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Ruthenium, rhodium, and iridium complexes featuring fluorenyl benzohydrazone derivatives: Synthesis and preliminary investigation of their anticancer and antibacterial activity
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OPEN
In-situ copper-based metal–organic
framework tailored clay synthesis
for efficient pharmaceutical
removal: Comprehensive
adsorption and optimization
studies
Elcin Tutus1, Nergiz Kanmaz2, Tugba Hayri-Senel1, Pelin Demircivi2, Gulhayat Nasun-Saygili1
& Nalan Erdol-Aydin1
The present study focuses on solvothermal in-situ synthesis of copper-based metal–organic framework
(Cu-MOF) supported montmorillonite (MMT) composites (CuMMT) with various Cu-MOF mass ratios
(5%, 10%, and 20%) and examine these composites as adsorbents for adsorption of tetracycline (TC)
antibiotic. CuMMT composites were characterized by performing FTIR, XRD, BET/N2, SEM, and zeta
potential analyses. The impacts of temperature, initial antibiotic concentration, contact time, and
solution pH on adsorption were investigated deeply. The pseudo-second-order and Elovich kinetic
models were consistent with the obtained kinetic results which proposed chemical interactions.
According to the studies, the monolayer Langmuir isotherm model fits the adsorption systems rather
well. TC adsorption constituted a spontaneous endothermic reaction. As a result of the adsorption
experiments, 10CuMMT composite showed the highest adsorption capacity as 319.57 mg g−1 at a
contact time of 240 min, pH 7.32 (natural pH of TC solution), at a temperature of 318 K. To enhance
the TC adsorption process, the Box–Behnken experimental design was used. The optimized conditions
(contact time = 200 min; solid/liquid ratio = 0.08 g L−1; temperature = 318 K) enhanced TC adsorption
capacity to 330.70 mg g−1.
Keywords Adsorption, Tetracycline, Montmorillonite, Cu-based metal–organic framework
Widespread industrialization, and uncontrolled growth in population have put more strain on available water
sources (surface/groundwater), reducing their quantity and quality1,2. Globalization and population growth have
not only led to an increase in waste, but pharmaceuticals and personal care products (PPCPs) have also recently
produced a number of emerging water contaminants, such as endocrine disruptors, pesticides, and herbicides2.
Pharmaceuticals are one type of PPCP that can be used to treat a variety of diseases. Wastewater has been shown
to include nearly every kind of pharmaceutical, the majority of which are persistent. Pharmaceutical compounds
(containing several groups such as anti-epileptic, antidepressant, antibiotic, hormone, anti-inflammatory, etc.)
are detected in surface waters, hospital sewage, and municipal sewage. Antibiotics in the environment appear to
be spreading increasing amounts and they include a growing range of compounds, and their concentrations in
some rivers across the world can be up to 300 times higher than “safe” levels1,3–5. The World Health Organization
(WHO) states that the main danger to food safety and global health is antibiotic resistance6. Since environmental
pollution reduces the effectiveness of antibiotics used to treat infections and diseases, more and more of these
diseases are becoming harder to treat1,3,4.
Antibiotic-removal from wastewater has been accomplished using a variety treatments like chlorination,
filtration, photocatalytic degradation, coagulation and sedimentation, adsorption, photo transformation,
ozonation, or processes of advanced oxidation3–11. Selecting an appropriate method for treating pharmaceutical
1Chemical Engineering Department, Chemical and Metallurgical Faculty, Istanbul Technical University, 34469,
MaslakIstanbul, Türkiye. 2Department of Chemical Engineering, Faculty of Engineering, Yalova University, 77200
Yalova, Türkiye. email: erdol@itu.edu.tr
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wastewater depends on factors such as wastewater characteristics, cost- effectiveness, practicality, environmental
compatibility, lack of sludge production, and the absence of hazardous secondary byproducts9,10 Among these,
adsorption method has been identified as superior to the others cited because of its simple application, effective
pollutant removal, and more rapid action; above all, it is easy to use in field conditions and in treatment
systems9–11
TCs are crucial antibiotics for human medication, animal disease-preventative medicine, and agricultural
feed supplements because of their wide range of activity, high efficacy, and cost-effectiveness. Over 75 percent of
TCs are excreted in an active state and discharged into the environment via human and animal urine and feces,
causing detrimental impacts on the ecological system and human health12.
TCs have long been challenging to efficiently remove from aqueous solutions due to their inherent
physicochemical properties, stable chemical structures, non-biodegradable characteristics, and low
concentrations in environmental matrices13. Numerous traditional techniques exist for the elimination of
TCs from wastewater, including biological treatment14, membrane separation15, electrochemical methods16,
photocatalysis17, ion exchange18, and advanced oxidation processes (AOP)19. Still, most of these old-fashioned
techniques need a lot of energy and have intricate, multi-step operating procedures. Adsorption has recently
shown itself to be a successful substitute in this area because of its straightforward and uncomplicated operation,
cost-effectiveness, environmental friendliness, and effective sustainability. Moreover, adsorption is frequently
recognized for its efficacy at low concentrations and does not generate detrimental by-products as secondary
pollutants throughout the process3,5,11,13.
Environmental remediation (e.g., treating air and water pollution), environmental decontamination (e.g.,
sterilization, disinfection, separation, etc.), and environmental substitution (to replace adsorbent materials with
high environmental load) are all uses for clay minerals. They may be successfully reconstructed through ion
exchange because of their complex and controllable structure, which offers a high economic value and a variety of
applications. In wastewater treatment, clay minerals have a special function as adsorbents20. MMT is a common
2:1 layered silicate clay mineral made up of two layers of silicon oxide tetrahedral layers and an interlayer of
aluminum oxide octahedral layers. It is also a remarkable adsorbent due to its high specific surface area and ion
exchange capacity. MMT’s layered structure allows for the adsorption of a variety of compounds both on the
surfaces and in the interlayer spaces, and chemical modifications of natural clay may be used to create different
MMT-related adsorbents21–23 with specific features. The interlayer space accounts for about 90% of the entire
clay surface and allows MMT to absorb water molecules and other compounds24,25. Po-Hsiang et al.26 studied
the adsorption of TC adsorption from an aqueous solution by different montmorillonite types at varying pH,
temperature, and ionic strength levels. They reported that four different smectite materials (SAz-1, SWy-2, SYn1, and SHCa-1) had a maximum TC adsorption capacity of 468, 404, 243, and 375 mg g−1, respectively.
MOFs are an unequaled type of porous crystalline materials with homogeneous and infinite coordination
networks. MOFs, which are made up of metal ions or clusters coordinated with a variety of organic linkers, have
a wide range of pH stability, high surface area, topologically diverse structures, and many sites for structural
modification.8,13. Additionally, the effectiveness of these MOF-based adsorbents for the adsorptive removal
of several classes of antibiotics has been further improved by the successful composite synthesis with carbon
nanotubes27, graphene oxides28, nanoparticles29, etc. Different pore sizes, large surface area, and simplicity of
synthesis are characteristics of copper-metal organic frameworks30. To detect TC in aqueous solution, Nehra et
al. synthesized two Cu-based metal–organic frameworks (Cu-btc) with the same stoichiometry under various
reaction conditions. They found that TC-Cu2+ complexes are formed because tetracycline contains many O- and
N-functional groups31.
In this study, Cu-MOF supported MMT composites were synthesized at three different mass ratios of CuMOF (5CuMMT, 10CuMMT, and 20CuMMT), and TC adsorption performances of these composites were
compared. The characterization of CuMMT composites were investigated using FTIR, XRD, BET, SEM and zeta
potential techniques. The effect of the initial concentration of TC, solution pH, contact time, and temperature
were examined. The data was analyzed using various isotherm and kinetic models. To enhance the TC adsorption,
the Box–Behnken experimental design method was applied to assess the impact of parameters such as contact
time, solid/liquid ratio, and temperature.
Notably, the CuMMT composite synthesized in this study represents a novel adsorbent material in the
context of environmental remediation. No prior studies have investigated the application of a Cu-MOF/MMT
hybrid structure for the removal of TC from aqueous solutions. Given the promising adsorption performance
of both metal–organic frameworks and clay-based materials in pharmaceutical contaminant removal reported
in recent literature, the integration of these two components is anticipated to yield a synergistic structure with
enhanced functionality. Therefore, this research not only introduces a new adsorbent to the field but also lays
the groundwork for further investigations into MOF-clay hybrid materials as effective and versatile platforms for
emerging contaminant remediation.
Materials and methods
Materials
Tetracycline antibiotic (C22H24N2O8; 98%Purity) used as a target pollutant was purchased from Thermo
Scientific, USA. MMT used for adsorption studies was purchased from Alya Mineral Health Training in
Turkey. Copper(II) nitrate trihydrate (Cu(NO3)2·3H2O, ≥ 98%), terephthalic acid ((C6H4(CO2H)2), ≥ 98%) and
N,N,dimethylformamide (DMF, ≥ 99.8%) used to obtain MOF were received from Merck Chemicals. Sodium
hydroxide (NaOH, ≥ 99%), hydrochloric acid (HCl, 37%), and ethyl alcohol (C2H6O, 99.90%) were also obtained
from Merck, Germany.
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In-situ synthesis of CuMMT composites
Cu(NO3)2·3H2O was weighted into a beaker in quantities corresponding to 5%, 10%, and 20% of the mass
of MMT, respectively. Terephthalic acid was weighed and added as 0.25 g, 0.5 g, and 1 g into the beakers for
5CuMMT, 10CuMMT, and 20CuMMT composites, respectively. As the amount of terephthalic acid increased,
the volumes of DMF and ethyl alcohol also increased. The powder mixtures were supplemented with 12.5 mL,
25 mL, and 50 mL of DMF, and with 6.25 mL, 12.5 mL, and 25 mL of ethyl alcohol, respectively. The mixtures
were mixed until a distinct blue solution was achieved. Ultimately, 5 g of MMT was transferred to each beaker,
and stirred for two hours.
CuMMT suspensions were placed in Teflon containers and treated trough solvothermal technique at 120 °C
for 12 h. After the treatment, the solid product was separated by using filter paper with a 0.45 µm pore diameter.
To get rid of the unreacted terephthalic acid, they were washed several times with DMF-ethyl alcohol, then
rinsed with distilled water, and dried for 24 h at 60 °C in a drying oven.
Batch experiments
To identify the optimum TC initial concentration for adsorption experiments, conduct adsorption isotherm
studies, and choose the adsorbent with the highest adsorption capacity for TC among the synthesized 5CuMMT,
10CuMMT, and 20CuMMT composites, adsorption experiments were conducted. The adsorbent amount
(5 mg), contact time (24 h), TC solution volume (50 mL), and mixing speed (150 rpm) were all held constant
while the adsorption studies were conducted at various TC concentrations (10–60 mg L−1).
To investigate the effects of contact time, pH, and temperature on TC adsorption, a series of batch experiments
were conducted using selected CuMMT composite under controlled conditions. For the kinetic and equilibrium
studies, a fixed amount of adsorbent (5 mg) was introduced into 50 mL of 50 mg L−1 TC solution, and the
mixture was stirred at 150 rpm for 5 h at room temperature.
To assess the influence of pH, experiments were performed at six different pH levels (ranging from 2 to 12),
with all other parameters (adsorbent dosage (5 mg), TC concentration (50 mg L−1), and solution volume (50 mL)
kept constant. The pH of the solutions was adjusted using 0.2 M HCl or 0.2 M NaOH, and samples were stirred
at 150 rpm for 240 min, corresponding to the established equilibrium time.
For thermodynamic analysis, the adsorption process was examined at varying temperatures (298 K, 308 K,
and 318 K) under otherwise identical conditions (adsorbent dosage (5 mg), TC concentration (50 mg L−1), and
solution volume (50 mL)), maintaining a contact time of 240 min with continuous stirring.
The initial and residual concentrations of TC were quantified using a UV–Vis spectrophotometer (Hach
DR6000 UV–Vis) at a maximum absorbance wavelength of 357 nm.
The adsorption capacity was determined by using the following equation11,32.
qe =
C0 − Ce
×V
W
qe is the amount of TC adsorbed per unit weight of CuMMT (mg g−1). W is the amount of CuMMT (g). C0 is the
initial TC concentration (mg L−1) and Ce is the concentration of TC in solution at equilibrium time (mg L−1), V
is the solution volume (L).
Characterization
The BrukerTM D8 Advanced Series powder diffractometer was used to carry out the X-ray diffraction analysis.
Patterns were recorded at 1.54 Å wavelength, 2θ degrees, and 0.05 Å degrees per scan speed. Data was gathered
between 10° and 90° degrees. Furthermore, the Perkin Elmer FT-IR Spectrum One was used to obtain the Fourier
transfer infrared spectrum (FTIR) in the 400–4000 cm−1 region. Brunauer Emmett-Teller (BET) measurements
of N2 adsorption isotherms were used to analyze the samples’ specific surface area using the Quantachrome
Quadrasorb SI instrument. The Malvern Nano ZS device was used to identify the point of net zero charge, or
zeta potential.
Experimental design
Response surface methodology (RSM), a well-known technique in the design of experiments, is applied in a
number of areas, including welding, material preparation conditions, removal of pollutants, etc. A well-liked
design in the RSM approach, the Box–Behnken design has numerous engineering benefits. The experiment
can be completed more rapidly and with significantly lower testing expenses when experimental design models
are used. Besides, one area of interest for laboratory researchers is experiment design, which aims to create the
ideal conditions for the best results32. The Box–Behnken design achieves the best results for a three-factor RSM
challenge33. Temperature, solid/liquid ratio, and contact time were selected as factors in this investigation. The
three levels were referred to as high (1), middle (0), and low (1), while the factors were named A (contact time),
B (solid/liquid ratio), and C (temperature).
Results and discussion
Characterization
Figure 1a shows XRD diagrams of 5CuMMT, 10CuMMT, and 20CuMMT composites before adsorption. The
diffraction peaks observed at 2θ = 19.61°, 27.92°, 35.09°, and 60.25° correspond to the MMT’s main mineral
component34. The peak observed at 2θ = 61.9° indicates that montmorillonite has a dioctahedral structure35.
The XRD diagrams in Fig. 1a showed that MMT’s main mineral component peaks at '2θ = 19.75°, 27.76°, 36.40°,
and 61.93°' for the 5CuMMT composite, '2θ = 19.67°, 27.60°, 36.40°, and 62.03°' for the 10CuMMT composite,
and '2θ = 19.67°, 27.71°, 36.35°, and 61.98°' for the 20CuMMT composite. The peaks at 2θ = 61.93° for the
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Intensity
(a)
10
30
50
70
2θ
Transmittance
(b)
10CuMMT
TC/10CuMMT
3530
2530
Wavenumber (cm-1)
1530
530
Fig. 1. (a) XRD patterns of samples, and (b) FTIR spectra of before and after TC adsorption for 10CuMMT.
5CuMMT composite, 62.03° for the 10CuMMT composite, and 61.98° for the 20CuMMT composite indicate
the dioctahedral structure of montmorillonite. Abdelmoaty et al.36 obtained particular peaks at approximately
2θ = 7°, 13.8°, 15.7°, 19.6°, 30.0°, 37.0°, 42.5°, 62.5° and 74° for the Cu-MOF. The observed peaks at 2θ = 13.66°,
15.63°, 19.80°, 29.76°, 36.51°, 42.36°, 62.06°, and 73.77° for 5CuMMT composite, at 2θ = 13.84°, 15.52°, 19.80°,
29.91°, 36.53°, 42.26°, 62.08°, and 73.85° for 10CuMMT composite, and at 2θ = 13.84°, 15.52°, 19.80°, 29.91°,
36.53°, 42.26°, 62.08°, and 73.85° for 20CuMMT can be attributed to Cu-MOF in the composite structure.
Figure 1b shows FTIR spectra from before and after TC adsorption on 10CuMMT. In the study by El Ouardi
et al.34, while the broadband at 3618 cm−1 was attributed to the –OH stretching vibration of MMT’s structural
hydroxyl groups, the broadband at 3448 cm−1 was attributed to the water molecules present in the interlayer.
The band at 1643 cm−1 is assigned to the adsorbed water’s bending vibration. The strong peak at 1010 cm−1 is
assigned to Si–O stretching vibrations, indicating the presence of numerous silanol (Si–OH) groups on the
montmorillonite clay surfaces. In this case, the FTIR spectrum of the 10CuMMT in Fig. 1b showed that the
broadband at 3593.70 cm−1 can be attributed to the –OH stretching vibration of MMT’s structural hydroxyl
groups present in the composite, and the broadband at 3252.84 cm−1 can be attributed to the water molecules
present in the interlayer. The band observed at 1651.25 cm−1 can be assigned to the adsorbed water’s bending
vibration. The strong peak at 991.71 cm−1 is assigned to the stretching vibrations of Si–O, indicating the presence
of numerous silanol (Si–OH) groups on the surfaces of MMT clay.
Abdelmoaty et al.36 observed a peak between 663 and 766 cm−1, which was attributed to the aromatic ring of
terephthalic acid. According to the study, the absorption band at 1390 cm−1 corresponding to the aromatic C=C
was identified. Furthermore, three absorption bands were detected at 970, 1500, and 1640 cm−1, corresponding
to the vibrations of the C–O, –C=C–, and C=O groups of terephthalic acid, respectively. The appearance of
three bands in the IR graphs was attributed to the effective Cu-MOF preparation. The FTIR spectrum of the
10CuMMT in Fig. 2 showed that the peak noticed at 734.26 cm−1 can be attributed to terephthalic acid’s aromatic
ring. The band characterizing the aromatic C=C is associated with the peak obtained at 1388 cm−1. The three
adsorption bands observed at 952.18 cm−1, 1500.83 cm−1, and 1582 cm−1 can be attributed to the vibration of
C–O, –C=C– and C=O groups of terephthalic acid, respectively. The appearance of these three bands in the
IR graph can be assigned to the successful synthesis of Cu-MOF. The bands obtained in the FTIR spectrum
of 10CuMMT/TC in Fig. 1b correspond mainly to adsorbed TC or TC interacting with 10CuMMT since the
spectrum of 10CuMMT shows much sharper and more distinct bands in the IR region studied.
BET method was used to determine the specific surface area (SBET), pore size, and pores volumes.
Figure 2a,b shows the isotherm and the pore size distribution curves, respectively. IUPAC classifies pores
as macropores > 50 nm, mesopores 2.0–50 nm, and micropores < 2.0 nm37. Adsorption mostly generated
mesopores at low pressure and macropores at high pressure, whereas the volume of adsorbed N2 increased with
rising P/Po. The pore size distribution curve shows that 10CuMMT has mostly mesopores. The N2 adsorption–
desorption curve matches IUPAC type II more closely38. The type II isotherm is typically observed in hierarchical
porous substances with micro, meso, and macroporosity, with significant porosity variation over the sample39.
10CuMMT has a surface area of 28.26 m2 g−1, a total pore volume of 0.08 cm3 g−1, and an average pore radius
of 11.81 nm. While Gülen and Demirçivi33 determined the BET specific surface area of montmorillonite clay as
72.20 m2 g−1, Abdelmoaty et al.36 determined the BET specific surface area of Cu-MOF as 1350.00 m2 g−1. The
lower 10CuMMT specific surface area may be due to the addition of Cu-MOF to the interlayer spaces of the
MMT.
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(a) 60
Adsorption
N2 Adsorbed (cm3 g-1)
50
Desorption
40
30
20
10
0
0
0.2
0.4
0.6
1
0.8
Relative Pressure (P/Po)
dV/dlog(w) (cm3 g-1 Å-1)
(b) 0.025
0.02
Specific surface area: 28.26 m2 g-1
Pore volume: 0.08 cm3 g-1
Mean pore diameter: 11.81 nm
0.015
0.01
0.005
0
0
20
40
60
80
100
120
140
160
Pore size (nm)
Fig. 2. (a) N2 adsorption–desorption isotherm and (b) pore size distribution of 10CuMMT composite.
SEM images of 10CuMMT are given in Fig. 3. Fil et al.40 confirmed that the surface morphology of
montmorillonite shows a layered surface with large flakes, which is a typical structure for montmorillonite.
Salama et al.41 found that the surface morphology of Cu-BDC consists of well-formed cubic microcrystals in
the SEM images obtained for Cu-BDC. In the SEM images of 10CuMMT in Fig. 3, due to the fact that the
layered structures of montmorillonite are doped with Cu-MOF, the layered structures, which are typical for
montmorillonite, and the cubic microcrystals of the Cu-MOF structure may not be observed.
Effect of initial TC concentration on adsorption
To examine the effect of initial TC concentration on adsorption capacity, 50 mL of TC solutions with initial TC
concentrations ranging from 10 to 60 g L−1 were stirred for 24 h at 298 K. Adsorbent amounts were kept constant
at 5 mg. The isotherm curves of the 5CuMMT, 10CuMMT, and 20CuMMT adsorbents for TC adsorption are
shown in Fig. 4a. Compared to the other two adsorbents, the 10CuMMT is found to have a greater adsorption
capacity for TC adsorption. For the further adsorption studies, 10CuMMT was selected as the adsorbent for
removal of TC from aqueous media. The results showed that the maximum adsorption capacity of 10CuMMT
for TC reached 280.55 mg g−1.
Figure 4b shows the maximum adsorption capacities of 10CuMMT adsorbent for TC adsorption for different
initial concentrations of TC. The adsorption capacity increased as the initial TC concentration increased.
However, it was observed that as the initial concentration of TC reached over 50 g L−1, the increase in adsorption
capacity reduced. The TC adsorption capacity of 10CuMMT remained relatively constant. 50 g L−1 was used as
the initial TC concentration for further adsorption studies.
Adsorption isotherms
Adsorption isotherms describe how adsorbates and adsorbents interact. The form of an isotherm provides
information about the adsorption affinity of the molecule and the stability of the interaction between the
adsorbent and the adsorbate11,42. In this study, the Langmuir, Freundlich, Temkin, and Dubinin-Redushkevich
models were used to investigate adsorption equilibrium characteristics. Table 1 presents the data obtained from
isotherm models for 5CuMMT, 10CuMMT, and 20CuMMT adsorbents.
The fit of the isotherm models used for TC adsorption is determined by evaluating the correlation coefficients
(R2). As a result, the Langmuir, Freundlich, and Temkin isotherm models with correlation values of 0.99 are fit
for TC adsorption of 5CuMMT adsorbent. The Langmuir isotherm model is the best fit for TC adsorption of
10CuMMT and 20CuMMT adsorbents, with a correlation value of 0.99. In accordance with the findings of the
literature review, the Langmuir isotherm model is better suited for TC adsorption from aqueous environments
for the various adsorbents investigated3,26,43,44.
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Fig. 3. (a,b) SEM images of 10CuMMT for 5 kX and (c,d) for 10 kX.
qe (mg g-1)
(a)
280
220
160
5CuMMT
10CuMMT
20CuMMT
100
0
5
10
15
20
25
30
35
50
60
Ce (mg L-1)
(b) 300
250
qe (mg g-1)
200
150
100
50
0
0
10
20
30
40
C0 (mg L-1)
Fig. 4. (a) Isotherm curves for 5CuMMT, 10CuMMT, and 20CuMMT, and (b) effect of initial TC
concentration on adsorption capacity for 10CuMMT.
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5CuMMT
Isotherm models
Parameters
qmax
Langmuir isotherm
Freundlich isotherm
Temkin isotherm
Dubinin-Redushkevich isotherm
(mg g−1)
243.90
KL (L g−1)
1.78
Kf (mg g−1)
165.64
N
8.54
10CuMMT
R2
Value
0.99
0.99
KT (L g−1)
11,585.41
bT (J mol−1)
131.12
qmax (mg g−1)
222.45
B (mol2 J−2)
8 × 10–9
E (J mol−1)
7905.69
0.99
20CuMMT
R2
Value
287.78
0.99
1.50
189.16
0.89
8.39
14,269.70
116.78
0.94
253.94
0.91
Value
263.16
2.92
184.23
8.90
26,879.48
128.47
R2
0.99
0.88
0.94
244.62
9 × 10–9
0.95
7453.56
9 × 10–9
0.97
7453.56
Table 1. Isotherm parameters of TC adsorption.
250
qe(mg g-1)
200
150
100
50
0
0
50
100
150
200
250
300
t (min)
Fig. 5. Effect of contact time.
Adsorption kinetics
To study the effect of contact time on TC adsorption, 50 mL of a 50 g L−1 TC solution and 5 mg of 10CuMMT
were stirred for 5 h at 298 K. The maximum adsorption capacities of 10CuMMT for TC adsorption at different
times are shown in Fig. 5. The adsorption capacity increased as contact time increased. The adsorption capacity
of 10CuMMT TC was 247.56 mg g−1 at 240 min and 244.64 mg g−1 at 300 min. After 240 min, it was observed
that the adsorbent had become saturated with TC, and lower adsorption capacity was obtained at longer contact
time. The time for adsorption to achieve equilibrium was determined as 240 min.
The detailed properties and mechanism of the adsorption process were clarified using pseudo-first-order,
pseudo-second-order, Elovich, and intraparticle diffusion kinetic models.
Pseudo-first-order kinetic model
ln (qe − qt ) = ln (qe ) −
k1
t
2.303
qe is the adsorption capacity of the adsorbent at equilibrium (mg g−1) and qt is the adsorption capacity of the
adsorbent at time t (mg g−1). k1 represents the rate constant for pseudo-first-order adsorption (min−1). The slope
and interception of the graph of ln (qe–qt) versus t provide a linear connection from which k1 and qe can be
calculated.
Pseudo-second-order kinetic model
[ ]
t
qt
=
1
1
+ (t)
qe
k2 qe2
k2 is the rate constant of pseudo- second-order adsorption (g mg−1 min−1). The plot of (t/qt) and t will give a
linear relationship whose slope and intercept can be used to determine qe and k2 respectively.
Elovich model
qt =
1
1
ln (∝ β) + ln(t)
β
β
α is the initial adsorption rate (mg g−1 min−1) and β is the desorption constant (g mg−1). A linear relationship
with slope '(1/β)' and intercept '(1/β) ln (αβ)' can be obtained from the plot of qt versus ln (t).
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Intraparticle diffusion kinetic model
qt = kint t1/2 + c
kint (mg (g min1/2)−1) is the intraparticle diffusion rate constant. The thickness of the layer that forms between
the adsorbent and the adsorbed substance is determined by a constant called c. kint and c values are determined
using the slope and intercept of the graph obtained by plotting qt versus t1/2 values. Table 2 presents the data
obtained from kinetics models.
When the correlation coefficients (R2) for the kinetic models used in TC adsorption are compared, the
correlation coefficient of 0.99 indicates that the pseudo-second-order kinetic model fits the data from TC
adsorption of 10CuMMT adsorbent better than the other kinetic models. When the Elovich model was examined,
a strong correlation coefficient of 0.98 was found. This suggests that TC adsorption on the 10CuMMT surface
involves chemisorption processes45. The pseudo-second-order kinetic model parameter qe value of 256.41 mg
g−1 is closely aligned with the qe value of 247.56 mg g−1 measured at 240 min. According to the literature review,
numerous investigations indicate that the pseudo-second-order kinetic model more accurately describes the
mechanism of tetracycline adsorption from aqueous solutions using various adsorbents26,43,44.
Effect of pH and zeta potential measurements
The pH of the solution is a crucial factor in controlling the adsorption process. To determine the ideal pH for TC
adsorption studies, 5 mg 10CuMMT and 50 mL of 50 mg L−1 TC solution were stirred for 240 min after adjusting
the pH of the solution ranging from 2 to 12. Figure 6a shows the adsorption capacities in relation to different pH
levels. TC adsorption capacity of the 10CuMMT adsorbent at acidic and neutral pH levels is slightly higher than
observed in a highly alkaline media. TCs are amphoteric compounds that include numerous ionizable functional
groups. The TC molecule contains three primary functional groups: tricarbonyl amide, phenolic diketone, and
dimethyl amino. Depending on the pH of the solution, TCs can undergo protonation-deprotonation reactions
and present many species, including the cationic form (H3TC+) at pH < 3.3, the zwitterionic form (H2TC±) at
pH < 7.68, and the anionic form (HTC−, TC2−) at pH > 7.6846. Parolo et al.47 reported that montmorillonite
contains structural negative charges. The study showed that not only cationic species of TC but also neutral
and monoanionic species were adsorbed on the negatively charged montmorillonite surface. They revealed that
electrostatics may play an important role in the adsorption of not only H3TC+ but also H2TC± and HTC-. In the
study, it was reported that the surface affinity decreased in the order of H3TC+ > H2TC± > HTC-.
Zeta potential measurements are essential for comprehending the interaction between the adsorbent and
the adsorbate since the adsorption process is primarily driven by electrostatic forces. A positive zeta potential
indicates that the adsorbent surface promotes the adsorption of negative ions, whereas a negative zeta potential
attracts positive ions to the adsorbent surface48. Figure 6b shows the zeta potential measurements of the
10CuMMT in the pH range of 2 to 10. The findings indicate that the 10CuMMT composite has a negative
zeta potential (mV) in the pH range under consideration. In this situation, the adsorbent surface will attract
positive ions. The higher TC adsorption capacity of 10CuMMT adsorbent at acidic and neutral pH is based
on the attraction of cationic, zwitterionic, and monoanionic (HTC-) forms of TC by the surface of 10CuMMT
adsorbent, which has a negative zeta potential (mV). In addition, a slight decrease in TC adsorption capacity
was observed at highly alkaline pH. This can be explained by the fact that the TC2− anionic form of TC, which
can be observed at the alkaline pH of the solution, is less attracted to the 10CuMMT surface than the cationic
and zwitterionic forms.
Electrostatic attraction is not the only dominant factor in TC adsorption. Despite the adsorption capacity
of the 10CuMMT adsorbent being comparatively lower in a highly alkaline pH environment than in acidic and
neutral pH environments, it demonstrated a significant adsorption capacity of 221.69 mg g−1 in a highly alkaline
pH environment.
The presence of π-electrons from the aromatic ring structure of the Cu-MOF structure in the prepared
adsorbent and the aromatic ring structures of TC formed π–π interactions during the adsorption process. The
aromatic rings found in TC function as acceptors for π-electrons. The presence of –COOH in the Cu-MOF
structure in the adsorbent makes the benzene ring a π-electron donor. Consequently, the interplay between
π-electron acceptors and donors contributed to improved adsorption efficiency through π–π interactions.
Overall, the pH of the solution did not influence the π–π interactions during the TC adsorption process49.
Kinetic models
Pseudo-first-order kinetic model
Pseudo-second-order kinetic model
Elovich model
Intraparticle diffusion kinetic model
Parameter
Value
(mg g−1)
144.68
qe
k1 (dk−1)
0.35 × 10–1
qe (mg g−1)
256.41
k2 (g (mg dk)−1)
0.25× 10–3
α (g (mg dk)−1)
0.43× 10–1
β (g mg−1)
40.83
kint (mg (g dk1/2)−1)
10.04
c
96.47
R2
0.88
0.99
0.98
0.85
Table 2. The data obtained from kinetics models applied for TC adsorption.
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(a) 300
qe(mg g-1)
250
200
150
100
50
1
3
5
7
9
11
13
pH
Zeta potential (mV)
(b) -55.00
-45.00
-35.00
-25.00
-15.00
-5.00
1
3
5
7
9
11
pH
Fig. 6. (a) Effect of pH, (b) zeta potential measurements.
350
qe(mg g-1)
310
270
230
190
150
295
300
305
310
315
320
T (K)
Fig. 7. Effect of temperature on adsorption.
Adsorption thermodynamics
The adsorption capacities of 10CuMMT for TC adsorption at 298 K, 308 K, and 318 K are given in Fig. 7, it
shows that the adsorption capacity of 10CuMMT for TC adsorption increased as the temperature got higher. At
318 K, the adsorption capacity was higher than at the other two temperatures, and the adsorption capacity of
10CuMMT for TC adsorption was 319.57 mg g−1 at 318 K.
Adsorption thermodynamic parameters should be assessed to determine the feasibility and spontaneity of
the process50. Experimental data from adsorption processes is used to calculate thermodynamic parameters,
including enthalpy change (ΔH), Gibbs free energy change (ΔG), and entropy change (ΔS)3,5,51. The
thermodynamic parameters ΔG°, ΔH°, and ΔS° were determined using Eqs. (1–5) provided below52, with the
ideal gas constant value of 8.314 J (mol K)−1.
(
dlnK
dT
ln
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(
)
o
Ce1
Ce2
=−
)
=
(
dlnCe
dT
∆H
R
o
(
)
=
o
∆H o
(1)
RT 2
)
1
1
(2)
−
T2
T1
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T (K)
∆H° (kJ mol−1)
298
308
11.09
318
∆G° (kJ mol−1)
∆S° (J (mol K)−1)
K
− 5.94
57.13
10.97
− 6.23
56.22
11.38
− 7.61
58.80
17.78
Table 3. Thermodynamic parameters of TC adsorption on 10CuMMT.
S
R2
R2(adj)
R2(pred)
4.07516
99.15%
97.63%
87.16%
Table 4. The summary data of the Box–Behnken model.
K=
(
)
Cads
(3)
Ce
∆Go = −RT lnK (4)
∆Go = ∆H o − T ∆S o (5)
K is the equilibrium constant, and Cads is the adsorbed substance concentration (mg L−1) at equilibrium. Ce
is the solution’s equilibrium concentration (mg L−1). T is the absolute temperature (K), and R is the ideal gas
constant (J (mol K)−1). Table 3 shows the thermodynamic parameter values obtained at 298 K, 308 K, and 318 K
respectively.
Table 3 shows a positive ∆H° value, indicating that 10CuMMT-TC adsorption occurs endothermically. For
chemisorption, the change in enthalpy of adsorption (∆H°) ranges between − 40 and − 80 kJ mol−1, while for
physisorption, it ranges between − 20 and 40 kJ mol−153. Thus, the enthalpy value of 11.09 kJ mol−1 suggests that
TC adsorption is physical. Table 3 shows that the change in free Gibbs energy (∆G°) values are negative and
decrease with increasing temperature. It indicates a spontaneous adsorption process, and spontaneity increased
as temperature increased. Positive ∆S° results indicate randomness at the solid-solution interface during
adsorption11,53.
Box–Behnken design and statistical assessment
Studies were conducted to investigate the effects of initial TC concentration, contact time, pH, and temperature
on TC adsorption. Experimental studies indicated that changing the initial TC concentration, contact time, and
temperature parameters had a significant effect on TC adsorption, however, changing the ambient pH had no
major impact on TC adsorption. Contact time, solid/liquid ratio, and temperature were selected as factors for
the experimental design.
The Box–Behnken experimental design was implemented using Minitab to conduct statistical analysis of the
obtained data, as well as investigation, modeling, and optimization of the impacts of selected parameters. Initially,
all major effects, as well as quadratic and binary interactions, were investigated. Table 4 shows model summary
data for the beginning circumstance, including all terms. The statistics indicate the model’s compatibility and
predictive power.
According to Table 4, the R2, R2(adj.), and R2(pred.) values are, respectively, 99.15%, 97.63%, and 87.16%. A high
degree of fit is indicated by such a high R2. A high R2(adj.) value suggests that there are no extraneous variables
and that the model is capable of explaining the process. Furthermore, the model’s predictive capability for the
process is represented by the R2(pred.) value, which is comparatively high for this process. The fact that these
numbers are so high and near to one another is agreeable. Additionally, the model’s residual points exhibit low
volatility, as evidenced by the standard error, or S, being as low as 4.
Table 5 lists the P-value and F-value values of the main, quadratic, and two-factor interactions of all parameters
whose impacts on the process are investigated in accordance with the Box–Behnken model. First, when the
overall significance of the model is examined the P-value (< 0.05) indicates that the model is significant. All
of the main effects’ P-values are less than 0.05, as intended, according to this analysis of the main effects. This
suggests that every main factor is statistically significant. A similar problem applies to quadratic interactions.
When the two-factor interactions are examined, the AC pairwise interaction is significant with a low P-value,
however, the AB and BC interactions are not significant with P-values of 0.746 and 0.608, respectively. The
P-value of Lack-of-Fit is > 0.05, indicating that the model generated by the design fits the data and there is no
lack of fit. Despite this, since the AB and BC interactions are known to be ineffective, these interactions were
eliminated from the model, and the analyses were rerun to improve the model’s predictive power and accuracy.
The model summary data for the new condition is shown in Table 6. After eliminating the AB and BC
interactions, all R2 values improved, as predicted. In particular, the increase in R2(pred.) suggests the new model
has more predictive power. Similarly, a decrease in the S value compared to the previous supports the idea that
the model’s accuracy has improved.
Table 7 shows the current analysis of variance results after eliminating the AB and BC interactions. The
F-value and P-value of the model in the table indicate that the model is significant. The P-values for all other
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Source
DF
Adj SS
Adj MS
F-value
P-value
Model
9
9739.59
1082.18
65.16
0.000
Linear
3
6238.59
2079.53
125.22
0.000
A
1
1445.83
1445.83
87.06
0.000
B
1
1855.43
1855.43
111.73
0.000
C
1
2937.33
2937.33
176.87
0.000
Square
3
3219.68
1073.23
64.63
0.000
A*A
1
574.01
574.01
34.56
0.002
B*B
1
2554.55
2554.55
153.82
0.000
C*C
1
456.01
456.01
27.46
0.003
2-Way
3
281.32
93.77
5.65
0.046
A*B
1
1.94
1.94
0.12
0.746
A*C
1
274.41
274.41
16.52
0.010
B*C
1
4.97
4.97
0.30
0.608
Error
5
83.03
16.61
Lack-of-Fit
3
78.17
26.06
10.71
0.087
Pure error
2
4.87
2.43
Total
14
9822.63
Interaction
Table 5. Analyses of variance data.
S
R2
R2(adj)
R2(pred)
3.58456
99.08%
98.17%
91.68%
Table 6. The summary data of the improved Box–Behnken model.
Source
DF
Adj SS
Adj MS
F-value
P-value
Model
7
9732.69
1390.38
108.21
0.000
Linear
3
6238.59
2079.53
161.84
0.000
A
1
1445.83
1445.83
112.52
0.000
B
1
1855.43
1855.43
144.40
0.000
C
1
2937.33
2937.33
228.60
0.000
Square
3
3219.68
1073.23
83.53
0.000
A*A
1
574.01
574.01
44.67
0.000
B*B
1
2554.55
2554.55
198.81
0.000
C*C
1
456.01
456.01
35.49
0.001
2-Way
1
274.41
274.41
21.36
0.002
Interaction
Table 7. Analyses variance data for reduced model.
effects and interactions are 0.05, suggesting that they are all significant. The P-value for Lack-of-Fit in the current
case is greater than in the prior case, showing that the model is more consistent with the data and that there is
no lack of fit.
The regression equation for the remaining factors is modeled as follows (Y represents the TC adsorption
capacity (mg g−1)).
Y = 261.11 + 13.44*A − 15.23*B + 19.16*C + 12.47*A2 + 26.30*B2 + 11.11*C2 + 8.28*A*C.
When the coefficients in the equation are examined, it appears that factor C has the greatest effect among
the main effects. The high coefficients of the squared factors suggest that the variables’ nonlinear effects are
also crucial for determining the optimum conditions. The most influential factor is B2, which has the highest
coefficient. The fact that the AC binary interaction has a lower coefficient than the other factors indicates that it
is significant, but its effect is lower than the other factors. The Pareto graph confirms all of these observations.
Figure 8a shows the Pareto graph for all factors.
The three-level, three-factorial Box–Behnken experimental design was used, and the contact time (150–
250 min) as factor A, solid/liquid ratio (0.08–0.12) as factor B, and temperature (298–318 K) as factor C were
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(a)
Predicted adsorption capacity (mg g-1)
(b)
340
y = 0.9953x + 1.758
R² = 0.9913
310
280
250
250
280
340
310
Observed adsorption capacity (mg g-1)
Fig. 8. (a) Pareto graph and, (b) comparison of the observed and predicted adsorption capacities.
TC adsorption
capacity (mg g−1)
Independent variables
Contact time
(min), A
Solid/liquid
ratio, B
Temperature
(K), C
10CuMMT
Run
Coded
Actual
Coded
Actual
Coded
Actual
Observed
Predicted
1
1
250
0
0.1
1
318
328
326
2
0
200
0
0.1
0
308
261
261
3
−1
150
0
0.1
1
318
285
282
4
0
200
0
0.1
0
308
262
261
5
−1
150
1
0.12
0
308
271
272
6
−1
150
−1
0.08
0
308
300
301
7
−1
150
0
0.1
−1
298
257
260
8
1
250
0
0.1
−1
298
267
271
9
1
250
1
0.12
0
308
297
297
10
1
250
−1
0.08
0
308
329
329
11
0
200
1
0.12
1
318
298
301
12
0
200
1
0.12
−1
298
268
265
13
0
200
−1
0.08
1
318
330
334
14
0
200
−1
0.08
−1
298
296
293
15
0
200
0
0.1
0
308
259
261
Table 8. Codes of independent variables and observed and predicted TC adsorption capacities of 10CuMMT.
chosen as variable parameters. The TC concentration of 50 mg L−1 remained constant during the experiments.
The factor levels were represented with − 1 (low), 0 (center point), and 1 (high).
The results of the TC adsorption capacity onto 10CuMMT were analyzed using the design matrix, and the
obtained data are shown in Table 8. The Box–Behnken experimental design’s optimized conditions (contact
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Fig. 9. (a), (b), and (c) Contour plots.
Fig. 10. (a), (b), and (c) Surface plots.
time = 200 min; solid/liquid ratio = 0.08; temperature = 318 K) enhanced the adsorption of TC on 10CuMMT
and the maximum adsorption capacity reached 330 mg g−1.
Figure 8b shows a graph of the comparison of the observed against predicted adsorption capacities. The data
is noticed to be spread quite close to the trend line. This demonstrates the model’s ability to make very accurate
predictions. Their distribution along a certain linear line also implies that there is no systematic mistake.
Contour Plots (Fig. 9a–c) and Surface Plots (Fig. 10a–c) were used to comprehend the connections between
the factors and their impacts on the dependent variable, as well as to identify the optimum points. Figures 9a
and 10a show that increasing the B factor decreases the dependent variable, TC adsorption capacity (Y), while
increasing the A factor increases Y values. The negative sign of the B factor’s coefficient in the model equation
confirms its observed negative influence on Y. It can be observed that areas with high A and low B constitute
optimum zones. Figures 9b and 10b illustrate that the value of Y rises when both the A and C factors increase.
Maximum values are achieved in Figs. 9c and 10c when B decreases and C increases. The change in the C factor
has a considerable effect on the dependent variable Y.
Conclusion
This study found that copper-based metal–organic framework doped Montmorillonite (CuMMT) composite is
an effective as a new adsorbent for the removal of TC from aqueous environment. In this study, three different
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CuMMT composites (5CuMMT, 10CuMMT, and 20CuMMT) were synthesized using varying Cu-MOF ratios.
The composites characterized via FTIR, XRD, BET, SEM, and zeta potential analyses, confirming successful
synthesis.
Adsorption experiments were conducted to evaluate the effects of initial TC concentration, contact time,
solution pH, and temperature on the adsorption behavior. 10CuMMT exhibited the highest adsorption capacity
and was selected for further detailed studies. Equilibrium was reached at 240 min, and optimal adsorption was
achieved at the natural pH of the TC solution (pH 7.32) and 318 K, yielding a maximum adsorption capacity
of 319.57 mg g⁻1. In addition to electrostatic interactions, π–π interactions between the aromatic rings of TC
and the Cu-MOF structure also played a crucial role in the adsorption mechanism. The π-electron donor–
acceptor dynamics between the Cu-MOF’s –COOH functionalized benzene rings and TC’s aromatic structures
significantly enhanced the overall adsorption efficiency. The adsorption process conformed to the Langmuir
isotherm model and pseudo-second-order kinetics, while thermodynamic analysis indicated that the process
was endothermic and spontaneous.
The optimized conditions (200 min, 0.08 g L⁻1 solid/liquid ratio, and 318 K) obtained by applying BoxBehnken experimental design enhanced the TC removal, achieving an adsorption capacity of 330.70 mg g⁻1. The
study concludes that 10CuMMT is a highly promising adsorbent for the effective removal of TC from wastewater,
with potential applicability in environmental remediation practices. In future studies, a comprehensive feasibility
and experimental study on the potential for industrial scale application is planned and this deficiency is aimed
to be addressed.
Data availability
The data presented in this study are available on request from corresponding author.
Received: 18 March 2025; Accepted: 19 May 2025
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Acknowledgements
This work was supported by the Istanbul Technical University (ITU) Scientific Research Projects Unit [Grant #
MYL-2022-44158].
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Author contributions
E.T. made substantial contributions to the conception or design of the work; made substantial contributions to
the acquisition, analysis, or interpretation of data; made substantial contributions to have drafted the work; have
approved the submitted version; have agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which
the author was not personally involved, are appropriately investigated, resolved, and the resolution documented
in the literature. N.K. made substantial contributions to the conception or design of the work; made substantial contributions to the acquisition, analysis, or interpretation of data; made substantial contributions to have
drafted the work; have approved the submitted version; have agreed both to be personally accountable for the
author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the
work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and
the resolution documented in the literature. T.H.S. made substantial contributions to the conception or design of
the work; made substantial contributions to the acquisition, analysis, or interpretation of data; made substantial
contributions to have drafted the work; have approved the submitted version; have agreed both to be personally
accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of
any part of the work, even ones in which the author was not personally involved, are appropriately investigated,
resolved, and the resolution documented in the literature. P.D. made substantial contributions to the conception
or design of the work; made substantial contributions to the acquisition, analysis, or interpretation of data; made
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integrity of any part of the work, even ones in which the author was not personally involved, are appropriately
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the conception or design of the work; made substantial contributions to have substantively revised work; have
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the author was not personally involved, are appropriately investigated, resolved, and the resolution documented
in the literature. N.E.A. made substantial contributions to the conception or design of the work; made substantial contributions to have substantively revised work; have approved the submitted version; have agreed both to
be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy
or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately
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