EVALUATION OF MOF-BASED ADSORBENTS FOR SAMPLING AND
MEASUREMENTS OF SEMI-VOLATILE ORGANIC COMPOUNDS (SVOCS)
FROM CONTAMINATED AIR

by

Yinchun Guo
B.Sc., Shanghai Institute of Technology, 2020
M.Sc., Hong Kong Baptist University, 2021

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENT FOR THE DEGREE OF
MASTER OF SCIENCE
IN
NATURAL RESOURCES ENVIRONMENTAL STUDIES

UNIVERSITY OF NORTHERN BRITISH COLUMBIA
November 2024

© Yinchun Guo, 2024

Abstract
Understanding the challenges of effectively capturing and analyzing semi-volatile organic
compounds (SVOCs) from contaminated air is crucial for environmental monitoring. In
this study, selected Zr-containing metal-organic frameworks (MOFs), UiO-67 and UiO-67Ni, have been investigated for their potential on adsorption of a group of nonpolar SVOCs
so called as poly-aromatic hydrocarbons (PAHs). Acenaphthene (Acp) was used as a model
SVOC, and adsorption tests were investigated using the static headspace sampling
technique, and desorption technique using methanol under ultrasonic conditions.
Parameters including desorption time and adsorbent dosage have been investigated as
independent variables for extraction performance, where the General Full Factorial design
method has been applied for an experimental design. Comparing two different MOFs with
same crystal structure and different pore size has demonstrated the exact effect of pore
accessibility and interaction of host-guest molecules in adsorption of SVOC.
The results showed superior sampling performance for UiO-67-Ni, with an adsorption
capacity of 0.226 µg mg-1, representing a 47.8% improvement compared to UiO-67. The
gas removal efficiency reached 17.45% for UiO-67-Ni, compared to 11.81% for UiO-67. A
collective interpretation of the data from characterization techniques demonstrated that the
presence of Ni in the framework substantially increased pore size and specific surface area,
which positively impacted the adsorption performance of the MOF. The pronounced pore
diameter difference between UiO-67 and UiO-67-Ni (22.4 Å to 25.3 Å), and the total
surface area of 1450.5 m² g⁻¹ in the Ni-containing MOF, are the driving forces behind higher
adsorption performance.
Keywords: MOF-based adsorbents; UiO-67; SVOCs; acenaphthene; contaminated air
sampling; static headspace sampling

ii

Table of Contents
Abstract ................................................................................................................................ii
List Of Tables....................................................................................................................... v
List Of Figures .................................................................................................................... vi
Acknowledgement .............................................................................................................vii
1. Introduction...................................................................................................................... 1
1.1 Metal Organic Frameworks (MOFs). ..................................................................... 3
1.1.1 MOF structure modifications........................................................................ 4
1.1.2 UiO-67 .......................................................................................................... 6
1.2 Volatile Organic Compounds and Semi-Volatile Organic Compounds .................. 7
1.2.1 Polycyclic aromatic Hydrocarbons (PAHs) .................................................. 9
1.2.2 Acenaphthene (Acp) ................................................................................... 10
1.3 Air sampling techniques and analysis methods .................................................... 12
1.3.1 Sampling techniques ................................................................................... 12
1.3.2 Extraction techniques.................................................................................. 14
1.3.3 Analytical methods ..................................................................................... 15
2. Research Objectives....................................................................................................... 16
3. Materials and methods ................................................................................................... 17
3.1 Materials ............................................................................................................... 17
3.2 Synthesis of UiO-67 and UiO-67-Ni .................................................................... 17
3.2.1 Synthesis of UiO-67 ................................................................................... 18
3.2.2 Synthesis of UiO-67-Ni .............................................................................. 19
3.3 Characterization .................................................................................................... 20
3.4 PAH sampling ....................................................................................................... 20
3.4.1 Prepare Acenaphthene standards ................................................................ 20
3.4.2 Static headspace sampling .......................................................................... 21
3.5 Desorption............................................................................................................. 22
3.5.1 GC-FID parameters used for analytes quantification ................................. 23
3.6 Experimental design ............................................................................................. 24
iii

3.7 Calculation method ............................................................................................... 26
4. Results and discussion ................................................................................................... 30
4.1 Characterizations of UiO-67, UiO-67-Ni ............................................................. 30
4.1.1 Fourier transform infrared (FT-IR) ............................................................. 31
4.1.2 X-Ray diffraction (XRD) ............................................................................ 32
4.1.3 Brunauer–Emmett–Teller (BET) ................................................................ 34
4.1.4 Scanning electron microscope (SEM) and energy dispersive spectroscopy
(EDS) ................................................................................................................... 36
4.1.5 Thermogravimetric analysis (TGA) ........................................................... 37
4.2 Characterization of UiO-67 and UiO-67-Ni after adsorption ............................... 40
4.2.1 Fourier transform infrared (FT-IR) after adsorption ................................... 40
4.2.2 X-Ray diffraction (XRD) after adsorption ................................................. 41
4.2.3 Thermogravimetric analysis (TGA) after adsorption ................................. 41
4.3 GC-FID analysis ................................................................................................... 42
4.3.1 Quantity adsorbed of acenaphthene ............................................................ 43
4.3.2 Removal efficiency ..................................................................................... 47
5. Conclusion and Further Studies ..................................................................................... 50
References.......................................................................................................................... 51

iv

List Of Tables
Table 1 Target polluted VOC, desorption mechanisms and applications of different
classes of porous material. .................................................................................... 2
Table 2 Health effect of SVOCs and their indoor sources. .......................................... 8
Table 3 Physical and chemical characteristics of acenaphthene. ............................... 11
Table 4 Sampling technique and desorption techniques and analysis method used in
gas-phase applications of MOF. .......................................................................... 14
Table 5 Detailed information on GC-FID conditions. ............................................... 23
Table 6 General Full Factorial design of experiments for 24 trial runs including two
independent variables. ......................................................................................... 25
Table 7 The value of each constant and calculated constant. ..................................... 29
Table 8 Specific surface area, total pore volume, micropore volume, micropore surface
area, and external surface area for both UiO-67 and UiO-67-Ni. ....................... 34
Table 9 Weight loss and mechanisms at different stages of UiO-67 during TGA. .... 39
Table 10 Weight loss and mechanisms at different stages of UiO-67-Ni during TGA.
............................................................................................................................. 39
Table 11 Quantity of acenaphthene adsorbed by UiO-67 and UiO-67-Ni. ................ 45

v

List Of Figures
Figure 1 Structure of Metal-Organic Frameworks (MOFs). ........................................ 4
Figure 2 Chemical structure of (a) UiO-66 [29], and (b) UiO-67 [30]. ....................... 7
Figure 3 Reaction scheme for the synthesis of UiO-67 [82]. .................................... 18
Figure 4 Reaction scheme for the synthesis of UiO-67-Ni [83]. ............................... 19
Figure 5 Gas adsorption- static headspace sampling set-up. ..................................... 22
Figure 6 FT-IR spectra of UiO-67 and UiO-67-Ni. ................................................... 32
Figure 7 XRD patterns for UiO-67 and UiO-67-Ni, with a particular emphasis on the
diffraction patterns observed in the 2θ range from 4° to 12°. ............................. 33
Figure 8 Nitrogen adsorption/desorption isotherms for UiO-67 and UiO-67-Ni, as well
as the enlarged graph of relative pressure from 0.1 to 0.3. .................................. 35
Figure 9 SEM analysis of (a) UiO-67 and (b) UiO-67-Ni together with EDS spectrum
of (c) UiO-67 and (d) UiO-67-Ni. ....................................................................... 37
Figure 10 Thermogravimetric analysis (TGA/DTG) of UiO-67, UiO-67-Ni. ........... 38
Figure 11 FT-IR of (a) UiO-67, UiO-67-Acp, UiO-67-Ni, UiO-67-Ni-Acp, and
acenaphthene, and (b) enlarged FT-IR peak at the range of wavelength between
1800 and 350 cm-1. .............................................................................................. 41
Figure 12 XRD pattern of (a) UiO-67, UiO-67-after, UiO-67-Ni, UiO-67-Ni-after, and
(b) enlarged XRD pattern with a particular emphasis on the diffraction patterns
observed in the 2θ range from 3° to 15°. ............................................................. 41
Figure 13 Thermogravimetric analysis (TGA and DTGA) of UiO-67, UiO-67-Ni, as
well as the curve after adsorbing the acenaphthene............................................. 42
Figure 14 Calibration cure of acenaphthene standard. .............................................. 44
Figure 15 Contour plot of quantity adsorbed of acenaphthene of different adsorbent
dosage and desorption time of (a) UiO-67, and (b) UiO-67-Ni. ......................... 46

vi

Acknowledgement
My supervisor, Dr. Hossein Kazemian, helped me to finish my research adventure at the
University of Northern British Columbia (UNBC), and for that, I am deeply grateful. It has
been an exciting and fulfilling experience. He has been a huge support and guide for me,
not only in academics but also in life.
My sincere gratitude goes out to Dr. Peter Jackson, Dr. Oliver Iorhemen, and Dr. Hoorieh
Jahanbani, who are members of my committee, for their invaluable advice, knowledge, and
steadfast support. Additionally, I would also like to thank Dr. S. Fatemeh Nouroozi for her
support with experimental design, data analysis, and thesis revisions. She always motivates
me when I am lost. I also want to express my gratitude to Erwin Rehl and Mya
Schouwenburg, my colleagues from NALS, for their invaluable assistance and support with
sample analysis.
Finally, I would like to express my sincere gratitude to my family for being my rock of
support and love, both financially and mentally — they have never hesitated. I am grateful
to my spouse, Aaron Y. Wei, especially for his unselfish love and unwavering support.
I could not have finished my study adventure in Canada without their unwavering love and
support. I dedicate this letter to each of you, and I thank you all for the beauty of this
research journey together.

vii

1. Introduction
Considering the evolution of society, people in North America and Europe now spend
approximately 90% of their time indoors [1]. Indoor air quality is crucial in creating healthy
and comfortable environments, whether at work, home, or vehicles. Unfortunately,
furniture and interior construction materials can release volatile organic compounds (VOCs)
and semi-volatile organic compounds (SVOCs).
As people start to pay more attention to the indoor environment, researchers have identified
various porous materials as adsorbents. Various adsorbents developed by researchers, such
as activated carbon, silica gel, zeolite, and other polymers, can effectively reduce the
pollution level of indoor VOC and SVOC pollutants. Various mechanisms, including
adsorption, absorption, and membrane separation, are employed to capture volatile organic
compounds (VOCs) and semi-volatile organic compounds (SVOCs) within the porous
structure of materials. These mechanisms enhance the efficiency of retrieving and
eliminating air pollutants by facilitating the interaction of these contaminants with the
porous matrix, thereby improving their removal from the air [2]. Table 1 provides a
comprehensive summary of the materials utilized in industrial and research contexts for
removing VOC from the environment, categorized according to the desorption mechanisms
employed and their respective applications.

1

Table 1 Target polluted VOC, desorption mechanisms and applications of different classes
of porous material.
Materials

Target
pollutants

Desorption
Applications
Mechanisms

Refs

Activated
carbon

Acetone and
Methyl ethyl
Ketone

Thermal
regeneration

Adsorbent

[3]

Coconut
shell

CHCl3, CCl4,
and CH2Cl2

Water bath
heating

High efficiency in removing
halomethanes from gases

[2]

Silica gel

Xylene and
Toluene

Thermal
desorption

Adsorbent for adsorption of
VOCs

[4]

MCM-41

CCl4 and nhexane

Thermal
desorption

Adsorbent for the removal of
VOCs in high concentrations
and humidity streams

[5]

Monolithic
carbon
Toluene
aerogels

Microwave
heating

Toluene adsorbents

[6]

MIL-101

Vacuum
desorption

VOC adsorbent

[7]

n-hexane

Nevertheless, several materials exhibit limitations, including restricted structure and
humidity stability [8]. Carbon-based materials are limited in adsorbing polar gas molecules
despite their non-polar nature. Furthermore, sampling temperature and humidity
substantially affect the gas adsorption process. Elevated temperature and humidity reduce
their adsorption capacity, indicating that carbon-based materials are less effective for
adsorbing high-boiling point VOCs [2], [9], [10]. Moreover, zeolites have emerged as
highly effective adsorbents for capturing VOC from gas streams, even under elevated
concentrations and humidity [5]. However, the hydrophilicity of zeolite can reduce its
adsorption capacity for VOCs due to competition from water molecules for available
adsorption sites. When sampling under high humidity, the water molecules preferentially
occupy the adsorption sites [11].
2

SVOCs, molecules with higher molecular weight and higher boiling point subgroups of
VOCs, can be found in indoor air, including residential apartments and industrial settings
where products containing these substances are utilized. SVOCs are challenging to detect
and quantify accurately due to their low concentrations and semi-volatile nature, which
causes them to partition between air and indoor surfaces. This results in continuous
desorption and fluctuating air concentrations. Variations in temperature and humidity
further affect the adsorption capacity and selectivity. This research addresses these
challenges by focusing on metal-organic frameworks (MOFs), a promising material for
effectively managing the complexities associated with SVOC adsorption under dynamic
conditions.
1.1 Metal Organic Frameworks (MOFs).
Metal-organic frameworks (MOFs) are a new family of coordination polymers with
crystalline structures formed by connecting metal ions or metal clusters as nodes to organic
ligands, so-called linkers, as illustrated in Figure 1. After zeolites and carbon nanotubes,
MOF have emerged as the most notable porous materials. They are identified as materials
with high surface area, well-defined channels, and customizable density [12].
Carbon-based adsorbents are widely utilized in commercial VOC air purification systems
and for effectively sampling atmospheric VOC, demonstrating their critical role in
enhancing air quality. However, their non-deterministic structure limits the potential for
structural optimization potential, which restricts improvements in pollutant adsorption
efficiency [13].
3

Figure 1 Structure of Metal-Organic Frameworks (MOFs).
In contrast, due to the structural variability and large metal pore dimensions, MOF offers
the potential for gas entrapment [14]. Furthermore, MOF benefits various applications,
including gas storage, separation, catalysis, and sensing [15]. The broad application of
MOF has brought considerable attention to their chemical, thermal, and mechanical
stability. This primarily concerns the resistance of MOF materials to high humidity, pH,
pressure, or temperature [16].
To fully leverage the potential of metal-organic frameworks (MOFs) in practical
applications, enhancing their physical stability is a critical priority, particularly in industrial
environments where sampling occurs at elevated temperatures. Strengthening MOF to
withstand such conditions can help prevent structural degradation or collapse, enabling
their successful use in demanding settings.
1.1.1 MOF structure modifications

At the molecular level, scientists can precisely control the porosity and functionality of
MOF. This approach can substantially improve their structural stability and gas separation
efficiency, leading to notable and optimized performance outcomes [17]. Therefore,

4

researchers actively investigate synthetic methods to obtain new compounds with different
properties and applications. Methods including choice of linker and metal nodes, mixedmetal MOFs, specific synthesis conditions, post-synthetic modification, guest molecule
inclusion, functionalization of linkers, and topochemical synthesis can be employed to
optimize the pore environment [16]. Post-synthetic modification (PSM) is one of the most
common methods used to tune the structural characteristics of MOF. Modification of linkers
and nodes can affect both pore structure and adsorptive characteristics. For example,
bimetallic MOFs developed by post-synthetic modification have greatly improved porosity,
active site, adsorption, selectivity, and stability compared to monometallic MOFs [18].
Although modification through metal substitution at the metal sites is less common than
incorporating side-chain functional groups into the MOF crystal structure, it has
demonstrated considerable advantages [19]. For example, Pal et al. reported that Cu(II)
substituted Zn(II)-MOF has higher water stability and selective adsorption of CO2 from H2
and CH4 [20]. Canossa et al. [21] investigated the effects of different guest metals during
the modification of IRMOF-9. Their findings revealed that substituting Zn(II) in IRMOF9 with Co(II) resulted in higher porosity and increased availability of open metal sites [21].
In terms of the adsorption and removal of SVOC from contaminated air, post-synthetic
modified MOF with increased porosity and more active sites can markedly enhance Van
Der Waals forces and π-π interaction between the metal nodes and the target pollutants [22].
This is particularly relevant for polycyclic aromatic hydrocarbons (PAHs) in this study.
Therefore, optimizing these interactions aims to achieve superior adsorption capacities for
the target pollutants, which is the primary objective of this study.
5

1.1.2 UiO-67
Zirconium (Zr) -based MOFs are highly regarded for their exceptional structural robustness
and durability, attributed to their high coordination numbers and the formation of bonds
with eight or more ligands [23]. Enhancing MOF stability is achieved through designing
rigid structures with high connectivity, which strengthens their overall framework integrity.
Zr serves as a central metal ion, enabling the formation of hexacoordinate solid bonds with
oxygen atoms in organic linkers [24]. These frameworks typically feature Zr(IV) nodes,
stabilized by oxygen-donating ligands, including water (H2O), hydroxyl groups (-OH), and
as many as 12 carboxylate-functionalized organic ligands [25]. The resulting threedimensional network contributes to the mechanical strength and stability of the MOF.
In the context of UiO (University of Oslo) MOFs, the Zr-terephthalate MOF UiO-66 is one
of the most studied structures with notable structure stability. By altering the length or
functional groups of the organic linker, typically terephthalic acid in UiO-66, the
researchers were able to develop iso-reticular variants [26]. UiO-67, also known as Zr (IV)
biphenyl dicarboxylate metal-organic framework, is an iso-reticular analog of UiO-66. Its
longer biphenyl-4,4'-dicarboxylate (BPDC) organic linker increases the spacing between
the zirconium-based nodes in the framework. This extended linker produces larger pore
diameters than UiO-66 [27]. The increased pore size enhances the potential of UiO-67 for
applications in gas adsorption. Figure 2 illustrates the exact chemical structure of UiO-66
and UiO-67.

6

Furthermore, biphenyl-4,4'-dicarboxylic acid (BPDC) provides a substantial number of
phenyl groups, which facilitate strong π-π interactions with SVOC, such as polycyclic
aromatic hydrocarbons. Therefore, UiO-67 exhibits better adsorption capacity in the gas
phase than UiO-66 [28].
(b)

(a)

Figure 2 Chemical structure of (a) UiO-66 [29], and (b) UiO-67 [30].
1.2 Volatile Organic Compounds and Semi-Volatile Organic Compounds
A family of organic compounds known as volatile organic compounds (VOCs) have a high
vapor pressure at room temperature, which makes them tend to evaporate and diffuse into
the atmosphere [31]. These chemical species, which include a wide range of compounds
such as benzene, toluene, formaldehyde, and xylene, present serious health risks [32]. Upon
inhalation, they can penetrate the pulmonary system, disseminating throughout the
respiratory tract before entering the bloodstream [9]. Prolonged exposure to some VOCs
can cause adverse health effects, such as neurological impairment, respiratory issues, and
an elevated risk of cancer. Many VOCs contribute substantially to air pollution in addition
to their direct impact on human health. When exposed to sunlight, they can go through

7

photochemical processes that generate secondary pollutants, including ozone and
particulate matter [33]. These processes can result in the formation of photochemical fog,
which can further harm ecosystems, human health, and air quality. The relevance of VOC
as environmental contaminants and public health threats is highlighted by their abundance
in indoor and outdoor contexts [34].
The U.S. Environmental Protection Agency (EPA) categorizes certain volatile organic
compounds (VOCs) as semi-volatile organic compounds (SVOCs). This classification
applies to VOCs that have a higher molecular weight. Additionally, these compounds
demonstrate reduced volatility within a temperature range of 240/260 to 380/400 °C [35].
These SVOC have properties that allow them to suspend in the air as tiny droplets or adhere
to dust and other surfaces. Consequently, they can penetrate human lungs, potentially
leading to immune system suppression and even cancer, as emphasized by Sonne, C. et al.
[36]. These compounds primarily originate from flame retardants, plasticizers present in
indoor building materials, and PAHs produced through combustion or incomplete
combustion, as noted by Melymuk, L. et al. [37]. Table 2 shows the health effects and
sources of seven classes of SVOCs in an indoor environment.
Table 2 Health effect of SVOCs and their indoor sources.
Pollutants Health Effect [38]

Sources of SVOCs

Refs

*PCBs

Melanomas, cancer,
endocrine disruption, and
osteoporosis.

Electronics; building materials

[36]

*PAHs

Cataracts, kidney and liver
damage, and skin disease.

Coal, crude oil, and gasoline

[39]

8

*PFASs

Cancer, endocrine
disruption, immune
suppression, and neonatal
mortality.

Clothing; electronics; furniture;
building materials; cleaning
products; cosmetics

[40]

*PBDEs

Diabetes, cancer,
reproductive health effects,
and thyroid disruption.

Clothing; electronics; furniture;
building materials

[40]

*BFRs
and
*EBFRs

Immune system
suppression, liver toxicity,
endocrine disruption, and
cancer.

Clothing; electronics; building
materials

[36]

*OPFRs

Cancer, endocrine
disruption, neurotoxicity,
and liver & kidney damage

Clothing; electronics; furniture;
building materials; cosmetics

[36]

*Phthalate
and
*Esters

Asthma, hypersensitivity,
obesity, diabetes, and
infertility.

Canadian indoor dust

[41]

*Seven classes of pollutants, including Polychlorinated Biphenyls (PCBs), Polycyclic Aromatic
Hydrocarbons (PAHs), Polyfluorinated Alkyl Substances (PFASs), Polybrominated Diphenyl Ethers (PBDEs),
Brominated Flame Retardants (BFRs), Emerging Brominated Flame Retardants (EBFRs), Organophosphate
Flame Retardants (OPFRs), and Phthalate Esters.

1.2.1 Polycyclic aromatic Hydrocarbons (PAHs)

Polycyclic aromatic hydrocarbons (PAHs) are compounds containing two or more benzene
rings, composed only of carbon and hydrogen atoms, interconnected in various ring
structural configurations [42], [43], [44]. PAHs originate from a variety of sources,
including both natural and anthropogenic activities [39]. They are released into the
atmosphere through various pathways, including the incomplete combustion of organic
matter in wildfires, emissions from volcanic eruptions, biological activities, and geological
processes [45], [46]. However, due to increased human activities, polycyclic aromatic
hydrocarbons (PAHs) have become a primary contributor to indoor pollution, stemming
from routine human activities and everyday necessities [47].
9

Additionally, workers in the mining and metal processing industries face a considerable
risk of exposure to respiratory exhaust fumes containing high concentration levels of PAHs
[48], [49]. According to the National Pollutant Release Inventory (NPRI) Substance
Overview [47], a total of 32 polycyclic aromatic hydrocarbons (PAHs) were reported, with
acenaphthene consistently exhibiting high levels of reported emissions between 2011 and
2021. Annual emissions of naphthalene peaked at 66 tons during this period [48]. Exposure
to PAHs pollutants can result in symptoms like eye irritation, skin disease, vomiting, and
diarrhea [52], [53]. Considering these challenges, there is a growing need to develop and
implement advanced technologies and innovative materials capable of efficiently capturing,
removing, and reducing PAH concentrations in the atmosphere.
1.2.2 Acenaphthene (Acp)
Among low molecular weight polycyclic aromatic compounds, acenaphthene is widely
used in industrial manufacturing and has attracted public attention due to its unique
reactivity and hazards to human health [54]. It is crucial for scientists to develop a method
to assess the health risks associated with exposure to individual PAH species. Instead of
relying on total PAH concentrations to evaluate their potential health risk to humans and
the environment [55], they have developed and assessed toxic equivalency factors (TEF)
for common PAHs through the toxic equivalency factor method [56], [57]. Given that
benzo[a]pyrene (BaP) is one of the most common toxic compounds, Nisbet and Lagoy
established the BaP equivalent concentration as 1, reflecting the carcinogenic potency of
other PAHs in comparison to BaP [58], [59]. Among them, acenaphthene (Acp) is listed

10

alongside 16 other compounds on the National Priorities List (NPL) as the compound with
the highest concentration at hazardous waste disposal sites, as well as the PAH most
commonly encountered by the public [60], from which the toxic equivalent factors of
acenaphthene is 0.001.
Acenaphthene, a versatile compound utilized in manufacturing dyes, plastics, and
medicines, is employed as an insecticide and fungicide in coal tar and presents unique
challenges in measurement. The physical and chemical characteristics of acenaphthene are
listed in Table 3. Occupational exposure limits for acenaphthene have yet to be established,
highlighting an area for future research and regulatory development [61]. According to the
United States Environmental Protection Agency (EPA) and the American Conference of
Governmental Industrial Hygienists (ACGIH), acenaphthene has been identified as a
hazardous substance due to its toxicological properties and potential adverse effects on
human health and the environment [62]. Acenaphthene targets the skin, blood, stomach,
intestines, and lymphatic system once it penetrates the body. Breathing in high quantities
over brief periods may damage these systems [63]. Therefore, acenaphthene was studied as
a model SVOC to explore the adsorption capacity of the synthesized MOF.
Table 3 Physical and chemical characteristics of acenaphthene.
Chemical

*b.p.
(℃)

*V.P. (hPa)

M.W. (g mol-1)

Radius (Å)

Ref

279

13.3 (131℃)

154.21

3.353-3.561

[64]

Acenaphthene
* b.p. is boiling point; V.P. is vapor pressure; M.W. is molecular weight.
11

1.3 Air sampling techniques and analysis methods
Air sampling and extraction techniques, as well as their associated analytical methods, are
critical for assessing the performance of MOFs in gas-phase applications. These methods
provide essential insights into the efficiency and effectiveness of MOF in various industrial
and research contexts.
1.3.1 Sampling techniques
In environmental research, air sampling techniques are essential, especially for assessing
air quality and identifying contaminants. These methods are critical for determining
pollutant concentrations and understanding the dispersion of pollutants in different contexts
[65]. The two primary air sampling methods are static sampling and active sampling. Active
sampling typically provides a more accurate estimate of the air volume sampled than
passive sampling. In passive sampling, the collection of contaminants relies on the natural
diffusion or movement of particles onto the collector.
In contrast, active sampling uses a pump to create a vacuum, drawing air into the sampler
and ensuring a more controlled and precise measurement of the sampled air volume [66].
However, because of the pump needed and the associated volumetric requirements for setup,
it is typically more expensive than passive sampling. In passive sampling, contaminants
diffuse through the air and are collected onto a substrate. Systems such as diffusion and
permeation collectors, which do not utilize pumps or other active air-moving components,
rely on the natural movement of contaminants [67].

12

In our study, minimizing changes in sample composition is critical for the quantitative
analysis of air pollutant removal using adsorbents. Static headspace sampling is a widely
recognized technique for its effectiveness in capturing volatile components, in which
specific volumes of the test sample and extractant gas are contacted in a closed system for
a given time, offering several notable advantages [68], [69]. This method is particularly
valued as being environmentally friendly, as it reduces the reliance on hazardous solvents
and reduces waste generation.
Static headspace sampling allows volatile compounds to equilibrate between the gas phase
above a sample and the sample matrix. In this process, the sample is sealed in a container,
and the VOCs that evaporate from the sample into the headspace above are allowed to reach
a state of equilibrium [70]. After putting the adsorbent into the system, the synthesized
MOFs were used as adsorbent materials in this study, and a new equilibrium was established
between adsorbents and adsorbate from the gas phase. This dynamic equilibrium enables
efficient capture of the pollutants, making the method highly suitable for quantitative
analysis in air purification applications [71], [72].
Additionally, the ease of operation associated with static headspace sampling makes it userfriendly for laboratory applications, facilitating straightforward implementation by
researchers [70]. Given these advantages, static headspace sampling emerges as the
preferred method for a range of analytical applications in this study, ensuring reliable and
accurate results while preserving sample integrity.

13

1.3.2 Extraction techniques
Various extraction methods are employed to isolate or extract PAHs. The combination of
thermal desorption (TD) and gas chromatography (GC) is recognized as a highly effective
technology. It is a widely employed technology for removing contaminants from materials
and quantitatively analyzing VOC [73]. However, this technology requires the material to
withstand high temperatures and pressures applied during desorption. Furthermore, the
effectiveness of thermal desorption can vary depending on the type of contamination.
Additionally, solid-phase extraction (SPE) and liquid-liquid extraction (LLE) are widely
utilized [74], as outlined in Table 4. These methods are essential for preparing samples
before analytical quantification, ensuring that PAHs are effectively separated from the
complex PM matrix. The National Institute for Occupational Safety and Health (NIOSH)
method 5515 [75] specifies using ultrasound-assisted and Soxhlet extraction methods for
PAHs extraction from filters. The ultrasonic bath method employs high-frequency sound
waves to interact with the adsorbent. This process facilitates the release of polycyclic
aromatic hydrocarbons (PAHs) from the material, which is valued for its simplicity and
relatively short extraction time.
Table 4 Sampling technique and desorption techniques and analysis method used in gasphase applications of MOF.
Materials

Target
molecules

Sampling and
desorption techniques

Analysis

Refs

MOF-5

Formaldehyde

Thermal desorption (TD)

GC- MS

[76]

TiO2/ MIL-101

Degrading
VOCs

Thermal desorption (TD)

GC-FID

[7]

14

Quartz glass filter
disk

PAHs

SPME

GC-MS

[58]

Coated SPME

VOCs

Headspace- SPME

GC-MS

[77]

DHA-80 filters

Nitro-phenolic
compounds

Ultrasonication- HFSPME

CE-ITRMS

[78]

XAD-2 adsorbent

PAHs

Ultrasonication- SPME

GC-MS

[79]

UiO-66

SVOCs

DI-SPME

GC-MS

[80]

From the literature review conducted in this section, UiO-67 has yet to be used for SVOC
removal in the air through the passive air sampling method, no similar studies could be
used for direct comparison. Therefore, in this research, the evaluation focuses on comparing
the removal efficiency of the material before and after modification of synthesized MOFs.
1.3.3 Analytical methods
Furthermore, analytical methods such as gas chromatography (GC), fluorescence
spectroscopy, and liquid chromatography (LC) are commonly used to provide detailed
information on targeted analytes. Additionally, GC can detect PAHs at deficient
concentrations when coupled with a sensitive detector, such as a flame ionization detector
(FID) or a mass spectrometer (MS). This is particularly important in environmental analysis,
where PAHs are often present in trace amounts.
A comprehensive method is necessary to rigorously investigate the adsorption and
analytical behavior of PAHs on MOF structures. The static headspace sampling technique
was used to accurately quantify PAHs in the gas phase. Ultrasonic extraction efficiently
desorbed PAHs from the MOF, ensuring high recovery without damaging the structure.
Moreover, GC-FID provided sensitive and accurate quantification of the desorbed analytes,
enabling a thorough assessment of the adsorption efficiency of synthesized MOF. These
15

methods allow for the quantification and identification of specific compounds, offering
valuable insights into the efficiency and selectivity of the adsorption process. Therefore, in
this project, the static headspace sampling technique was employed as the adsorption
method, ultrasonic was used to extract analytes from the MOF structure, and gas
chromatography coupled with a flame-ionized detector was employed as an analytical
method to quantify the desorbed analytes accurately.
2. Research Objectives
The objectives of this research were twofold:
(i) To compare the efficiency and selectivity of different MOF-based adsorbents for
sampling and measuring SVOC from contaminated air.
(ii) To identify the key factors influencing the adsorption capacity and stability of MOFbased adsorbents in the presence of SVOC in contaminated air and to optimize these factors
for environmental sampling applications.

16

3. Materials and methods
3.1 Materials
This section outlines the reagents utilized in this investigation, including their respective
purities and sources. Notably, all chemicals were used as received without further
purification to maintain their efficacy and ensure the integrity of the synthesized materials.
Zirconium (IV) chloride (ZrCl4, ≥ 98%, anhydrous, for synthesis, Millipore sigma), 4,4′Biphenyl dicarboxylic acid (BPDC, > 97.0%, TCI), Nickel (II) nitrate hexahydrate (Ni
(NO3)2· 6H2O, > 98%, Thermo Scientific), Acenaphthene (C12H10, 99%, Millipore sigma).
N, N-Dimethylformamide (DMF, ≥99.9%, Millipore sigma), Hydrochloric Acid (HCl,
VWR chemicals), Methanol (CH4O, Fisher chemical), and Reagent Alcohol 95% (VWR
chemicals).
3.2 Synthesis of UiO-67 and UiO-67-Ni
MOFs have garnered significant research interest, particularly regarding developing
alternative synthesis methods beyond conventional thermal heating. Diverse synthetic
approaches enable the production of novel MOF compounds with distinct properties and
tailored applications. Furthermore, the pursuit of convenient, cost-effective, and rapid
synthesis methodologies aligns closely with the demands of contemporary industrial
advancements. From our preliminary experiments in different synthesis methods,
solvothermal synthesis allows precise control over reaction parameters such as temperature,

17

solvent type, and time, optimizing material properties, including pore size, surface area,
and adsorption capacity [81].

3.2.1 Synthesis of UiO-67
Following the synthesis method reported by Audu, C. O., et al. [82], UiO-67 was
synthesized using the solvothermal method, following the detailed procedure described
below. The reaction scheme for the synthesis of UiO-67 is shown in Figure 3.
In a 50 mL beaker, zirconium solution ZrCl4 (335.58 mg, 1.44 mmol) was dissolved in 25
mL of N, N-Dimethylformamide (DMF), and 2.5 mL of HCl. At the same time, biphenyl4,4′-dicarboxylic acid (BPDC, 450.55 mg, 1.86 mmol) was partially dissolved by
sonicating in 25 mL of DMF. The metal and organic linker solution mixture was poured
into Teflon reactors, sealed, and left in a programmable oven set to 80 °C for 24 hours.
After that, it cooled to room temperature.

Figure 3 Reaction scheme for the synthesis of UiO-67 [83].
The solid was collected followed by centrifuge at 7500 rpm for 10 minutes and washed
twice with 20 mL DMF and 20 mL ethanol. Finally, the collected UiO-67 was dried using
18

a vacuum oven at room temperature for 24 hours. The resulting white solid was then stored
at room temperature for subsequent analysis and application use.

3.2.2 Synthesis of UiO-67-Ni
The post-synthetic modification (PSM) method introduced the nickel metal ions to the UiO67 structure following the synthesis method reported by Fatemeh et al. [19]. The reaction
scheme for the synthesis of UiO-67-Ni is shown in Figure 4, and the green dot represents
the possible sites of nickel metal ions within the UiO-67 crystal structure.

Figure 4 Reaction scheme for the synthesis of UiO-67-Ni [84].
Freshly synthesized UiO-67 was suspended in a nickel nitrate hexahydrate Ni(NO₃)₂·6H₂O
(209.37 mg, 0.72 mmol) in 25 mL DMF. This resulting mixture was subjected to
solvothermal aging at 100 °C for 36 hours. The procedure was then continued as described
in Section 3.2.1. The resulting light-green solid was collected, followed by centrifuge at
7500 rpm for 5 minutes, and washed twice with 20 mL of DMF, followed by two washing
with 20 mL of ethanol. Finally, the collected UiO-67 was dried using a vacuum oven at
room temperature for 24 hours.

19

3.3 Characterization
Fourier transform infrared (FTIR) spectra, Platinum ATR Alpha ΙΙ FTIR spectrophotometer,
Bruker, in a 400-4000 cm-1 range was employed to characterize the functional groups of
UiO-67 and UiO-67-Ni. The X-ray diffraction (XRD) pattern of the synthesized MOFs was
recorded with a Rigaku Mini-flex 600 6G X-ray using CuKα1 radiation (λ = 1.5418 Å, 40
kV, 15 mA), in the 2θ range from 3° to 60°. Brunauer–Emmett–Teller (BET) analysis and
adsorption/desorption isotherm was performed using Micromeritics 3Flex, using Nitrogen
gas, 12 mm flat bottom sample tube with check seal, samples adsorption-desorption
isotherms were measured at 77.300 K using ultra-pure grade N2 gas. The morphologies and
structures were investigated by the Hitachi SU5000 Scanning Electron Microscope (SEM)
with a decelerated landing voltage of 2 kV, using a BSE detector, and energy dispersive
spectroscopy (EDS) using an Oxford Ultima 65, the approximate detection limit of EDS is
around 0.1wt% to 1wt%. All the samples were prepared on a 15 mm aluminum screw-style
stub with double-sided carbon tape as an adhesive before measurement. The thermal
gravimetric analysis (TGA) was performed to investigate the thermal stability of MOFs
using (TA Instruments 954000.901) on an aluminum pan from 35 °C to 600 °C at a rate of
10 °C min−1.

3.4 PAH sampling
3.4.1 Prepare Acenaphthene standards
A standard solution of 1000 ppm acenaphthene was prepared. An analytical balance was
employed to accurately weigh 10 mg of acenaphthene into a 10 mL volumetric flask. The
20

acenaphthene was diluted to the calibration mark on the volumetric flask using methanol,
a solvent well-known for its high purity and compatibility with the analyte.
Subsequently, a series of standard solutions with concentrations of 10, 5, 2, 1, 0.5, 0.2, and
0.1 ppm were prepared to establish a calibration curve necessary for quantitative analysis.
These concentrations were selected based on the anticipated detection limits of the gas
chromatography-flame ionization detector (GC-FID) system. Each standard was carefully
prepared by serial dilution from the 1000 ppm stock solution.
For method validation, a standard solution of 50 ppm was utilized to determine the retention
time of the target analyte under the specified given GC-FID conditions, ensuring
consistency and reliability in the chromatographic separation. A standard solution of 100
ppm was also prepared for further quantitative analysis. After the standard solutions were
prepared, each solution was analyzed using the GC-FID. The peak area corresponding to
each concentration was recorded, providing the necessary data for plotting the calibration
curve. This curve is critical for determining the concentration of acenaphthene in unknown
samples based on their GC-FID responses.
3.4.2 Static headspace sampling
As discussed in Section 1.3, the static headspace sampling method was chosen as the gas
adsorption technique for this study. In this method, the synthesized MOFs were used to do
the static sampling of gas samples from the equilibrated headspace atmosphere within a
sealed environment maintained at room temperature. As shown in Figure 5, a sealed bottle
containing 4 mL of 100 ppm acenaphthene standard solution was prepared for the
21

experiment. The sampling plastic tube was packed following the NIOSH-5515 standard [75]
to guarantee an appropriate surface of adsorbent interaction with the gas-phase
acenaphthene. A minimum of 50 mg of MOF was used and packed with quartz wool to
prevent direct contact between the MOF and any liquid phase.

Figure 5 Gas adsorption- static headspace sampling set-up.
Following the standard practices in environmental sampling, indoor and outdoor samples
are typically collected over 24 hours. In this study, the sealed vial was carefully opened at
room temperature after the 24-hour sampling period to avoid contamination or loss of the
adsorbed samples. The adsorbents, after sampling, were divided into 10 mg portions to
meet the designed extraction condition, then transferred into an Eppendorf tube and sealed
for subsequent analysis. The specific mass value was written down for each sample for
calculation.
3.5 Desorption

This study employed an ultrasonic bath was employed to assess gas desorption efficiency
over different time intervals. Based on preliminary experiments, different adsorbent

22

dosages subjected to ultrasonic treatment for 5, 10, 15, and 20 minutes were explored and
followed by ultrasonication at 7500 rpm for 5 minutes. The resulting solution was injected
into the gas chromatograph (GC) for further analysis.
3.5.1 GC-FID parameters used for analytes quantification
Analysis was conducted using an Agilent 6890 GC gas chromatograph connected to a
Flame-ionized detector (FID) equipped with a split/splitless inlet and autosampler. The
detailed programming information is listed in Table 5.
Table 5 Detailed information on GC-FID conditions.
Oven Program
#

Rate

Final temperature

Final time

Initial

-

120 ℃

1

1

25 ℃ min-1

240 ℃

0.00

2

10 ℃ min-1

300 ℃

8.20

Front Inlet (Split/splitless)
Mode

Split

Initial temperature

300 ℃

Pressure

22.7 psi

Split ratio

20:1

Total Flow (He)

38.1 mL min-1

Purge Flow to Split Vent

34.0 mL min-1

Column
Flow Set Point

1.7 mL min-1

Post Run

1.7 mL min-1

Column Information

ValcoBond VB-5MS

Temperature Range

-60 ℃- 325 ℃ (350 ℃)

Dimensions

30 m x 250 mm x 0.25 mm

Heater

Oven
23

In

Front inlet

Out (Initial)

Front Detector FID, 40 ℃

Pressure

22.7 psi

Average Velocity

41 cm sec-1

Control Mode

Constant Flow

Column Outlet Pressure

Ambient

Front Detector FID
Makeup

N2

Heater

300 ℃

H2 Flow

35 mL min-1

Air Flow

300 mL min-1

Makeup Flow

13.3 mL min-1

Carrier Gas Flow Correction

Constant Col + Makeup

Flame

On

3.6 Experimental design
The experimental design of this study was created using the General Full Factorial design
method, which enables the creation of factors with varying levels. It will create an
experiment that includes all possible combinations of all factor levels.
The impact of varying adsorbent dosages on extraction efficiency was systematically
evaluated based on the method reported by Liu and colleagues [85] and subsequent
optimizations. To identify the key factors affecting the adsorption capacity of Acp from the
gas phase using different MOFs (UiO-67 and UiO-67-Ni), two independent variables have
been investigated, including the desorption time and adsorbent dosage.
a. Adsorbent dosage
▪

Low Level (-1): 2.5 g L-1
24

▪

High Level (+1): 10 g L-1

Different adsorbent dosages, specifically 2.5 g L-1 and 10 g L-1, were related to the
extraction solvent (methanol) volume of acenaphthene from MOFs, which is 4 mL and 1
mL, respectively, were evaluated to assess their impact on extraction efficiency. The
performance was quantified by analyzing the resulting gas chromatography (GC) signals,
providing a detailed assessment of the extraction efficiency for each adsorbent dosage.
b. Desorption time
▪

5 mins, 10 mins, 15 mins, and 20 mins

A series of desorption times ranging from 5 to 20 minutes were systematically evaluated to
optimize and ensure maximum extraction efficiency. This approach was involved in
determining the optimal duration for achieving the highest level of analyte recovery during
the desorption process.
In this study, each condition was tested in triplicate to ensure the reliability and repeatability
of the results. These values are reported to explain variations and provide a more accurate
representation of the results. Therefore, 24 experimental runs for each synthesized MOF
were investigated for the desorption conditions process in Table 6 for this purpose.
Table 6 General Full Factorial design of experiments for 24 trial runs including two
independent variables.
Run Order

Dosage (g L-1)

Minutes (mins)

1

2.5

15

2

10

20

3

10

5

4

10

15
25

5

2.5

20

6

2.5

5

7

10

15

8

2.5

20

9

2.5

20

10

10

20

11

2.5

5

12

10

10

13

2.5

15

14

2.5

10

15

2.5

10

16

10

15

17

10

10

18

10

20

19

10

5

20

2.5

5

21

10

10

22

10

5

23

2.5

15

24

2.5

10

3.7 Calculation method
The relationship between the liquid phase and the gas phase within the headspace system
is governed by Henry’s Law, which describes how the concentration in the liquid phase
affects the equilibrium concentration in the gas phase, as well as the distribution constant.
The evaporation process is dynamic, and the adsorbent adsorbs analyte, as well as the
system compensates by transferring more analyte from the liquid to the gas phase [72], [86].

26

In systems with analytes distributed between the three phases, the equation of material
balance has the following form expressed in Equation 1.
‫ܥ‬௅଴ ܸ௅଴ = ‫ܥ‬௅ ܸ௅ + ‫ ீܸ ீܥ‬+ ‫ܥ‬ௌ ܸௌ (Equation 1)
where CL, CG, and CS are the equilibrium concentration of the analyte in liquid phase,
gaseous phase, and sorbing phase, respectively. VL, VG, and VS are the volumes of these
phases.
Given the system volume of 20 mL with the headspace volume of 16 mL and the
concentration of the acenaphthene stock solution of 100µg mL-1 employed in this study,
the analytes that adsorbed in the sorbing phase will establish a second equilibrium. In
addition, Cs is a relatively small amount compared to the whole system, therefore, ignoring
its presence in the equilibrium can be an assumption. To investigate the amount of error by
this assumption, MOFs accessible surface area can be calculated by Equation 2.
ܸௌ = ܸ௧௢௧௔௟ × ݉஺ௗ௦௢௥௕௘௡௧ (Equation 2)
where Vtotal is the total pore volume of synthesized MOFs (cm3 g-1), mAdsorbent is the mass
of the dosed MOF into the system, which is 50 mg in this study.
The calculated surface volumes based on BET analysis and assuming 50 mg of the solid
used in each setup, which occupies 0.31 cm3 in the column, are 0.0285 and 0.0345 cm3 for
UiO-67 and UiO-67-Ni, respectively. Consequently, this assures that the assumption is
within the context of the static headspace extraction concept.

27

According to Henry’s Law and distribution coefficient formula, the distribution coefficient
of acenaphthene from methanol to air (KMA) can be evaluated. Based on the Octanol-Water
partition coefficient of Methanol (KMW), Octanol-Air partition coefficient of acenaphthene
(KOA), Octanol-Water partition coefficient of acenaphthene (KOW), Henry’s Law constant
of acenaphthene (HA), the value of each constant and calculated constant was listed in Table
7 [87], [88].
The constant should be converted to dimensionless form to use HA for partitioning
calculations. Assuming 1 atm pressure and 1 mol of gas occupies 22.4 L (0.0224 m³) at
standard conditions, which can be described as Equation 3, the dimensionless form is
HA’ =

‫ܪ‬஺ൗ
0.0224 ݉ଷ ݉‫ି݈݋‬ଵ (Equation 3)

Given the distribution coefficient formula and Henry's law constant, calculated KOA values
from dimensionless Henry’s law constant (HA’) and Octanol–Water partition coefficient
(KOW) can be expressed by the following Equation 4 [89],
KOA =

‫ܭ‬ைௐ
൘‫ܪ‬′ (Equation 4)
஺

From the relationship between partition coefficients,
KMA =

‫ܭ‬ை஺
ൗ‫ܭ‬
(Equation 5)
ைெ

and

KOM =

‫ܭ‬ைௐ
ൗ‫ܭ‬
(Equation 6)
ெௐ

Thus,
KMA =

‫ܭ‬ை஺ × ‫ܭ‬ெௐ
ൗ‫ܭ‬
(Equation 7)
ைௐ

The initial amount of acenaphthene in methanol
M (g) = ‫ܥ‬஺ × ܸ஺ (Equation 8)
28

where CA is the concentration of acenaphthene; VA is the volume of the methanol solution.
Use the partition coefficient and mass balance Equation 9,
‫ܸ = ܯ‬௠௘௧௛௔௡௢௟ × ‫ܥ‬௠௘௧௛௔௡௢௟ + ܸ௔௜௥ × ‫ܥ‬௔௜௥
= ܸ௠௘௧௛௔௡௢௟ × ‫ܭ‬ை஺ × ‫ܥ‬௔௜௥ + ܸ௔௜௥ × ‫ܥ‬௔௜௥ (Equation 9)
where Cmethanol and Cair are the equilibrium concentrations of acenaphthene in methanol and
air, respectively.
Thus, the equilibrium concentration of acenaphthene in air can be calculated using Equation
10,
‫ܥ‬௔௜௥ (݃ ‫ିܮ‬ଵ ) = ‫ܯ‬ൗ(ܸ

௠௘௧௛௔௡௢௟ × ‫ܭ‬ை஺ + ܸ௔௜௥ )

(Equation 10)

Thus, the equilibrium amount of acenaphthene in the air phase can be obtained according
to Equation 11,
‫ܯ‬௔௜௥ (µ݃) = ܸ௔௜௥ × ‫ܥ‬௔௜௥ (Equation 11)
Table 7 The value of each constant and calculated constant.
Constant [88]

Value

Log KOW

3.92 (at 25 ℃)

HA (atm m3 mol-1)

1.84 x 10-4 (at 25 ℃)

Log KMW

-0.77 (at 25 ℃)

HA’

8.21 x 10-3

KOA

967513.08

Log KOA

5.99

KMA

20.17

Cair (g L-1)

4.05 x 10-3

Mair (µg)

64.75
29

4. Results and discussion
A comprehensive characterization was performed to evaluate the stability of all adsorbents
in terms of crystal structure and molecular composition before and after gas adsorption.
The structural integrity and functional properties of the synthesized MOF have been
carefully analyzed and compared with the published data from peer-reviewed journals,
which have been discussed in sections 4.1 and 4.2. Subsequently, results from sampling and
extraction are discussed in section 4.3, following the complete analysis of data collected
from the experimental work.
4.1 Characterizations of UiO-67, UiO-67-Ni
The synthesized UiO-67 and UiO-67-Ni were extensively characterized using various
analytical techniques, including Fourier Transform Infrared Spectroscopy (FT-IR), X-ray
Diffraction (XRD), Brunauer-Emmett-Teller (BET) analysis, Scanning Electron
Microscopy

(SEM),

Energy

Dispersive

X-ray

Spectroscopy

(EDX),

and

Thermogravimetric Analysis (TGA). XRD was employed to assess the crystallinity and
confirm the successful synthesis of MOF. At the same time, BET analysis provided detailed
information regarding the surface area and porosity, which are critical factors for enhancing
gas uptake. The thermal stability of the frameworks was evaluated using TGA, ensuring
that the materials can withstand the operational conditions of industrial gas separations.
SEM and EDX were used to analyze surface morphology and elemental composition.

30

4.1.1 Fourier transform infrared (FT-IR)
FT-IR characterization analysis was conducted on the synthesized UiO-67 and UiO-67-Ni,
as illustrated in Figure 6. The FTIR spectrum of UiO-67 is dominated by a strong and broad
band centered at 3412 cm−1, highlighted in a blue band, which is due to the O–H stretching
peak of inter-crystalline water and physically adsorbed water in the pores [90]. The
stretching vibration peaks of aromatic C–H of benzene rings can be seen in the range of
3100–3000 cm−1, and there is an apparent aliphatic C–H stretching vibration peak of methyl
(–CH3) at 2975 cm−1. Both were highlighted in orange bands. The absorption peak at 1660
cm−1 belongs to the C=O stretching vibration peak of BPDC, while the absorption peaks at
1597, 1545, and 1406 cm−1 are related to the C=C stretching vibration of benzene rings,
and the symmetrical and asymmetrical stretching of COO− vibration pattern [91]. In the
green bar highlighted area, which represents the Zr-O2 mode, there is a triplet peak of
longitudinal and transverse modes at 768, 660, and 458 cm−1, respectively [92].
On the other hand, UiO-67-Ni exhibits an infrared spectrum closely resembling that of the
parent structure. Notably, UiO-67-Ni displays a distinctive absorption peak near 1041 cm1

[93], attributable to the Ni-O-Ni stretching vibration mode.

31

Figure 6 FT-IR spectra of UiO-67 and UiO-67-Ni.
4.1.2 X-Ray diffraction (XRD)
The XRD characterization of UiO-67 and UiO-67-Ni composites is shown in Figure 7. The
information illustrates that the XRD patterns of MOF crystal structures before and after
modification are similar. They both have firm diffraction patterns at 2θ at 5.65°, 6.53°, 9.26°,
10.86°, and 11.35°, which corresponds to the (111), (200), (220), (311) and (222) crystal
planes, respectively [94] and are consistent with the standard crystal structure information
of UiO-67 [27], which indicated that they were highly crystalline.
After modification with nickel, as shown in the enlarged graph on the diffraction pattern
observed in the 2θ range from 4° to 12°, the enlarged section of the XRD pattern within
this range provides detailed insight into the structural modifications induced by the
incorporation of nickel into the UiO-67 framework. Despite these modifications, the XRD
32

pattern of UiO-67-Ni closely resembles that of the unmodified UiO-67, indicating
successful retention of the framework structure after post-treatment. Notably, distinct
diffraction patterns are observed at 2θ values of 13.17°, 37.28°, and 48.56°, corresponding
to the (001), (101), and (102) crystal planes of Ni-O-Ni, as reported by Pramana Y. B. [95],
However, in our findings, the nickel-related diffraction patterns are less pronounced, which
suggests a lower amount of nickel incorporated into the structure in comparison with other
species or possible differences in the crystallinity of the nickel species within the
framework.

Figure 7 XRD patterns for UiO-67 and UiO-67-Ni, with a particular emphasis on the
diffraction patterns observed in the 2θ range from 4° to 12°.

33

4.1.3 Brunauer–Emmett–Teller (BET)
Based on the data presented in Table 8, the BET surface area of UiO-67-Ni is considerably
higher than that of the original UiO-67. The post-synthetic process, wherein nickel was
incorporated into the UiO-67 framework, resulted in an increase in the specific surface area
of UiO-67-Ni, reaching 1450.5 m² g⁻¹.
Table 8 Specific surface area, total pore volume, micropore volume, micropore surface area,
and external surface area for both UiO-67 and UiO-67-Ni.
Specific
surface
area

Total
pore
volume

(m2 g-1)

(cm3 g-1)

UiO-67

1137

UiO-67-Ni

1450

MOF

Micropore Micropore
volume
surface area
3 -1
(m2 g-1)
(cm g )

External
surface area
(m2 g-1)

Pore
size
(Å)

0.57

0.23

563

573

22.4

0.69

0.30

752

698

25.3

Additionally, a substantial increase in pore size and other vital parameters, such as total
pore volume, micropore volume, micropore surface area, and external surface area, which
also refers to the t-plot outer surface area, were also observed to be more prominent in UiO67-Ni. These findings align with results from complementary analyses, indicating a
substantial enhancement in the porosity and surface characteristics of the material due to
the nickel modification.
Furthermore, the N2 adsorption-desorption isotherm of UiO-67 and UiO-67-Ni are shown
in Figure 8. The information represents the qualitative and descriptive performance of the
adsorption and desorption processes, along with the material surface area and pore structure.
Referring to the IUPAC classification of isotherms resembles the type IV isotherm. The

34

presence of a continuous hysteresis loop belonging to the H4 type at P/Po of 0.1–0.3 for
both UiO-67 and UiO-67-Ni represents the microporosity of these structures.
Nonetheless, as shown in the enlarged graph of relative pressure from 0.1 to 0.3, the gap
between the adsorption/desorption curve, depicted for UiO-67-Ni, is much narrower than
that of the original MOF, as shown in Figure 8. The narrow hysteresis curve corresponds to
the mild development of micropores [78]. This behavior can be attributed to the variation
in pore width between UiO-67 and UiO-67-Ni, and the discrepancy in pore dimensions
between these two frameworks results in the narrow hysteresis loop observed in the
adsorption-desorption isotherms.

Figure 8 Nitrogen adsorption/desorption isotherms for UiO-67 and UiO-67-Ni, as well as
the enlarged graph of relative pressure from 0.1 to 0.3.

35

4.1.4 Scanning electron microscope (SEM) and energy dispersive spectroscopy (EDS)
The SEM images of (a) UiO-67 and (b) UiO-67-Ni, together with EDS analysis, are shown
in Figure 9. As the results show, no pronounced changes in morphology have been observed
under the post-synthetic modification conditions. Additionally, the presence of 2.252% of
Nickel in EDS analysis (Figure 9 (c) and 9 (d)) means that during the post-modification,
nickel ions have been incorporated into the structure of UiO-67. As the collective
interpretation of the characterization technique data shows, this study suggests that Nickel
ions are in contact with the zirconium metal nodes. The atomic ratio of the introduced Ni
and Zr was 1:6.3, which is consistent with the analysis data from triple quadrupole
inductively coupled plasma mass spectrometry (ICP-QQQ).

36

(c)

(d)

Figure 9 SEM analysis of (a) UiO-67 and (b) UiO-67-Ni together with EDS spectrum of
(c) UiO-67 and (d) UiO-67-Ni.
4.1.5 Thermogravimetric analysis (TGA)
The TGA test results, illustrated in Figure 10, reveal distinctive features. The TGA of UiO67 exhibits two distinct stages of mass loss, which is consistent with the findings reported
by Peng et al. [90].
37

In the first stage, from 43 to 162 °C, the UiO-67 lost 30.5 % of its initial mass, which was
attributed to the volatilization of residual DMF solvent within UiO-67. During the plateau
period of 162~526 ℃, due to the stable crystal structure, UiO-67 material experiences
minimal loss until 526 ℃. Subsequently, weight loss in the third stage was marked by a
sharp drop in the range of 526~534 °C, with a total weight loss of 38.3 %, which signifies
the destruction of the crystal structure attributed to the cleavage of the benzene ring and
carboxylic acid group in the UiO-67 organic chain [96]. A second plateau appears at
524~600 °C, where only a trace of ZrO2 with very low crystallinity is present, accounting
for 31.2 % of the initial mass. Therefore, UiO-67 exhibits commendable thermal stability
and maintains a stable crystal structure below 526 °C. The detailed information is listed in
Table 9.

Figure 10 Thermogravimetric analysis (TGA/DTG) of UiO-67, UiO-67-Ni.

38

Table 9 Weight loss and mechanisms at different stages of UiO-67 during TGA.
Temperature

Weight loss

Mechanism

Stage 1

43-162 ℃

30.5 %

Volatilization of DMF solvent

Stage 2

162-526 ℃

-

MOF structure stable

Stage 3

526-534 ℃

38.3 %

Cleavage of the benzene ring and
carboxylic acid group

Stage 4

534-600 ℃

-

ZrO2, low crystallinity

UiO-67 incorporated with nickel exhibits a decrease in thermal stability, as evidenced by
the TGA/ DTG curve of UiO-67-Ni (drawn in red solid and dotted line in Figure 10,
portraying two different stages of mass loss. In the first stage, from 47 to 162 °C, the
material experiences a loss of 31.3 % of its initial mass, possibly due to the volatilization
of the residual DMF solvent in synthesized MOF. With temperatures between 162 and
422 °C, UiO-67-Ni has a stable structure, slightly decreasing compared to UiO-67.
Subsequently, in the third stage, a sharp drop in weight loss occurs within the range of 422428 °C, with a total of 38.0 %, indicating the destruction of the UiO-67 crystal structure in
the material. Finally, a plateau appears at 428~600 °C, where only 29.8% of ZrO2 and NiO
with very low crystallinity is present. The detailed information is listed in Table 10.
Table 10 Weight loss and mechanisms at different stages of UiO-67-Ni during TGA.
Temperature

Weight loss

Mechanism

Stage 1

47-162 ℃

31.3 %

Volatilization of DMF solvent

Stage 2

162-422 ℃

-

MOF structure stable

Stage 3

422-428 ℃

38.0 %

Destruction of UiO-67 crystal
structure

39

Stage 4

428-600 ℃

-

ZrO2, NiO, low crystallinity

4.2 Characterization of UiO-67 and UiO-67-Ni after adsorption
4.2.1 Fourier transform infrared (FT-IR) after adsorption
The FT-IR spectra of UiO-67 and UiO-67-Ni, as presented in Figure 11 (a), exhibit
absorption peaks at 1597, 1545, and 1406 cm−1 are related to the C=C stretching vibration
of benzene rings, and the symmetrical and asymmetrical stretching vibration of the COO−
groups. The similarity of the spectra before and after adsorption indicates that the MOF
structure remains unchanged upon interaction with analytes. Figure 11 (b) provides an
enlarged view of the FT-IR region from 1800 to 350 cm-1. The FT-IR spectrum of
acenaphthene, represented by the blue line, overlaps with the original MOF peaks because
of the small quantity of analyte captured onto the structures. Furthermore, after the
adsorption of acenaphthene, distinct variations in the spectral peaks, highlighted in gray,
were observed. These changes are likely attributable to the interaction of the analyte with
the MOF structure, suggesting its successful adsorption.

40

Figure 11 FT-IR of (a) UiO-67, UiO-67-Acp, UiO-67-Ni, UiO-67-Ni-Acp, and
acenaphthene, and (b) enlarged FT-IR peak at the range of wavelength between 1800 and
350 cm-1.
4.2.2 X-Ray diffraction (XRD) after adsorption
The XRD patterns of (a) UiO-67 and UiO-67-Ni, as well as the patterns after desorption
illustrated in Figure 12 (a), and the enlarged XRD pattern of the grey color heightened
region in (a) is shown in Figure 12 (b). The data in the figure indicates that after desorption,
the crystal structures of UiO-67 and UiO-67-Ni substantially hold constant to their original
forms. Notably, after the modification of UiO-67, the crystal structure is preserved
effectively. This suggests that the nickel atoms play a crucial role in enhancing the stability
of the MOF.

Figure 12 XRD pattern of (a) UiO-67, UiO-67-after, UiO-67-Ni, UiO-67-Ni-after, and (b)
enlarged XRD pattern with a particular emphasis on the diffraction patterns observed in the
2θ range from 3° to 15°.
4.2.3 Thermogravimetric analysis (TGA) after adsorption
The TGA curves for the various adsorbents post-sampling are presented in Figure 13. As
outlined in Section 4.1.5, the observed weight loss corresponds well with the temperature
variations, indicating that the UiO-67 framework retains its structural integrity following
41

acenaphthene adsorption. The two distinct weight loss stages in the initial stage are
attributed to the desorption of methanol, which was used as solvent, and acenaphthene,
respectively.

Figure 13 Thermogravimetric analysis (TGA and DTGA) of UiO-67, UiO-67-Ni, as well
as the curve after adsorbing the acenaphthene.
4.3 GC-FID analysis
The effects of different dosages of adsorbents and extraction intervals on gas desorption
efficiency were systematically investigated. The adsorption and desorption efficiency of
acenaphthene on different materials was assessed using the partition coefficient of
acenaphthene in methanol in conjunction with gas chromatography data. This approach
provided critical evidence regarding the potential applicability of modified MOF in
industrial gas capture systems. By analyzing these parameters, we obtained critical insights

42

into the performance and feasibility of the MOF for efficient gas separation and capture
applications.

4.3.1 Quantity adsorbed of acenaphthene
Following the calculation introduced in section 3.7, the partition coefficient between
methanol and air KMA is 20.71. To calculate the amount of acenaphthene transferred from a
4 mL 100 ppm acenaphthene solution in methanol to 16 mL of air in a sealed container, the
methanol- air partition coefficient KMA and Equation 12 was used.
To establish a calibration curve necessary for quantitative analysis, a series of standard
solutions were used with concentrations of 10, 5, 2, 1, 0.5, 0.2, and 0.1 ppm. The peak areas
of the standard solutions were obtained through GC-FID analysis. The linear regression
equation and correlation coefficient R2 of the acenaphthene standard solution are shown in
Figure 14. The linear regression equation is Y= 1.6893×X- 0.1983, and the correlation
coefficient R2= 0.9996, where X is the concentration of standard solutions, while Y
represents the corresponding peak area.

43

Figure 14 Calibration curve of acenaphthene standard.
After obtaining the chromatogram of each sample, the EIC technique is used for data
analysis. The peak area of the extraction solution was recorded, and the quantity adsorbed
of acenaphthene of different conditions was calculated by Equation 12. After calculation, a
linear regression equation was needed to calculate the concentration of components (ppm).
Madsorbed (µg mg-1) =

൫‫ ܣ‬+ 0.1983ൗ1.6893൯ × ܸ௠
൘
‫ܯ‬ெைி (Equation 12)

where, A is the peak area of the analyte, Vm is the desorption solvent volume (mL), and
MMOF is the dosage of ultrasonic desorption (mg).
According to the experimental design, the quantity adsorbed of acenaphthene was measured
for UiO-67 and UiO-67-Ni under eight distinct conditions, including three replicates
expressed in µg mg⁻¹. The processed data from GC are listed in Table 11.

44

Table 11 Quantity of acenaphthene adsorbed by UiO-67 and UiO-67-Ni.
Run
Order

Dosage
(g L-1)

Minutes
(mins)

*Response1 (µg mg⁻¹)

*Response2 (µg mg⁻¹)

1

2.5

15

0.08

0.08

2

10

20

0.16

0.23

3

10

5

0.11

0.11

4

10

15

0.12

0.20

5

2.5

20

0.10

0.12

6

2.5

5

0.03

0.04

7

10

15

0.10

0.20

8

2.5

20

0.11

0.11

9

2.5

20

0.10

0.11

10

10

20

0.16

0.22

11

2.5

5

0.03

0.04

12

10

10

0.13

0.11

13

2.5

15

0.08

0.06

14

2.5

10

0.05

0.08

15

2.5

10

0.05

0.06

16

10

15

0.10

0.20

17

10

10

0.14

0.11

18

10

20

0.14

0.23

19

10

5

0.11

0.14

20

2.5

5

0.03

0.05

21

10

10

0.11

0.11

22

10

5

0.12

0.10

23

2.5

15

0.08

0.08

24

2.5

10

0.05

0.06

*Response1 represents the quantity adsorbed of acenaphthene by UiO-67, while Response2 represents the
quantity adsorbed of acenaphthene by UiO-67-Ni.

45

To identify the relationship between two independent variables, a general linear model
ANOVA analysis was conducted, and the adsorption capacities for all replicates were
plotted in a contour map to explore the relationship between two independent variables,
which is drawn in Figure 15.

Figure 15 Contour plot of quantity adsorbed of acenaphthene of different adsorbent dosage
and desorption time of (a) UiO-67, and (b) UiO-67-Ni.
As the information displayed in Figure 15, the extraction efficiency of analytes increased
progressively as the desorption time was increased from 5 to 20 minutes. For UiO-67, the
extraction efficiency exhibited minimal variation when the time was increased from 5 to 15
minutes. However, a considerable increase was observed at 20 minutes, reaching the
highest extraction efficiency of 0.153 µg mg⁻¹. Moreover, the extraction efficiency
exhibited a positive correlation with increased adsorbent dosage. But according to p-value
analysis, when the desorption time was 10–15 minutes, the group of independent variables
did not exhibit a statistically significant association with the dependent variable (p>0.05),
which is also in line with the data displayed in the contour plot.

46

Meanwhile, for UiO-67-Ni, the higher adsorbent dosage was associated with higher
extraction efficiency. The extraction efficiency exhibited minimal variation as the
desorption time was extended from 15 to 20 minutes, 0.200 and 0.227 µg mg⁻¹, respectively.
However, desorption time shows a non-linear pattern, when experimental conditions are 5
and 10 minutes, the quantity of adsorbed acenaphthene decreases significantly, while when
time increased from 15 to 20 minutes, there would be an increase in adsorption efficiency.
Considering the high efficiency and expedited sample extraction requirements, 15 minutes
was the optimal ultrasonic duration.
In this study, the adsorption and desorption behavior of the acenaphthene were investigated
using different methods. The adsorption of gaseous acenaphthene molecules on the MOF
surface was facilitated by π-π interaction, which belongs to the physisorption mechanisms
[97]. Given the reversible nature of physisorption, ultrasonic was employed as the energy
source. However, although the π-π interactions have relatively weak strength, the ultrasonic
energy may not be sufficient to destroy these interactions. Therefore, this could result in
incomplete desorption of the adsorbed gas molecules, potentially limiting the efficiency of
the desorption process. This also explains why the gas desorption efficiency is not linear
when the desorption time is around 10 to 15 minutes.
4.3.2 Removal efficiency
50 mg MOF dozed into the plastic tube for the static headspace sampling set-up. Therefore,
the total gas-phase acenaphthene adsorbed amount can be expressed as Mtotal (µg), using
Equation 13,
47

‫ܯ‬௧௢௧௔௟ (µ݃) = 50 × ‫ܯ‬௔ௗ௦௢௥௕௘ௗ (Equation 13)
Thus, the removal efficiency (%) of the optimal condition can be calculated through
Equation 14,
Removal efficiency, R (%) =

‫ܯ‬௧௢௧௔௟ൗ
64.75 (Equation 14)

Where the amount of acenaphthene in the headspace was calculated according to Equation
3 to Equation 11, which is 64.75 µg.
The improvement of removal efficiency between two different adsorbents was calculated
using Equation 15.
Improvement (%) =

ܴଶ − ܴଵ
ൗܴ (Equation 15)
ଵ

where, R1 is the removal efficiency of UiO-67, and R2 is the removal efficiency of UiO-67Ni.
Therefore, the calculated removal efficiency of UiO-67 is 11.81%, while the removal
efficiency of UiO-67-Ni is 17.45% by using the equation illustrated above. The results
showed that UiO-67-Ni has a 47.8% improvement in removal efficiency compared to UiO67.
Incorporating nickel (Ni²⁺) ions into the structure of UiO-67 considerably alters the
coordination environment of the metal nodes, leading to a denser metal node population.
Due to this, densification weakens coordination bonds between the zirconium-based metal
nodes and the biphenyl-4,4'-dicarboxylate (BPDC) linkers. Therefore, this weakened

48

interaction enhances the π-π stacking interactions between the BPDC linkers and guest
molecules such as acenaphthene.
Brunauer-Emmett-Teller (BET) analysis reveals that the Ni-modified UiO-67 (UiO-67-Ni)
exhibits a larger pore size than the parent UiO-67 framework. This observation is consistent
with the hypothesis that incorporating Ni²⁺ ions weaken the connectivity between the
ligands and metal nodes, resulting in a more open pore structure. The increase in pore size
facilitates more substantial guest molecule adsorption, further corroborating the
strengthened π-π interactions observed with acenaphthene.

49

5. Conclusion and Further Studies
This study has explored two Zr-containing MOFs, UiO-67 and UiO-67-Ni, for their
potential for adsorption of acenaphthene as a model compound for PAHs. Both MOFs
demonstrated considerable adsorption capacities at an adsorbent dosage of 10 g L-1.
Specifically, UiO-67 achieved the highest extraction efficiency of 0.153 µg mg⁻¹ within 20
minutes, while UiO-67-Ni reached an extraction efficiency of 0.200 µg mg⁻¹ within 15
minutes. In addition, the adsorption efficiency of acenaphthene on different MOF was
further quantified, where the removal efficiency of acenaphthene reached 11.81% and 17.45%
for UiO-67 and UiO-67-Ni, respectively. Notably, UiO-67-Ni exhibited a 47.8%
improvement in removal efficiency compared to UiO-67, highlighting its superior
performance in acenaphthene adsorption.
The results of the study indicate that larger pores and post-synthetic modification improved
acenaphthene adsorption. With increased porosity, the modified UiO-67 framework
demonstrated enhanced gas desorption efficiency while maintaining crystallographic
integrity. These characteristics align with operation conditions on demand for sampling and
analysis in environmental and industrial applications.
While this study showed improved gas capture efficiency, future research could explore the
scalability of MOF for industrial use, such as the membrane material for portable
autosamplers in the workplace. Besides, for future studies, systematically analyze PAHs of
varying molecular weights to understand their π–π interactions.

50

References
[1] G. P. Marchildon, S. Allin, and S. Merkur, “Canada: health system review,” Health
Syst. Transit., vol. 22, no. 3, 2020, Accessed: Aug. 20, 2024. [Online]. Available:
https://apps.who.int/iris/bitstream/handle/10665/336311/HiT-22-3-2020-eng.pdf
[2] X. Zhang, B. Gao, A. E. Creamer, C. Cao, and Y. Li, “Adsorption of VOCs onto
engineered carbon materials: A review,” J. Hazard. Mater., vol. 338, pp. 102–123,
2017.
[3] Q. Qian, C. Gong, Z. Zhang, and G. Yuan, “Removal of VOCs by activated carbon
microspheres derived from polymer: a comparative study,” Adsorption, vol. 21, pp.
333–341, 2015.
[4] S. Kutluay and F. Temel, “Silica gel based new adsorbent having enhanced VOC
dynamic adsorption/desorption performance,” Colloids Surf. Physicochem. Eng. Asp.,
vol. 609, p. 125848, 2021.
[5] X. S. Zhao, Q. Ma, and G. Q. Lu, “VOC removal: comparison of MCM-41 with
hydrophobic zeolites and activated carbon,” Energy Fuels, vol. 12, no. 6, pp. 1051–
1054, 1998.
[6] F. J. Maldonado-Hódar, C. Moreno-Castilla, F. Carrasco-Marín, and A. F. PérezCadenas, “Reversible toluene adsorption on monolithic carbon aerogels,” J. Hazard.
Mater., vol. 148, no. 3, pp. 548–552, 2007.
[7] Y. Hu, Z. Huang, L. Zhou, D. Wang, and G. Li, “Synthesis of nanoscale titania
embedded in MIL-101 for the adsorption and degradation of volatile pollutants with
thermal desorption gas chromatography and mass spectrometry detection,” J. Sep. Sci.,
vol. 37, no. 12, pp. 1482–1488, 2014.
[8] K. Dettmer and W. Engewald, “Adsorbent materials commonly used in air analysis
for adsorptive enrichment and thermal desorption of volatile organic compounds,”
Anal. Bioanal. Chem., vol. 373, no. 6, pp. 490–500, 2002.
[9] H. Wang, J. Xiong, and W. Wei, “Measurement methods and impact factors for the
key parameters of VOC/SVOC emissions from materials in indoor and vehicular
environments: A review,” Environ. Int., vol. 168, p. 107451, 2022.
[10] X. Li, L. Zhang, Z. Yang, P. Wang, Y. Yan, and J. Ran, “Adsorption materials for
volatile organic compounds (VOCs) and the key factors for VOCs adsorption process:
A review,” Sep. Purif. Technol., vol. 235, p. 116213, 2020.

51

[11] B. Yu, H. Deng, Y. Lu, T. Pan, W. Shan, and H. He, “Adsorptive interaction between
typical VOCs and various topological zeolites: Mixture effect and mechanism,” J.
Environ. Sci., vol. 136, pp. 626–636, Feb. 2024, doi: 10.1016/j.jes.2023.02.015.
[12] L. Jiao, J. Y. R. Seow, W. S. Skinner, Z. U. Wang, and H.-L. Jiang, “Metal–organic
frameworks: Structures and functional applications,” Mater. Today, vol. 27, pp. 43–
68, 2019.
[13] H.-Y. Li, S.-N. Zhao, S.-Q. Zang, and J. Li, “Functional metal–organic frameworks as
effective sensors of gases and volatile compounds,” Chem. Soc. Rev., vol. 49, no. 17,
pp. 6364–6401, 2020.
[14] Q.-G. Zhai, X. Bu, X. Zhao, D.-S. Li, and P. Feng, “Pore space partition in metal–
organic frameworks,” Acc. Chem. Res., vol. 50, no. 2, pp. 407–417, 2017.
[15]H.-C. Zhou, J. R. Long, and O. M. Yaghi, “Introduction to metal–organic frameworks,”
Chemical reviews, vol. 112, no. 2. ACS Publications, pp. 673–674, 2012. Accessed:
Aug. 20, 2024. [Online]. Available: https://pubs.acs.org/doi/full/10.1021/cr300014x
[16] M. Ding, X. Cai, and H.-L. Jiang, “Improving MOF stability: approaches and
applications,” Chem. Sci., vol. 10, no. 44, pp. 10209–10230, 2019.
[17] Y. Feng et al., “Engineering the pore environment of metal–organic framework
membranes via modification of the secondary building unit for improved gas
separation,” J. Mater. Chem. A, vol. 8, no. 26, pp. 13132–13141, 2020.
[18] J. Luo et al., “Advantages of bimetallic organic frameworks in the adsorption,
catalysis and detection for water contaminants,” Nanomaterials, vol. 13, no. 15, p.
2194, 2023.
[19] Fatemeh Nouroozi, Hossein Kazemian, “Tuning The Adsorptive Characteristics Of
MOFs Through Trans-Metalation: A Successful Strategy For Designing Selective
Adsorbents For Aquas And Gas-Phase Applications,” presented at the 6th Euro-Asia
Zeolite Conference, EAZC-2025.
[20] T. K. Pal, D. De, S. Senthilkumar, S. Neogi, and P. K. Bharadwaj, “A partially
fluorinated, water-stable Cu (II)–MOF derived via transmetalation: significant gas
adsorption with high CO2 selectivity and catalysis of Biginelli reactions,” Inorg.
Chem., vol. 55, no. 16, pp. 7835–7842, 2016.
[21] S. Canossa et al., “MOF transmetalation beyond cation substitution: defective
distortion of IRMOF-9 in the spotlight,” CrystEngComm, vol. 21, no. 5, pp. 827–834,
2019.
[22] Y. Cao et al., “A MOF-derived carbon host associated with Fe and Co single atoms
for Li–Se batteries,” J. Mater. Chem. A, vol. 9, no. 29, pp. 16196–16207, 2021.
52

[23] Y. Bai, Y. Dou, L.-H. Xie, W. Rutledge, J.-R. Li, and H.-C. Zhou, “Zr-based metal–
organic frameworks: design, synthesis, structure, and applications,” Chem. Soc. Rev.,
vol. 45, no. 8, pp. 2327–2367, 2016, doi: 10.1039/C5CS00837A.
[24] Y.-S. Kang, Y. Lu, K. Chen, Y. Zhao, P. Wang, and W.-Y. Sun, “Metal–organic
frameworks with catalytic centers: From synthesis to catalytic application,” Coord.
Chem. Rev., vol. 378, pp. 262–280, Jan. 2019, doi: 10.1016/j.ccr.2018.02.009.
[25] C. Healy et al., “The thermal stability of metal-organic frameworks,” Coord. Chem.
Rev., vol. 419, p. 213388, 2020.
[26] V. Guillerm et al., “A Series of Isoreticular, Highly Stable, Porous Zirconium Oxide
Based Metal–Organic Frameworks,” Angew. Chem. Int. Ed., vol. 51, no. 37, pp. 9267–
9271, Sep. 2012, doi: 10.1002/anie.201204806.
[27] S. Øien et al., “Detailed structure analysis of atomic positions and defects in zirconium
metal–organic frameworks,” Cryst. Growth Des., vol. 14, no. 11, pp. 5370–5372, 2014.
[28] Q. Zhao et al., “Effects of regulator ratio and guest molecule diffusion on VOCs
adsorption by defective UiO-67: Experimental and theoretical insights,” Chem. Eng.
J., vol. 433, p. 134510, 2022.
[29] “UiO-66 - CD Bioparticles.” Accessed: Sep. 02, 2024. [Online]. Available:
https://www.cd-bioparticles.net/p/10359/uio-66
[30] “UiO-67 (Zr) - CD Bioparticles.” Accessed: Aug. 20, 2024. [Online]. Available:
https://www.cd-bioparticles.net/p/9156/uio-67zr?gad_source=1&gclid=CjwKCAjw_ZC2BhAQEiwAXSgClvaGM0kmDbGvJ6v7
MpjBkcY-nLYsIgmNBFEJvqOXfGtZ9yck-KqKFBoCS9gQAvD_BwE
[31] J. (Jim) Zhang and K. R. Smith, “Indoor air pollution: a global health concern,” Br.
Med. Bull., vol. 68, no. 1, pp. 209–225, Dec. 2003, doi: 10.1093/bmb/ldg029.
[32] E. Durmusoglu, F. Taspinar, and A. Karademir, “Health risk assessment of BTEX
emissions in the landfill environment,” J. Hazard. Mater., vol. 176, no. 1, pp. 870–
877, Apr. 2010, doi: 10.1016/j.jhazmat.2009.11.117.
[33] Y. Sadanaga, J. Matsumoto, and Y. Kajii, “Photochemical reactions in the urban air:
Recent understandings of radical chemistry,” J. Photochem. Photobiol. C Photochem.
Rev., vol. 4, no. 1, pp. 85–104, 2003.
[34] R. H. F. Kwok, K. R. Baker, S. L. Napelenok, and G. S. Tonnesen, “Photochemical
grid model implementation and application of VOC, NO x, and O 3 source
apportionment,” Geosci. Model Dev., vol. 8, no. 1, pp. 99–114, 2015.

53

[35] O. US EPA, “Technical Overview of Volatile Organic Compounds.” Accessed: Sep.
04, 2024. [Online]. Available: https://www.epa.gov/indoor-air-quality-iaq/technicaloverview-volatile-organic-compounds
[36] C. Sonne, C. Xia, P. Dadvand, A. C. Targino, and S. S. Lam, “Indoor volatile and semivolatile organic toxic compounds: Need for global action,” J. Build. Eng., vol. 62, p.
105344, 2022.
[37] L. Melymuk, M. Robson, P. A. Helm, and M. L. Diamond, “Application of land use
regression to identify sources and assess spatial variation in urban SVOC
concentrations,” Environ. Sci. Technol., vol. 47, no. 4, pp. 1887–1895, 2013.
[38] X. Zhou, X. Zhou, C. Wang, and H. Zhou, “Environmental and human health impacts
of volatile organic compounds: A perspective review,” Chemosphere, vol. 313, p.
137489, 2023.
[39] K. Sam et al., “Prospects of emerging PAH sources and remediation technologies:
insights from Africa,” Environ. Sci. Pollut. Res., vol. 30, no. 14, pp. 39451–39473,
2023.
[40] L. Lucattini, G. Poma, A. Covaci, J. de Boer, M. H. Lamoree, and P. E. Leonards, “A
review of semi-volatile organic compounds (SVOCs) in the indoor environment:
occurrence in consumer products, indoor air and dust,” Chemosphere, vol. 201, pp.
466–482, 2018.
[41] C. Kubwabo et al., “Analysis of selected phthalates in C anadian indoor dust collected
using household vacuum and standardized sampling techniques,” Indoor Air, vol. 23,
no. 6, pp. 506–514, 2013.
[42] A. T. Lawal, “Polycyclic aromatic hydrocarbons. A review,” Cogent Environ. Sci., vol.
3, no. 1, p. 1339841, 2017.
[43] H. Choi, R. Harrison, H. Komulainen, and J. M. D. Saborit, “Polycyclic aromatic
hydrocarbons,” in WHO guidelines for indoor air quality: selected pollutants, World
Health Organization, 2010. Accessed: Aug. 20, 2024. [Online]. Available:
https://www.ncbi.nlm.nih.gov/books/NBK138709/?report=printable
[44] S. K. Samanta, O. V. Singh, and R. K. Jain, “Polycyclic aromatic hydrocarbons:
environmental pollution and bioremediation,” TRENDS Biotechnol., vol. 20, no. 6, pp.
243–248, 2002.
[45] M. Howsam and K. C. Jones, “Sources of PAHs in the environment,” PAHs Relat.
Compd. Chem., pp. 137–174, 1998.
[46] M. S. Callén, J. M. López, A. Iturmendi, and A. M. Mastral, “Nature and sources of
particle associated polycyclic aromatic hydrocarbons (PAH) in the atmospheric
environment of an urban area,” Environ. Pollut., vol. 183, pp. 166–174, 2013.
54

[47] Y. Ataei, Y. Sun, W. Liu, A. S. Ellie, H. Dong, and U. M. Ahmad, “Health effects of
exposure to indoor semi-volatile organic compounds in Chinese building environment:
a systematic review,” Int. J. Environ. Res. Public. Health, vol. 20, no. 1, p. 678, 2022.
[48] H. F. L. Leachi, M. H. P. Marziale, J. T. Martins, P. Aroni, M. J. Q. Galdino, and R. P.
Ribeiro, “Polycyclic aromatic hydrocarbons and development of respiratory and
cardiovascular diseases in workers,” Rev. Bras. Enferm., vol. 73, p. e20180965, 2020.
[49] E. T. Ny, D. Heederik, H. Kromhout, and F. Jongeneelen, “The relationship between
polycyclic aromatic hydrocarbons in air and in urine of workers in a Söderberg
potroom,” Am. Ind. Hyg. Assoc. J., vol. 54, no. 6, pp. 277–284, 1993.
[50] M. Delcroix and L. Howard, “Pulmonary arterial hypertension: the burden of disease
and impact on quality of life,” Eur. Respir. Rev., vol. 24, no. 138, pp. 621–629, 2015.
[51] R. T. Schermuly, H. A. Ghofrani, M. R. Wilkins, and F. Grimminger, “Mechanisms of
disease: pulmonary arterial hypertension,” Nat. Rev. Cardiol., vol. 8, no. 8, pp. 443–
455, 2011.
[52] A. Berthiaume, E. Galarneau, and G. Marson, “Polycyclic aromatic compounds
(PACs) in the Canadian environment: Sources and emissions,” Environ. Pollut., vol.
269, p. 116008, Jan. 2021, doi: 10.1016/j.envpol.2020.116008.
[53] P. V. Hodson et al., “Polycyclic aromatic compounds (PACs) in the Canadian
environment: The challenges of ecological risk assessments,” Environ. Pollut., vol.
266, p. 115165, Nov. 2020, doi: 10.1016/j.envpol.2020.115165.
[54] W. Xiang et al., “Volatility and chemical composition of secondary organic aerosol
derived from acenaphthylene and acenaphthene under various oxidant conditions,”
Atmos. Environ., vol. 330, p. 120563, 2024.
[55] H. Canada, “ARCHIVED - Polycyclic Aromatic Hydrocarbons - PSL1.” Accessed:
Sep.
02,
2024.
[Online]. Available:
https://www.canada.ca/en/healthcanada/services/environmental-workplace-health/reportspublications/environmental-contaminants/canadian-environmental-protection-actpriority-substances-list-assessment-report-polycyclic-aromatic-hydrocarbons.html
[56] D. Delistraty, “Toxic equivalency factor approach for risk assessment of polycyclic
aromatic hydrocarbons,” Toxicol. Environ. Chem., vol. 64, no. 1–4, pp. 81–108, 1997.
[57] J. F. Collins, J. P. Brown, G. V. Alexeeff, and A. G. Salmon, “Potency equivalency
factors for some polycyclic aromatic hydrocarbons and polycyclic aromatic
hydrocarbon derivatives,” Regul. Toxicol. Pharmacol., vol. 28, no. 1, pp. 45–54, 1998.
[58] P.-J. Tsai, H.-Y. Shieh, W.-J. Lee, and S.-O. Lai, “Health-risk assessment for workers
exposed to polycyclic aromatic hydrocarbons (PAHs) in a carbon black manufacturing
industry,” Sci. Total Environ., vol. 278, no. 1–3, pp. 137–150, 2001.
55

[59] I. C. Nisbet and P. K. Lagoy, “Toxic equivalency factors (TEFs) for polycyclic
aromatic hydrocarbons (PAHs),” Regul. Toxicol. Pharmacol., vol. 16, no. 3, pp. 290–
300, 1992.
[60] U. E. N. C. for E. Assessment and T. Shannon, “Provisional Peer-Reviewed Toxicity
Values for Acenaphthene.” Accessed: Aug. 20, 2024. [Online]. Available:
https://cfpub.epa.gov/ncea/pprtv/recordisplay.cfm?deid=338895
[61] B. M. Sahoo, B. V. Ravi Kumar, B. K. Banik, and P. Borah, “Polyaromatic
hydrocarbons (PAHs): structures, synthesis and their biological profile,” Curr. Org.
Synth., vol. 17, no. 8, pp. 625–640, 2020.
[62] “Provisional Peer-Reviewed Toxicity Values for Acenaphthene (CASRN 83-32-9)”.
[63] J. A. O. Oyekunle, S. A. Inalegwu, A. O. Fagbuyi, A. S. Adekunle, and O. T. Ore,
“Evaluation of polycyclic aromatic hydrocarbons and potentially toxic metals in
commonly consumed pasta products available in the Nigerian markets,” J. Trace Elem.
Miner., vol. 4, p. 100077, Jun. 2023, doi: 10.1016/j.jtemin.2023.100077.
[64] PubChem, “Acenaphthene.” Accessed: Aug. 24, 2024. [Online]. Available:
https://pubchem.ncbi.nlm.nih.gov/compound/6734
[65] Jr and J. P. Lodge, Methods of Air Sampling and Analysis, 3rd ed. New York:
Routledge, 2017. doi: 10.1201/9780203747407.
[66] J. He and R. Balasubramanian, “A comparative evaluation of passive and active
samplers for measurements of gaseous semi-volatile organic compounds in the
tropical atmosphere,” Atmos. Environ., vol. 44, no. 7, pp. 884–891, Mar. 2010, doi:
10.1016/j.atmosenv.2009.12.009.
[67] F. Wania and C. Shunthirasingham, “Passive air sampling for semi-volatile organic
chemicals,” Environ. Sci. Process. Impacts, vol. 22, no. 10, pp. 1925–2002, 2020, doi:
10.1039/D0EM00194E.
[68] A. C. Soria, M. J. García-Sarrió, and M. L. Sanz, “Volatile sampling by headspace
techniques,” TrAC Trends Anal. Chem., vol. 71, pp. 85–99, 2015.
[69] O. V. Rodinkov, A. S. Bugaichenko, and L. N. Moskvin, “Static Headspace Analysis
and Its Current Status,” J. Anal. Chem., vol. 75, no. 1, pp. 1–17, Jan. 2020, doi:
10.1134/S106193482001013X.
[70] A. C. Soria, M. J. García-Sarrió, and M. L. Sanz, “Volatile sampling by headspace
techniques,” TrAC Trends Anal. Chem., vol. 71, pp. 85–99, Sep. 2015, doi:
10.1016/j.trac.2015.04.015.
[71] B. Kolb and L. S. Ettre, Static headspace-gas chromatography: theory and practice.
John Wiley & Sons, 2006. Accessed: Sep. 30, 2024. [Online]. Available:
56

https://books.google.com/books?hl=zhCN&lr=&id=VyLNDwAAQBAJ&oi=fnd&pg=PR11&dq=static+headspace+sampli
ng&ots=48TjeDSJjL&sig=3im8rvH2yZYvgr5195LFMptHAv4
[72] O. V. Rodinkov, A. S. Bugaichenko, and L. N. Moskvin, “Static Headspace Analysis
and Its Current Status,” J. Anal. Chem., vol. 75, no. 1, pp. 1–17, Jan. 2020, doi:
10.1134/S106193482001013X.
[73] Y.-H. Kim, P. Kumar, E. E. Kwon, and K.-H. Kim, “Metal-organic frameworks as
superior media for thermal desorption-gas chromatography application: A critical
assessment of MOF-5 for the quantitation of airborne formaldehyde,” Microchem. J.,
vol. 132, pp. 219–226, 2017.
[74] N. N. Naing and H. K. Lee, “Microextraction and analysis of contaminants adsorbed
on atmospheric fine particulate matter: A review,” J. Chromatogr. A, vol. 1627, p.
461433, 2020.
[75] “Page:NIOSH Manual of Analytical Methods - 5515.pdf/1 - Wikisource, the free
online
library.”
Accessed:
Aug.
21,
2024.
[Online].
Available:
https://en.wikisource.org/wiki/Page:NIOSH_Manual_of_Analytical_Methods__5515.pdf/1
[76] Z.-Y. Gu, G. Wang, and X.-P. Yan, “MOF-5 metal- organic framework as sorbent for
in-field sampling and preconcentration in combination with thermal desorption
GC/MS for determination of atmospheric formaldehyde,” Anal. Chem., vol. 82, no. 4,
pp. 1365–1370, 2010.
[77] C. Bicchi, C. Cordero, E. Liberto, B. Sgorbini, and P. Rubiolo, “Headspace sampling
of the volatile fraction of vegetable matrices,” J. Chromatogr. A, vol. 1184, no. 1–2,
pp. 220–233, 2008.
[78] J. A. Kumar et al., “Acenaphthene adsorption onto ultrasonic assisted fatty acid
mediated porous activated carbon-characterization, isotherm and kinetic studies,”
Chemosphere, vol. 284, p. 131249, 2021.
[79] H. C. Menezes and Z. de Lourdes Cardeal, “Determination of polycyclic aromatic
hydrocarbons from ambient air particulate matter using a cold fiber solid phase
microextraction gas chromatography–mass spectrometry method,” J. Chromatogr. A,
vol. 1218, no. 21, pp. 3300–3305, 2011.
[80] J. Gao, C. Huang, Y. Lin, P. Tong, and L. Zhang, “In situ solvothermal synthesis of
metal–organic framework coated fiber for highly sensitive solid-phase
microextraction of polycyclic aromatic hydrocarbons,” J. Chromatogr. A, vol. 1436,
pp. 1–8, Mar. 2016, doi: 10.1016/j.chroma.2016.01.051.

57

[81] S.-H. Feng and G.-H. Li, “Chapter 4 - Hydrothermal and Solvothermal Syntheses,” in
Modern Inorganic Synthetic Chemistry (Second Edition), R. Xu and Y. Xu, Eds.,
Amsterdam: Elsevier, 2017, pp. 73–104. doi: 10.1016/B978-0-444-63591-4.00004-5.
[82] C. O. Audu et al., “The dual capture of As V and As III by UiO-66 and analogues,”
Chem. Sci., vol. 7, no. 10, pp. 6492–6498, 2016.
[83] S. Øien-Ødegaard et al., “UiO-67-type Metal–Organic Frameworks with Enhanced
Water Stability and Methane Adsorption Capacity,” Inorg. Chem., vol. 55, no. 5, pp.
1986–1991, Mar. 2016, doi: 10.1021/acs.inorgchem.5b02257.
[84] T. N. Tu, M. V. Nguyen, H. L. Nguyen, B. Yuliarto, K. E. Cordova, and S. Demir,
“Designing bipyridine-functionalized zirconium metal–organic frameworks as a
platform for clean energy and other emerging applications,” Coord. Chem. Rev., vol.
364, pp. 33–50, 2018.
[85] L. Liu et al., “Zirconium (IV)-based metal organic framework (UIO-67) as efficient
sorbent in dispersive solid phase extraction of plant growth regulator from fruits
coupled with HPLC fluorescence detection,” Talanta, vol. 154, pp. 23–30, Jul. 2016,
doi: 10.1016/j.talanta.2016.03.038.
[86] J. Pawliszyn, “Solid Phase Microextraction,” in Headspace Analysis of Foods and
Flavors: Theory and Practice, R. L. Rouseff and K. R. Cadwallader, Eds., Boston,
MA: Springer US, 2001, pp. 73–87. doi: 10.1007/978-1-4615-1247-9_6.
[87] C. Hansch, A. Leo, and D. Hoekman, Exploring QSAR: hydrophobic, electronic, and
steric constants, vol. 2. American Chemical Society Washington, DC, 1995. Accessed:
Aug.
20,
2024.
[Online].
Available:
https://hero.epa.gov/hero/index.cfm/reference/details/reference_id/224462
[88] H. A. Bamford, Di. L. Poster, and J. E. Baker, “Temperature dependence of Henry’s
law constants of thirteen polycyclic aromatic hydrocarbons between 4 C and 31 C,”
Environ. Toxicol. Chem. Int. J., vol. 18, no. 9, pp. 1905–1912, 1999.
[89] W. M. Meylan and P. H. Howard, “Estimating octanol–air partition coefficients with
octanol–water partition coefficients and Henry’s law constants,” Chemosphere, vol.
61, no. 5, pp. 640–644, Nov. 2005, doi: 10.1016/j.chemosphere.2005.03.029.
[90] Y. Peng et al., “Stability and adsorption performance of UiO-67 for uranium (VI) in
solution,” J. Radioanal. Nucl. Chem., vol. 333, no. 1, pp. 305–315, 2024.
[91] M. Y. Ying, Z. M. A. Merican, A. Haruna, and S. G. Musa, “UiO-67 MOF-Based
Catalyst as A Potential Photocatalyst for Co2 Conversion to Methanol Complimented
by RSM Studies,” Platf. J. Sci. Technol., vol. 6, no. 2, pp. 2–16, 2023.

58

[92] M. Yuen, Z. Merican, A. Merican, A. Haruna, and S. G. Musa, “UiO-67 MOF-Based
Catalyst as A Potential Photocatalyst for Co2 Conversion to Methanol Complimented
by RSM Studies,” Dec. 2023, doi: 10.61762/pjstvol6iss2art25229.
[93] D. Saravanakkumar et al., “Synthesis of NiO doped ZnO/MWCNT Nanocomposite
and its charecterization for photocatalytic & antimicrobial applications,” J Appl Phys,
vol. 10, pp. 73–83, 2018.
[94] Y. Jin, D. Wu, N. Ma, and W. Dai, “Well-design and synthesis of a water-and heatresistant UiO-67@ CNTs composite for Congo red efficient capture,” Water Sci.
Technol., vol. 85, no. 5, pp. 1636–1647, 2022.
[95] Y. B. Pramana, B. Setiawan, P. Prihono, Y. Utomo, M. Subandowo, and K.
Budipramana, “A simple synthesis of nickel oxide nanotube using high voltage
electrolysis,” J. Neutrino J. Fis. Dan Apl., vol. 13, no. 1, pp. 13–18, 2020.
[96] J. H. Cavka et al., “A new zirconium inorganic building brick forming metal organic
frameworks with exceptional stability,” J. Am. Chem. Soc., vol. 130, no. 42, pp.
13850–13851, 2008.
[97] J. Rouquerol, F. Rouquerol, P. Llewellyn, G. Maurin, and K. Sing, Adsorption by
powders and porous solids: principles, methodology and applications. Academic
press,
2013.
Accessed:
Aug.
26,
2024.
[Online].
Available:
https://books.google.ca/books?hl=zh-CN&lr=&id=UOEZscCYncC&oi=fnd&pg=PP1&dq=gas+adsorption+principles&ots=0UWWKFnjiz&
sig=psWd_uMHHNXHgRlMrTjDHdgvFlc

59