Modeling of removal of an organophosphorus pesticide from aqueous solution by amagnetic metal-organic framework composite

Abdolraouf Samadi-Maybodi*,Mohsen Nikou

Analytical Division,Faculty of Chemistry,University of Mazandaran,Babolsar,Iran

Keywords:Fenitrothion Magnetic iron-based metal-organic framework Fuzzy logic Adsorption Isotherm Mamdani

ABSTRACT Today,a variety of pesticides are used to fight plant pests in the world.The entry of these resistant pollutants into water resources can have devastating effects on human health and the environment,hence their removal from the environment is a vital task.In the present work,the magnetic iron-based metalorganic framework(Fe3O4/MIL-101(Fe))was synthesized by a simple and feasible method and characterized by FT-IR,XRD,BET,FESEM,TEM,TGA,and VSM techniques.The synthesized nanocomposite was successfully applied for the removal of fenitrothion (FEN) pesticide from the aqueous solutions.The isothermal and kinetic models were also investigated.The Langmuir isotherm model (type I) and pseudo-second-order kinetic model were more consistent in the adsorption process.The thermodynamic parameters of fenitrothion sorption were also calculated.The results revealed that the adsorption of fenitrothion onto Fe3O4/MIL-101 (Fe) was spontaneous and endothermic under optimized conditions.Moreover,the removal efficiency of FEN was predicted using the developed fuzzy logic model.Four input variables including the initial concentration of FEN (mg·L-1),pH of the solution,adsorbent dosage (mg),and contact time (min) versus removal efficiency as output were fuzzified by the usage of an artificial intelligence-based method.The fuzzy subsets consisted of Triangular and Trapezoidal membership functions (MFs) with six levels and a total of 23 rules in IF-THEN format which was applied on a Mamdani inference system.The obtained coefficient of determination value (proved the excellent accuracy of the fuzzy logic model as a powerful tool for the prediction of FEN removal efficiency.

1.Introduction

In recent years,pollution of surface water and groundwater by pesticides has become a significant concern throughout the world because many of these compounds are detrimental to both human health and the environment.Increasing the use of pesticides in agriculture and domestic activities for controlling pests has caused considerable amounts of these materials to enter the environment and are polluting the water resources day by day.Pesticides form a strong class of water pollutants because most of them are nonbiodegradable,thus the removal of these compounds from the environment is an essential task [1,2].

There are various types of pesticides such as organochlorine,organophosphorus,and carbamate which are used in agriculture[3].Organochlorine pesticides are used less because of resistance to plant pests,but organophosphorus and carbamate pesticides have the highest levels of use to fight plant pests.Fenitrothion(FEN) or dimethoxy-(3-methyl-4-nitrophenoxy)-thioxophosphor ane is one of the high consumption of organophosphorus pesticides [4].The World Health Organization (WHO) has categorized this pesticide as a medium-grade poison in Class II.The FEN solubility in water is 38 mg·L-1,nonpolar and it remains in the soil for a long time and causes pollution of surface water and groundwater[5].

Many techniques have been used to remove organophosphorus pesticides from the water and wastewater such as photocatalytic degradation [6],electrochemical [7],biodegradation [8],Photo-Fenton[9],and adsorption[10].Among these methods,adsorption is more attractive due to design simplicity,cost-effectiveness,and higher efficiency.Removal of organophosphorus pesticides with this technique from aqueous solutions have performed by the various adsorbent,for instance,activated carbon [11],carbon nanotubes [12],NaX zeolite [13],graphene oxide [14],imprinted polymer [15],and silica [16].

The metal-organic frameworks (MOFs) are new nanoadsorbents with high adsorption capacity which have attracted the highest attention of researchers.MOFs exhibit desirable characteristics such as high porosity,high surface area,various and simple synthesis methods,tunable of pore size,low density,environmentally friendly,high mechanical and thermal stability [17,18].Due to such characteristics,these nanoparticles are widely used in adsorption [19],catalyst [20],gas storage [21],and drug delivery [22].

MIL-101 is one of the MOFs families containing the metals of chrome,aluminum,and iron which have high thermodynamic and kinetic stability in liquid and gas phases[23-25].Among them,MIL-101(Fe) has shown good ability in the adsorption process for removing a variety of pollutants including organic and inorganic effluents [26-31].

Collecting of the nanoparticles from the solution is a main problem in the adsorption process,the magnetizing nanoparticles are a desirable and feasible way for the solving of this problem[32-34].

Several magnetized MOFs have been used to remove pollutants from water.Wanget al.[35] synthesized Fe3O4@SiO2@UiO-67 for simultaneous detection and removal of glyphosate.Zhanget al.[36] prepared Fe3O4@MIL-101 (Cr) for the decolorization of acid red 1 and orange G.Yanget al.[37] used Fe3O4@UiO66for the removal of nitrophenol.Zhenget al.[38] used Fe3O4@ MOF-100(Fe) for adsorption sodium diclofenac in the aqueous solution.Wuet al.[39] synthesized Fe3O4/HKUST-1 and used it for the removal of ciprofloxacin and norfloxacin.To the best of our knowledge,no reports have been done on the removing fenitrothion(FEN) pesticide by Fe3O4/MIL-101(Fe).

Modeling is an appropriate tool for decision making and prediction of environmental phenomena,often expressed as conceptual models with mathematical relations.Due to the complexity and non-linearity of environmental phenomena such as the water purification process,the use of fuzzy logic with a similar function to the human brain as a suitable tool is justifiable[40].For modeling the adsorption process various techniques were implemented,for instance,principal component analysis(PCA)[41],multiple linear regression (MLR) [42],least-square support vector machines(LS-SVM) [43],random forest (RF) [44],artificial neural networks(ANN) [45] and fuzzy logic [40].Among forgoing techniques,the FL is a powerful method particularly for modeling the nonlinear and complex systems [40].

In this work,the magnetic nanocomposite(Fe3O4/MIL-101(Fe))was synthesized with the conventional solvothermal method as a simple and feasible method.The synthesized nanocomposite was successfully applied for the removal of fenitrothion pesticide from the aqueous solutions.Various factors influencing the adCsorption process including,the adsorbent dosage,the pH of the solution,initial concentration,and contact time were investigated in detail.The fuzzy logic was used for modeling and estimating the removal efficiency of FEN.Moreover,the adsorption isotherms,adsorption kinetics,and adsorption thermodynamics of the Fe3O4/MIL-101(Fe) adsorbents were explored.

2.Experimental

2.1.Materials

The chemical compounds of FeCl3.6H2O,FeCl2.4H2O,NH4OH(25%),HCl (35.5%),NaOH (99%),methanol (99.8%),dimethyl formaldehyde (DMF),and polyvinylpyrrolidone (PVP) were purchased from Merck Company and the fenitrothion and terephthalic acid were bought from the Sigma Company.All chemical materials were used without any further purification.Double distilled water(DDW) was used throughout.

2.2.Instruments and software

The powder X-ray diffraction pattern of the prepared samples was recorded by X-Ray diffractometer (PHILIPS-PW1730) using Cu Kα radiation (1.5 ?,1 ?=0.1 nm) in the range of 2θ (5.0°-80.0°) with a scanning rate of 0.04 degree/second.FTIR spectra of the synthesized nanoparticles were recorded using a Thermo/FTIR AVATAR spectrophotometer over the range of 400-4000 cm-1at ambient temperature.The morphology of the samples was acquired by field emission scanning electron microscopy coupled with energy dispersive X-ray spectrometer (FESEM/EDX) model TESCAN-MIRA3 (BELSORP MINI 2).The nanocomposite size and shape were observed by the transmittance electron microscopy(TEM) model Zeiss em900.The differential thermal analysis(DTA/TGA)was carried out in a thermogravimetric analyzer(BAHR,model STA 504).The samples (10 mg) were heated from 50 to 600 °C at a rate of 10 °C·min-1.The surface area of the samples was studied by the Brunauer,Emmet,and Teller (BET) method using nitrogen adsorption/desorption on a BELSORP MINI2 apparatus.The value of saturated magnetic was studied by vibrating sample magnetometer (VSM) (Daghigh kavir CO;Kashan kavir,Iran).The Zeta potential of nanocomposite was determined by zetasizer nano ZS90.Electronic absorbance spectra were recorded using a UV-Vis spectrophotometer (PG Instrument Ltd,double beam-Model T90+).The artificial neural network toolbox was implemented by MATLAB R2012a software.Also,Design-Expert?Software Version 10 was used for experimental design.

2.3.Synthesis of MIL-101(Fe)

MIL-101(Fe)was synthesized using the conventional solvothermal method.Briefly,0.338 g of FeCl3·6H2O and 0.103 g of terephthalic acid were separately dissolved in 7.5 ml of DMF and then mixed together.The resultant mixture was heated for 20 h at 110 °C.The orange precipitate was centrifuged,washed several times with ethanol,and dried at 50 °C for 24 h [46].

2.4.Synthesis of magnetite

Magnetic nanoparticles were synthesized through the coprecipitation method.At the first 2.162 g of FeCl3·6H2O (8 mmol)and 0.795 of FeCl2·4H2O (4 mmol) were dissolved in 100 ml of DDW.Then the solution was agitated with a magnetic stirrer at room temperature ((22 ± 2) °C) under nitrogen gas,and added 5 ml of NH4OH dropwise.The black sediment (Fe3O4) was formed after a few minutes,the mixture was shaked for 10 min and finally,it was separated with a magnetic device (1.4 T).The produced magnetite was washed 5 times with deionized water and dried overnight [47].

2.5.Synthesis of Fe3O4/MIL-101(Fe)

In order to prepare the Fe3O4/MIL-101 nanocomposite,0.050 g of Fe3O4was added in 5 ml of DMF(A).Then 0.025 g of PVP added to this solution (A) and mixed using ultrasonic (mixture B).In another vessel,0.226 g of FeCl3·6H2O was dissolved in 5 ml of DMF and added it to the mixture B and sonicated for 5 min.Finally,5 ml of DMF solution consisting of 0.070 g of terephthalic acid added to the mixture B (mixture C).Then mixture C sonicated for 5 min and transferred into the autoclave.The autoclave was heated for 20 h at the temperature of 120°C.The obtained solid was separated by the magnet device,washed several times with hot ethanol,and dried for 24 hours at the temperature of 50 °C.

2.6.Measurements of FEN uptake

At first,the initial standard solution of FEN was prepared with a concentration of 1000 mg·L-1and the other standard solutions(5-50 mg·L-1)were made with the stock solution.Due to the low solubility of fenitrothion in water,all the solutions were prepared by the methanol/water(50:50,v/v)solvent.The calibration curve was constructed by measuring the absorption of the corresponding solutions.The effect of experimental conditions was investigated to achieve maximum FEN removal.To optimize the adsorption process,different parameters including,initial FEN concentration,pH,sorbent dosage,and contact time were considered.In each step,an appropriate amount of adsorbent was added to 30 ml of a solution containing FEN,and the pH was adjusted to the desired value with 0.1 mol·L-1NaOH or 0.1 mol·L-1HCl solutions.The mixture was stirred at room temperature and then the magnetic adsorbents were isolated from the solution by a magnet device.The absorption of each corresponding supernatant was recorded at 270 nm by UVVis spectrophotometer.The removal percentage of FEN and adsorbent capacity was calculated by equations 1 and 2 respectively.

whereCoandCeare initial and equilibrium concentrations(mg·L-1)respectively.Vis the volume (L) of the solution andWis the mass(g) of the adsorbent.

2.7.Definition of the fuzzy logic model

Fuzzy logic is a powerful technique for the modeling of nonlinear,uncertain,and complex systems.Fuzzy models use logical connections to create qualitative relationships between model variables.This structure makes the models transparent to interpretation and analysis [48].Zadeh introduced this concept,in which fuzzy numbers are assigned to variables to represent uncertainties.A fuzzy number describes the relationship between an uncertain quantityxand a membership function μ,which ranges between 0 and 1 [49].

There are various the shape of membership functions (MFs) of fuzzy sets,can be triangular,trapezoidal,bell-shaped,sigmoidal,Gaussian,or another apposite form,depending on the features of the system understudied [50].Among them,triangular and trapezoidal shaped MFs,have the most application in the fuzzy set theory,due to their simplicity in both design and implementation[51].

For fuzzy inference systems,two types of rule-based models are described,namely,Mamdani models and Takagi-Sugeno models[49].The relations between the output MFs of Takagi-Sugeno models and the input variables are either constant or linear functions,whereas,in the Mamdani models,the output MFs are the fuzzy sets,which can integrate linguistic information into the model[49].Mamdani models are more appropriate for modeling qualitative information,due to allowing a simplified representation and commentary of the fuzzy rules [52].

Fuzzy logic begins with a set of human language rules provided by the user.Then,these rules convert to mathematical equivalents by the fuzzy systems.This could streamline the job of the system designer and the computer,and results in much more accurate depictions of the way systems behave in the real world.Another benefit of fuzzy logic is can noted to its simplicity and flexibility.Additionally,Fuzzy logic can solve problems regarding imprecise and incomplete data,and it can model nonlinear functions in each level of complexity [53].

In the fuzzy model,the relationships between the input and output are described using fuzzy if-then rules(fuzzy propositions).Expert knowledge can have a key role in the creation of fuzzy rules.In the Mamdani schema as a rule base of the fuzzy model,each fuzzy rule is represented in the format of if-then relationships which the first part ‘‘if”is named antecedent and the second part‘‘then”is named the consequent [54].

The general format of an if-then rule in the Mamdani model is shown below [54]:

whereX,Y,andZare named as the linguistic variables of fuzzy sets.In each given input(x0,y0),the value of outputZorCis determined.Generally,each fuzzy expert system (FES) model is developed through three stages,namely,fuzzification,inference engine,and defuzzification as exhibited in Fig.S1(See Supplementary Material).

3.Results and Discussion

3.1.Characterization of materials

Fig.1. FT-IR spectra for Fe3O4 (a),MIL-101 (Fe) (b) and Fe3O4/MIL-101(Fe) (c).

Fig.2. XRD patterns for Fe3O4 (a),MIL-101 (Fe) (b) and Fe3O4/MIL-101(Fe) (c).

Fig.3. Thermogravimetric curves of Fe3O4(a),MIL-101(Fe)(b)and Fe3O4/MIL-101(Fe) (c).

Fig.1(a)-(c) exhibits the FT-IR spectra of Fe3O4nanoparticles,MIL-101(Fe),and the Fe3O4/MIL-101(Fe) composite.The peaks located at 449.96 and 579.73 cm-1in Fig.1(a) were ascribed to the tensile modes of Fe—O in the tetrahedral and octahedral sites,respectively.The appeared signal at 750.35 cm-1(Fig.1(b)) was attributed to the out-of-plane bending vibration of the C—H bond in the benzene ring of MIL-101(Fe).Also,the bands observed at 1601.99 and 1503.99 cm-1are related to asymmetric stretching of carboxyl groups (COOH) whereas the band at 1390.53 cm-1was assigned to the symmetric stretching of the carboxylic groups in the H2BDC of MIL-101(Fe)[55-58].These mentioned signals are observable in Fig.1(c) with slight displacement,which demonstrates the successful synthesis of Fe3O4/MIL-101 composite [58].Fig.2(a)-(c) shows the XRD patterns of Fe3O4,MIL-101(Fe),and Fe3O4/MIL-101(Fe)nanoparticles.In Fig.2(a),the peaks positioned at 30.15°,35.8°,44.4°,53.75°,57.35°,and 63.4°,2θ are related to the indexes of 220,311,400,422,440,and 511 respectively,which belong to the cubic structure of Fe3O4nanoparticle.Fig.2(c)shows the XRD patterns of the Fe3O4/MIL-101(Fe) nanocomposite,as can be observed this pattern almost include both peaks in XRD patterns of Fe3O4and MIL-101(Fe).These results are in good agreement with the FTIR,revealing that this hybrid material (Fe3O4/MIL-101(Fe)) was composed of Fe3O4and MIL-101(Fe) [58,59].Fig.3(a)-(c) illustrates the thermograms of the Fe3O4,MIL-101(Fe),and Fe3O4/MIL-101(Fe).In Fig.3(a) can be observed there is no mass loss (from 25-600 °C).Meanwhile,two thermograms of MIL-101(Fe) (Fig.3b) and Fe3O4/MIL-101(Fe) (Fig.3(c)) have two stages of the mass loss at the temperatures of 370 and 490 °C.The first mass loss is associated to the departure of solvent from the pores and the latter one is related to the decomposition of the terephthalic acid in the MIL-101(Fe)compound.Obviously,the mass loss of MIL-101(Fe) is more than that of Fe3O4/MIL-101(Fe) since the nanocomposite (Fe3O4/MIL-101(Fe) has less amount of MIL-101(Fe)and consequently has less terephthalic acid[36].The morphology analysis of Fe3O4,MIL-101(Fe),and the Fe3O4/MIL-101(Fe)composite was performed and the FESEM,as well as TEM images of corresponding nanoparticles,are illustrated in the Fig.4(a)-(d).The Fe3O4particles with spherical shape can be observed in Fig.4(a) and the octahedral shape of MIL-101(Fe) particles can be seen in Fig.5(b).The FESEM image of the Fe3O4/MIL-101(Fe)nanocomposite is illustrated in Fig.4(c).As clearly observed the Fe3O4nanoparticles are properly placed on the surface of MIL-101(Fe) without any changes in the morphology of MIL-101(Fe).Also,the TEM image of this composite is presented in Fig.4(d).These results confirm the successful synthesis of Fe3O4/MIL-101(Fe)[59].Fig.5(a)-(c)shows the adsorption/desorption isotherms of Fe3O4,MIL-101(Fe),and Fe3O4/MIL-101(Fe) nanoparticles.The BET surface areas (and the pore volumes) of the Fe3O4,MIL-101(Fe),and Fe3O4/MIL-101(Fe)were obtained 67.13 m2·g-1(0.14 cm3-·g-1),1624.91 m2·g-1(1.23 cm3·g-1)and 957.48 m2·g-1(0.78 cm3-·g-1) respectively.The reduction in the surface area and pore volume of the Fe3O4/MIL-101(Fe) in comparison with the MIL-101(Fe)is due to the presence of Fe3O4nanoparticles on the surface of MIL-101(Fe)[58].The VSM analysis of Fe3O4and Fe3O4/MIL-101(Fe) was performed and corresponding magnetization curves are shown in Fig.6 which indicating the superparamagnetic behavior of the magnetite.As can be expected the magnetic property of Fe3O4/MIL-101(Fe) is less than that Fe3O4,nevertheless,the amount of saturated magnetic (26.38 emg·g-1) for Fe3O4/MIL-101(Fe) is good enough for separating it with the magnetic device.The analysis of zeta potential for Fe3O4/MIL-101(Fe) was carried out and the corresponding plot is presented in Fig.7.According to this plot,the pHpzsof this compound is equal to 8.47.As a result,this adsorbent has positively charged below pH 8.47 and is negatively charged above pH 8.47,this matter is important for a better understanding of the adsorption mechanism.

3.2.The effect of pH and adsorption mechanism

The pH of the solution is one of the most important and effective factors in the adsorption process.The pH of the solution affects the charge of the adsorbent surface,the degree of ionization of organic pollutants,the dissociation of the active functional groups on the adsorbent surface,and the structure of the adsorbate.Forasmuch as the fenitrothion degrades rapidly in low or high pH of the solution,the variation of pH was done in the range of 3-10[60].As it is demonstrated in Fig.6(a),the highest amount of FEN sorption is achieved at pH 7,with the removal efficiency is 88.53%.

The FEN molecule has a positive and negative head(see Table 1).In acidic pH,between the negative head of the FEN molecule and the solution cations [H+],electrostatic interaction is desirable.Moreover,in alkaline pH,between the positive head of the FEN molecule and the solution anions [OH-],the same thing happens.As a result,can be said that the FEN molecule has a positive and negative charge in acidic and alkaline conditions,respectively.

The zeta potential graph of Fe3O4/MIL-101(Fe) is depicted in Fig.S3.As can be observed the value of the zeta potential for this adsorbent is about 8.Based on the above results,it can be deduced that at the pH higher than 8 both adsorbent and adsorbate are negatively charged while at the pH lower than 6 both adsorbent and adsorbate are positively charged.Accordingly,in both conditions the adoption processes are unfavorable and consequently,removal efficiency would be expected low.At around pH of 7,the interaction of FEN with the nanoparticles would be favorable since,at this pH,the surface of the adsorbent is negatively charged while the FEN molecule is in the zwitterion situation,consequently,the electrostatic interaction between FEN and nanoparticles is desirable.Also,the hydrogen bond can take place between the hydrogen of the carboxyl group of the MIL-101 with the oxo site on nitro function of the fenitrothion.Moreover,the hydrogen bond can occur between the hydroxyl groups of Fe3O4(hydrogen bond donor)and the oxo site on phosphate function (as the hydrogen bond acceptor) of the fenitrothion.Another possible interaction that can be expected is the π-π interaction between aromatic rings of FEN and MIL-101(Fe).

3.3.The other effective parameters

Fig.4. FESEM images of the Fe3O4 (a),MIL-101(Fe) (b),Fe3O4/MIL-101(Fe) (c) and TEM image of the Fe3O4/MIL-101(Fe) (d).

The potential effective parameters of the contact time,the dosage of adsorbent (Fe3O4/MIL-101(Fe)),and the initial concentration of the adsorbate (FEN) were also investigated.Results obtained from the one at a time method,in this way one factor is changed while the other ones were kept unchanged.The optimized removal efficiency of FEN was obtained under the following conditions:the FEN initial concentration 10 mg·L-1,pH value of 7.0,the adsorbent dosage of 30 mg,and the contact time of 60 min.Fig.6(b)-(d) shown the effect of the mentioned parameters on the removal efficiency of FEN by the Fe3O4/MIL-101(Fe) nanocomposite.In general,it can be deduced that by decreasing initial concentration the removal efficiency is enhanced while with increasing contact time as well as adsorbent dosage the removal efficiency is increased.

3.4.Results of fuzzy modeling

Fuzzy logic modeling was carried out by MATLAB R2012a version and developed using four input variables including pH of the solution,contact time(min),adsorbent dosage(mg),and initial concentration of FEN(mg·L-1)with ranges considered between[0,10],[0,90],[0,40] and [0,40],respectively.The designed fuzzy inference system has an output named removal efficiency of FEN in the ranges of [0,100] as shown in Fig.7.Multiple membership functions consisting of VVL,VL,L,M,H,and VH were considered for input variables (pH,CT,AD,and IC).The names of these six fuzzy sets were defined as ‘‘very very low”,‘‘very low”,‘‘low”,‘‘medium”,‘‘high”,and ‘‘very high”.Several membership functions such as VVL,VL,L,LM,LM1,LM2,M,M1,M2,MH,H,VH,and VVH were used to call the output variable ‘‘very very low”,‘‘very low”,‘‘low”,‘‘low moderate”,‘‘low moderate one”,‘‘low moderate two”,‘‘moderate”,‘‘moderate one”,‘‘moderate two”,‘‘moderate high”,‘‘high”,‘‘very high”,and ‘‘very very high”(Fig.7).Triangular and trapezoidal shapes of membership functions (MFs) were chosen for all input and output variables.Triangular curves rely on three parameters (a,b,c),while the trapezoidal curves work with four parameters (a,b,c,d) as illustrated in the following equations:

Fig.5. Nitrogen adsorption/desorption (BET) and pore size distribution curves (BJH) of the Fe3O4 (a),MIL-101 (Fe) (b) and Fe3O4/MIL-101 (Fe) (c).

The compact forms of two functions are represented as follows;

In triangular MFs,aandcare at the‘‘feet”of the triangle andbis at the ‘‘peak”.As well,in trapezoidal MFs,the parametersbandcare at the‘‘top”of the trapezoid,whileaanddare at the‘‘bottom”of the trapezoid [61].

The schematic of the Mamdani fuzzy inference system (FIS)applied in this study for the FEN removal efficiency is shown in Fig.S4.Mamdani FIS consists of four steps:fuzzification,rule evaluation,aggregation of the rule outputs,and transforming the fuzzy output into a crisp output (defuzzification).In the first step,numerical inputs and outputs (crisp variables) are changed to linguistic terms (X,Y,Z,etc.) or some specific adjectives (warm,hot,cold,low,high,big,small,etc.),and the corresponding degrees of the one or more several membership functions are determined.In the second step,the fuzzy inputs are taken and applied them to the qualified fuzzy rules.Then,the fuzzy operators such as AND,OR,and NOT are used in case of any uncertainty to get a single value.In the third step,using with the inference operators such as max-min,max-product,and sum-product,can combine the scaled rules into a single fuzzy set for each variable [62].The max-min technique was applied as an inference operator in this study,due to its ability to represent a computationally good and expressive setting for constraint propagation[52].In the defuzzification step as the final step of the fuzzy system,linguistic results obtained from the FIS converted into crisp numerical outputs(real values).There are several defuzzification methods,such as mean of maxima (MOM),leftmost maximum (LM),rightmost maximum(RM),the bisector of area(BOA),and center of gravity(COG or centroid),which for the present study,the COG as the most common defuzzification method was employed [62].

Fig.6. Effect of pH on the FEN removal efficiency(a)Effect of adsorbent dose on the FEN removal efficiency(b)Effect of contact time on the FEN removal efficiency(c)Effect of initial concentration the FEN removal efficiency (d).

The removal efficiency of FEN pesticide as a response was calculated for each input variable using fuzzy modeling in Matlab software.As can be seen in Fig.8,there is a small deviation between predicted data obtained from the fuzzy model and experimental data,undoubtedly confirming the acceptable performance of the fuzzy inference system.Fig.9 presents the correlation graph of all experimental and predicted data plus the full information of four input variables in each run.As can be observed,the fairly good overlapping between the two curves mentioned shows that there is good agreement between experimental and predicted data.

Moreover,the coefficient of determination(R2)equal to 0.98205 proves that exists a good agreement between the predicted data obtained from dynamic simulation and experimental data(Fig.10).

The performance of the fuzzy model was evaluated by calculating several significant statistical parameters such as average relative error (ARE),absolute average relative error (AARE),standard deviation (SD),and root mean squared error (RMSE),which listed in Table 2.Results approve that the fuzzy model has considerable ability in the prediction of FEN removal efficiency.The values of ARE,AARE,SD,and RMSE are computed by the following equations[61]:

Moreover,a comparison of the performance of the fuzzy logic model used in this study with other relevant models applied in previous works in the field of organic pollutions is given in Table 3.Based on the presentedas a statistical parameter for the evaluations of models,it can be said that the model employed in this work had a reasonably precise performance in comparison to other models.

Fig.7. Fuzzy membership functions of input(pH,contact time(min),adsorbent dosage(mg),initial concentration of FEN(mg·L-1))and output(Removal efficiency of FEN(%)variables.

3.5.The isothermal and kinetic studies

The adsorption isotherm explains how the adsorbent can interact with the adsorbate and demonstrate the mechanism of the adsorption process.

In this study,the Langmuir type I-IV,Freundlich,and Dubinin-Radushkevich models were used to find the adsorption behavior of FEN by Fe3O4/MIL-101(Fe)(Fig.S5(a)-(f)).The isotherm models were examined by the coefficient of determination (R2) and the standard test of chi-square(χ2)using the following equation[69]:

Table 1 Main properties of fenitrothion pesticide used in this study (1 atm=101325 Pa)

whereqeis the equilibrium adsorption capacity of the experiment(mg·g-1),andqmis the equilibrium capacity calculated according to the dynamic model (mg·g-1).The small value of (χ2) indicates that the data obtained from the model is more adaptable with the experimental results (Table 4).Based on results (R2and χ2),it can be deduced that the Langmuir I model is more consistent and consequently the monolayer adsorption is the dominant adsorption mechanism [69].

The comparison of the maximum monolayer adsorption(qm)of FEN onto Fe3O4/MIL-101(Fe) nanocomposite with other various adsorbent is given in Table 5.As can be seen in this table,the adsorption capacity of Fe3O4/MIL-101(Fe) is higher than other reported adsorbent.

Fig.8. Response of fuzzy inference model (FEN removal efficiency) related to (a) pH,(b) contact time,(c) adsorbent dose and (d) initial concentration of FEN.

Fig.9. Correlation graph of experimental data of FEN removal efficiency and the predicted data obtained of the fuzzy logic model.

The adsorption kinetic is depended on the physical and chemical characteristics of the adsorbent which influences the adsorption behavior.The kinetic studies were performed and the corresponding graphs were depicted in Fig.S6(a)-(b) and also the kinetic parameters are presented in Table 6.According to the parametersR2and χ2,it can be concluded that the kinetic adsorption process of FEN on the Fe3O4/MIL-101(Fe) follows the pseudosecond-order kinetic and the rate of reaction originated to be controlled by the chemical interaction [72].

3.6.Thermodynamic study

Thermodynamics parameters were estimated to investigate the adsorption nature of FEN onto Fe3O4/MIL-101(Fe)nanoparticles by running sorption tests at different temperatures (293,303,313,and 323 K) in optimized conditions.The thermodynamic parameters,including the free Gibbs energy (ΔG),enthalpy (ΔH),and entropy (ΔS) were calculated by the following equations [69]:

Fig.10. Comparison of the experimental and predicted data for the FEN removal efficiency (%).

The thermodynamic parameters of FEN onto Fe3O4/MIL-101(Fe)at various temperatures are summarized in Table 7.It can be concluded from this table that the amount of sorption of FEN wasenhanced by increasing solution temperature.The sorption process of FEN onto Fe3O4/MIL-101(Fe) nanocomposite was found endothermic with ΔHvalue of 15.587 kJ·mol-1.The negative values of ΔG° also decreased as the temperature was increased from 298 K to 328 K,showing that the adsorption process was thermodynamically feasible at room temperature but it will be more spontaneous and faster at higher temperatures.Moreover,the value of ΔSwas calculated as 60.382 J·K-1·mol-1verifying the high affinity of FEN toward Fe3O4/MIL-101(Fe)and increased randomness at the interface of the adsorbent-solution over the adsorption process[73].

Table 2 ARE,AARE,SD,and RMSE for removal efficiency of FEN modeled by Fuzzy logic

Table 3 Comparison between the present study and other performed models on the removal efficiency of organic pollutants by various adsorbents/technique

Table 4 Isotherm parameter of FEN adsorption onto Fe3O4/MIL-101 (Fe),conditions:Adsorbent dose=30 mg,pH=7,the concentration of FEN=10-100 mg·L-1 and contact time=60 min

Table 5 Comparison of the maximum monolayer adsorption (qm) of FEN onto various adsorbents

3.7.Regeneration studies

The reusability of Fe3O4/MIL-101(Fe) composite in the removal of Fenitrothion (FEN) under optimal conditions for four consecutive cycles was evaluated and the results are shown in Fig.S7.For this purpose,30 mg of adsorbent was added to 20 ml of FEN solution with concentration of 10 mg·L-1under the stirrer for 60 min.The adsorbent was separated from the solution by the magnet,and then,it was washed with ethanol and water several times followed by drying under vacuum at 50 °C overnight and reused for another adsorption.The results showed that the decrease and adsorption efficiency of FEN after five cycles were low and there was no significant reduction in removal efficiency.As shown in Fig.S7,the removal efficiencies of FEN is reduced from 87%in the first cycle to 73%in the fifth cycle.These results indicate that the synthesized composite has excellent recoverability and significant recycling potential.It can also be stated that the Fe3O4/MIL-101(Fe) composite is easily regenerated and thus can significantly reduce the cost of synthesis and purchase of materials.

4.Conclusions

In this study,the removal of fenitrothion (FEN) has performed using a magnetized metal-organic framework (Fe3O4/MIL-101(Fe)).The successful synthesis of nanocomposite was confirmed by different methods such as FT-IR,XRD,BET,FESEM,TEM,TGA,and VSM techniques.One at a time method was assisted to be achieved optimized removal efficiency.The optimized removal efficiency of FEN was obtained under the following conditions:the FEN initial concentration 10 mg·L-1,pH value of 7.0,the adsorbent dosage of 30 mg,and the contact time of 60 min.According to the coefficient of determination (R2) and the standard test of chisquare (χ2) values obtained from the isothermal and kinetic models,it can be mentioned that the sorption process was controlled by the pseudo-second-order model,and equilibrium data were fitted well to Langmuir (type I) model.Thermodynamic parameters revealed that the FEN sorption onto nanocomposite was endothermic,feasible,and spontaneous.In addition,fuzzy logic (FL) was applied for building up a predictive model and prediction of the FEN removal efficiency.A Mamdani type of fuzzy inference system was developed using 23 IF-THEN rules along with six levels of both trapezoidal and triangular membership functions.Results showed that a good agreement between experimental and predicted data was obtained by the FL model as a powerful tool for prediction of removal percentage of FEN ().

Credit Authorship Contribution Statement

Abdolraouf Samadi-Maybodi:Supervision,Writing -original draft,Writing -review &editing.Mohsen Nikou:Conceptualization,Methodology.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Supplementary Material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cjche.2020.09.072.

Table 6 The pseudo-first-order and pseudo-second-order kinetic parameters at various FEN concentration (Adsorbent dosage:30 mg,pH=7,and contact time=60 min)

Table 7 Thermodynamic parameters for the adsorption of FEN onto Fe3O4/MIL-101 nanocomposite