Anal. Method Environ. Chem. J. 3 (3) (2020) 25-31
Magnetic bentonite nanocomposite for removal of
amoxicillin from wastewater samples using response surface
methodology before determination by high performance
liquid chromatography
Mohammad Reza Rezaei Kahkha
a,*
, Ali Faghihi Zarandi
b
, Nahid Shaghi
a
, Saeedeh Kosari
a
and Batool Rezaei Kahkha
a
a
Department of Environmental Health Engineering, Faculty of Health, Zabol University of Medical Sciences, Zabol.Iran.
b
Department of Occupational Health Engineering, Faculty of Health, Kerman University of Medical Sciences, Kerman. Iran.
ABSTRACT
In this study, feasibility of magnetic bentonite nanocomposite for
removal of amoxicillin from wastewater samples was evaluated
by high performance liquid chromatography (HPLC). Magnetic
bentonite synthesized by co-precipitation of bentonite and Fe
3
O
4
and used for removal of amoxicillin from water samples. Response
surface methodology on central composition design (CCD) was
applied for designing of experiment and building of model. Three
factors including pH, adsorbent dose and temperature were studied
and used for quadratic equation model to prediction of optimal points.
By solving the equation and considering regression coefcient (R
2
=0.98). The optimal points of main parameters were obtained as a pH
of 4.68, the adsorbent dosage of 1.50 g and the temperature of 48.9
0
C.
Results showed that three factor are signicant on removal efciency
and experimental data are in good agreement with predicted data.
Proposed methods were used to analysis of amoxicillin in three real
samples.
Keywords:
High performance liquid
chromatography,
Amoxicillin removal,
Response surface methodology (RSM),
Central composition design,
Magnetic bentonite nanocomposite
ARTICLE INFO:
Received 11 Jun 2020
Revised form 5 Aug 2020
Accepted 28 Aug 2020
Available online 29 Sep 2020
* Corresponding Author: Mohammad Reza Rezaei Kahkha
Email: m.r.rezaei.k@gmail.com
https://doi.org/10.24200/amecj.v3.i03.108
------------------------
1. Introduction
Nowadays, antibiotic residual is a serious concern
for many of environmental researchers. Because
of insufcient ability of conventional sewage
treatment, some antibiotics such as ampicillin,
erythromycin, tetracycline and penicillin are not
even removed in sewage treatment processes and
cause many environmental hazards [1]. Recent
studies have shown that some antibiotics have toxic
effects on the life of microorganisms and, over the
long term, have undesirable effects on ecological
sustainability [2]. Amoxicillin (C
16
H
19
N
3
O
5
S
3
) is a
β- lactam antibiotic with a molecular weight of 365
gram per mole is used to treat bacterial infections
[3]. The concentration of this type of antibiotics
groups in surface waters is 48 ng L
-1
and in the
hospital sewage between 28 -82 mg L
-1
have been
reported. In many pharmaceutical efuent output
higher concentration of these drugs can also be
found. Several methods such as; the ozonation [4],
Research Article, Issue 3
Analytical Methods in Environmental Chemistry Journal
Journal home page: www.amecj.com/ir
AMECJ
26
Anal. Method Environ. Chem. J. 3 (3) (2020) 25-31
the Fenton process [5], electrochemical methods
[6], the nano ltration [7] and the adsorption
process [8] were applied for removal of antibiotics
from aqueous environments samples. Absorption
is one of the most effective methods to removal
of antibiotics compounds from water and sewage
even at low concentrations (less than 1 mg L
-1
).
Adsorption is very simple and low cost method
in comparison to other techniques that applied
for removal of common pollutants from aqueous
samples [9]. Natural clay compounds are one of
the best adsorbents to removal of contaminants
from air and water samples. This ability obtained
from their high surface area, the porous structure,
the chemical stability, and their layered structure.
Bentonite is a natural clay that used as an adsorbent
to removal of pollutants from water and wastewater
samples. Response surface methodology (RSM)
is appropriate technique that used in many elds
[10]. The main objective of RSM is to determine
optimum operating conditions for the system or
designated area of the practical satisfaction [11].
Experimental data points were obtained during our
optimization and used to build a model for CCD
which was ideal for sequential testing and allows
the right amount of information to test the lack
of t a large number of unusual design points.
In this study, removal of amoxicillin by nano-
composite made of multi-walled carbon nanotubes
and iron nanoparticles were studied. Design of
Experiments were conducted using the RSM as
well as factors affecting on absorption process
of amoxicillin such as pH, amount of adsorbent,
and the temperature were optimized. Finally, the
data obtained from experiments compared with
model output to optimize and predict the results.
The concentration of amoxicillin determined by
high performance liquid chromatography. HPLC
is simple, accurate and precise technique that used
for separation, identication and analysis of drugs.
It can be successfully and efciently adopted for
routine quality control analysis of drugs in bulk and
pharmaceutical dosage form. It can also be used
in combination with other analytical methods to
further elucidate the components of mixtures.
2. Material and methods
2.1. Apparatus and reagent
The measurement of amoxicillin was performed
using high performance liquid chromatography
accessory (CECIL Corporation, HPLC, England)
equipped ACE C
18
column and UV-VIS detector at
230 nm. The mobile phase is ACN: water (60:40).
Analytical grade of different reagents such as; HCl
and NaOH were purchased from Merck (Darmstadt,
Germany). The amoxicillin was prepared from
Aldrich chemical Co. HPLC grade of acetonitrile
and water purchased from Sharloa (Espain).
Bentonite clay was purchased and from Merck
(Darmstadt, Germany) and used for further work.
The bentonite samples were powdered and sieved
by 80-mesh sieve and washed with double distilled
water (DDW) for 4 times before using by procedure.
2.2. Synthesize of adsorbent
Synthesize of magnetic bentonite are performed by
co-precipitation methods by Hashem et al. First, 20 g
of bentonite was added into 100 ml of distilled water
containing FeCl
2
(0.02 mol L
-1
) and FeCl
3
(0.04 mol
L
-1
). The pH of solution was set around 10 by adding
NH
4
OH buffer solution (1 mol L
-1
) and stirred for 30
min at 300 rpm. Next, 40 ml of HNO
3
solution (2 M)
was added with stirring for 5 minutes and then 60
ml of Fe(NO
3
)
3
solution (0.35M ) was added to the
previous solution and solution boiled for one hour.
After settling suspension, the residual was ltered
and solid of magnetic bentonite was separated by
washing of DDW for 3-5 times. Finally, the product
was heated in an oven at 80ºC for 24 h [12].
2.3. Removal Procedure
Experiments were performed with the central
composite design (CCD) methodology. A standard
solution of 1000 mg L
- 1
amoxicillin was prepared
by dissolving of 1 g of amoxicillin in 1 liter of
deionized water. All standard working prepared
from this solution. Experiments were performed at
a batch reactor in 500 ml beaker that containing of
50 ml of amoxicillin concentration and the solution
was shaked for the 30 minutes in incubation shaker
at 200rpm by controlling of temperature. The pH
27
Magnetic bentonite nanocomposite for removal of amoxicillin Mohammad Reza Rezaei Kahkha et al
of the solution was adjusted by adding 0.1 M of
NaOH and HCl. After completion of experiments,
magnetic nanocomposite was removed by an
external magnet and remaining amoxicillin was
measured. The removal percentage of Amoxicillin
(% removal) was calculated as Equation 1:
(Eq. 1)
Where Ce and C
0
are initial and nal Amoxicillin
concentration (mg l
-1
) in solution, respectively.
2.4. Experimental design
A central composition design (CCD) was carried
out in this research for evaluate the variables for
adsorption of amoxicillin from aqueous solution
using in a batch reactor. The CCD method for
three variables (pH, amount of nanocomposite and
temperature), with two levels (the minimum and
maximum) was used as experimental design model.
In the experimental design model, the pH between
2-9, the adsorbent dosage from 0.5 g to 1.5 g and
the temperature between 20-60
o
C were employed
as the input variables. Percentage removal of
amoxicillin was the response of the system. Table.
1 showed the experimental design matrix that
obtained from CCD procedure. The amoxicillin
concentration determined by HPLC. The quadratic
equation model for predicting the optimal point of
adsorption processes was expressed by Equation 2.
(Eq.2)
Where Y is the response of the system and X
i
and
X
J
are the variables of action. R
2
is coefcient of
determination of polynomial model . The statistical
signicance was veried with adequate precision
ratio and by the F-test. Design expert (version 8)
program was used for regression and graphical
analysis. A total of 19 experiments were necessary
to estimate of the full model (Table 1)
Predicted Value(%)Actual Value (%)adsorbent( g)T(
o
c)pHRun
92.20981455.51
19.20201.0045.0011.392
92.20961.0045.005.503
60.2957.05065.009.004
92.20831.0045.005.505
92.20951.8445.005.506
91.64991.5065.002.007
77.97971.5025.002.008
92.20981.0045.005.509
100861.0011.365.5010
65.08830.5065.002.0011
100900.1645.005.5012
66.82701.50659.0013
74.58830.5025.009.0014
84.41970.5025.002.0015
100931.0078.645.5016
92.20921.0045.005.5017
48.14461.5025.009.0018
92.20901.0045.005.5019
Table 1. Central composite design matrix with experimental and predicted values
28
Anal. Method Environ. Chem. J. 3 (3) (2020) 25-31
3. Result and discussion
3.1. Regression model and statistical analysis
The CCD method has been successfully used for
optimizing affecting factor that inuenced on the
percentage of amoxicillin removal. For the best
response of system, the experimental results were
analyzed through RSM to obtain an empirical
model. The regression model equations (second-
order polynomial) relating the removal efciency of
amoxicillin and related parameters were developed
as Equation 3:
%Removal=+35.00926+7.71191×pH+
0.91252×T+23.48479×Dose-7.14286e
-
004
×pH × T+0.38571 × pH* × Dose+5.e
-3
× T × Dose-
0.68596 × pH
2
-0.013048 × T
2
-8.99753 × Dose
2
(Eq. 3)
The coefcients of Equation 3 are determined
by using software Design-Expert 8. The optimal
parameters are as follows: pH = 4.68, the adsorbent
dosage = 1.5 g, and the temperature = 48.90
o
C.
The model prediction of the amoxicillin removal
was obtained %99.48 while the experimental
amount of removal efciency achieved %99. These
results conrmed that the RSM could be effectively
applied to optimize the factors and parameters in
complex processes. The term of encoded factor
expressed relation between the independent
variables and dependent response of system.
Apart from the line are effects of the parameter
for the amoxicillin removal, the RSM also gives
an insight in to the quadratic and interaction effect
of the parameters. Table 2 showed the results of
regression analysis of obtained quadratic model.
The F-values and p-values are related to signicant
of each coefcient (Table 2). High magnitude of
the F-values and small magnitude of the p-values
indicated that corresponding coefcients was
more signicant. Also, Values of “probe> F” less
than 0.0500 implied high signicant regression
at 95 percent condence level. According to the
F-value and p-values, the amount of adsorbent was
found more effective on the adsorption process
of amoxicillin. The “Lack of Fit F-value” of 0.26
Mean P-value
Source sum of square df Square F Value Prob> F
Model 3120.63835 14 222.902739 148.517383 < 0.0001
A-pH 84.4231475 1 84.4231475 56.2501161 < 0.0001
B-adsorbent dose 537.787036 1 537.787036 358.320959 < 0.0001
C-temperature 0.66785645 1 0.66785645 0.44498463 0.5091
D-time 116.402257 1 116.402257 77.5574075 < 0.0001
AB 0.01125 1 0.01125 0.00749574 0.9315
AC 25.56125 1 25.56125 17.0311499 0.0002
AD 4.5 1 4.5 2.99829525 0.0922
BC 15.40125 1 15.40125 10.2616655 0.0029
BD 29.645 1 29.645 19.7521028 < 0.0001
CD 26.645 1 26.645 17.7532393 0.0002
A^2 1107.54922 1 1107.54922 737.946571 < 0.0001
B^2 225.03502 1 225.03502 149.938096 < 0.0001
C^2 745.166816 1 745.166816 496.495584 < 0.0001
D^2 897.389678 1 897.389678 597.919825 < 0.0001
Residual 52.52985 35 1.50085286
Lack of Fit 5.00385003 10 0.500385 0.26321645 0.9840
Pure rror 47.526 25 1.90104
Table 2. ANOVA analysis for removal of amoxicillin