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Biosorption of copper(II) ions from aqua solutions using dried yeast biomass

Biosorption of copper(II) ions from aqua solutions using dried yeast biomass
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  Colloids and Surfaces A: Physicochem. Eng. Aspects 335 (2009) 181–188 Contents lists available at ScienceDirect Colloids and Surfaces A: Physicochemical andEngineering Aspects  journal homepage: Biosorption of copper(II) ions from aqua solutions using dried yeast biomass Corneliu Cojocaru a , Mariana Diaconu a , Igor Cretescu a , ∗∗ , Jasmina Savi´c b , ∗ , Vesna Vasi´c b a Department of Environmental Engineering and Management, Technical University of Iasi, Bvd. D. Mangeron 67, 700050 Iasi, Romania b Department of Physical Chemistry, Vinˇ ca Institute of Nuclear Sciences, P.O. Box 522, 11001 Belgrade, Serbia a r t i c l e i n f o  Article history: Received 18 April 2008Received in revised form 5 October 2008Accepted 7 November 2008Available online 14 November 2008 Keywords: BiosorptionAdsorption isothermHeavy metalsResponse surface methodology (RSM) a b s t r a c t Theabilityofdriedyeast Saccharomyces biomasstoremoveCu(II)ionsfromaqueoussolutionswasinves-tigated by using of batch techniques. The influence of different parameters on copper uptake by driedyeast, such as initial Cu(II) concentration, initial pH of solution and temperature, was studied. The Fre-undlich,Langmuir,Redlich–PetersonandSipsisothermswereappliedtotheobtainedexperimentaldata.According to Langmuir isotherm the maximum adsorption capacity of investigated non-living biomasswas found to be 2.59mg/g. The thermodynamic parameters (e.g. free energy and enthalpy) were cal-culated and discussed. The adsorption of Cu(II) onto the dried cells of   Saccharomyces cerevisiae  is anendothermic process and become more favorable with the increasing of temperature in pH range from 3to 4. Optimization studies by means of response surface methodology were carried out, which resultedin improvement of the efficiency of sorption removal by using of biomass. The removal efficiency of real wastewater srcinating from electroplating industry which contains Sn(II) ions was determined andcompared with synthetic wastewater obtained in laboratory.© 2008 Elsevier B.V. All rights reserved. 1. Introduction Wastewater contamined with heavy metals is a serious envi-ronmental problem because they do not undergo biodegradationand are accumulated into the organism entering into the foodchains.Duringrecentyears,theintensiveindustrialactivities,suchas electroplating, microelectronics, battery manufacture, dyestuff,chemical, metallurgical, pharmaceutical and other, greatly con-tribute to the increase of heavy metals in the environment [1].The stringent limits of different pollutant concentrations inindustrial and municipal wastewaters, imposed by the envi-ronmental legislation, make the treatment to be imperative.Conventional methods for removing of metal ions from aqueoussolutions, like chemical precipitation, ion exchange, electrochemi-cal treatment, and adsorption on activated carbon, have significantdisadvantages. Chemical precipitation and electrochemical treat-mentbecomeineffectiveparticularlywhenmetalionconcentrationin the solution is low (in the range from 10 to 100mg/L), becausethey produce large quantity of sludge to be treated and whichrequiredisposal.Ionexchangeandactivatedcarbonadsorptionareextremely expensive processes, especially for the treatment of alarge amount of wastewater containing low heavy metal concen-trations [2,3]. ∗ Corresponding author. Tel.: +381 11 2453 967; fax: +381 11 2447 207. ∗∗ Corresponding author. Tel.: +40 741 914342; fax: +40 232 271311. E-mail addresses: (I. Cretescu), (J. Savi´c). Thesedisadvantagesofconventionalmethodstogetherwiththeneed of more effective and low-cost methods for the metal ionsremovalfromwastewaterresultedinthedevelopmentofnewsep-arationtechnologies.Thebiosorptionhasattractedtheattentionasa low-cost treatment technology for the removal of heavy metalsfrom wastewaters [1–15]. The biosorbents are prepared from nat-urally abundant materials and from by-products or waste biomassfrom other industries [16]. Among biosorbents, those of microbio-logical srcin (e.g. bacteria, fungi, yeast and algae biomass) are of especial interest and were studied as potential heavy metal sor-bents in various environments [4,6,7,11,12,14,17–23].Although many effects on biosorption have been widely stud-ied, such as initial metal concentration, pH, temperature, andsorbent dose, the mechanism of metal biosorption is not com-pletely understood because of its complexity. The metal uptake bybiosorption can occur due to physico-chemical interactions suchas complexation, coordination, chelation, ion exchange, physicaladsorption or microprecipitation. The biosorption (passive sorp-tion) by biomass occurs through interactions between metal ionsand functional groups at the cell surface like amino, phosphoryl,carboxyl,sulphydrilandhydroxylgroups[3].Inspiteofgreatnum-ber of papers reported for biosorption, this process is still at thestage of laboratory-scale study [3]. Therefore complex research isnecessary in order to select the best sorbent for certain pollutant[12].This work is focused on Cu(II) ions removal from aqueoussolutions using the dried non-living yeast biomass ( Saccharomycescerevisiae ) as biosorbent. An effort to discuss the equilibrium 0927-7757/$ – see front matter © 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.colsurfa.2008.11.003  182  C. Cojocaru et al. / Colloids and Surfaces A: Physicochem. Eng. Aspects 335 (2009) 181–188 adsorption isotherms on basis of Freundlich, Langmuir,Redlich–Peterson (R–P) and Sips models as well as to opti-mize the sorption process using response surface methodology(RSM) is also included in this work. In order to check the RSMoptimization of sorption process, the real sample obtained fromelectroplating industry was compared with synthetic wastewaterattained in laboratory conditions. 2. Experimental  2.1. Reagents Commercial yeast biomass  S. cerevisiae , available from localcommercial company, was prepared as non-living biomass by dry-ing in a hot air oven at 105 ◦ C for 24h. Copper stock solution of 1000mg/L was prepared using CuSO 4 · 5H 2 O of analytical reagentgrade. This salt was chosen since sulphate ions exist in mostwastewater and mine drainage and thus simulating the potentialapplication of biosorption for copper ions removal. All solutionsandtheirdilutionswerepreparedbyusingofbidistilledwater.Theacidity of the solutions was adjusted by addition of 0.1N sulphuricacid or 0.1N sodium hydroxide solutions.  2.2. Instrumentation The concentrations of copper solutions were determinedin aqueous solutions spectrophotometrically by measuring theabsorbance of complex formed between rubeanic acid and Cu(II)at 390nm. The absorbance was recorded using the direct read-ing spectrophotometer (HELIOS, USA). For pH measurements a pHmeter BOECO PT-370, with a combined glass electrode, was used.The batch experiments were carried out using an orbital shaker.The temperature adjustment, in the range from 20 to 50 ◦ C, wasperformed by using of BMT Ecocell incubator, with the precision of  ± 0.5 ◦ C.  2.3. Sorption studies In order to obtain the adsorption isotherms, batch experimentswere conducted with copper aqueous solutions of initial concen-tration varying from 25 to 200mg/L and in pH range from 3 to5. Samples were mixed and left for 24h to reach the equilibrium.The resulting filtrate was analyzed for copper. The biosorbent dose(BSD)intheseexperimentswasmaintainedconstant,i.e.1.5%(w/v)thatcorrespondsto1.5gofbiosorbentaddedto100mLofaqueoussolution. The adsorption isotherms were determined as the func-tion of pH and temperature. The equilibrium adsorption capacitywas determined by the following expression: Q  e  = ( C  o  − C  e )  V m 1000 (1)where  Q  e  is the equilibrium adsorption capacity, i.e. the amount of copper ions adsorbed onto the biosorbent (in mg/g);  C  o  and  C  e  arethe initial and final metal concentrations in the solution, respec-tively (in mg/L);  V   is the volume of the solution (in mL) and  m represents the weight of the dried biosorbent (in g).In addition, another set of the experiments was performed inorder to optimize the process efficiency. The experiments con-cerning optimization were conducted in batch mode. In theseexperiments,theinitialcopperconcentrationwasvariedfrom20upto80mg/L.Afterreachingtheequilibriumtheresultingfiltratewasanalyzed for final copper concentration and the removal efficiencywas determined.In this respect the experimental design was applied and theremoval efficiency of copper ions from aqueous solution was cho-senastheresponseforoptimization.Theremovalefficiency Y  %was  Table 1 pH dependence of the isotherm parameters for copper biosorption onto  Saccha-romyces  from aqueous solutions at  T  =293K.Type of isotherm pH 3 pH 4 pH 5Freundlich n =2.234  n =2.226  n =2.814 K  F   =0.260  K  F   =0.252  K  F   =0.287 R 2 =0.623  R 2 =0.673  R 2 =0.686  ARE  =24.230  ARE  =21.889  ARE  =16.653Langmuir Q  max  =2.533  Q  max  =2.489  Q  max  =1.794 K  L  =4.241 × 10 − 2 K  L  =3.980 × 10 − 2 K  L  =4.872 × 10 − 2 R 2 =0.958  R 2 =0.940  R 2 =0.975  ARE  =18.832  ARE  =16.256  ARE  =11.422Redlich–Peterson  A =5.858 × 10 − 2  A =6.551 × 10 − 2  A =4.698 × 10 − 2 B =9.407 × 10 − 4 B =2.313 × 10 − 3 B =1.583 × 10 − 3  g  =1.637  g  =1.482  g  =1.550  ARE  =10.883  ARE  =10.423  ARE  =5.342Sips Q  SM   =2.060  Q  SM   =2.019  Q  SM   =1.524 b =1.274 × 10 − 5 b =4.114 × 10 − 5 b =1.977 × 10 − 4 n S   =0.271  n S   =0.303  n S   =0.348  ARE  =2.743  ARE  =1.823  ARE  =1.852 determined by means of subsequent equation: Y   =  1 − C  e C  0  100 (2) 3. Results and discussion  3.1. Adsorption isotherms In the design of sorption systems, the equilibrium sorptionisotherms are very important from fundamental point of view. Theequation parameters and the underlying thermodynamic assump-tions of these equilibrium models often provide some insight intoboth the sorption mechanism and the surface properties and affin-ity of the sorbent [1]. In order to investigate the sorption isothermFreundlich,Langmuir,Redlich–PetersonandSipsequilibriummod-els were applied.The well-known expressions of the Freundlich and Langmuirmodels with two parameters are given by the following Eqs. (3)and (4).TheFreundlichequationisanempiricalrelationshipestablishedupon sorption onto a heterogeneous surface supposing that differ-ent sites with several adsorption energies are involved [5]. Q  e  =  K  F   C  1 /ne  (3)where  K  F   denotes the relative adsorption capacity and  n  the inten-sity of adsorption.The Langmuir isotherm: Q  e  = Q  max K  L C  e 1 + K  L C  e (4)where  Q  max  and  K  L  are Langmuir isotherm constants. Thus,  Q  max denotes the maximum adsorption capacity (mg/g) while  K  L  is theequilibriumconstantconnectedtotheenergyofsorptionthatquan-titatively reflects the affinity between the biosorbent and sorbate[5].The simplest method to determine isotherm constants for twoparameter isotherms is to transform the corresponding equationintothelinearformandthentoapplylinearregression[1].Thus,theapplicability of Freundlich and Langmuir sorption isotherms havebeen checked by plotting log( Q  e ) versus log( C  e ) and ( C  e / Q  e ) versus( C  e ),respectively.ThevaluesofparametersinFreundlich( K  F   and n )and Langmuir ( Q  max  and  K  L ) were determined and are reported inTables 1 and 2.The Redlich–Peterson and Sips isotherm equation with threeparameters are given by Eqs. (5) and (6).  C. Cojocaru et al. / Colloids and Surfaces A: Physicochem. Eng. Aspects 335 (2009) 181–188  183  Table 2 Temperature dependence of the isotherm parameters for copper biosorption onto Saccharomyces  from aqueous solutions at pH 4.Type of isotherm  T  =308K  T  =323KFreundlich n =2.444  n =3.176 K  F   =0.323  K  F   =0.546 R 2 =0.707  R 2 =0.890  ARE  =19.075  ARE  =8.409Langmuir Q  max  =2.402  Q  max  =2.595 K  L  =5.619 × 10 − 2 K  L  =7.466 × 10 − 2 R 2 =0.982  R 2 =0.994  ARE  =13.652  ARE  =5.524Redlich–Peterson  A =6.326 × 10 − 2  A =0.118 B =1.021 × 10 − 3 B =2.007 × 10 − 2  g  =1.630  g  =1.153  ARE  =6.345  ARE  =4.073Sips Q  SM   =2.154  Q  SM   =2.592 b =3.16518 × 10 − 4 b =4.110 × 10 − 2 n S   =0.369  n S   =0.861  ARE  =2.649  ARE  =4.498 The Redlich–Peterson isotherm: Q  e  =  AC  e 1 + BC   g e (5)where  Q  e  and  C  e  have the same definitions as in Eqs. (3) and (4),  A  (L/g) and  B  (Lmmol − 1 )  g  are the Redlich–Peterson isotherm con-stants, while  g   is the Redlich–Peterson isotherm exponent whichhas values between 0 and 1. For  g  =1 the Redlich–Peterson modelconverts to the Langmuir model.The Sips isotherm: Q  e  = Q  SM   bC  1 /n S e 1 + bC  1 /n S e (6)where  Q  SM   is the Sips maximum adsorption capacity (mg/g) and b  is the Sips constant related to affinity between solute andsorbent.It is noticeable that, at low solute concentrations the Sips equa-tion is reduced to a Freundlich isotherm, while at high soluteconcentration it predicts a monolayer adsorption capacity charac-teristic of Langmuir equilibrium equation [24].The parameters of Redlich–Peterson (  A ,  B ,  g  ) and Sips ( Q  SM  ,  b , n S  ) isotherms, listed in Tables 1 and 2, were determined by meansofnon-linearregressionmethodusingaGauss–Newtontechnique.The non-linear regression method provides a mathematically rig-orous method for determining isotherm parameters by using of the srcinal form of the isotherm equation. This is an advantagecomparing to linear regression approach where the deviation biasappears as a result of linearization.Inordertorevealtheagreementoftheequilibriummodelswithexperimentalresults,theaveragerelativeerror(  ARE  )wascomputedas follows [20]:  ARE   (%)  = 100  z   z   i = 1  | Q  exp  − Q  calc  | Q  exp  i (7)where  z   is the number of data points;  Q  exp  and  Q  calc   are theexperimentalsorptioncapacityandcalculatedsorptioncapacitybytheoretical models, respectively. Fig. 1.  Adsorption isotherms obtained at 293K for pH 4 (a) and pH 5 (b). Fig. 2.  Adsorption isotherms obtained at pH 4 for  T  =308K (a) and  T  =323K (b).  184  C. Cojocaru et al. / Colloids and Surfaces A: Physicochem. Eng. Aspects 335 (2009) 181–188 The average relative errors, represented in Tables 1 and 2,approve the effectiveness of the applied models in fitting of theexperimental data.According to results represented in Tables 1 and 2, the cop-per uptake was in the range from 2.40 to 2.59mg/g at pH 4.0 andthe values of the Langmuir isotherm parameters indicate that themonolayer saturation capacity of Cu(II) ions,  Q  max , is 2.59mg/g for S. cerevisiae  dried cells. This value is comparable with the cop-per ions uptake by yeast biomass reported in the literature [2,25].As the illustration, the copper uptake by  S. cerevisiae  was rangedfrom 2.04 to 9.05mg/g for adapted and growing cells, while forcommercially available dry cells it was in the range from 2.98to 12.03mg/g [2,25,26]. The Sips model provided slightly lowervalues of maximum adsorption capacity than those achieved byLangmuirequation.AccordingtoSipsmodel,themaximumadsorp-tion capacity for dry yeast cell was in the range from 2.02 to2.59mg/g for pH 4.0 and different temperature conditions. TheFreundlich isotherm constant  n  indicates that the intensity of adsorption is increasing with the increment of temperature. TheRedlich–Petersonisothermconstantsindicatethatthismodeltendstoward a Langmuir isotherm.Usingtheisothermparametersandmassbalanceequations,thepredicted amount of copper adsorbed per gram of sorbent wasdetermined and compared with experimental data (Figs. 1 and 2).As can be seen from Figs. 1 and 2 and  ARE   values listed inTables 1 and 2, the Sips isotherm give the best fit with experimen-talresults.Theothermodels,inorderofdecreasingagreementwithexperimentally obtained results, are as follows: Redlich–Peterson,Langmuir and Freundlich. Thus, the equilibrium models with threeparameters (Sips and R–P) fit better to the experimental resultscomparing to the two parameters models (Langmuir and Fre-undlich).Likewise,Fig.1showstheeffectofpHontheadsorptioncapacity.IncreasingpHfrom4to5leadtoadecreaseofadsorptioncapacity.This could be attributed to the strong effect of initial concentra-tion of solute which is very high comparing with the relatively lowsorptioncapacityofbiosorbent.Also,parallelprecipitationofCu(II)hydroxideispossiblewhichmeansthattheinitialconcentrationof Cu(II) ion is lower than it should be. Besides, increase in densityof the negative charge on the cell surface, causing proton removalfromthesolution,therebydecreasingbiosorptioncapacityofCu(II)because of competitively adsorption of protons.The adsorption capacities, reported for different temperatures,arerepresentedinFig.2.Ascanbeseen,aslightincreaseofadsorp-tion capacity was induced with temperature growth. According tothe obtained results, the isotherm obtained by using of Langmuirmodel at 323K fit the experimental data better than the isothermsreportedforlowertemperaturesandalsobetterthanthreeparam-eters models.On the basis of additional analysis of the Langmuir equation,the dimensionless parameter of the equilibrium ( R L —known alsoas separation factor) may be calculated as [27] R L  = 11 + K  L C  o (8)where  C  o  (in mg/L) is the initial concentration of solute. Accordingto the value of separation factor  R L , following types of adsorp-tion exists [27]: (1) favorable adsorption 0< R L <1; (2) unfavorableadsorption  R L >1; (3) linear adsorption  R L =1; (4) irreversibleadsorption  R L =0. In our experiments, the initial solute concentra-tion was in the range of 25–200mg/L and corresponding valuesof the separation factor  R L  has been found to be in the range of 0.063–0.501. Hence, the results underlines that the adsorption of Cu(II) onto the dried cells of biomass is favorable under all condi-tions considered in this work. Fig. 3.  Dependence of ln( K  L ) vs. 1/ T   for Cu(II) adsorption on dried cells of   Saccha-romyces cerevisiae .  3.2. Thermodynamics of adsorption The thermodynamics of sorption of the copper onto the driedcells of   S. cerevisiae  biosorbent was evaluated using the followingequations: G  = − R  g  T   ln  K  L  (9)ln  K  L  = − H R  g  T   + const   (10) G  =  H   − T S  (11)where   H  ,   S  ,   G  and  T   are the enthalpy, entropy, Gibbs freeenergy, and absolute temperature, respectively;  R  g   is the gas con-stant and  K  L  the equilibrium constant (L/mol). The computationof thermodynamic parameters gives the following numerical val-ues of    G =( − 20.98 ± 1.72)kJ/mol,   H  =(14.33 ± 0.51)kJ/mol and  S  =(114.67 ± 0.11)J/molK. The negative value of    G  indicates thespontaneous nature of adsorption process. Positive value of    H  (computedfromtheslopeoflineardependenceofln( K  L )versus1/ T  ( R 2 =0.997) as shown in Fig. 3) indicates the endothermic enthalpyof adsorption, favored by increased temperatures, while the pos-itive value of    S   shows the affinity of the adsorption for copperions.As we noticed, according to data shown inFig. 3, the adsorptionof Cu(II) onto the dried cells of   S. cerevisiae  is an endothermic pro-cesswhichbecomesmorefavorablewiththeincreasingofsolutiontemperature.  3.3. Response surface modeling  The response surface methodology deals with experimentaldesign strategy, statistical modeling and process optimization. TheRSM applications concerns the particular situations where severalinput variables (  factors ), which can be set by experimenter, influ-ence the result of measurement ( response ). Also, RSM includes theempirical statistical modeling in order to develop an appropriateapproximatingrelationshipbetweentheresponseandfactorstobeused for process optimization. In this work the response surfacemodeling was applied to develop an appropriate regression modelto find out the optimal values of experimental factors in order toenhanceremovalefficiency.Themostsignificantfactorsthataffectsorptionremovalefficiency(response)aretheinitialconcentrationofcopperionsinaqueoussolutions C  o  (mg/L),initialpHofsolutionand the biosorbent dose (%, w/v). The real values of independent  C. Cojocaru et al. / Colloids and Surfaces A: Physicochem. Eng. Aspects 335 (2009) 181–188  185  Table 3 Central composite orthogonal design and experimental response.Run number ( N  ) Factors (input variables) ResponseInitial concentration of Cu(II) pH of solution Biosorbent dose (%, w/v) Removal efficiency, b Y  % C  o  (mg/L) Level a  x 1  pH Level a  x 2  BSD Level a  x 3 1 75 1 5 1 2.25 1 32.732 25  − 1 5 1 2.25 1 37.963 75 1 3  − 1 2.25 1 36.984 25  − 1 3  − 1 2.25 1 41.225 75 1 5 1 1.50  − 1 29.476 25  − 1 5 1 1.50  − 1 34.697 75 1 3  − 1 1.50  − 1 34.208 25  − 1 3  − 1 1.50  − 1 38.949 80  ˛  4 0 1.88 0 20.6410 20  − ˛  4 0 1.88 0 37.4011 50 0 5.2  ˛  1.88 0 33.9612 50 0 2.8  − ˛  1.88 0 32.9813 50 0 4 0 2.33  ˛  39.9214 50 0 4 0 1.42  − ˛  35.0215 50 0 4 0 1.88 0 36.3316 50 0 4 0 1.88 0 36.06 a 1=low value, 0=center value, +1=high value,  ± ˛ =star point value. b The removal efficiency  Y  % was determined experimentally by using Eq. (2). variables as well as their coded limits are listed in the central com-positional experimental design given in Table 3.As can be seen from Table 3, the coefficients of the empiricalmodelhavebeencalculatedbymeansofmultiplelinearregression(MLR) method [28] and according to MLR empirical model (withcoded variables) may be written as follows:ˆ Y   =  33 . 87 − 3 . 633  x 1  − 1 . 397  x 2  + 1 . 602  x 3  − 2 . 205  x 21  + 3 . 523  x 23 subjected to :  − ˛  ≤  x  j  ≤ + ˛ ;  j  =  1 , 2 , 3; (12)where  x 1 ,  x 2  and  x 3  are the coded levels of factors and  ˛ =1.215 isthe star point in experimental design that gives the limits of thevalid region (region of experimentation).TheregressioncoefficientsweretestedforsignificancebymeansofStudent’s t  -test[28]retaininginEq.(12)onlythesignificantones. For the graphical representation and analysis of the factors’ influ-ence upon response it is worth converting the response surfacemodel in terms of coded variables to an empirical model in termsofactualvariables.Forthispurposethesubstitutiontechniquewasapplied and the empirical coefficients were computed. Thus, interms of actual variables the empirical model may be written asˆ Y   =  117 . 969 + 0 . 207  C  0  − 1 . 397pH − 89 . 675BSD − 3 . 528 × 10 − 3 C  20  + 25 . 052 BSD 2 subjected to : 20  ≤  C  0  ≤  80 (mg/L); 2 . 8  ≤  pH  ≤  5 . 2;1 . 42  ≤  BSD  ≤  2 . 33(% ,  w/v) (13)The goodness-of-fit between empirical model and experimen-tal data was verified using  F  C  -ratio test [28] for a confidence level  p =0.05 and degrees of freedom  f  1 =9 and  f  2 =1. The  F  C  -ratio wascomputed as the ratio between the variance of residual and thevarianceofexperimentalerror(replication).The F  C  -ratiowasfoundto be  F  C  =234.1 that is smaller than tabulated value  F  tab  (  p ,  f  1 ,  f  2 )=240.5. This means that the mathematical model is validatedfrom statistical standpoint. Fig. 4 compares the experimental andpredicted data of removal efficiency. Likewise, the model ade-quacy has been investigated by the examination of the residuals( e  =  Y  exp  −  ˆ Y   predic  ).TheresidualanalysisisalsoshowninFig.4out-lining a good concordance between experimental and predictedresponses.As one can see the response model shows a goodness-of-fit toexperimental data (Fig. 3). Therefore, the model has been consid-ered adequate for the prediction and optimization. Having a validmodel the graphical representations of the response surface were Fig. 4.  Removal efficiency, experimental data plotted against the predicted ones and residual analysis of empirical model.
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