Anal. Methods Environ. Chem. J. 6 (4) (2023) 37-51
Research Article, Issue 4
Analytical Methods in Environmental Chemi s try Journal
Journal home page: www.amecj.com/ir
AMECJ
Removal and determination of carbon monoxide based on
copper oxide immobilized on Zeolite 13X Nanocatalys t
by catalytic oxidation process and gas ow analyzer
Bahar Parsazadeha, Hasan Asilian Mahabadi a,*, and Niloofar Damyarb
a Department of Occupational Health, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
bDepartment of Occupational Health, Damghan School of Public Health, Semnan University of Medical Sciences, Semnan, Iran
ABS TRACT
Carbon monoxide is one of the main air pollutants, mainly
produced from the incomplete combus tion of fossil fuels.
This s tudy aims to oxidize carbon monoxide by copper oxide
nanoparticles immobilized on zeolite13X subs trate. The present
inves tigation was conducted to determine the eect of carbon
monoxide concentration parameters (in the range of 200-1400
ppm) and reaction temperature (in the range of 100-500 °C) on the
eciency of carbon monoxide conversion by CuO/Zeolite 13X
nanocatalys t. The design of the experiment and the determination
of the number of experiments were analyzed using the central
composite design method, and the s tatis tical tes t of analysis of
variance was done using the response surface method. Also, the
s tructural and morphological characteris tics of the nanocatalys t
were inves tigated using BET, BJH, FE-SEM, EDX, and XRF tes ts.
The results show that CuO/Zeolite 13X nanocatalys t eciently
oxidizes carbon monoxide. The highes t conversion eciency
of 82.6% was obtained at a temperature of 400 °C and a carbon
monoxide concentration of 500 ppm as the optimal conditions.
According to the EDX tes t results, copper oxide nanoparticles
with a weight percentage of 5.9% were loaded on the Zeolite 13X
subs trate. Design Expert11 software reduced the cubic model with
an R2 coecient of 0.98.
Keywords:
Carbon monoxide,
Copper Oxide nanoparticles,
Zeolite 13X,
Catalytic oxidation,
Response surface methodology,
Central composite design
ARTICLE INFO:
Received 2 Aug 2023
Revised form 14 Oct 2023
Accepted 3 Nov 2023
Available online 28 Dec 2023
*Corresponding Author: Hasan Asilian Mahabadi
Email: asilian_h@modares.ac.ir
https://doi.org/10.24200/amecj.v6.i04.259
1. Introduction
According to the World Health Organization
(WHO), 7 million deaths occur yearly due to air
pollution [1]. Carbon monoxide is a colourless,
odourless, and non-irritating gas mainly produced
from the incomplete combus tion of carbonaceous
materials (coal, diesel, natural gas, oil, propane,
and so forth) [2]. Indus tries and vehicles are the
mos t important sources of this gas emission [3].
Carbon monoxide is a poisonous gas for humans
and animals and also has adverse eects on the
environment [2] Regarding the concentration
------------------------
38
of carbon monoxide in the exhaus t air of Iran’s
indus tries, several inves tigations have been
carried out, for example, in 2010, Junadi et al.
inves tigated the exhaus t of was te incinerators of
several hospitals in Hamedan city, and the highes t
concentration of carbon monoxide reported as
1041 ppm, which 10.4 times the s tandard of the
Environmental Organization in 2002 [4]. Also,
in another s tudy, Guderzi et al. inves tigated the
pollutants from cement indus tries in Lores tan city,
and based on the results, the highes t concentration
of carbon monoxide is related to winter and spring
seasons and is equal to 630 ± 53.04 and 2378 ±
76 ppm, respectively, which are higher than the
permissible exposure limit [5]. About 95% of the
absorbed carbon monoxide is easily combined
with hemoglobin to form carboxyhemoglobin.
Complications such as muscle paralysis, coma,
cardiovascular complications, and eventual death
occur when the percentage of carboxyhemoglobin
increases by more than 50%. Also, nano-palladium
embedded on the mesoporous silica nanoparticles
was used for CO and mercury removal from
air [2, 6]. Hence, controlling and reducing the
concentration of carbon monoxide is essential in
maintaining public health. Oxidation of carbon
monoxide to carbon dioxide is a practical and
s traightforward way to control this pollutant [7].
Using heterogeneous catalys ts to oxidate various
chemical compounds is among the leading
technologies in advanced environmental science
and engineering [8-10]. Many s tudies have reported
that platinum group metals (PGM), including
Platinum (Pt), Palladium (Pd), Radium (Rd), and
Iridium (Ir), have high catalytic activity for carbon
monoxide oxidation [11, 12]. However, their use
as catalys ts is limited due to challenging problems
such as low natural abundance, high cos t, and
sulphur poisoning [13]. Therefore, transition metals
such as copper [14, 15], nickel [16, 17] and cobalt
[18] and their combinations have been considered
for carbon monoxide oxidation due to their high
natural abundance and high s tability [13, 14, 19,
20] To date, many s tudies have been performed to
inves tigate the catalytic activity of nanocatalys ts.
According to previous s tudies, metal nanoparticles
used as catalys ts can help improve the reaction
eciency due to their high surface-to-volume ratio,
specic surface area, and high chemical and thermal
resis tance [21, 22]. Hence, attempts have been
made to place active nanos tructures on mesoporous
supports to increase the s tability of nanocatalys ts
and prevent them from c. So far, various porous
materials, such as metal oxides, diatomite, zeolites,
activated carbon, and alumina, have been s tudied
to make copper oxide catalys ts [23-27]. Zeolites
are available in synthetic and natural forms and
generally consis t of a basic s tructure consis ting of
an aluminosilicate framework and a quadrilateral
set of silicate cations (SiO4
+) and aluminum
(Al3+) cations surrounded by four oxygen anions.
Synthetic zeolites have a higher surface area,
more pore volume, and no impurities compared to
natural zeolites [28, 29]. NaX faujasite (with the
brand name of zeolite 13X) is an alkaline metal
aluminosilicate with the sodium form of zeolite X
[30, 31]. However, the inves tigation of the catalytic
activity of copper oxide nanoparticles immobilized
on zeolite 13X was not found by our research team.
This s tudy aimed to catalyze carbon monoxide
oxidation by zeolite 13X s tabilized copper oxide
nanocatalys t. The CCD central composite design
method (a subset of the RSM response surface
method) and the Design-Expert software version
11 were used to design, model, and optimize the
experiments.
2. Experimental
2.1. Catalys t preparation
This s tudy synthesized the copper oxide using
aqueous copper acetate salt Cu(OAc)2·H2O by co-
precipitation. Previous research reported 4-5%
by weight of copper oxide xed on the support as
the optimal catalys t [20, 32]. Accordingly, CuO/
Zeolit13X nanocatalys t with 4% by weight was
inves tigated in this s tudy. To make a copper oxide
nanocatalys t of 4% by weight, 2.8 g of the aqueous
copper acetate salt (Cu(OAc)2·H2O) was added to 100
ccs of dis tilled water to reach a clear blue solution.
The solution temperature was adjus ted to 80 °C, and
Anal. Methods Environ. Chem. J. 6 (4) (2023) 37-51
39
Removal and Analysis of carbon monoxide by Nano Catalys t Bahar Parsazadeh et al
20 g of zeolite 13X was added. The suspension was
then placed on a mixer and gently s tirred until all
the water in the suspension was evaporated. Next,
zeolite pellets impregnated with copper acetate salt
were calcined at 450 °C for 3 hours (Fig.1).
2.2. Characterization
Elemental analysis of zeolite13x was performed
by X-ray uorescence tes t (XRF) using an XRF
device (model PW 2404 Philips Company) with
the LIO method (loss-on-ignition) on pressed
powders. The Brunauer-Emmett-Teller (BET) and
Barrett-Joyner-Halenda (BJH) tes ts determined
specic surface area, pore volume, and pore
size. The tes ts were performed with the physical
adsorption of N2 at -196 °C using a micrometric
TriS tar II 3020 analyzer. Field Emission Scanning
Electron Microscope (FE-SEM) and Energy-
dispersive X-ray spectroscopy (EDX) examined
the morphology of the catalys t and quantitative
and elemental identications appended with FE-
SEM, respectively. Using FE-SEM analysis, high-
resolution images of the catalys t surface were
provided with a magnication of 5,000 x and an
operating voltage of 20 kV.
2.3. Procedure based on catalytic activity and
gas ow analyzer
Catalytic activity tes ts of copper oxide nanocatalys ts
were performed in a xed bed reactor (quartz glass
tube with an inner diameter of 13 mm and a catalys t
length of 20 mm were used) at the atmospheric
pressure and with a 1 liter per minute ow rate. As
shown in Figure 2, a carbon monoxide cylinder with
a purity of 99.99% was used as the main gas, and
puried ambient air (silica gel and soda-lime) was
used as the carrier gas. The concentration of carbon
monoxide was adjus ted by needle valves, rotameters,
and owmeters with control valves in the path before
the reactor, and its amount was determined both before
and after the reactor (reactor outlet) by a continuous
gas ow analyzer (MRU Vario Plus, Germany) with
10-ppm accuracy which was calibrated before all
tes ts. This device is Suitable for indus trial applications
using combined infrared (NDIR) technology and
electrochemical sensors for maximum versatility. It can
measure 9 gases, including O2, CO, NO, NO2, NOx,
SO2, CO-high, CO-very high, H2S or H2, CH4 or C3H8.
This device can measure carbon monoxide, carbon
dioxide, and oxygen simultaneously. To perform the
tes ts, the supported copper oxide nanocatalys t was
Fig. 1. Schematic view of the manufacturing s teps of CuO/Zeolite13X nanocatalys t
40
placed in an oven at 150 °C for 2 hours with the aim
of dehumidifying, and then 1 g of it was placed in a
reactor (Fig.3). Refractory breglass was also used to
hold the catalys ts in place. The conversion eciency
of carbon monoxide to carbon dioxide was calculated
by Equation 1.
(Eq. 1)
In this equation: Cin: input concentration (ppm),
Cout: output concentration (ppm), and ɳ: eciency
in percentage.
2.4. Experimental design and s tatis tical analysis
To achieve maximum eciency, there is a need to
optimize the process variables. Traditional and old
optimization methods are done by considering the
eect of a parameter on the process at a certain time.
In recent years, to overcome the shortcomings, the
Fig. 2. Schematic design of the tes t set-up, 1: Gas valves, 3: Pressure measurement, 4: Needle valve,
5: Flowmeter, 6: Heat control thermos tat, 7: Furnace, 8: Three-way valve, 9: Vent, 10: Gas analyzer
Fig. 3. The packed CuO/Zeolite 13X reactor
Anal. Methods Environ. Chem. J. 6 (4) (2023) 37-51
41
RSM response surface method, which is a powerful
optimization method, has been widely used [33-
35]. This method is based on mathematical and
s tatis tical techniques that nd optimal conditions by
unders tanding the eects of various factors and their
interaction on the response. CCD (Central Composite
Design) is one of the mos t common RSM methods [36]
[37]. As shown in Table 1, temperature variables and
carbon monoxide concentration in 5 levels α, -1,0, +α,
+1 were examined. Also, the qualitative catalys t type
variable was s tudied in CuO/Zeolite 13X and Zeolite
13X (uncoated catalys t base). After carrying out the
carbon monoxide oxidation tes ts, the eect of each
independent variable (carbon monoxide concentration
and temperature) on the response and their relationship
with each other was obtained by Equation 2 [38].
(Eq.2)
In this formula, Y is the predicted response, β0 is a
cons tant coecient, βi is the linear eects, βii squared
eect, βij interaction, εij random error, Xi and Xj
are the temperature and concentration of carbon
monoxide (independent variables), respectively.
3. Results and Discussion
3.1. S tatis tical analysis of the catalytic conversion
of CO
This s tudy aimed to determine the inuential factors
on carbon monoxide conversion eciency (CEC),
s tudy the interaction of variables, and determine
their optimal values. Table 1 lis ts the variables under
consideration, which were reaction temperature
(A) and CO concentration (B) under two reactors’
conditions (reactor packed with Zeolite13X (uncoated
base) and reactor packed with CuO/Zeolite13X
nanocatalys t). The minimum and maximum values of
the main variables (CO gas concentration and reaction
temperature) were selected according to the reported
conditions of the exhaus t gas concentration of many
indus tries and reneries, according to the values given
in Table 1. Firs tly, CO adsorption by uncoated Zeolite
13X pellets was inves tigated to ensure that they had no
adsorption and catalytic eects. The central composite
design (CCD) method in 5 levels has been used to
obtain logical results in this research. To evaluate the
eect of variables, the reaction temperature (A), carbon
monoxide gas concentration (B) and type of packed
reactor (C), 26 tes ts were designed using the CCD
method, and the results of each tes t are given in Table 2.
3.2. Model matching and s tatis tical analysis
The experimental data from 26 runs dened by
the CCD were analyzed to achieve signicant
and insignicant eects of each variable and their
interactions. Then, the bes t regression model will be
obtained to predict the carbon monoxide conversion
eciency of the reactor packed with CuO/Zeolite
13X and zeolite 13X. ANOVA results for the selected
reduced cubic model, which can predict the response
(carbon monoxide conversion eciency), are given
in Table 3. According to the obtained results (Table
2), the carbon monoxide conversion eciency for the
reactor packed with Zeolite 13X is 0.11-7.43%, which
is signicantly higher than that of the reactor packed
with CuO/Zeolite 13X nanocatalys t. In this s tudy, the
maximum carbon monoxide conversion eciency
is achieved by CuO/Zeolite 13X nanocatalys t
(92.96%), which is 12.5 times greater than that of the
reactor packed with Zeolite 13X. Therefore, it can be
concluded that Zeolite 13X is catalytically inactive,
which is consis tent with other s tudies conducted in
this area. To achieve the carbon monoxide conversion
eciency (CEC) regression model, according to the
results obtained from 26 tes ts, Equations 3 and 4
were created by Design-Expert software version 11.
Table 1. The experimental range and levels of the factors in the CCD
Factor Name Units Type Minimum Maximum Coded low Coded high Mean
A Temperature °C Numeric 100.00 500.00 -1 ↔ 200.00 +1 ↔ 400.00 300.00
B Concentration ppm Numeric 200.00 1400.00 -1 ↔ 500.00 +1 ↔ 1100.00 800.00
C packing type Categoric Zeolite13X CuO/Zeolite13X Levels: 2
Removal and Analysis of carbon monoxide by Nano Catalys t Bahar Parsazadeh et al
42
The following equations can predict the CEC at the
levels of the original units determined for each factor.
R% CuO/Zeolite13X= +17.19889
+0.112493A-0.054183B+0.000013AB
+0.000205A2+0.000022B2
(Eq.3)
R% Zeolite13X = + 3.32835
+ 0.000295A-0.006330B-3.58333E-06AB
+ 0.000042A2 + 2.83118E-06B2
(Eq.4)
Analysis of variance (Table 3) showed that the
selected reduced cubic model with P-values of less
than 0.05 and a 95% condence interval is suitable for
predicting CEC. The factors of reaction temperature
(A), CO concentration (B), packed reactor type (C),
and AC interaction) have P-values less than 0.05 and
are signicant factors in carbon monoxide conversion
eciency. This model is robus t, with more than
99.99% accuracy. Based on the s tatis tical results,
the values predicted by the model with the results of
regression experiments are equal to 0.98. Also, the
R-square (R2) and adjus ted R-squared (adj, R2) are
equal to 0.98 and 0.97, respectively, with a dierence
of less than 0.2, indicating the model’s suitability. The
precision also shows the signal-to-noise ratio, which
is 31.54 for the prediction model, which is more than
four and is desirable. Also, the s tandard deviation
values (SD) and the coecient of variation (CV) equal
68.4 and 18.50, respectively. Based on these results,
the predicted values and the values obtained from the
experimental tes ts for the CO conversion eciency
Table 2. Results of CO conversion eciency experiments
Run A: Temperature (◦C) B: CO concentration (ppm) C: Catalys t type (%) ƞpredicted (%) ƞactual
1 300 800 ZeoliteX13 3.74 3.08
2 400 1100 ZeoliteX13 5.16 5.70
3 100 800 CuO/ZeoliteX13 2.31- 6.77
4 300 800 ZeoliteX13 3.74 3.10
5 300 200 ZeoliteX13 5.47 5.76
6 300 800 CuO/ZeoliteX13 46.85 41.57
7 300 1400 ZeoliteX13 2.02 2.10
8 300 800 ZeoliteX13 3.74 2.77
9 500 800 ZeoliteX13 8.31 9.10
10 300 800 ZeoliteX13 3.74 2.68
11 400 1100 CuO/ZeoliteX13 66.84 78.89
12 400 500 CuO/ZeoliteX13 76.02 82.60
13 200 500 CuO/ZeoliteX13 26.86 22.10
14 300 800 ZeoliteX13 3.74 3.13
15 300 800 CuO/ZeoliteX13 46.85 41.20
16 200 500 ZeoliteX13 2.32 2.50
17 300 1400 CuO/ZeoliteX13 37.67 38.02
18 200 1100 CuO/ZeoliteX13 17.68 16.81
19 300 800 CuO/ZeoliteX13 46.85 42.85
20 500 800 CuO/ZeoliteX13 96.01 92.96
21 100 800 ZeoliteX13 0.82- 0.11
22 400 500 ZeoliteX13 6.89 7.43
23 300 800 CuO/ZeoliteX13 46.85 42.05
24 200 300 CuO/ZeoliteX13 56.03 61.06
25 200 1100 ZeoliteX13 0.60 1.20
26 300 800 CuO/ZeoliteX13 46.85 42.20
Anal. Methods Environ. Chem. J. 6 (4) (2023) 37-51
43
response have good regression, which indicates that
this model can be used with good reliability to predict
the response. Parameters A and B represent the actual
reaction temperature and CO concentration values,
respectively. Positive regression coecients indicate
a positive linear eect, and negative regression
coecients indicate a negative linear eect on
carbon monoxide conversion eciency. It should be
noted that the P-value determines the eect of each
parameter. For signicant parameters, the smaller
the P-value, the greater the eect of that parameter
on the response, and if the P-values are the same, the
F-value should be considered; the higher this value is,
the greater the eect of the parameter on the response.
The signicant parameters in this s tudy based on their
importance in carbon monoxide conversion eciency
are C > A > AC > B. The comparison of the predicted
values obtained from the selected model agains t the
actual values obtained from the experimental tes ts
for the intended response is shown in Figure 4. The
scatter of points around the diagonal line (Figure 4)
shows a good correlation between the experimental
data and the predicted values and, consequently, the
model’s s trength. Based on these results, the predicted
values and the values obtained from experiments for
the CO conversion eciency response showed a good
regression, revealing that this model can be used with
good reliability to predict the response.
Fig. 4. Comparison of values predicted by the model and obtained from experimental tes ts
Table 3. ANOVA for Reduced Cubic model
Source Sum of Squares df Mean Square F-value p-value
Model 19803.02 11 1800.27 82.23 < 0.0001
A-Temperature 4330.10 1 4330.10 197.78 < 0.0001
B-Concentration 178.38 1 178.38 8.15 0.0127
C-Type of reactor 4647.60 1 4647.60 212.28 < 0.0001
AB 0.1653 1 0.1653 0.0076 0.9320
AC 2982.63 1 2982.63 136.23 < 0.0001
BC 83.37 1 83.37 3.81 0.0713
A² 69.88 1 69.88 3.19 0.0957
B² 56.45 1 56.45 2.58 0.1306
ABC 0.5050 1 0.5050 0.0231 0.8815
A²C 30.17 1 30.17 1.38 0.2600
B²C 33.51 1 33.51 1.53 0.2364
Removal and Analysis of carbon monoxide by Nano Catalys t Bahar Parsazadeh et al
44
3.3. Effect of carbon monoxide concentration
and reaction temperature on carbon monoxide
conversion eciency
The eect of the main parameters, i.e. reaction
temperature and CO concentration, on carbon
monoxide conversion eciency is shown in Table 3.
Based on the obtained results and as expected, there
is a linear relationship between carbon monoxide
conversion eciency and reaction temperature, CO
concentration, and type of reactor.
Figure 5a shows the carbon monoxide conversion
eciency by CuO/Zeolite 13X and Zeolite 13X
nanocatalys ts as a function of reaction temperature.
The composition of the incoming feed s tream
contains 800 ppm of CO gas with a space velocity of
22641 h-1. Based on the analysis of variance, reaction
temperature (A) has a positive linear eect on
carbon monoxide conversion eciency. According
to the results shown in Figure 5a, the CuO/Zeolite
13X nanocatalys t is inactive at temperatures below
100°C, and the carbon monoxide conversion
eciency is low. At a temperature of 100 °C, the
conversion eciency is equal to 6.77% (experiment
number 3) and with increasing temperature, the
eciency of carbon monoxide conversion also
increases so that it reaches 41.57% on average at a
temperature of 300°C and 92.96% at a temperature
of 500 °C (experiment number 20) found that the
obtained results are consis tent with similar s tudies
[20, 32]. Of course, according to previous s tudies
[39], by using copper oxide nanoparticles with
a smaller diameter (up to 5 nm), it is possible to
achieve a carbon monoxide conversion eciency of
99.5% at a temperature of 250 °C, which indicates
the eect of the catalys t preparation method and the
size of the nanoparticles.
Figure 5b shows the eect of CO concentration of
1100-500 ppm on CEC during carbon monoxide
oxidation by CuO/Zeolite13X and Zeolite13X
nanocatalys ts at 300°C and with a space velocity
of 22641 h-1. Based on the analysis of variance, CO
concentration has a linear negative eect on carbon
monoxide conversion eciency. The experimental
results show that when the concentration of CO is
equal to 500 and 1100 ppm at a cons tant temperature
of 400 °C, the conversion eciency of carbon
monoxide is equal to 82.60% (experiment number 12)
and 78.89% (experiment number 11), respectively.
Theoretically, the conversion eciency decreases
with the increase in pollutant concentration, which
is consis tent with the results of this s tudy. Of course,
using a pure oxygen cylinder can limit this eect and
increase the catalys t’s eciency [40], which could
not be used in this s tudy due to exis ting limitations.
Fig. 5. The results of the eect of (a) reaction temperature and
(b) carbon monoxide concentration on carbon monoxide conversion eciency
Anal. Methods Environ. Chem. J. 6 (4) (2023) 37-51
45
3.4. Three-dimensional (3D) response surfaces
and contour plots
Figures 6a and 6b show three-dimensional (3D)
response surfaces and contour plots indicating
the main variables’ eect (CO concentration and
reaction temperature) on CEC by CuO/Zeolite
13X nanocatalys t. According to Figures 6a and
6b, there is a positive linear relationship between
reaction temperature and CEC and a negative linear
relationship between CO concentration and CEC,
which means that the CEC increases with increasing
temperature and decreasing CO concentration.
3.5. Determining the optimal conditions for
carbon monoxide conversion eciency and
validation
In the carbon monoxide conversion process by
the CuO/Zeolite 13X nanocatalys t, based on
the optimization, the highes t carbon monoxide
conversion eciency of 75.94% was obtained
at a concentration of 500 ppm and a reaction
temperature of 400 °C as optimal conditions.
Further, to conrm the correctness and validity of
the model obtained from the experimental results
of carbon monoxide conversion eciency by
CuO/Zeolite 13X nanocatalys t, experiments were
conducted under optimal conditions, and the results
are reported in Table 4. As shown in Table 4, the
result of the conrmation tes t lies between the lower
and upper limits of the 95% condence interval,
and so on, the results of predictions by the model
and experimental tes ts are in good agreement.
3.6. Characterization of CuO/Zeolite 13X
catalys ts
The results of the XRF tes t are shown in Table 5.
According to the results obtained from the XRF
tes t, Al2O3 and SiO2, with percentages of 23.397 and
45.108, are the main components of zeolite X13,
which indicates the aluminosilicate s tructure of this
material. The s tructure of this article conrms this.
The results of the BET tes t are presented in Table 6.
Fig. 6. a) surface view and b) three-dimensional view of the eect of main variables
on carbon monoxide conversion eciency by CuO/Zeolite 1 nanocatalys t
Table 4. Optimized conditions with predicted and experimental values for the intended response
Response Catalys t Type Concentration (ppm) Temperature (°C) Predicted
Response
Conrmation
Experiment
C.I (95%)
Low High
CEC CuO/Zeolite13X 500 400 75.94 82.6 68.88 82.99
Removal and Analysis of carbon monoxide by Nano Catalys t Bahar Parsazadeh et al
46
Based on the results, the specic surface area and
total pore volume of Zeolite 13X equal 495.85 m2
g-1 and 0.265368 cm3 g-1, respectively. According
to the s tudies conducted on other types of zeolite,
it was found that Zeolite 13X has a higher specic
surface area compared to USY and ZSM-5 zeolite
[42-44] because increasing the specic surface
increases the active sites, and as a result, the
reaction eciency increases, Zeolite 13X can be a
more suitable subs trate for the catalys t compared
to other zeolites. According to the BJH tes t shown
in Figure 7, based on adsorption and desorption, N2
isotherms zeolite 13X has a s tructure composed of
mesoporous and microspores (Table 7). So 62.77%
of the zeolite13X is microporous, and 37.23% is
mesoporous.
The images obtained from the FE-SEM tes t are
shown in Figure 8, and the size dis tribution of
nanoparticles calculated using MIPCloud software
is shown in Figure 9. The images presented in Figure
8 show the uniform dis tribution of copper oxide
nanoparticles on the surface of zeolite X13. Figure
9 shows the size dis tribution of nanoparticles.
The cumulative percentage of nanoparticle size is
dierent; particles of 20-40 nm have the highes t
cumulative rate, and particles larger than 100 nm
have a cumulative percentage of less than 25%.
The results of the EDX tes t are shown in Figure 10,
which shows the weight percentage of the copper
element s tabilized on the X13 zeolite bed. Based
on the results, copper oxide nanoparticles with a
weight percentage of 5.4% have been s tabilized
Table 5. Elemental analysis tes t results(XRF) of zeolite X13
LOINa2OMgOAl2O3
SiO2
K2OTiO2
Fe2O3
Si/Al
Components
15.9511.3361.9223.39745.1080.5040.0171.0271.928Zeolite13X
----12.49ND30.1749.28NDNDND2.77Components
Table 7. Characterization of microporous and mesoporous pores in CuO/Zeolite13X
Microspore
volume
Microspores
surface area
Mesoporous
volume
Mesoporous
surface area
Microspores
%
Mesoporous
%
0.16657 453.847 0.0988 42.003 62.77 37.23
Table 6. S tructural features of zeolite 13X (BET Equation) in CuO/Zeolite13X
Adsorption average pore
diameter by BET A0
The total pore volume
of pores cm³ g-1
BET Surface
Area m²/g BJH Adsorption
average pore A0
21.40 0.265368 495.8508 28.172
Anal. Methods Environ. Chem. J. 6 (4) (2023) 37-51
47
on the X13 zeolite subs trate. Based on previous
s tudies, the optimal eciency of carbon monoxide
conversion by copper nanocatalys t with a weight
percentage of 4-5% s tabilized on dierent subs trates
has been reported and based on these results
[20, 32], increasing the loading of copper oxide
nanoparticles on the subs trate does not only lead to
an increase in eciency but also the accumulation
of nanoparticles and collection of masses causes
a decrease in catalytic activity. As a result of the
EDX tes t obtained from Zeolite13X, silica and
aluminum, which are the main components of
zeolite, can be seen.
Relative Pressure (p/p°)
0.00.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantity Adsorbed (cm³/g STP)
00
50
100
150
Isotherm
Fig. 7. Adsorption and desorption isotherms N2 for CuO/Zeolite 13X nanocatalys t
Fig. 8. Scanning electron microscope image of CuO/Zeolite 13X and zeolite 13
Removal and Analysis of carbon monoxide by Nano Catalys t Bahar Parsazadeh et al
48
4. Conclusion
This s tudy was conducted to determine the
eciency of carbon monoxide conversion by CuO/
Zeolite 13X nanocatalys t. Based on the results, this
nanocatalys t has acceptable eciency and thermal
s tability in carbon monoxide oxidation. However,
it is signicant that compared to other natural and
synthetic zeolites, zeolite 13X has a much higher
surface area (according to the 495-bat tes t), which
can provide many active sites for nanoparticles.
However, based on the results obtained from the
XRD images, it was found that the mos t signicant
Fig. 10. Energy-dispersive X-ray spectroscopy (EDX) tes t results
Fig. 9. Size dis tribution of copper oxide nanoparticles immobilized on zeolite13X
Anal. Methods Environ. Chem. J. 6 (4) (2023) 37-51
49
number of particles s tabilized on zeolite 13X having
a diameter equal to 60-40 nm, which indicates the
clumping of nanoparticles and the reduction of
the contact surface of the carbon monoxide ow
with the nanocatalys t. Based on previous s tudies,
nanoparticles with smaller dimensions can be
produced by changing the manufacturing method,
increasing eciency and decreasing reaction
temperature.
5. Acknowledgement
Dr. Hasan Asilian Mahabadi was responsible for
guiding and advising on the research. Miss Bahar
Parsazadeh was accountable for doing experiments,
analyzing and interpreting the data, and writing the
manuscript. Dr. Niloofar Damyar was responsible
for data analysis, interpretation of the data, and
correction of the written manuscript.
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