Highly efficient recovery of biophenols onto graphene oxide nanosheets: Valorisation of a biomass

Accepted Manuscript Highly efficient recovery of biophenols onto graphene oxide nanosheets: Valorisation of a biomass

Zeynep İlbay, Aydın Haşimoğlu, Oğuz Kaan Özdemir, Fadime Gedik, Selin Şahin PII: DOI: Reference:

S0167-7322(17)33490-6 doi: 10.1016/j.molliq.2017.09.046 MOLLIQ 7883

To appear in:

Journal of Molecular Liquids

Received date: Revised date: Accepted date:

2 August 2017 11 September 2017 13 September 2017

Please cite this article as: Zeynep İlbay, Aydın Haşimoğlu, Oğuz Kaan Özdemir, Fadime Gedik, Selin Şahin , Highly efficient recovery of biophenols onto graphene oxide nanosheets: Valorisation of a biomass, Journal of Molecular Liquids (2017), doi: 10.1016/ j.molliq.2017.09.046

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Highly Efficient Recovery of Biophenols onto Graphene Oxide Nanosheets: Valorisation of A Biomass Zeynep İlbaya, Aydın Haşimoğlub,c, Oğuz Kaan Özdemird, Fadime Gedikeand Selin Şahine Uşak University, Engineering Faculty, Department of Chemical Engineering, 64200,

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Institute of Nanotechnology, Gebze Technical University, 41400, Gebze, Kocaeli,

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Uşak, Turkey.

c

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Turkey.

Material Science and Engineering Department, Engineering Faculty, Gebze Technical

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University, 41400, Gebze, Kocaeli, Turkey.

Department of Metallurgical and Material Engineering, Yıldız Technical University,

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Istanbul, Turkey.

Istanbul University, Engineering Faculty, Department of Chemical Engineering, 34320

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Avcılar, Istanbul, Turkey.

Corresponding Author: Zeynep İlbay 1

Permanant Address: Uşak University, Engineering Faculty, Department of Chemical

Engineering, 64200, Uşak, Turkey E-mail:[email protected]

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ABSTRACT In this study, graphene oxide (GO) nanosheets were evaluated for the recovery of biophenols from an agricultural biomass, olive leaf. Modified Hummer's method was used to synthesize GO by natural oxidation of graphite. The adsorbent was

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characterized by several novel analysis methods such as Fourier transform infrared

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spectroscopy (FTIR), scanning electron microscopy (SEM) and X-ray diffraction (XRD). On the other hand, optimization of the adsorption process was applied utilizing

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multivariate statistic technique such as Response Surface Methodology (RSM) in order

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to consider any possible interaction between variables with less number of experiments as well as to model a response affected by several variables. The outcome of the

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present study indicates that the optimum conditions for the adsorption of were 4.57/10 of pH together with 24.62/30 °C of temperature and 3 mg of GO to achieve the

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maximum yields of each dependent variable such as total biophenol content (TBC) and

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the most prevalent compound, oleuropein (OC). The verification of the calculated

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models was held by several error function analysis.

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Keywords: Biomass valorization, graphene oxide, oleuropein, adsorption, optimization.

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1.

Introduction

Hydrophilic biophenols of plant materials are a great value since they have been proved to have so many therapeutical effects such as antioxidative, antimicrobial, antiviral, antiatherogenic, and cardioprotective, antihypertensive and anti-inflammatory [1,2].

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Therefore, recovery of those natural materials rich in biophenols is significant for food,

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pharmaceutic and cosmetic industries. Recovery of these high-added value compounds through adsorption processes have been widely applied by adsorbents such as activated

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carbons [3–5], chitin and chitosan [6,7], molecularly imprinted polymers [8–10] and

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macroporous resins [11–14]. Recently, carbonaceous materials such as porous carbon, carbon nanotubes and graphene oxide other than activated carbon have gathered great

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interest owing to their high surface area and pore size, ease of functionalization and chemically stable structure [15]. Hence, it is extremely important to try to find new

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carbon adsorbents with high adsorption capacity, rapid adsorption, and specific surface

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reactivity. Graphene oxide (GO) is a two dimensional (2D), carbonaceous, conjugated aromatic ring structure which has a huge amount of oxygenated functional groups on its

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surface such as epoxy, hydroxyl and carboxylic acid groups as seen in Fig 1[16]. These groups make graphene oxide layer highly hydrophilic and allow graphene oxide to

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disperse easily in polar solvents such as water, ethanol, and methanol. Furthermore, surface groups acting as binding sites for adsorbed species is also a well known fact. Specifically, phenolic structures might be bounded by π-π interactions [17,18]. All these properties and interactions make GO an ideal material for adsorption studies. It has begun to be used in adsorption of heavy metal ions [19], stain materials [20,21] and antibiotics [22] lately.

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Archeological investigations have proved that societies in Mediterranean regions have been using up olive and its byproducts since the copper age [23]. These products have been consumed as cosmetic and pharmacologic materials due to their biophenol content which shows antioxidant, anti-inflammatory, anti-cancer, antimicrobial, antiviral, anti-

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atherogenic, hypoglycemic, hepatic-,cardiac- and neuro-protective effects [24–26]. Among the biophenols in olive, oleuropein is the most prevalent and famous phenolic

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component and its percentage may vary up to 14% in olive leaf [23]. Therefore, olive

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leaf has been valorized as biophenol resource in the present study. Studies are really scarce in olive leaves with the exception of flavonoid and oleuropein enrichment by the

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adsorption on several resins [11]. Additionally, Bayçin et al. [27] and Altıok et al. [28]

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used silk fibroin as a novel adsorbent in order to adsorb biophenols (oleuropein and rutin) from olive leaf extracts. Lately, a molecularly imprinted polymer was applied to

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recover oleuropein from the crude extract of olive leaves [29]. To the best of our

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knowledge, graphene oxide has not been evaluated for the adsorption of biophenols from olive leaf. On the other hand, the experimental design also ensured to consider any possible interaction between variables with less number of experiments as well as to

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model a response affected by several variables and to optimize the responses (TBC and

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OC).

2. Experimental 2.1. Materials

Oleuropein (<98%), gallic acid and Folin & Ciocalteu's phenol reagent were purchased from Sigma Aldrich (St. Louis, MO, USA). Additionally, graphite, sodium nitrate and potassium permanganate were also purchased from Sigma Aldrich in order to synthesize 4

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graphene oxide. Concentrated H2SO4, 30% hydrogen peroxide, HCI and NaOH were supplied from Merck (Darmstadt, Germany). All chemicals were analytical reagentgrade and used without further purification. 0.1 N NaOH and 0.1 N HCl were used for adjusting pH levels of stock solutions. Bidistilled water was used for all experiments.

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Leaf samples were collected from Ayvalik (Edremit) cultivar growing in Agean part of

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Turkey during harvesting time of olives (November, 2016). Olive leaves were dried in the ambient conditions for four days. The mass losses of fresh leaves were measured as

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approximately 16 % (w/w). After drying process, the leaf samples were crushed into

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particles (nearly 1 mm of diameter).”

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2.2. Preparation of GO

The GO nanosheets were produced from natural graphite flakes by using modified

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Hummer’s method. Typically, graphite flakes (3 g) and sodium nitrate (1.2 g) were

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added into a 500 ml flask and then concentrated sulfuric acid (H2SO4, 150 mL) was slowly poured into the flask, while stirring in an ice bath at 1 °C for almost 30 min. Finally, potassium permanganate (KMnO4, 12 g) was added carefully into the above

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mixture since it is a very strong oxidizing agent. The flask was removed from ice bath

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and stirred for 90 min at 35 °C. Distilled deionized water was poured drop by drop, and the mixture was kept at 98 °C for 10 min. After further stirring for another 30 min, the reaction mixture diluted with more deionized water (300 mL), and the reaction was completed with the addition of 30% hydrogen peroxide (20 mL). The resultant mixture was centrifuged (Nüve, Turkey) at 800 rpm for 6 min to remove supernatants. The remaining solid material was washed with distilled water for 3 times. The filtered powder was dried in vacuum-oven (Nüve EV 018, Turkey) at 80 °C overnight. 5

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2.3. Characterization of GO 2.3.1. Fourier Transform Infrared Analysis Fourier

transform

infrared

(FTIR)

spectra

were

recorded

on

a

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Perkin Elmer Spectrum 100 FTIR spectrometer (Perkin Elmer Inc., Wellesley, MA).

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2.3.2. Scanning electron microscopy analysis

The scanning electron morphology of graphene oxide was conducted at scanning

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electron device (SEM-EDX, FEI Quanta FEG-450- The Netherlands) together with an

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operating voltage of 20 kV under high vacuum after coating the sample with gold film

2.3.3. X-ray diffraction analysis

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in order to avoid an electrostatic charge on the surface.

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X-ray diffraction (XRD) patterns were obtained by Rigaku D/max 2200 diffractometer

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(Japan) using Cu Ka irradiation (1.5406 A ° ) for structural characterization. 2.4. Preparation of olive leaf extract

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Homogenizer-assisted extraction was chosen as the extraction process. The same

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experimental operation was also used in published paper by [30] where several details of the method related to extraction conditions were given. 2.5. Analysis of TBC and OC The total biophenolic content in the olive leaf was measured according to the FolinCiocalteu method based on colorimetric oxidation/reduction reaction. TBCs of the extracts were determined according to Malik and Bradford [31]. Results are stated as mg of gallic acid equivalent per g of graphene oxide (mg-GAEg-1-GO). 6

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Oleuropein analyses were conducted on an Agilent 1260 chromatographic system (Agilent, Waldbronn, USA) equipped with quaternary pump, a degasser, manual injector and a diode-array- detector (DAD). Agilent Eclipse Plus C18 RRHD 18 column (3.0 mm × 5.0 mm id, 1.8 μm particle size) was used to separate the extracts. The

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column temperature was maintained at 40 °C. A gradient elution of (A) 0.1 % formic acid in H2O and (B) 0.1 % formic acid in acetonitrile. A gradient program was written

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according to the following profile: 0-14 min 0% B, 14-14.2 min 40% B, 14.2-17.2 min

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100% B, 17.4-20 min 0% B. Injection volume was 20μL and the detection wavelength

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was set at 276  nm, 4nm. Ref. off [32].

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2.6. Adsorption Studies

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Desired pH levels (4-10) of extract solution was adjusted by using 0.1 N NaOH and 0.1

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N HCl. Then, a certain amount of adsorbent (4-6 mg) was added to erlen mayer flasks. The extract solution put into erlen mayer and mixed via shaker at a speed of 150 rpm.

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After adsorption process, samples were taken from the flask for TBC and OC analysis of the solution. All experiments were repeated at three times for calculating standard

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deviations.

The biophenols and oleuropein uptake loading capacity (mg/g) of graphene oxide adsorbent at equilibrium was determined with Eq. (1)

qe=

Ci -Ce ×V M

(1)

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where Ci and Ce are initial and equilibrium concentration for oleuropein and total biophenols, V is the volume of the solution in mL, and M is the mass of the dry adsorbent (g).

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2.7. Experimental Design In this study, face-centered central composite design (FCCD) was applied to design of

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experiments due to its efficient and flexible properties [33]. For modeling and

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optimizing adsorption process, RSM was used.

The optimization of oleuropein and total biophenols capacity of the adsorbent was

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conducted with three chosen independent variables (pH, adsorption temperature and the

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amount of adsorbent). Values of the independent factors and their coded levels with their symbols employed in RSM were displayed in Table 1.

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Twenty experiments were calculated for six replicated central points in Eq. (2) [34]. (2)

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N=2k +2k+n c

where k is the number of factors; nc used for predicting the residual error is the

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replicates in central points.

For estimating the optimal conditions, the quadratic equation model was expressed with

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Eq. (3)

k

k

i=1

i=1

k-1

k

Y=β0 + βi x i + βii x i2 +  βij x i x j +ε

(3)

i=1 j=i+1

where Y is the response;  0 is the constant coefficient,  i ,  ii ,  ij are coefficients for the linear, quadratic and interaction effect; xi and xj are factors; ε is error.

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2.8. Statistical Analysis Three replicate experiments were conducted with each of the samples followed by three spectrophotometric measurements from each sample. The analysis of variance (ANOVA) test was used to identify the interaction between the independent factors and

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the dependent factors via Design Expert Version 9.0.6 (Stat Ease, USA). The statistical indicators such as correlation coefficients (adjusted correlation coefficient (R2adj) and

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predictive correlation coefficient (R2pred)), adequate precision and also coefficient

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variation were calculated to get the best model.

Error function analysis was also performed to verify the models for the adsorption

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system. The list of error functions used in this study is given in Table 2 with their

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formulas [35].

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3.1. FT-IR Analysis of GO

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3. Results and Discussions

Fig 2 demonstrates the FT-IR spectra of the adsorbent material (GO) used in this study. The broad and intense absorption band centered 3300 cm-1 is originated due to

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stretching mode of O-H bond. One reason for peak broadening (band formation) was

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the O-H groups which were bonded to different sites of carbon sheets (carbon skeleton). O-H groups at different sites have different O-H vibration frequencies ranging in a wide interval and leads band formation. Another reason is the water molecules between graphene oxide layers. Peaks at 2921cm-1 and 2852 cm-1 can be attributed to – CH2 – . The strong peaks at 1719 cm-1 and 1606 cm-1 are because of the stretching vibrations of C=O (Carbonyl / Carboxyl), respectively. Finally, absorption peak at 1159 cm-1 is

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related with C-OH and C-O-C and 1044 cm-1 is originated from epoxide and C-O vibrations.

3.2. Surface Morphology of GO

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As seen in Fig.A.3, a surface formed by several crumpled, thin layers was observed. These layers were arranged irregularly and showed characteristic stacking. It had a

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wrinkled structure with multiple folds. Randomly wrinkled layers of graphene oxide

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structure were much clear in Fig.B.3. This wrinkled form can be explained by the insertion of oxygenated functional groups such as

hydroxyl, epoxy, and carbonyl

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groups. The addition of all these groups changed the hybridization of carbon sp2 (planar

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structure) to sp3 (tetrahedral structure). The interplanar spacing between the graphite

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3.3. XRD Analysis of GO

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layers increased with chemical oxidation.

XRD technique is widely used to characterize the carbonaceous structures such as active carbon, carbon nanotubes and graphene. XRD diffraction patterns of graphite and

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graphene oxide are shown in Fig. 4. Characteristic X-ray diffraction peak of graphite

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that is originated from (002) plane was appeared at about 27.0° (2Ɵ). This peak is the indication of highly ordered graphite structure. When graphene was oxidized using Hummers method, the interplanar distance was expanded due to the intercalation of oxygenated groups such as carboxylic acid, hydroxyl groups, water molecules and etc. After destruction of the highly ordered hexagonal structure of graphite, sharp characteristic peak (about 27°) was disappeared. A new slightly broader at 10.26° was formed. According to XRD data, interplanar distance for graphite and graphene oxide 10

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were calculated as 3.30 Å and 8.59 Å, respectively. Complete disappearance of hexagonal structure of graphite, the formation of new peak at 10.29° (2Ɵ) and the increase of interplanar distance from 3.30 Å to 8.59 Å indicate successful exfoliation and oxidation of graphene sheets. It is clear that FT-IR data is in correspondence with

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XRD data, which proves the complete oxidation of graphite. It can be concluded that the oxidation of the graphite was entirely completed by modified Hummer’s method

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depending on the FTIR and XRD data.

3.4. Development of Regression Model Equation

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Table (3) indicates the effects of pH (4-10), adsorption temperature (20-30 oC) and the

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amount of adsorbent (3-5 mg) on adsorption capacity (qe) for the recovery of oleuropein and total biophenols.

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The quadratic models for the removal of total biophenols and oleuropein were

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recommended by Design Expert program. The final equations in terms of actual factors are given in Eqs.(4) and (5), respectively.

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TBC (mg/g)=145.95-17.59X1 +31.14X 2 -131.78X3 +1.47X1X 2 +0.50X 2X3 +

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+0.81X12 -0.66X 22 +8.99X32

Oleuropein (mg/g) =10540.99-1822.47X1 -42.77X2 -783.46X3 +8.98X1X2 +131.67X12

Eq. (4)

Eq. (5)

ANOVA of the quadratic models for both total biophenols and oleuropein were given in Tables 4-5. The probability (p) of these models had lower value (p<0.05). This value indicates that there is a relationship between independent and dependent factors. Moreover, correlations coefficients (R2, R2adj and R2pred) were found as 0.9893, 0.9816 11

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and 0.9470; 0.9717, 0.9616 and 0.9175 for adsorbed TBC and OC, respectively. Another important statistical indication is that the model F-value assessing whether the model is crucial. Higher F-value is always desirable for model. The Model F-values were calculated as 127.50 and 96.25 in terms of adsorbed TBC and OC, respectively. If

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the effects of the factors on adsorption capacity is considered, the amount of adsorbent was found to be the strongest factor due to the highest F value regarding TBC.

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Additionally, square of temperature and binary interaction of pH and amount of

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adsorbent were also found influential (table 4). With respect to OC, adsorbent amount was the most effective factor among all the independent factors. Besides, square of pH

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was also found to be an effective factor (Table 5). Lower coefficient of variation (CV) is

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a favorable statistical indicator since this value (<10%) showed higher reproducibility of a model [36]. CV of these two models was computed as 2.82 and 9.00 for the adsorbed

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TBC and OC. Another indicator is adequate precision (AP) measuring the signal to

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noise. The values of AP were got to be 35.033 and 34.370 for TBC and OC, respectively. A ratio bigger than 4 for AP is desirable so these two model can be applied

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to navigate the design space [37].

3.5. Results for Error functions Analysis

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In this study, eleven descriptive performance indices corresponding to the testing data of each model were calculated to verify the model for the batch experiments. These eleven error functions are showed in Table (6). Variance account for (VAF) of 98.93 and 96.20 were obtained for TBC and OC models. That means that model data accounts for the experimental data adequately. The higher VAF value, the closer the model data to the experimental data [38]. On the other hand, it is desirable that root mean square error (RMSE) and mean absolute percentage error (MAPE) should be close to zero contrary to 12

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VAF [39]. RMSE values were found to be 2.83, 188.73, whereas models have 1.79 and 8.80 of MAPE for TBC and OC. An acceptable amount of correlation coefficient was obtained for OC although its RMSE value was relatively high. 3.6. Effects of Process Parameters on the Responses and Optimization

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Three-dimensional response surface with contour plots were drawn related to the Eqs. 4

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and 5 to assess the synergetic interaction between the response and the factors. Fig. A.5

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displays the influence of interaction between the adsorbent amount and pH of the media on the adsorbed TBC onto GO. The relationship between the amount of adsorbent and

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adsorption temperature on adsorbed TBC is indicated in Fig B.5.

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It can be clearly figured out that adsorbed TBC decreased with the amount of adsorbent (Figs.A.5 and B.5). Number of available active sites and surface area are enhanced by

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increasing adsorbent dosage up to a point [40]. Although recovery percentage increased

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with a certain amount of adsorbent, adsorption capacity was affected inversely. The optimal amount of adsorbent for adsorbent TBC was found to be 3 mg. Optimal pH of the media was detected at pH 4.57 for TBC adsorption. The drop of adsorbed TBC was

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only 3% when the pH of the solution ranged from 4.57 to 9. Actually, a sharp effect on

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the adsorption of TBC onto GO was not observed as for pH (Fig.A.5). Three different temperature values were investigated (20, 25 and 30 °C ) on adsorbed TBC and OC due to the fact that structure of antioxidant was altered above 40 °C [41]. 24.62 °C of adsorption temperature was calculated as the optimal condition. There was no significant increase on adsorbed TBC. In adsorption processes, lower temperature values favor the industrial scale-up owing to economic and applicability concerns. When the temperature ascended up to 24.62 °C, adsorption rate increased. This might 13

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be attributable to the incensement of diffusion coefficient. However, there was no certain phenomenon for the extension of increase in adsorption rate with raising temperature (Fig.B.5). Even so, it can be concluded that this process is endothermic. As seen in Fig. 6, it was observed that the impact of pH on adsorbed OC was higher

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than temperature. Optimal conditions were found to be 10 of pH, 30 °C of temperature and 3 mg of adsorbent. The adsorbed OC under the given optimal conditions was gained

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as 4543.99 mg per gram of GO.

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Interaction between graphene oxide and oleuropein is a complex phenomenon due to the complex structure of oleuropein and highly oxygenated graphene oxide. Both

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oleuropein and graphene oxide have many hydroxyl groups and graphene oxide have

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additional oxygen containing functional groups such as epoxy and carboxylic acid groups. All these groups can form strong hydrogen bonds. What’s more, oleuropein

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whose structure contained the electron-rich aromatic ring can form strong π- π bonds

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with GO.

A comparative literature search was also conducted as seen in Table 7. The finding of the present study shows that GO has proved to be superior over the other adsorbents.

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Moreover, batch experiments reached to the equilibrium in a lesser time because of the

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advantageous properties of graphene oxide (Table 7).

4. Conclusion The output of the present study suggests that graphene oxide should be considered as a highly effective adsorbent for the recovery of biophenols from a cheap, renewable and abundant biomass. The recovery of oleuropein, considered to be the most valuable ingredient of the olive leaf, has been found to be ≈11 to 277 times higher comparing to 14

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those of other studies on oleuropein recovery. Considering the issues such as shorter processing time and cost (lower adsorbent dosage), the concerned adsorbent can be a reasonable selection for scaling the process up owing to its high surface area and pore size, ease of functionalization and chemically stable structure. Besides, the estimated

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data through RSM indicates consistency with the experimental data which permits its application, modeling, and optimization of the adsorption of biophenols onto GO

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depending on the error function analysis.

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Formatting of Funding

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commercial, or not-for-profit sectors.

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This research did not receive any specific grant from funding agencies in the public,

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Nomenclature AP Adequate precision β0 constant coefficient C-CNT Multi-walled carbon nanotube CV Coefficient of variation Ce final concentration in the solution (mg/L) Ci initial concentration in the solution (mg/L) k the number of factors nc the replicates in central points M the mass of the dry adsorbent (g) N the number of the experiments qe,i exp adsorption capacity of the experiment i qe,i cal calculated adsorption capacity value of the i OC Oleuropein content PS Polystrene R2adj adjustment of correlation coefficient R2pred predicted of correlation coefficient TBC total biophenol content X1 pH of the solution X2 Temperature (oC) X3 Amount of adsorbent (mg) V the volume of the solution (mL) Y response y experimental data y' model data

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[24] F. Visioli, C. Galli, Biological Properties of Olive Oil Phytochemicals, Crit. Rev. Food Sci. Nutr. 42 (2002) 209–221. doi:10.1080/10408690290825529. [25] S. Cicerale, X.A. Conlan, A.J. Sinclair, R.S.J. Keast, Chemistry and Health of Olive Oil Phenolics, Crit. Rev. Food Sci. Nutr. 49 (2008) 218–236. doi:10.1080/10408390701856223.

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[26] S. Cicerale, L. Lucas, R. Keast, Biological Activities of Phenolic Compounds Present in Virgin Olive Oil, Int. J. Mol. Sci. 11 (2010) 458–479. doi:10.3390/ijms11020458.

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[28] E. Altıok, D. Bayçın, O. Bayraktar, S. Ülkü, Isolation of polyphenols from the extracts of olive leaves (Olea europaea L.) by adsorption on silk fibroin, Sep. Purif. Technol. 62 (2008) 342–348. doi:10.1016/j.seppur.2008.01.022.

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[29] A.A. Özcan, Ş. Demirli, Molecular Imprinted Solid-Phase Extraction System for the Selective Separation of Oleuropein from Olive Leaf, Sep. Sci. Technol. 49 (2014) 74–80. doi:10.1080/01496395.2013.814678.

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[36] R.B. Bendel, S.S. Higgins, J.E. Teberg, D.A. Pyke, Oecologia and Gini coefficient as inequality measures within populations *, (1989) 394–400. [37] Q. Khalil, V. Sahai, R. Gupta, Statistical media optimization and alkaline protease production from Bacillus moja v ensis in a bioreactor, 39 (2003). doi:10.1016/S0032-9592(03)00064-5. [38] Z. Rahamneh, S. Aljahdali, Forecasting Stock Exchange Using Soft Computing Techniques, (n.d.).

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[41] O.G.U.Z.B. Ayraktar, Adsorption of Olive Leaf ( Olea europaea L .) Antioxidants on, (2007).

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[42] Y. Liu, Y. Liu, J. Zhang, X. Wu, J. Wei, D. Pei, Adsorption behaviors for oleuropein from olive leaves extracts by porous materials with carbon nanotubes, (2015) 2395–2404. doi:10.1007/s00396-015-3643-3.

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Tables Table 1. Values of the independent factors and their coded levels with their symbols employed in RSM. Coded Levels (0) 7 30 4

Factors

(+1) 10 35 5

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(-1) 4 25 3

pH (X1) Adsorption Temperature (°C, X2) Amount of adsorbent (mg, X3)

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Table 2. Descriptive performance indices.

Error Functions Root mean square error

1 N  ( y  y ' )2 N i1

RMSE 

 var( y  y ' )  VAF  1  x100 var( y)  

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The variance accounted for

1 N y  y' MAPE   x100 N i1 y

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Mean absolute percentage error

Mean absolute error

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Mean squared error

MSE 

1 N  ( y '  y )2 N i1 N

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The correlation coefficient

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Non-linear chi-square

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The sum of square of the error

The sum of the absolute error

 ( y  y) '

R2  1 

i 1 N

(y  y '

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The coefficient of determination

Average relative error

1 N '  y y N i1

MAE 

i 1

m

)

R  Mean ( y  mean( y))( y '  mean( y ' )  / ( y)( y ' )) N

2   i 1

( y '  y)2 y

N

SSE   ( y  y ' )2 i 1

N

SAE   y '  y i 1

ARE 

1 N y'  y  N i1 y

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Table 3. Face-centered central composite design results for the adsorption of TBC and OC onto graphene oxide.

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4 10 4 10 4 10 4 10 4 10 7 7 7 7 7 7 7 7 7 7

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18 15 14 20 6 13 9 11 3 4 8 1 19 7 17 2 10 16 5 12

Adsorption Amount of Adsorbed TBC Adsorbed OC o Temperature( C) Adsorbent(mg) (mg/g) (mg/g) 20 3 184.76 3053.79 20 3 166.07 4292.00 30 3 173.26 2535.65 30 3 168.46 4669.19 20 5 91.42 1324.80 20 5 99.67 2447.10 30 5 99.16 1499.70 30 5 102.74 2804.34 25 4 140.20 1850.42 25 4 139.39 3414.02 20 4 114.80 1265.27 30 4 117.22 1878.98 25 3 177.50 2249.49 25 5 105.54 889.56 25 4 135.29 1500.00 25 4 140.03 1859.16 25 4 137.27 1455.51 25 4 138.94 1680.00 25 4 137.85 1575.42 25 4 133.32 1687.42

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pH

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Table 4. ANOVA for the quadratic equations of Design Expert 9.0.6 for the adsorption of TBC onto graphene oxide.

30.17 14997.28

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significant

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Pure Error Cor. Total

p-value Prob>F <0.0001 0.3232 0.7387 <0.0001 0.0074 0.0920 0.0091 <0.0001 0.0024

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8 1 1 1 1 1 1 1 1 11 6

Mean F Value Square 1854.66 127.50 15.56 1.07 1.70 0.12 13801.50 948.78 155.85 10.71 49.56 3.41 144.92 9.96 750.72 51.61 222.06 15.27 14.55 21.64 3.59

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df

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Model X1 X2 X3 X1X2 X2X3 X12 X22 X32 Residual Lack of fit

Sum of Squares 14837.27 15.56 1.70 13801.50 155.85 49.56 144.92 750.72 222.06 160.01 129.84

0.0912

Not significant

6.03

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Table 5. ANOVA for the quadratic equations of Design Expert 9.0.6 for the adsorption of OC onto graphene oxide.

Pure Error Cor. Total

1.086E+5 1.937E+7

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F Value 96.25 138.56 2.58 156.91 3.71 179.49

p-value Prob>F < 0.0001 < 0.0001 0.1304 < 0.0001 0.0746 < 0.0001

significant

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5 1 1 1 1 1 14 9

Mean Square 3.765E+6 5.420E+6 1.010E+7 6.138E+6 1.452E+5 7.021E+6 39118.40 48789.28

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df

2.25

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Model X1 X2 X3 X1X2 X12 Residual Lack of Fit

Sum of Squares 1.883E+7 5.420E+6 1.010E+7 6.138E+6 1.452E+5 7.021E+6 5.477E+5 4.391E+5

0.1930

Not significant

21710.83

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Table 6. Descriptive performance indices corresponding to the testing data of each model.

TBC

OC

Root mean square error

2.83

188.73

The variance accounted for

98.93

Mean absolute percentage error

1.79

Mean absolute error

2.50

Mean squared error

8.00

The coefficient of determination

0.9945

The correlation coefficient

0.9893

Non-linear chi-square

1.0962

410.87

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Explanations

96.20

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8.81

35620.16 0.9811 0.9626

The sum of square of the error

160.01

712403.2

The sum of the absolute error

50.01

3283.18

Average relative error

0.02

0.09

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164.16

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Table 7. Summary of the studies on the oleuropein adsorption. Adsorbent

Operating Conditions C0=3.5 mg/mL, 25 oC, 100 rpm, 540 min, 1 g adsorbent

PS

Adsorption Capacity qe,exp (mg/g) 16.38

[42]

20.77 44.77

LSA-21

C0=0.1 mg/mL, 25 oC, 180 min, pH:8, 3 g adsorbent 25 oC, static adsorption,0.2 g adsorbent 25 oC, 250 rpm, 30 h, 0.025 solid/liquid

24.31 28.81 58.13

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C0=5 mg/mL, 25 oC, 100 rpm, 540 min, 1 g adsorbent

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PS PS/C-CNT-1 PS/C-CNT-2

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PS/C-CNT-1 PS/C-CNT-2

Reference

Graphene oxide

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Powdered silk fibroin Powdered silk fibroin

2 mg/mL, 100 rpm, 120 min, 3 mg adsorbent

420.02

[11]

96.41

[28]

108

[41]

4543.99

[Present study]

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*PS:Polystryene., LSA-21: Semi-polar commercial resin, C-CNT: carboxyl-modified multi-walled carbon nanotube.

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Figure Captions Fig.1. Molecular structure of graphene oxide. Fig.2. FT-IR spectra of graphene oxide. Fig.A.3: SEM images of graphene oxide 50 µm scale, Fig.B.3: 10 µm scale. Fig. 4: XRD for graphite and graphene oxide.

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Fig.A.5: 3-D graphs for adsorbed TBC (A) as a function of amount of adsorbent to pH

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(temperature 23.356 oC), Fig.B.5:as a function of amount of adsorbent to temperature

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(pH 4.9).

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Fig 6: 3-D graphs for adsorbed OC as a function of temperature to pH (amount of adsorbent: 3 mg).

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Fig.1: Molecular structure of graphene oxide.

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Fig.2: FT-IR spectra of graphene oxide

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(A)

(B) Fig.A.3: SEM images of graphene oxide 50 µm scale, Fig.B.3: 10 µm scale

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Fig. 4: XRD for graphite and graphene oxide

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A

B Fig.A.5: 3-D graphs for adsorbed TBC (A) as a function of amount of adsorbent to pH (temperature 23.356 oC), Fig.B.5:as a function of amount of adsorbent to temperature (pH 4.9). 33

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Fig 6: 3-D graphs for adsorbed OC as a function of temperature to pH (amount of adsorbent: 3 mg).

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Graphical abstract

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Highlights

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Modified Hummer's method was used to synthesize graphene oxide. GO were evaluated for the recovery of biophenols from olive leaf. RSM was used to optimize adsorption process. The verification of calculated models was held by several error function analysis.

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1. 2. 3. 4.

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