-order and Langmuir model via high, economic and profitable adsorption capacity of 214.0, 189.7 and 211.6mgg(-1) for MG, EY and AO, respectively.

Adsorptive amputation of hazardous azo dye Congo red from wastewater: a critical review

Increasing amount of dyes in an ecosystem has propelled the search of various methods for dye removal. Amongst all the methods, adsorption occupies a prominent place in dye removal. Keeping this in mind, many adsorbents used for the removal of hazardous anionic azo dye Congo red (CR) from aqueous medium were reviewed by the authors. The main objectives behind this review article are to assemble the information on scattered adsorbents and enlighten the wide range of potentially effective adsorbents for CR removal. Thus, CR sorption by various adsorbents such as activated carbon, non-conventional low-cost materials, nanomaterials, composites and nanocomposites are surveyed and critically reviewed as well as their sorption capacities are also compared. This review also explores the grey areas of the adsorption performance of various adsorbents with reference to the effects of pH, contact time, initial dye concentration and adsorbent dosage. The equilibrium adsorption isotherm, kinetic and thermodynamic data of different adsorbents used for CR removal were also analysed. It is evident from a literature survey of more than 290 published papers that nanoparticle and nanocomposite adsorbents have demonstrated outstanding adsorption capabilities for CR. Graphical abstract ᅟ.

Ultrasonically assisted hydrothermal synthesis of activated carbon-HKUST-1-MOF hybrid for efficient simultaneous ultrasound-assisted removal of ternary organic dyes and antibacterial investigation: Taguchi optimization

Activated carbon (AC) composite with HKUST-1 metal organic framework (AC-HKUST-1 MOF) was prepared by ultrasonically assisted hydrothermal method and characterized by FTIR, SEM and XRD analysis and laterally was applied for the simultaneous ultrasound-assisted removal of crystal violet (CV), disulfine blue (DSB) and quinoline yellow (QY) dyes in their ternary solution. In addition, this material, was screened in vitro for their antibacterial actively against Methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa (PAO1) bacteria. In dyes removal process, the effects of important variables such as initial concentration of dyes, adsorbent mass, pH and sonication time on adsorption process optimized by Taguchi approach. Optimum values of 4, 0.02 g, 4 min, 10 mg L(-1) were obtained for pH, AC-HKUST-1 MOF mass, sonication time and the concentration of each dye, respectively. At the optimized condition, the removal percentages of CV, DSB and QY were found to be 99.76%, 91.10%, and 90.75%, respectively, with desirability of 0.989. Kinetics of adsorption processes follow pseudo-second-order model. The Langmuir model as best method with high applicability for representation of experimental data, while maximum mono layer adsorption capacity for CV, DSB and QY on AC-HKUST-1 estimated to be 133.33, 129.87 and 65.37 mg g(-1) which significantly were higher than HKUST-1 as sole material with Qm to equate 59.45, 57.14 and 38.80 mg g(-1), respectively.

Highly efficient simultaneous ultrasonic assisted adsorption of brilliant green and eosin B onto ZnS nanoparticles loaded activated carbon: Artificial neural network modeling and central composite design optimization

In this work, central composite design (CCD) combined with response surface methodology (RSM) and desirability function approach (DFA) gives useful information about operational condition and also to obtain useful information about interaction and main effect of variables concerned to simultaneous ultrasound-assisted removal of brilliant green (BG) and eosin B (EB) by zinc sulfide nanoparticles loaded on activated carbon (ZnS-NPs-AC). Spectra overlap between BG and EB dyes was extensively reduced and/or omitted by derivative spectrophotometric method, while multi-layer artificial neural network (ML-ANN) model learned with Levenberg-Marquardt (LM) algorithm was used for building up a predictive model and prediction of the BG and EB removal. The ANN efficiently was able to forecast the simultaneous BG and EB removal that was confirmed by reasonable numerical value i.e. MSE of 0.0021 and R(2) of 0.9589 and MSE of 0.0022 and R(2) of 0.9455 for testing data set, respectively. The results reveal acceptable agreement among experimental data and ANN predicted results. Langmuir as the best model for fitting experimental data relevant to BG and EB removal indicates high, economic and profitable adsorption capacity (258.7 and 222.2 mg g(-1)) that supports and confirms its applicability for wastewater treatment.

Response surface methodology approach for optimization of adsorption of Janus Green B from aqueous solution onto ZnO/Zn(OH)2-NP-AC: Kinetic and isotherm study

The Janus Green B (JGB) adsorption onto homemade ZnO/Zn(OH)2 nanoparticles loaded on activated carbon (AC) which characterized by FESEM and XRD analysis has been reported. Combination of response surface methodology (RSM) and central composite design (CCD) has been employed to model and optimize variables using STATISTICA 10.0 software. The influence of parameters over pH (2.0-8.0), adsorbent (0.004-0.012g), sonication time (4-8min) and JGB concentration (3-21mgL(-1)) on JGB removal percentage was investigated and their main and interaction contribution was examined. It was revealed that 21mgL(-1) JGB, 0.012g ZnO/Zn(OH)2-NP-AC at pH 7.0 and 7min sonication time permit to achieve removal percentage more than 99%. Finally, a good agreement between experimental and predicted values after 7min was achieved using pseudo-second-order rate equation. The Langmuir adsorption is appropriate for correlation of equilibrium data. The small amount of adsorbent (0.008-0.015g) is applicable for successful removal of JGB (RE>99%) in short time (7min) with high adsorption capacity (81.3-98.03mgg(-1)).

Fe3O4@P(DVB/MAA)/Pd composite microspheres: preparation and catalytic degradation performance

Fe₃O₄@P(DVB/MAA)/Pd composite microspheres were synthesized with a combination of coating and loading processes using Fe₃O₄ nanoparticles as a core.

Application of least squares support vector regression and linear multiple regression for modeling removal of methyl orange onto tin oxide nanoparticles loaded on activated carbon and activated carbon prepared from Pistacia atlantica wood

Two novel and eco friendly adsorbents namely tin oxide nanoparticles loaded on activated carbon (SnO2-NP-AC) and activated carbon prepared from wood tree Pistacia atlantica (AC-PAW) were used for the rapid removal and fast adsorption of methyl orange (MO) from the aqueous phase. The dependency of MO removal with various adsorption influential parameters was well modeled and optimized using multiple linear regressions (MLR) and least squares support vector regression (LSSVR). The optimal parameters for the LSSVR model were found based on γ value of 0.76 and σ(2) of 0.15. For testing the data set, the mean square error (MSE) values of 0.0010 and the coefficient of determination (R(2)) values of 0.976 were obtained for LSSVR model, and the MSE value of 0.0037 and the R(2) value of 0.897 were obtained for the MLR model. The adsorption equilibrium and kinetic data was found to be well fitted and in good agreement with Langmuir isotherm model and second-order equation and intra-particle diffusion models respectively. The small amount of the proposed SnO2-NP-AC and AC-PAW (0.015 g and 0.08 g) is applicable for successful rapid removal of methyl orange (>95%). The maximum adsorption capacity for SnO2-NP-AC and AC-PAW was 250 mg g(-1) and 125 mg g(-1) respectively.

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.

Isotherm and kinetics study of malachite green adsorption onto copper nanowires loaded on activated carbon: artificial neural network modeling and genetic algorithm optimization

In this study, copper nanowires loaded on activated carbon (Cu-NWs-AC) was used as novel efficient adsorbent for the removal of malachite green (MG) from aqueous solution. This new material was synthesized through simple protocol and its surface properties such as surface area, pore volume and functional groups were characterized with different techniques such XRD, BET and FESEM analysis. The relation between removal percentages with variables such as solution pH, adsorbent dosage (0.005, 0.01, 0.015, 0.02 and 0.1g), contact time (1-40min) and initial MG concentration (5, 10, 20, 70 and 100mg/L) was investigated and optimized. A three-layer artificial neural network (ANN) model was utilized to predict the malachite green dye removal (%) by Cu-NWs-AC following conduction of 248 experiments. When the training of the ANN was performed, the parameters of ANN model were as follows: linear transfer function (purelin) at output layer, Levenberg-Marquardt algorithm (LMA), and a tangent sigmoid transfer function (tansig) at the hidden layer with 11 neurons. The minimum mean squared error (MSE) of 0.0017 and coefficient of determination (R(2)) of 0.9658 were found for prediction and modeling of dye removal using testing data set. A good agreement between experimental data and predicted data using the ANN model was obtained. Fitting the experimental data on previously optimized condition confirm the suitability of Langmuir isotherm models for their explanation with maximum adsorption capacity of 434.8mg/g at 25°C. Kinetic studies at various adsorbent mass and initial MG concentration show that the MG maximum removal percentage was achieved within 20min. The adsorption of MG follows the pseudo-second-order with a combination of intraparticle diffusion model.

Enhanced simultaneous removal of malachite green and safranin O by ZnO nanorod-loaded activated carbon: modeling, optimization and adsorption isotherms

Highly improved, safe, green and rapid adsorption by loading trace amounts of ZnO nanorods on AC with high uptake capacity.

Random forest model for the ultrasonic-assisted removal of chrysoidine G by copper sulfide nanoparticles loaded on activated carbon; response surface methodology approach

Copper sulfide nanoparticle-loaded activated carbon (CuS-NP-AC) was prepared and used as an adsorbent for the accelerated removal of chrysoidine G (CG) assisted by ultrasound.