Powering the next wave of portable fuel cells;
Covalent Organic Frameworks for hydrogen
storage
Alex Lacey
CE30122
12/5/2020
Supervisor: Tina Dü ren
Department of Chemical Engineering
University of Bath, Bath, BA2 7AY
Authorship Declaration
I certify that I have read and understood the entry in the Programme Handbook for the
Department of Chemical Engineering on Cheating and Plagiarism and that all material in this
report is my own work, except where I have indicated with appropriate references or
acknowledgements. I agree that, in line with Regulation 15.3(e), if requested I will submit an
electronic copy of this work for submission to a Plagiarism Detection Service for quality
assurance purposes.
Name:
Alex Lacey
Signature:
Date:
12/5/20
i
Abstract
Portable hydrogen fuel cells offer many distinct advantages over traditional batteries; In
particular, high energy density’s (5-10 times greater), quick recharge times and long operating
cycles. This has led to the emergence of portable fuel cells as a viable alternative to batteries
for applications such as UAVs. The further expanse of the fuel cell market is hindered upon the
adequate storage of hydrogen. A portable hydrogen storage system must be light weight, store
a high amount of hydrogen per unit volume and not be too expensive; therefore, many
traditional hydrogen storage solutions, including storing hydrogen as a liquid or under very
high pressures, are not appropriate here. Covalent organic Frameworks (COFs) are an exciting
new class of materials which show promise as excellent hydrogen adsorption materials.
This research analysed a large database of 309 COFs in order to identify the COFs (and specific
trends in these COFs), which show the most promise as hydrogen adsorption materials.
Molecular simulations were used to model the deliverable hydrogen in a COF for a given
temperature and pressure. The deliverable hydrogen at 185 K was calculated for just 24 of the
original 309 COFs. Nevertheless, on a mass basis, COF number 70 and 74 showed exceptional
hydrogen uptakes of 33 wt% and 24.1 wt% respectively. These are some of the highest values
for the weight-based hydrogen adsorption in COFs, seen to date. These COFs would show
excellent applications in lightweight portable hydrogen storage such as in UAVs. This research
also found the mass-based surface area to be the most significant factor affecting the weightbased hydrogen adsorption in COFs. COF number 70 had a mass specific surface area of 25,917
m2 g-1 which was over 3 times higher than any other COF in the study.
In conclusion COFs offer exceptional potential as hydrogen adsorption materials on a mass
basis; however, further research is required to explore their hydrogen adsorption on a volume
basis. Future research should focus on increasing the mass-based surface area of future COFs;
thus, allowing the discovery of more materials with high weight-based hydrogen adsorption,
ideal for portable power applications.
ii
Table of Contents
Authorship Declaration ............................................................................................................... i
Abstract ...................................................................................................................................... ii
Table of Contents ...................................................................................................................... iii
1 Introduction ............................................................................................................................. 1
2 Background and Literature Review......................................................................................... 2
2.1 Department of Energy (DOE), hydrogen storage targets ................................................. 2
2.2 Portable hydrogen storage ................................................................................................ 2
2.2.1 A review of the portable fuel cell sector ................................................................... 2
2.2.2 Applications of portable fuel cells ............................................................................ 4
2.3 An overview of hydrogen storage technologies ............................................................... 6
2.3.1 Physical-based storage .............................................................................................. 7
2.3.2 Material-based storage .............................................................................................. 8
2.4 Covalent Organic Frameworks (COFs) for hydrogen storage ......................................... 9
2.4.1 Introduction to COFs and MOFs ............................................................................... 9
2.4.2 Early molecular simulations of hydrogen adsorption in COFs ............................... 11
2.4.5 Addition of metals in COFs .................................................................................... 12
2.5 Aims and Objectives ...................................................................................................... 14
3 Simulation Methods .............................................................................................................. 15
3.1 Grand Canonical Monte Carlo (GCMC) ........................................................................ 15
3.2 Periodic Boundary Condition (PBC).............................................................................. 16
3.3 Simulation details ........................................................................................................... 17
3.3.1 Guest molecule (H2) ................................................................................................ 17
3.3.2 Force fields .............................................................................................................. 18
3.3.1 Simulation input ...................................................................................................... 18
3.4 Ideal number of cycles ................................................................................................... 19
3.4 Scripting ......................................................................................................................... 21
4 Results and Discussion .......................................................................................................... 22
iii
4.1 Validation of simulation results ..................................................................................... 22
4.2 Determining the storage pressure ................................................................................... 24
4.3 Screening 309 COFs for the deliverable hydrogen ........................................................ 25
4.4 Identifying the most promising COFs for hydrogen adsorption .................................... 27
4.5 Observable trends in the characteristics of COFs .......................................................... 31
4.6 Project planning and design ........................................................................................... 38
5 Conclusions and Future Work ............................................................................................... 40
Acknowledgements .................................................................................................................. 43
References ................................................................................................................................ 44
Appendix A – Health & Safety ................................................................................................ 49
Appendix B – Simulation files and scripts ............................................................................... 50
Appendix C – Additional simulation results ............................................................................ 54
iv
1 Introduction
The first working hydrogen fuel cell is credited to Sir William Robert Grove, whom in 1839,
demonstrated how combining hydrogen and oxygen to form water and heat could produce an
electrical current. Since then, research into hydrogen fuel cell technologies remained nearly
untouched until the 1960s. In the 1960s NASA began researching proton exchange membrane
(PEM) fuel cell technologies, for use in the project Gemini and project Apollo missions, which
required a power source able to last a long duration of time [1]. Although this first PEM fuel cell
was unsuccessful, this sparked extensive research into fuel cell technology for the remaining
1900s and early 2000s.
It has long been prophesied that fuel cell technology has an essential role to play in the growing
energy demands of the future, with three times the mass-based energy content of gasoline at
120 MJ/Kg
[2]
. Major advancements in the motor industry have already been seen with the
launch of the first commercially available fuel cell vehicle (FCV), the Hyundai Tucson 2013
[3]
. Despite this, many obstacles still prevent the large-scale adoption of fuel cell technology; in
particular, adequate storage of hydrogen which is both commercially safe and economically
viable. In stark contrast to the promising mass-based energy content, the volume based-energy
content for hydrogen is a quarter of that of gasoline, at only 8 MJ/Kg
[2]
. As a result, the
development of enhanced hydrogen storage techniques is essential to increase the energy
density of hydrogen.
1
2 Background and Literature Review
2.1 Department of Energy (DOE), hydrogen storage targets
Hydrogen storage applications are categorised into three main classes by the Department of
Energy (DOE): automotive, material handling and portable power. The DOE lists two
significant target parameters for each application which should be met for viable, commercial
use of hydrogen storage. Firstly, the system gravimetric capacity, this is often expressed as the
weight percentage of hydrogen in the system. The second parameter is the system volumetric
capacity or the usable hydrogen density (gH2/ L). The first parameter offers insight into the
practicality of the storage system, in particular, for light weight portable operations. The second
parameter however is important in showing the actual storage capacity of the system. Table 1
shows these DOE targets for the three classes of applications.
Table 1. A comparison of the 2020 DOE targets for gravimetric and volumetric capacity
across automotive, portable and material handling applications [4–6].
Gravimetric
Automotive
Portable
Material handling
6.5 wt%
4 wt%
4 wt%
50 g/L
50 g/L single use (30g/L
50 g/L single use (30g/L
rechargeable)
rechargeable)
capacity
Volumetric capacity
This research will focus on portable power applications due to the lower DOE targets and less
research when compared to automotive applications.
2.2 Portable hydrogen storage
2.2.1 A review of the portable fuel cell sector
According to a 2014 review paper, portable fuel cell technology had a promising start, with
over 50% of fuel cells shipped in 2008 belonging to the portable sector [7]. In principle, the fuel
cell has many advantages over traditional batteries which make it especially useful in the
2
portable power sector. In particular, the high energy density of the cell (being 5-10 times higher
than traditional batteries
[7]
) and long operating cycles. However, high production cost and
safety concerns have been the main caveats to the widespread adoption of the portable fuel cell.
Problems such as heat dissipation, recyclability of fuel containers, and operation under various
operating conditions were all cited in the 2014 review paper as issues preventing the expansion
of this sector [7]. A more recent review paper in 2019 by E4tech [8], summarised the current state
of the portable fuel cell industry. It noted the above limitations as the main reason for product
failures and company collapses, causing the portable fuel cell market to shrink. Figure 1 shows
the trend in the number of fuel cells shipped across different fuel cell sectors in the years 2015
- 2019 [8].
Figure 1. A comparison of the number of fuel cells shipped (per 1000 units) from the years
2015 – 2019 in the Portable, Stationary and Transportation sectors [8].
As can be seen from Figure 1, the portable fuel cell sector has stagnated over recent years,
despite the continued growth in other sectors. The main challenges to the technology preventing
market growth (mentioned previously), are primarily hindered upon the adequate storage of
hydrogen. Improvements in the storage systems gravimetric capacity, volumetric capacity, cost
of production and safety are all needed to mitigate these challenges
[9]
. Specifically,
improvements in the gravimetric capacity and volumetric capacity would improve the
efficiency of the fuel cell and make it economically more advantageous over battery
technology.
3
2.2.2 Applications of portable fuel cells
The application of commercial, portable fuel cells is vast. Including portable power generators
for applications such as camping, emergency relief power, portable signage and surveillance.
While also encompassing applications in consumer electronics, including mobile phones,
laptops, power tools, and anything that traditionally uses a battery [7].
One new market for portable fuel cell technology is in UAVs (Unmanned Aerial Vehicles) or
drones. The first commercial hydrogen fuel cell drone was the HyDrone 1800, created by a
Chinese company in 2016 [10]. Since then, the commercial market for hydrogen fuel cell drones
has grown rapidly. The sudden emergence of this new market is accredited to the numerous
advantages hydrogen fuel cells offer as a power source over batteries for UAVs. Namely, the
greatly increased flight time, lighter weight, lack of emissions compared to petrol based options
and very fast recharge times (typically 5 minutes)
[11]
. The applications of such UAVs are
increasingly growing, from agricultural crop analysis to aiding search and rescue missions and
even delivery. With the increased uses of UAVs in the future, the unparalleled advantages from
fuel cell systems have an integral part to play in this developing industry.
Currently, integrated fuel cell systems are pioneered by Doosan Mobility, whom in February
2019, demonstrated a drone flight time of 2 hours using fuel cells, compared to 30 minutes for
the same drone on a battery power source. At present, this drone is being used by a power
company KEPCO, to inspect transmission lines
[8]
. In April of 2019, a drone from Korean
company MetaVista, broke the world record for the longest UAV flight time, with a time of 12
hours 7 minutes and 5 seconds
[12]
. This exceptional flight time is accredited to the liquid
hydrogen storage system used in place of pressurised storage vessels in other drones. This liquid
system offered much more hydrogen for the same weight of vessel. Looking into the future,
Dubai wants 25% of its total transportation to be autonomous such as UAVs by 2030
[13]
.
Hydrogen fuel cells could be used to meet this demand, offering the best flight times and light
weight systems for higher powered uses.
Another key application for portable fuel cells is for military personal. It is estimated that in
2012, soldiers in Afghanistan on a three-day operation would carry more than 8kg of batteries
[14]
. The advantages of fuel cells previously discussed would result in much less weight for
soldiers to carry, reducing the exhaustion of military personal and aiding in longer missions.
4
Furthermore, the ability to quickly refuel fuel cells without electricity would be further
advantageous in remote missions and consecutive operations requiring a swift changeover. The
U.S army in 2004, initiated a Foreign Comparative Test (FCT) programme
[15]
aimed at
acquiring light weight portable fuel cell technologies from around the globe, for evaluation as
portable power sources in military operations. The FCT programme concluded that replacing
battery power sources with fuel cells provided significant reductions in weight for portable
military operations. It also cited these mass saving benefits were increasingly more significant
for longer mission lengths. However, for very short missions or for missions of very low energy
requirement, there was little to no mass saving benefits of using fuel cell technologies over
traditional batteries. This was because the initial weight of the fuel cell system was more
significant than the weight of smaller battery powered systems. As the power requirement
increased however, the further hydrogen fuel added much less weight to the system than larger
batteries. A simplified schematic adapted from the results of this FCT programme is shown
below in Figure 2.
Figure 2. Showing the advantage of hydrogen fuel cells over traditional batteries for military
missions [14].
In recent times, portable fuel cells are receiving more military funding. Diffusing into
applications such as charging platforms, for missions requiring silent watch and for missions
of long duration [8]. Other, non-portable applications are also being seen such as base generators
and even submarines. The market trend for military applications is predicted to continue
growing, with more countries realising the benefits of implementing this technology in their
own forces.
5
2.3 An overview of hydrogen storage technologies
Hydrogen storage is divided into two main categories by the DOE. Firstly, physical storage,
which changes the internal storage conditions (namely temperature and pressure), to increase
the energy density. Secondly, material storage uses the addition of adsorbent materials or
chemical reactions to store the hydrogen in different compounds. Figure 3 shows the
classification structure of hydrogen storage according to the DOE [2].
Figure 3. Classification of hydrogen storage technologies, taken from the DOE [2].
The ideal hydrogen storage for portable fuel cells, should be lightweight, safe, inexpensive,
have a high energy density and be relatively simplistic; however, achieving all of these criteria
is not possible with the storage technologies of today.
6
2.3.1 Physical-based storage
Compressed gas storage is the most common hydrogen storage technology, involving the
compression of gaseous hydrogen in a pressurised vessel at ambient temperature. This physical
storage method is frequently used in Fuel Cell Vehicles (FCVs) with an industrial standard
pressure of 700 bars [16]. The main advantage of compressed storage is the simplicity and lower
material cost compared to other technologies. A major caveat however is the lower energy
density compared to alternative storage methods
[17]
. Greater energy densities can be reached
by using very high pressures (as seen in FCVs). Nevertheless, this is not practical in portable
applications due to the safety concerns of highly flammable gases like hydrogen, stored at
higher pressures. Furthermore, increasing the storage pressure results in thicker vessel walls,
which increases the cost and weight of the system. Another issue is the heat released when
hydrogen is pressurised such as in refuelling. To avoid overheating in larger systems (for
example FCVs), the hydrogen is pre-cooled
[16]
(although this requires large components and
further engineering). Pressurised hydrogen storage is still seen in many portable applications;
however, the above limitations show this storage method is far from optimal in portable uses.
The other main type of ‘physical’ storage is liquid hydrogen storage. The basic requirement for
liquid storage involves cooling the hydrogen to its boiling point of -253 ℃ at ambient pressure
[16]
. The tank is insulated to reduce heat transfer to the environment as much as possible,
however it is impossible to reduce all heat transfer. Liquid storage tanks are only built for
atmospheric pressure and cannot withstand higher pressures; therefore, as the hydrogen heats
up and the pressure increases in the tank, it is necessary to release some hydrogen to the
environment through a relief valve. This is often referred to as ‘boil-off’ and eventually all the
hydrogen in a tank will have dissipated if left long enough
[16]
. The main advantage of liquid
hydrogen storage over compressed hydrogen storage is the increased energy density. With
liquid hydrogen having a density of 70.8 g/L
[18]
, liquid storage is capable of surpassing the
volumetric capacity targets set by the DOE. As a result, some portable fuel cell companies have
developed high performing liquid storage systems, such as the Korean drone manufacturer MetaVista (discussed previously). Despite this, there are many engineering problems when
storing liquid hydrogen on a smaller scale. The surface area to volume ratio is much larger for
smaller vessels than larger vessels. Thus, the effective heat transfer is greater as the vessel is
scaled down [19]. This, coupled with the expensive cost in cooling hydrogen to a liquid, makes
this storage method unfeasible for wide spread commercial use in portable fuel cells [17].
7
Cryo-compressed hydrogen storage uses a combination of the compressed gas storage and
liquid storage methods discussed above. It offers exceptional volumetric capacities and
gravimetric capacities but is accompanied by the limitations of both these storage systems,
discussed previously. As a result, cryo-compressed storage is certainly not a feasible option for
portable hydrogen storage.
2.3.2 Material-based storage
Material-based storage is the second category of hydrogen storage according to the DOE,
capable of elevating some limitations of the physical storage methods discussed previously.
Although many storage materials for hydrogen exist, they can be broken down into two
different adsorption mechanisms: chemisorption or physisorption.
Chemisorption involves a chemical reaction to form a subsequent product containing the
adsorbate and material. Chemisorption materials describe most of the material-based storage
sub-types from Figure 3, with the exception of Adsorbents. One type of chemisorption storage
material is metal hydrides. Here, hydrogen anions (H-) react with metal cations (M+) to form
metal hydrides in a process known as dissociative chemisorption. With the addition of heat, the
reaction can then be reversed, reforming hydrogen
[20]
. These metal hydrides have many
advantages over other storage methods, including very high volumetric capacities, surpassing
that of liquid hydrogen storage and targets set by the DOE. Furthermore, the energy required
to store hydrogen in metal hydrides is half of that of compressed storage (700 bar), and a sixth
of liquid storage [21]. However, the gravimetric capacity for metal hydrides is not as good as the
volumetric capacity, with typical values ranging from 1 wt% to 9 wt% (not ideal for lightweight
applications)
[21]
. Further limitations, including irreversible metal hydride reactions and high
desorption temperatures (typically > 300 ℃
[22]
), prevent any practical applications of metal
hydride storage in portable fuel cells.
Physisorption based material storage is the alternate to chemisorption storage like metal
hydrides. In physisorption, hydrogen adsorbed into a solid material is held in place by weak
Van der Waal forces between the solid and the hydrogen molecules, as shown in figure 4
8
Figure 4. The process of physisorption between hydrogen molecules and a solid material
Physisorption materials offer many advantages for portable hydrogen storage over the storage
of hydrogen by chemisorption processes seen in metal hydrides. In particular, complete
reversibility, light weight, fast kinetics and much lower desorption temperatures [23]. Although
physisorption materials exhibit high gravimetric uptakes and volumetric uptakes at low
temperatures (77 K), they are limited by much less hydrogen adsorption at ambient
temperatures. If physisorption materials with a high hydrogen uptake at ambient temperature
could be discovered, they would be the ideal candidate for effective portable hydrogen storage.
In the past few decades, materials such as activated carbon and Metal Organic Frameworks
(MOFs) have been the focus of extensive research, offering some potential for high hydrogen
uptake at ambient conditions. As of yet, none of these materials offer a high enough uptake at
ambient conditions to offer an effective solution for portable hydrogen storage. This research
will explore an exciting new type of these materials – Covalent Organic Frameworks (COFs).
2.4 Covalent Organic Frameworks (COFs) for hydrogen storage
2.4.1 Introduction to COFs and MOFs
Covalent Organic Frameworks (COFs) are a new class of materials with exceptional properties.
They are credited to the work of Yaghi and co-workers, who in 2005, successfully designed
and synthesised the first of these materials
[24]
. These crystalline, nanoporous materials, are
comprised entirely of light elements (H, C, O, N, B) covalently bonded together
[25]
. Metal
Organic Frameworks (MOFs) are a class of materials, developed prior to COFs. Unlike COFs,
they contain metal ions linked together by organic ligands. This metallic structure allowed far
9
easier synthesis of MOFs than of COFs, which in part, lead to more research into the uses of
MOFs as hydrogen storage mediums. Both COFs and MOFs, have many properties that make
them excellent candidates as physisorption hydrogen storage materials, exhibiting, large surface
areas with a high porosity. Unlike MOFs however, COFs, show excellent molecular stability at
higher temperatures and low densities. As a result, COFs are a more attractive option for
portable hydrogen storage than MOFs (due to the importance of low-density storage mediums).
Currently, MOF structures show higher volumetric uptakes than COFs, but with a much greater
library of structures, this is unsurprising. Nevertheless, COFs are already demonstrating the
highest gravimetric uptakes for any physisorption material, making them the most promising
new materials for hydrogen adsorption.
COFs can be categorised as either 2D or 3D COFS. In the case of the former, the resulting
structure is composed of layered 2D sheets which are held together by 𝜋-interactions. For 3D
COFs, the entire structure is covalently bonded and is composed of repeating tetrahedral blocks
[26]
. Figure 5 shows a comparison between a 2D COF (COF-1) and a 3D COF (COF-108).
Figure 5. A comparison between the structures of a 3D COF and a 2D COF [27].
3D COFs frequently exhibit superior properties over 2D COFs, including greater molecular
stability and greater hydrogen uptake; however, they often face challenges with synthesis
compared to their 2D cousins [28]. Both 2D and 3D COFs will be explored in this research.
10
2.4.2 Early molecular simulations of hydrogen adsorption in COFs
Yaghi and co-workers published the first paper in 2008 which reported COFs as exceptional
materials for hydrogen storage
[29]
. They used Grand Canonical Monte Carlo (GCMC)
simulations to predict the hydrogen adsorption in six COFs: COF-1, COF-5, COF-102,
COF103, COF-105 and COF-108. These molecular simulation methods used were confirmed
to be very accurate at predicting hydrogen adsorption in COFs when later compared to
experimental results. Figure 6 shows the structures of these 6 COFs including their molecular
building blocks.
Figure 6. Molecular structures and building blocks of the 6 COFs used in the first simulations
by Yaghi and co-workers [29].
As can be seen in Figure 6, the structures of COF-1 and COF-5 are 2D, while COF-102, COF103, COF-105 and COF-108 have 3D structures. The results of the simulations by Yaghi
predicted that the hydrogen storage capacity in 3D COFs was 2.5 – 3 times higher than in 2D
COFs. This was said to be due to the higher free volume and larger surface area in 3D COFs.
COF-108 and COF-105 showed the greatest potential with the highest gravimetric uptakes of
18.9 wt% and 18.3 wt% respectively at 100 bar and 77 K. This was predicted to be due to the
mostly free volumes in these COFs. COF-102 and COF-103 showed the greatest volumetric
uptakes with total values of 49.9 g/L and 49.8 g/L respectively at 100 bar and 77K. These COFs
outperformed many well-known MOFs for volumetric hydrogen uptake but could not compete
with MOFs that had exposed Mn2+ sites. Nevertheless, these materials showed the highest
11
known gravimetric uptakes of any physisorption materials; hence, 3D COFs were concluded as
the most promising materials for hydrogen storage.
2.4.5 Addition of metals in COFs
A paper published in 2011 by Goddard III and co-workers [30], expanded upon the initial work
from Yaghi. The work from Yaghi showed excellent adsorption potential of COFs at 77 K, but
not at ambient temperature (a much more practical storage temperature). Goddard III set out to
investigate the adsorption of COFs at ambient temperature by expanding on the work of Yaghi.
To increase the hydrogen adsorption at these conditions, they proposed the ‘doping’ of COFs
with alkaline metal ions including Li-, Na- and K-. It was found that metalating COFs greatly
increased their physisorption potential. The highest gravimetric uptakes in these COFs at 298
K and 100 bar were cited as: COF-102-Li (5.16 wt%), COF-103-Li (4.75 wt%) and COF-102Na (4.75 wt%). Similarly, the highest volumetric uptakes in these COFs at 298 K and 100 bar
were: COF-102-Na (24.9 g/L), COF102-Li (23.8 g/L), COF103-Na (22.8 g/L). These materials
showed some of the greatest promise for hydrogen adsorption in COFs.
In affiliation with the Department of Energy (DOE), Yaghi and Goddard III continued to
research the addition of heavy metals in COFs for hydrogen storage, publishing a final report
in 2013
[31]
. They researched the hydrogen adsorption potential of metalated COFs using
compounds such as palladium chloride (PdCl2) and platinum chloride (PtCl2). An example of
the metalation of one of these COFs (COF-301 to COF-301-Pd) is shown in Figure 7.
Figure 7. A schematic adapted from Yaghi and Goddard III showing the structure of the
metalated COF-301 (COF-301-Pd) [31].
12
Through simulations, it was found that the addition of palladium sites in COF-301 greatly
improved the binding enthalpy of hydrogen; thus, improving the hydrogen adsorption potential.
Figure 8 shows the total isotherms of the total volumetric uptake for different concentrations of
palladium (Pd) in COF-30-Pd (taken from the report by Yaghi and Goddard III).
Figure 8. Isotherms of the total volumetric uptakes for different concentrations of Pd in COF301-Pd at 298K [31].
As can be seen from Figure 8 the increased concentration of palladium metal sites greatly
increased the hydrogen adsorption in COF-301. At 100% Pd concentration, the total volumetric
uptake in COF-301-Pd was 60 g/L at 100 bar and 298K. The total gravimetric uptake was 4.2
wt% at these conditions. These COFs demonstrate some of the highest gravimetric and
volumetric capacities of any physisorption materials to date.
Metalated COFs show some of the most promising potential of any material for hydrogen
adsorption at ambient temperatures. They are; however, fairly novel materials and synthesis
can be troublesome. Furthermore, the addition of heavy metals such as palladium and platinum,
will drastically increase the cost of the storage system. For this reason, metalated COFs may
not be the most feasible solution for portable power as of yet. This research will focus on a
selection of ordinary COFs and metalated COFs, considering the practicality of implementing
either as hydrogen storage mediums for portable power applications.
13
2.5 Aims and Objectives
There have been many excellent papers into the use of COFs for hydrogen storage; however,
the majority of this research is centred around fuel cell vehicle (FCV) applications. The current
understanding is that no specific research into using COFs for portable storage applications
exists. This work will attempt to explore these novel materials with respect to the requirements
of portable fuel cells.
Project aim statement: Asses the practicality of COFs as hydrogen storage materials in portable
applications through computer simulations.
Project Objectives:
1. Screen an entire database of COF structures to determine the COFs with the
highest gravimetric and volumetric capacities. Compare the hydrogen
adsorption of all screened COFs to the DOE parameters.
2. Simulate whole isotherms for the COFs with the highest gravimetric and
volumetric uptakes. These isotherms should be compared to the DOE
parameters to conclude the practicality of using such COFs for portable
applications.
3. Analyse correlation data between the hydrogen adsorption in COFs and the
underlying properties of such COFs. This allows the identification of the most
important properties for COFs with a high hydrogen adsorption.
14
3 Simulation Methods
3.1 Grand Canonical Monte Carlo (GCMC)
The Monte Carlo method is a class of computational algorithms dating back to the 1940s which
uses random sampling methods to get results. Molecular dynamics is another type of a
computational molecular model; it calculates the specific time dependent properties of
individual particles based on newtons laws of motion. Molecular dynamics is often used for
smaller systems to investigate the dynamic interactions of molecules over short periods of time.
However, to investigate larger systems or the macroscopic properties from equilibrium data,
Monte Carlo offers a more computationally practical solution. As this project involves the
macroscopic properties of hydrogen storage at an equilibrium state, the Monte Carlo method
was chosen.
In thermodynamics, the Grand Canonical ensemble is a statistical ensemble used to represent
the possible states of a system in equilibrium with a reservoir of particles. In this ensemble, the
chemical potential (𝜇), temperature (T) and volume (V) of such system is fixed, while other
properties such as the number of particles (N), density (𝜌) and energy (E) of the system are
allowed to change
[32]
. Grand Canonical Monte Carlo (GCMC) uses random sampling of
potential states based on the thermodynamic probability of that state
[33]
. GCMC computer
simulations first begin with the potential commands for particles in a system. Namely, particle
insertion, particle translation, particle rotation and particle deletion [34]. A command is randomly
selected according to a programmed probability e.g. 40% insertion, 40% deletion, 20%
translation. Before executing the command, the energy (E) of the current configuration is
calculated (denoted as E(o) where o corresponds to the old state of the system). The command
is then executed to produce a trial configuration. The energy of the trial configuration is
calculated, (denoted by E(n) where n corresponds to the new state of the system). The
probability of the trial configuration being accepted is given by a form of the Metropolis
criterion [35], as seen below.
𝑎𝑐𝑐(𝑜 → 𝑛) = min 01, 𝑒
15
45
67(8)47(9):
>
;< =
?
(1)
Equation 1 shows that if the energy of the system for the new configuration is lower than the
old configuration (E(o) > E(n)), then the new configuration is always accepted with a
probability of 1. If however the energy of the system for the new configuration is greater than
the old configuration (E(n) > E(o)), then the new configuration is accepted with a probability
between 0 and 1. This probability is a function of the energy difference between the two
configurations, the system temperature (T) and the Boltzmann constant (𝑘C ), as represented by
the exponential in equation 1.
3.2 Periodic Boundary Condition (PBC)
Molecular simulations of entire storage systems are very computationally demanding,
involving millions of atoms. Periodic boundary conditions can be used to approximate a large
system by using a much smaller section of the system known as a unit cell. This unit cell size
is generally in the range of a few nanometres, containing a few hundred to a few thousand
atoms, requiring much less computational power. The unit cell most commonly has the
dimensions of a cubic box and is imagined as being surrounded by repeated copies of itself in
all dimensions, hence, ‘periodic’[36]. As one particle exits the unit cell boundary from one side,
it instantaneously reappears on the other side. Figure 9 shows an example of the PBC.
Figure 9. A visual explanation of the Periodic Boundary Condition (PBC). As a particle
leaves the boundary of the simulation it reappears on the other side [36].
16
Another important characteristic is the cut-off radius. Due to the nature of intermolecular
interactions between particles, only particles very close to each other will have any significant
interaction. To simplify the molecular model, a cut-off radius can be used. This dictates that
any two particles with a distance apart greater than the cut-off radius, have no interaction. It is
also necessary that this cut-off radius be less than half of the minimum box length. This is
known as the minimum image convention and ensures no particles interact with themselves
[37]
. For the simulations in this project, the cut-off radius was set to 12.8 Å or 1.28 nm. The unit
cell size was then adjusted accordingly to fit the minimum image convention.
3.3 Simulation details
‘RASPA’ is a software package for simulating the adsorption and diffusion of molecules in
nanoporous materials or ‘frameworks’ such as COFs [38]. It makes use of the latest Monte Carlo
algorithms, as previously discussed. For this work, preinstalled RASPA software was accessed
by a Secure Shell (SSH) to the high-performance computer at the University of Bath, ‘Balena’.
Within the working directory which RASPA is loaded, a collection of key files describing the
framework material (COF), the simulation guest molecule (H2) and force field interactions
between atoms are all required. Files used in the basic simulations are available in Appendix
B-1. The RASPA manual provided information on the correct formatting and technical details
of these files
[39]
. The core COF 3.0 database
[40]
provides all the COF files used for these
simulations, accessible online at: https://core-cof.github.io/CoRE-COF-Database/.
3.3.1 Guest molecule (H2)
One important file contains specific details about the guest molecule (H2). This file contains
the critical constants: critical temperature, critical pressure and acentric factor, used to compute
the fugacity from the pressure based on an equation of state
[39]
. Values for these critical
constants were obtained from literature sources such as Coulson and Richardson
[41,42]
. The
other key information this file contains is the spatial coordinates of the hydrogen atoms relative
to each other. As hydrogen is a linear molecule, the spatial coordinates can be calculated from
the bond length of hydrogen. The ‘x y z’ coordinates of the first hydrogen atom and ‘y z’
coordinates of the second hydrogen atom were all set to zero as a reference point. The ‘x’
coordinate of the second hydrogen atom was set as the bond length of hydrogen which was
obtained from literature sources [43].
17
3.3.2 Force fields
Force field data is essential in any molecular based simulation to describe the intermolecular
potentials between atoms. RASPA uses two key files to describe the force field in this molecular
model. The first file is used to initiate all the atoms in the COF and hydrogen molecules. It
contains data such as the atomic mass, charge and radii of all the atomic elements in the
simulation. The molecular model used for the simulations in this project neglected factors such
as atomic charge and other more complicated parameters. The charge of the atoms was assumed
to have a negligible effect as hydrogen is a non-polar molecule; hence, the scope of this project
did not require a more complicated molecular model containing charges for each atom. The
second file uses the Lenard-Jones model to approximate the intermolecular potentials, the
equation describing this model is show below [44].
𝜎 LM
𝜎 O
𝑉 = 4𝜀 GH K − H K P
𝑟
𝑟
(2)
The intermolecular potential between the two atoms is denoted as ‘𝑉’, ‘𝜀’ is the maximum
attraction between two atoms, ‘𝑟’ is the distance of the two atoms (measured from the centre of
both atoms) and ‘𝜎’ is the distance of separation where the intermolecular potential is zero [44].
This file requires the Lenard-Jones parameters of ‘𝜀’ and ‘𝜎’ for all the atomic elements in the
simulation, which allows RASPA to compute the intermolecular potentials of such species at
any distance. These Lenard-Jones parameters for the COF structures were taken from the
DRIEDING force field parameters [45] and changed into the appropriate units. The Lenard-Jones
parameters for the hydrogen molecule were taken from other literature sources [46].
3.3.1 Simulation input
In order for the simulation files to run on RASPA, a final file containing all the general
simulation parameters is needed. This file specifies the number of cycles and initiation cycles,
the simulation cut off radius, the external pressure and temperature, the framework and
molecule name, the framework cell size and the helium void fraction (a measure of the
frameworks voidage). The helium void fraction for each COF was included with the core COF
3.0 database [40].
18
This file also specifies the particle commands for Monte Carlo: Particle reinsertion, Particle
swap, Particle rotation and Particle translation (as mentioned previously). Each command was
given an equal probability of 1.0, meaning each move was equally as likely to occur. A fugacity
coefficient of 1.0 was also specified in this file which prevents RASPA calculating the fugacity
from the specified pressure. The inbuilt equations of state that RASPA uses to approximate the
fugacity are inherently inaccurate with hydrogen, as a result the fugacity was calculated
externally and inputted as the specified pressure. The fugacity of hydrogen at a specific pressure
was obtained from the computer programme REFROP
[47]
, as this gave a more accurate
approximation for the hydrogen fugacity. Unlike the other files, this simulation input file
depended on the specific simulation parameters and thus, was adapted to each simulation. A
copy of this simulation input file can be found in appendix B-1.4.
3.4 Ideal number of cycles
The number of cycles and number of initiation cycles are important simulation parameters in
the simulation input file. In RASPA, one cycle consists of a Monte Carlo move being carried
out on each molecule, either successfully or not [39]. A sufficient amount of cycles is required
for the system to reach equilibrium and give valid results on the macroscopic properties such
as adsorption. An increasingly high number of cycles, however, requires much more computing
power and time. Thus, it is necessary to find the minimum number of cycles required for the
system to reach equilibrium.
To determine the exact ideal cycle count for each COF at every pressure would be
counterproductive and near impossible. As a result, the ideal cycle number was determined for
one COF (COF-103) at the highest and lowest simulation pressures of 300 bar and 1.5 bar
respectively. For the lower pressure, a simulation on COF-103 was ran for 25,000 cycles, with
0 initiation cycles, printing results every 100 cycles. A similar simulation on COF-103 was ran
for the higher pressure; however, this simulation only ran for 12,000 cycles as the higher
computational power required for high pressure simulations, meant that 25,000 cycles exceeded
the available computing time available on Balena. The results for these simulations are shown
below in Figure 10 and Figure 11.
19
Figure 10. Adsorption of hydrogen at 1.5 bar and 77 K in COF-103 (using gravimetric
uptake) against the number of cycles.
Figure 11. Adsorption of hydrogen at 300 bar and 77 K in COF-103 (using gravimetric
uptake) against the number of cycles.
20
As can be seen in Figure 10, the average hydrogen adsorption stabilises at about 6,000 cycles
for a pressure of 1.5 bar. In Figure 11, the average adsorption stabilises at about 4,000 cycles
for a pressure of 300 bar. From this, the chosen number of cycles to run all future simulations
at was 8000 cycles. This value was chosen as it was greater than the minimum number of cycles
for both the lower and higher pressures, yet also had a 2000 cycle contingency to account for
potential variation in the cycle number to reach system stability between different COFs. The
number of initiation cycles were chosen as 3000, data before this point for both the high
pressure and low pressure was rapidly variable and had not equilibrated. Setting an initiation
cycle parameter also increased the accuracy of any variation between different COFs or
pressures as the highly fluctuating initial data is discarded, thus the average H2 loading takes
less recorded cycles to stabilise. Ultimately applying the chosen number of cycles (8,000) to all
COFs and pressures is an assumption. It could result in less accurate data for systems that
require longer cycle numbers to equilibrate; however, the given contingency and reduction in
required computing power justifies this assumption.
3.4 Scripting
For running of multiple simulations and for the initiation of more complicated simulations, it
was necessary to use scripting, allowing repetitive tasks to be carried out automatically. BASH
(Bourne-Again SHell) is a command language innate to LINUX operating systems which was
used to write the scripting tasks for this project [48].
The first BASH script written was a submission script, allowing multiple simulations to be
executed and ran simultaneously. This allowed simulations to be distributed over the maximum
available computing cores, optimising the computational time to complete tasks. This script
was implemented for all simulations and a copy can be found in Appendix B-2.1. A variety of
scripts were used to format the multiple simulation input files and specific subdirectories for
screening the whole COF database. These scripts allowed personalised simulation input files to
be created for each COF, containing the specific framework name, helium void fraction and
unit cells associated with that COF. A copy of these scripts can be found in Appendix B-2.2:2.5.
The final script implemented in this project allowed the required simulation results to be
outputted to a file and formatted correctly. A copy of this script can be found in Appendix B2.6.
21
4 Results and Discussion
4.1 Validation of simulation results
Hydrogen adsorption was first simulated for COF-103 at 77 K. Simulations were done at 1.5,
10, 25, 50, 75, 100, 200 and 300 bar. The first pressure of 1.5 bar was chosen as this is the
discharged pressure for portable hydrogen storage according to the DOE
[5]
. The discharged
pressure refers to the pressure of the storage vessel when effectively emptied of hydrogen, some
residual hydrogen will remain in the vessel, but this is not practical to extract. Pressures
proceeding this were chosen at sensible intervals in order to construct a full adsorption isotherm
for 77 K. Hydrogen adsorption is measured using the gravimetric uptake (wt% of H2). As an
example, Figure 12 shows the hydrogen adsorption isotherm for COF-103 at 77 K.
Figure 12. Gravimetric hydrogen uptake as function of pressure for COF-103 at 77 K (lines
have been added between points for reference).
22
Error bars were calculated and included in Figure 12, yet they cannot be seen as they are within
the data point. This shows the error in these simulations is negligible.
The excess adsorption refers to the additional hydrogen adsorbed in a unit volume of COF,
relative to the amount of hydrogen ordinarily in a unit volume at a given pressure. The absolute
adsorption is a measure of the total hydrogen adsorbed by a COF. Equation 3 demonstrates this
concept.
Absolute adsorption = Base hydrogen amount + Excess adsorption
(3)
Figure 12 was used to verify the validity of the simulation methods used. The first paper
investigating hydrogen adsorption in COFs, by Yaghi et al.
[29]
, used similar grand canonical
Monte Carlo (GCMC) molecular simulations as found in this work. Yaghi and co-workers
simulated an excess adsorption isotherm for COF-103 at 77 K, which showed a maximum
excess adsorption of 9.1 wt% at 100 bar. In comparison, the above isotherm for COF-103
showed an excess adsorption of 9.8 wt% at 100 bars. The simulation methods used by Yaghi
and co-workers showed excellent agreement with experimental adsorption in COF-5 (3.3 wt%
simulated compared to 3.4 wt% experimental uptake at 50 bar). This verifies the accuracy of
the simulation methods used by Yaghi.
Disparity between the results of Yaghi et al. and the results obtained in this work originates in
differences between the simulation methods used. Specifically, the force fields used in the
simulations by Yaghi et al. were derived from ab initio second order Møller-Plesset (MP2)
calculations using quadruple-QZVPP basis set and basis set superposition error correction.
Whereas in this work, the Lenard-Jones parameters taken from the DRIEDING force field were
used
[45]
. Despite this, the difference in gravimetric uptake was only 0.7 wt% showing the
simulation methods in this work could be used to identify the best COFs for hydrogen
adsorption. Caution should be exercised, however, when drawing conclusions from the specific
values of hydrogen adsorption in COFs; the nature of the simulation methods used will likely
cause some discrepancy between the adsorption results obtained from these simulations and the
actual adsorption results obtained from experiments. Ultimately, this discrepancy is not overly
significant and meaningful predictions can still be drawn about the COFs with the highest
hydrogen adsorption potential from these simulations.
23
4.2 Determining the storage pressure
With the results of the simulation verified, COF-103 was then simulated at 298 K and 185 K,
using the same pressures above (1.5, 10, 25, 50, 75, 100, 200 and 300 bar). This was used to
construct 2 more adsorption isotherms for COF-103. Individual plots showing the absolute and
excess adsorption isotherms for COF-103 at 185 K and 298 K can be found in Appendix C.
Figure 13. shows the absolute adsorption isotherms for 77 K, 185 K and 298 K.
Figure 13. Gravimetric hydrogen uptake as function of pressure for COF-103 at 298 K, 185
K, 77 K (lines have been added between points for reference).
Figure 13 shows that the absolute adsorption in COF-103 was greatest at 77 K and smallest at
298 K. Hydrogen adsorption significantly reduces with temperature - as the kinetic energy of
hydrogen molecules increases, the Van der Wall forces between the COF and hydrogen
molecules weaken [49]. Because the aim of this research is concerned with portable applications
(requiring a moderate gravimetric capacity), the hydrogen adsorption at 298 K was too minimal
to justify any further simulations of different COFs at this temperature.
24
Determining the storage pressure for COFs in portable applications involves a compromise.
Higher pressures yield a greater gravimetric uptake; although, they also inflate the drawbacks
associated with compressed hydrogen storage (increased costs, safety concerns and more
weight). The adsorption isotherm in Figure 13 for 77 K showed a much steeper gradient at
lower pressures, with an increasingly flatter gradient going into higher pressures. This
demonstrates that the benefit of using a COF over an ordinary compressed vessel is most
apparent in lower pressures (this is shown by the excess adsorption isotherm in Figure 12). For
this reason, 100 bar was chosen as an appropriate storage pressure for COF-103 at 77 K. For
the adsorption isotherm at 185 K, the gradient is much flatter at lower pressures than for the 77
K isotherm. This shows that the benefit gained by the COF at this higher temperature is
significantly less (due to the weaker Van der Waal forces). As a result, a higher storage pressure
of 200 bar was chosen for COF-103 at 185 K, allowing a greater gravimetric capacity to be
achieved than at 100 bar. This gives two interesting storage conditions. The first uses a higher
pressure and temperature, while the second uses a lower temperature and pressure.
4.3 Screening 309 COFs for the deliverable hydrogen
The deliverable hydrogen is an important quantity which refers to the usable amount of
hydrogen stored in a COF. The deliverable hydrogen is calculated from the difference between
the adsorbed hydrogen (gravimetric uptake) at the storage pressure and discharged pressure.
Figure 14 demonstrates this concept.
Figure 14. Showing how the deliverable hydrogen is obtained from an adsorption isotherm
25
To identify the most effective COFs for hydrogen adsorption, it was necessary to screen all 309
COFs in the core COF 3.0 database [40] – obtaining the deliverable hydrogen for each. This was
achieved by simulating all the COFs at the discharged pressure (1.5 bar) and storage pressure
(100 bar at 77 k and 200 bar at 185 K). All 309 COFs were successfully simulated at the
discharged pressure of 1.5 bar for the temperatures of 77 K and 185 K. Unfortunately, due to
the COVID-19 pandemic, time constraints to this work prevented more computationally
demanding simulations of all these COFs at the storage pressures. To accommodate for this, a
small number of COFs from the screening at 1.5 bar and 185 K were selected for simulations
at the storage pressure of 200 bar. The 24 COFs with the highest gravimetric uptake at this
lower pressure were selected. Generally, a higher uptake at the discharged pressure is not
optimal for hydrogen adsorption as the amount of usable hydrogen is less. Nevertheless, it was
assumed that the trends in COFs with the highest adsorption at lower pressures would continue
into higher pressures; thus, the adsorption at higher pressures would be significantly greater,
making the adsorption at lower pressures negligible. Figure 15 shows the results of screening
all 309 COFs at 1.5 bar and 185 K, indicating selected COFs for simulations at the storage
pressure.
Figure 15. A screening showing the gravimetric uptake of all 309 COFs at 185 K and 1.5 bar.
26
It should be noted that the ‘COF number’ shown in Figure 15 refers to the numbering system
used in the core COF 3.0 database
[40]
. This does not correspond to the typical numbering of
COFs in literature.
Constraints of this work caused from the COVID-19 pandemic, also prevented any simulations
at the storage pressure (100 bar) for 77 K. The results of the screening for COFs at 1.5 bar and
77 K can be found in Appendix C.
4.4 Identifying the most promising COFs for hydrogen adsorption
With the 24 selected COFs successfully simulated at the discharged pressure and storage
pressure, the deliverable hydrogen could be obtained by the taking the difference in the
hydrogen adsorption at these two pressures. The deliverable hydrogen was calculated from the
gravimetric uptake and volumetric uptake; thus, giving the gravimetric capacity and volumetric
capacity for each COF. Figure 16 shows the deliverable gravimetric capacity and volumetric
capacity for each of the 24 selected COFs at 185 K and 200 bar.
Figure 16. A comparison of the gravimetric capacity and volumetric capacity for the 24 selected
COFs at 185 K and 200 bar.
27
Similar to Figure 15, the COF numbering for Figure 16 refers to the numbering system of the
COF 3.0 database and is not how these COFs are commonly numbered in literature.
As can be seen from Figure 16, the variation in the volumetric capacity was much less than the
variation in the gravimetric capacity between all 24 COFs. COF number 82 (commonly known
as CoPc-PorDBA) had the highest volumetric capacity of 28.0 g/L. COF number 70 (COFDL229-4fold) had the lowest volumetric capacity of 22.9 g/L. This gave a range of 5.1 g/L in
the volumetric capacities of these COFs. In stark contrast, COF number 70 displayed the highest
gravimetric capacity of 33.0 wt%, while COF number 82 showed the lowest gravimetric
capacity of 8.3 wt%. This gave a range of 24.7 wt% in the gravimetric capacities of these COFs.
The COFs with the second and third highest volumetric capacities were COF number 9 (3DPy-COF-2P) and COF 149 (IISERP-COF3), both showing volumetric capacities of 27.4 g/L.
COF number 74 (COF-DL229-0fold) and COF 33 (CCOF-2) showed the second and third
highest gravimetric capacities of 24.1 wt% and 17.9 wt % respectively. A relationship between
the gravimetric capacity and volumetric capacity is discernible from Figure 16: COFs with the
highest volumetric capacity generally exhibit the lowest gravimetric capacity. Figure 17 more
accurately depicts this trend.
Figure 17. Relationship between the volumetric capacity and gravimetric capacity for
selected COFs.
28
This negative relationship between the volumetric capacity and gravimetric capacity is seen in
literature for hydrogen adsorption in MOFs. Similarly, MOFs with the highest volumetric
uptake generally exhibit poorer gravimetric uptakes
[50]
. MOFs or COFs with more atoms and
heavy atoms (heavy metals) show stronger interactions with hydrogen, typically acting as
greater physisorption materials capable of storing more hydrogen. As a result, they generally
show greater hydrogen adsorption per unit volume (volumetric capacity). Unfortunately, more
and heavier atoms present in materials with the highest volumetric capacity adds weight to the
framework. Therefore, the COFs and MOFs with the highest hydrogen adsorption on a mass
basis (gravimetric capacity) are generally comprised of less and lighter atoms. This highlights
the difficulty in finding adequate storage materials with both a high volumetric capacity and a
high gravimetric capacity.
As the initial screening of all 309 COFs at 185 K and 1.5 bar (see Figure 15), measured the
hydrogen adsorption in gravimetric uptake, the 24 selected COFs are likely to show more
promise as hydrogen adsorption materials in terms of gravimetric capacity than volumetric
capacity. This is shown as all 24 COFs surpassed the DOE portable gravimetric target of 4 wt%.
However, none of the COFs approached the DOE volumetric targets of 50 g/L single-use and
30 g/L rechargeable [5].
Selecting the most promising COFs for portable hydrogen storage was a challenge, involving a
trade-off between the gravimetric capacity and volumetric capacity. COF number 82 was the
closest to the DOE targets with a volumetric capacity of 28 g/L and 8.3 wt%; therefore, a case
could be made that this COF is the most promising for hydrogen adsorption (identified in this
research). However, as the range of volumetric capacities were relatively small (5.1 g/L), it was
decided that COF number 70 and 74 with significantly higher gravimetric capacities, showed
the most promise as portable hydrogen storage materials in this research.
Comparing the adsorption of COF number 70 and 74 with other well-performing COFs from
literature is difficult due to the unique conditions used in these simulations (185 K and 200 bar).
COF number 70 and 74 showed gravimetric capacities of 33 wt% and 24.1 wt%. On a
gravimetric basis, they were capable of outperforming two well-known COFs at 77 K and 100
bar: COF-108 (18.9 wt%) and COF-105 (18.3 wt%) [29]. On a volumetric basis, the hydrogen
adsorption for COF number 70 and 74 was 22.8 g/L and 25.2 g/L respectively. This could not
compete with the volumetric uptake of COF-102 (49.9 g/L) and COF-103 (49.8 g/L) at 100 bar
29
and 77K
[29]
. Furthermore, COF number 70 and 74 were drastically outcompeted in terms of
volumetric uptake by the metalated COF, COF-301-Pd, which showed a total volumetric uptake
of 60 g/L at 100 bar and 298K
[31]
. Nevertheless, the ability of COF number 70 and 74 to
outcompete COF-108 and COF-105 on a weight basis and at a lower temperature, should not
be understated for portable storage applications. Specifically, the use of a 185 K storage
temperature over a 77 K temperature reduces the high energy costs of cryogenic storage and
the associated loss of hydrogen through ‘boil off’
[16]
. Furthermore, the high gravimetric
capacity of both these COFs would be especially useful in lightweight applications such as
UAV’s.
Images of COF number 70 and 74 were taken from visualisation software to understand their
structure. Van der Waal atomic radii were used, as this gave the most accurate representation
of the available hydrogen volume in the COF. Figure 18 and 19 show these images for COF
number 70 and COF number 74 respectively.
67 Å
100 Å
Figure 18. A visual representation of COF number 70 using Van der Waals atomic radii.
30
65 Å
127 Å
Figure 19. A visual representation of COF number 74 using Van der Waals atomic radii
The first thing to note from the structures of these two COFs is that they are 3D in nature. This,
compliments the initial predictions of Yaghi and co-workers, suggesting 3D COFs offer more
promise as hydrogen adsorption materials than 2D COFs
[29]
. As expected, both these COFs
were not doped with metals and were comprised of only light elements (H, C, N). Furthermore,
they exhibited large cavities which would allow for a larger weight fraction of hydrogen in the
COF. These two factors play a significant role in their exceptional gravimetric uptakes. Specific
factors that make particular COFs such as COF number 70 and 74 better hydrogen adsorption
materials will be quantitatively analysed in the next section.
4.5 Observable trends in the characteristics of COFs
Properties of COFs were correlated against the gravimetric and volumetric uptake. The COF
specific properties included: the density, specific volume, voidage, Pore Limiting, Diameter
(PLD), Largest Cavity Diameter (LCD), volume specific surface area and mass specific surface
area. This was done for all 24 selected COFs using the deliverable hydrogen for 200 bar and
185 K (see Figure 20). Correlations were also made for all 309 COFs from the screening at 1.5
bar and 185 K (see Figure 21). Values for the properties of each COF were obtained from the
‘COF properties spreadsheet’ found in the core COF 3.0 database [40].
31
Figure 20. Deliverable gravimetric and volumetric capacities for the 24 selected COFs at
185 K and 200 bar, plotted against different COF properties.
32
Figure 21. Gravimetric uptake and volumetric uptake plotted against the different COF
properties for 185 K and 1.5 bar.
33
As this research is concerned with identifying the specific properties of COFs with a high
deliverable hydrogen, Figure 20 contains the most useful correlations. Nevertheless, the data
set for deliverable hydrogen storage only contained 24 COFs, which may not be enough to draw
accurate correlations between the properties of those COFs with exceptional hydrogen
adsorption. To increase the reliability of these trends, correlations were made for the larger
dataset of all 309 COFs at 1.5 bar. As can be seen, both Figure 20 and 21 shared the same trends
in hydrogen uptake for these properties. This confirms the reliability of the correlations showed
in Figure 20. Furthermore, this helps to confirm the initial assumption made when selecting
these 24 COFs, stating that the COFs which exhibited superior hydrogen uptake at 1.5 bar will
exhibit superior hydrogen uptake at 200 bar.
Trends in the density and specific volume of COFs
The first trends in Figure 20 and 21 explores the relationship between hydrogen uptake and the
densities of the COFs. As the specific volume is just the inverse of the density, correlations in
the hydrogen uptake relating to specific volume will be discussed in conjunction to the trends
observed for density. As expected, the gravimetric uptake shows a negative exponential
relationship with the density. The reason behind this correlation is twofold. Firstly, COFs with
a lower density will have fewer and lighter atoms; thus, the amount of adsorbed hydrogen
relative to the weight of the COF (gravimetric uptake) will be greater. Secondly, these COFs
with lower densities will generally have more ‘empty space’ to store an even greater weight
fraction of hydrogen. These two reasons likely result in the exponential nature of this
relationship.
Unsurprisingly, the specific volume shows the opposite of this relationship, showing a positive
increase in the gravimetric uptake with increasing specific volume. As this trend is the inverse
of the exponential correlation between the gravimetric uptake and density, it exhibits a linear
relationship. The volumetric uptake in Figure 21 shows a positive correlation with density and
a negative correlation with specific volume (opposite to with gravimetric uptake). COFs with
higher densities often have more and heavier atoms which increases the bonding energy
between hydrogen and the COF allowing for a greater hydrogen uptake per unit volume. These
trends once again highlight the trade-off between a high gravimetric and volumetric uptake.
34
Trends in the voidage of COFs
A significant correlation is seen in both Figure 20 and 21, between hydrogen uptake and the
voidage. The gravimetric uptake shows an exponential positive relationship as a function of
voidage. This is expected as the COFs with the greatest empty space (voidage), will have the
least atoms; thus, the relative mass of hydrogen adsorbed in relation to the mass of the structure
will be more. The exponential nature of this relationship is due to the twofold effects of
increasing the empty space (voidage) in the COF (discussed previously).
A study analysing the trends in hydrogen adsorption for 18,383 porous crystalline structures,
found the same relationship between the gravimetric uptake and structure density [51]. This study
also found an interesting relationship between the volumetric uptake and structure density.
Namely, they recorded a broad range of volumetric uptakes at different structure densities,
citing that the volumetric uptake depended on a wide variety of properties and was not strongly
correlated with the voidage. Nevertheless, this study stated that the volumetric uptakes peaked
for COFs with a voidage of about 0.75.
This is seen in Figure 20 and 21 which did not show a strong correlation between the volumetric
uptakes and voidage. Figure 21 showed a similar trend as this study with the volumetric uptake
generally peaking for COFs with a voidage between 0.7 and 0.8. All COFs (except 1) in Figure
20 had a voidage greater than 0.8 and showed a negative relationship between the volumetric
uptake and voidage. If Figure 20 had contained the volumetric uptakes for a greater range of
COFs, it would likely see the volumetric uptake peak at a voidage of about 0.75.
One interesting point is that the relative increase in the gravimetric uptake is much greater than
the relative decrease in volumetric uptake at increasing voidage fractions. As a result, slightly
higher void fractions (0.9 - 0.95), may provide the optimal range for the COFs most ideal for
portable hydrogen storage.
Trends in the Pore Limiting Diameter (PLD) of COFs
As can be seen in Figure 20, there was no notable correlation between the Pore Limiting
Diameter (PLD) and the deliverable hydrogen (gravimetric capacity and volumetric capacity).
As the 24 COFs used in Figure 20 were selected as having the highest gravimetric uptake from
35
the initial screening, they are likely to have the lowest densities for the reasons discussed
previously. Consequentially, all these COFs had a PLD greater than 10 Å, which is much less
than the size of hydrogen (bond length of 0.74 Å); therefore, it is unlikely to be a limiting factor
in hydrogen adsorption.
Figure 21 shows a weak positive correlation between the gravimetric uptake and PLD. COFs
with a very small PLD are likely to see limitations with hydrogen uptake; however, hydrogen
is such a small molecule that, for the vast majority of these COFs, this is not the causation of
this weak correlation. It is much more probable that the COFs with greater PLDs had a greater
voidage, which is already seen to show a strong correlation with the gravimetric uptake.
Similar to Figure 20, Figure 21 showed no notable correlation between the PLD and volumetric
uptake. As there was no strong correlation between the voidage and volumetric uptake, it was
unlikely there would be any correlation between the volumetric uptake and PLD. Ultimately,
the PLD in itself, was not considered a significant factor that directly influences hydrogen
uptake in COFs.
Trends in the Largest Cavity Diameter (LCD) of COFs
For both Figure 20 and 21, the Limiting Cavity Diameter (LCD) showed the same trends as the
PLD. No notable trends were present in Figure 20 between the LCD and the deliverable
hydrogen (gravimetric capacity and volumetric capacity), for the same reasons mentioned
previously. Similarly, the weak trend between the gravimetric uptake and LCD shown in Figure
21 is most likely a correlation between a secondary factor like voidage. As a result, LCD was
not considered a significant factor affecting hydrogen uptake in COFs.
Trends in the volume specific surface area of COFs
A relationship between the volume specific surface area (m2 cm-3) and gravimetric uptake is not
innately clear from Figure 20 or 21; however, when compared to literature findings, a
relationship becomes more apparent. The same study discussed above which analysed the
trends in hydrogen adsorption for 18,383 porous crystalline structure, found two independent
correlations between the volume specific surface area and gravimetric uptake
36
[51]
. These two
correlations were due to the classification of these structures into high voidage and low voidage
materials.
Firstly, materials with a high voidage showed a steep negative correlation between the
gravimetric uptake and volume specific surface area (m2 cm-3). For this class of structures, those
materials with a high-volume specific surface area, also had a higher number of atoms; thus,
the amount of hydrogen adsorbed on a mass basis was less. Figure 20 shows a weak negative
trend as these 24 selected COFs were in the high voidage class of structures.
The second class of structures in the study (with a low voidage) showed a positive correlation
between the gravimetric uptake and volume specific surface area (m2 cm-3). These materials,
show more complex structures, comprising of many atoms; therefore, on a mass basis the
hydrogen adsorption in these materials is much less. The study cited that the volume specific
surface increases due to more porosity and ‘exposed’ atoms in these structures; therefore, the
overall hydrogen adsorption increases (including the weight-based hydrogen adsorption).
Figure 21 shows both a weak positive relationship (COFs of low voidage) and a faint negative
correlation (COFs of high voidage) for gravimetric uptake as a function of volume specific
surface area.
The volumetric uptake showed a weak positive correlation with the volume-based surface area
for both Figure 20 and 21. This is expected as a larger surface area allows for more
physisorption of hydrogen in a given volume.
Overall, the correlation between hydrogen adsorption and the volume specific surface area is
not pronounced and depends more on the individual structure of the COF. As a result, the
volume specific surface area was not considered a significant factor affecting the hydrogen
adsorption in COFs.
Trends in the mass specific surface area of COFs
The final correlation to discuss involves the hydrogen adsorption as a function of the mass
specific surface area (m2 g-1). As expected, a positive correlation was shown between the
gravimetric uptake and mass-based surface area in both Figure 20 and 21. Evidently, COFs
with a greater surface area per unit mass will generally show greater hydrogen uptake per unit
37
mass. In particular, COF number 70, which showed an exceptionally high gravimetric uptake,
had an exceptionally high mass-based surface area of 25,917 m2 g-1 (over 3 x higher than any
other COF). Surprisingly, there did not appear to be an overly discernible correlation between
the volumetric uptake and mass-based surface area in either Figure 20 or 21. Nevertheless, the
mass-based surface area is a significant parameter in COFs with exceptional gravimetric
uptakes.
Overall these relationships once again demonstrate the difficulty in optimising COFs with a
high gravimetric uptake and volumetric uptake. Characteristics that make COFs exceptional
hydrogen adsorption materials on a gravimetric basis are often not akin to the characteristics
which make COFs exceptional hydrogen adsorption materials on a volumetric basis.
Nevertheless, it was concluded that the most important characteristics present in COFs that
showed excellent promise as portable hydrogen adsorption materials were a moderately low
density, high voidage (0.9-0.95) and a high mass-based surface area.
4.6 Project planning and design
Due to the interruptions to this work caused by the COVID-19 pandemic, the collecting of
simulation data was ceased on the 19th of March. Some of the impacts to this have been briefly
discussed in the Results and Discussion section. This section will mention the planned
simulations that could not be completed and their effects on this work due to these
unprecedented circumstances.
These interruptions prevented the screening of all 309 COFs at the storage pressure of 200 bar
and temperature of 185 K. This meant that the deliverable hydrogen could not be calculated for
all the COFs at these conditions. To obtain some data for the deliverable hydrogen, 24 COFs
were selected from the initial screening at 1.5 bar and 185 K. These COFs were selected as they
showed the greatest gravimetric uptake at 1.5 bar and 185 K. As a result, it was most likely
these materials would show exceptional gravimetric capacities but not volumetric capacities.
Not being able to obtain the deliverable hydrogen for all 309 COFs at 185 K also prevented the
accurate selection of the most optimum COFs for portable hydrogen storage. Furthermore, it
was difficult to draw accurate conclusions about the properties of the COFs which showed the
best hydrogen adsorption from a small data set of just 24 COFs.
38
It was also planned to screen all 309 COFs at the storage pressure of 100 bar and a temperature
of 77 K; thus, calculating the deliverable hydrogen at these conditions. This would have allowed
the comparison between the hydrogen uptakes at a lower pressure and temperature (100 bar and
77 K), compared to a higher pressure and temperature (200 bar and 185K). The practicalities
of implementing either storage condition for portable uses could have also been compared with
respect to their difference in hydrogen storage. The volumetric and gravimetric capacity for
each COF at 77 K and 100 bar would have been plotted against the specific properties of such
COFs (similar to the correlations at 185 K and 200 bar). This would have allowed for the
characterisation of the properties of COFs with the best hydrogen uptake at these conditions.
Simulations of adsorption isotherms for the most promising COFs (COF number 70 and 74)
were also planned (similar to the adsorption isotherms shown for COF-103). Specifically, the
excess adsorption isotherm would have given a better insight into their actual adsorption benefit
when compared to just compressed hydrogen.
The final area of simulations which were affected was for error analysis. It was planned to
repeat the simulations isotherm simulations of COF-103. Namely, repeating simulations at 298
K 185 K and 77 K all for pressures of 1.5, 10, 25, 50, 75, 100, 200 and 300 bar. These were
planned to be repeated 5 times in order to calculate the difference between simulation outputs
of RASPA. This would have allowed the accurate determination of the error and could have
confirmed the reliability of RASPA simulations.
39
5 Conclusions and Future Work
The aim of this research was to assess the practicality of COFs as hydrogen storage materials
in portable applications through computer simulations. This was done by three key objectives.
Unfortunately, due to the unprecedented circumstances, not all of these objectives could be
completely satisfied. Nevertheless, some of these objectives were satisfied and the aim of the
research was, in part, fulfilled.
The first objective of this project was to screen a large dataset of 309 COFs to obtain the
deliverable hydrogen for a variety of temperatures in order to identify the most effective COFs
and conditions for hydrogen adsorption. This objective was partly met. Unfortunately, due to
the situation, this was only done for 24 of the initial 309 COFs at a temperature of 185 K and
200 bars. Disappointingly, none of these COFs satisfied the volumetric portable power targets
set by the DOE of 50 g/L (single use) and 30 g/L (reusable). Nevertheless, the deliverable
gravimetric capacity of all 24 COFs exceeded the target of 4 wt% set by the DOE for portable
power. From these simulations, two COFs were identified as promising materials for portable
hydrogen adsorption: COF number 70 - COF-DL229-4fold (gravimetric capacity of 33 wt%
and volumetric capacity of 22.8 g/L) and COF number 74 (gravimetric capacity of 24.1 wt%
and volumetric capacity of (25.2 g/L). On a gravimetric basis, these two COFs showed some
of the highest hydrogen adsorption for porous materials in literature, outperforming many wellknown COFs (such as COF-108 and COF-105) at a lower temperature. The high gravimetric
capacities of these two COFs would have significant benefits in portable hydrogen storage,
providing a lightweight framework. This would be particularly useful in UAV’s, where these
mass saving benefits offer extended flight times.
The second objective of this research, which was to simulate isotherms for the most promising
COFs, could not be achieved due to time restraints. If this objective had been accomplished, it
would have allowed for a greater understanding into the adsorption benefit of these COFs, and
allowed more accurate optimisation of the storage conditions to meet the DOE targets for
portable applications
The final objective of this research was to establish any similarities in the properties of COFs
showing exceptional hydrogen adsorption. This objective was met for the available COFs. It
40
was found the most significant factor in a high gravimetric uptake in COFs was a high massbased surface area. Specifically, COF number 70, showing the greatest gravimetric uptake also
had the highest mass-based surface area of any COF at 25,917 m2 g-1 (over 3 x higher than any
other COF from the database). Correlations in this research also described the difficulty in
optimising COFs with both a high gravimetric uptake and volumetric uptake. From this research
it was concluded that a high voidage of (0.9-0.95) offered the best trade-off between the
gravimetric and volumetric uptakes for portable power applications.
The results of these simulations were in good agreement with similar simulation results from
literature, showing a difference in gravimetric uptake of just 0.7 wt% for COF-103 at 100 bar
and 77 K. This confirmed the validity of the simulation methods and allowed the identification
of the most effective COFs for hydrogen adsorption. Nevertheless, it was likely there was some
error in the simulation methods used. If recent time constraints to the work did not take place,
the simulations to construct the isotherm results for COF-103 would have been repeated. This
would have allowed comparisons to be made between the same simulations at the same
conditions; thus, allowing for the effective error in the simulation methods to be calculated.
If there was more time available for this research, it would be practical to run simulations at
different cycle counts for different COFs. It was assumed when determining the ideal cycle
number in COF-103, that the ideal cycle count between different COFs, at the same
thermodynamic conditions, did not fluctuate too much. The only way to validate this
assumption, however, is to run simulations of different cycle numbers over different COFs to
determine any fluctuations in the hydrogen adsorption. If, the assumption made did not hold
true and the ideal cycle number varied greatly between COFs, then this would be a cause of
error in this work. Specifically, if the hydrogen uptake for COFs was determined before the
system equilibrated, then these results would be invalid.
One area of future work would be the continuation of screening the COF database at various
temperatures to calculate the deliverable hydrogen (as originally planned in this research). This
would offer great promise in identifying the materials with the highest hydrogen uptake at a
variety of thermodynamic conditions. Specifically, it would show materials with a high
volumetric uptake, which were likely missed in this research.
41
Another area of future work could involve the investigation into the effects of the adsorption
enthalpies for the physisorption of hydrogen in different COFs. High adsorption enthalpy’s is
a known factor akin to high hydrogen uptakes in COFs. Investigating specific trends in the
adsorption enthalpy of different COFs would likely allow for the better design of future COFs
with higher hydrogen uptakes.
A final area of future work could be in the synthesis and design of COFs with exceptionally
high mass-based surface areas. As demonstrated in this research, COFs such as COF number
70 which had very high mass-based surface areas, showed exceptional gravimetric uptakes. The
design and synthesis of COFs with these properties could lead to new and exciting COFs with
these exceptional hydrogen adsorption properties.
42
Acknowledgements
This research made use of the Balena High Performance Computing (HPC) Service at the
University of Bath.
I would like to acknowledge Dr. Matthew Lennox, Dr. Joe Manning and Dr. Stephen Wells
for assisting my understanding of molecular simulation software and scripting.
I would also like to acknowledge James Osola and Bethany Arendt for aiding my report
writing.
43
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48
Appendix A – Health & Safety
49
Appendix B – Simulation files and scripts
B-1 Basic simulation files
B-1.1 ‘pseudo_atoms.def’
# Number of pseudo-atoms
18
# type print as chem oxidation mass charge polarization B-factor
anisotropy anisotropic-type tinker-type
H
yes H
H
0
1.00800000
0.00000000
Li
yes Li
Li
0
6.94000000
0.00000000
B
yes B
B
0
10.81000000
0.00000000
C
yes C
C
0
12.01100000
0.00000000
N
yes N
N
0
14.00700000
0.00000000
O
yes O
O
0
15.99900000
0.00000000
F
yes F
F
0
18.99840316
0.00000000
Si
yes Si
Si
0
28.08500000
0.00000000
P
yes P
P
0
30.97376200
0.00000000
S
yes S
S
0
32.06000000
0.00000000
Cl
yes Cl
Cl
0
35.45000000
0.00000000
Ni
yes Ni
Ni
0
58.69340000
0.00000000
Cu
yes Cu
Cu
0
63.54600000
0.00000000
Zn
yes Zn
Zn
0
65.38000000
0.00000000
Br
yes Br
Br
0
79.90400000
0.00000000
I
yes I
I
0
126.90447000
0.00000000
Co
yes Co
Co
0
58.93319400
0.00000000
H_H2
yes H
H
0
1.00800000
0.00000000
radius connectivity
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
absolute
absolute
absolute
absolute
absolute
absolute
absolute
absolute
absolute
absolute
absolute
absolute
absolute
absolute
absolute
absolute
absolute
absolute
B-1.2 ‘force_field_mixing_rules.def’
#
General rule:
shifted
#
General rule:
no
#
Number of
18
#
Interactions
B
LENNARD_JONES
Br
LENNARD_JONES
C
LENNARD_JONES
Cl
LENNARD_JONES
Co
LENNARD_JONES
Cu
LENNARD_JONES
F
LENNARD_JONES
H
LENNARD_JONES
I
LENNARD_JONES
Li
LENNARD_JONES
N
LENNARD_JONES
Si
LENNARD_JONES
Ni
LENNARD_JONES
O
LENNARD_JONES
P
LENNARD_JONES
S
LENNARD_JONES
Zn
LENNARD_JONES
H_H2
LENNARD_JONES
#
General rule:
Lorentz-Berthelot
‘shifted’
or
‘truncated’
‘yes’
‘no’
tail
or
potentials
correction
defined interactions
(epsilon
in
“Kelvin,”
sigma
47.80587153
3.581412844
186.1912891
3.519049936
47.8561935
3.472990471
142.5621411
3.519317207
7.045076
2.030810677
2.516099
2.4713382
36.48342827
3.093200348
7.64893945
2.846421401
170.591478
3.181980514
12.580493
1.733118722
38.94920481
3.262560198
202.29432
3.037023622
7.548296
2.00394062
48.15812532
3.033153776
161.0303041
3.69722968
173.1075769
3.59032183
62.399243
1.953736038
10
2.158865431
‘Lorentz-Berthelot’
or
‘Jorgensen’
50
in
Angstrom):
mixing rule
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
B-1.3 ‘H2.def’
# critical constants: Temperature [T], Pressure [Pa], and Acentric factor [-]
33.18
1300000
-0.22
# Number Of Atoms
2
# Number of groups
1
# H2-group
rigid
# number of atoms
2
q# atomic positions
0 H_H2
0.00 0.0 0.0
1 H_H2
0.74 0.0 0.0
# Chiral centers Bond BondDipoles Bend UrayBradley InvBend Torsion Imp. Torsion Bond/Bond
Stretch/Bend Bend/Bend Stretch/Torsion Bend/Torsion IntraVDW IntraCoulomb
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
# Bond stretch: atom n1-n2, type, parameters
0 1 RIGID_BOND
# Number of config moves
0
B-1.4 ‘Simulation.input’
SimulationType
NumberOfCycles
NumberOfInitializationCycles
PrintEvery
PrintPropertiesEvery
Forcefield
RemoveAtomNumberCodeFromLabel
CutOffVDW
MonteCarlo
8000
3000
100
100
local
yes
12.8
Framework 0
FrameworkName
UnitCells
HeliumVoidFraction
ExternalPressure
ExternalTemperature
Component 0 MoleculeName
MoleculeDefinition
TranslationProbability
ReinsertionProbability
SwapProbability
RotationProbability
CreateNumberOfMolecules
FugacityCoefficient
38
1 1 1
0.797
1.4963e5
77
H2
local
1.0
1.0
1.0
1.0
0
1.0
51
B-2 Scripting
B-2.1 Submission script
#!/usr/bin/env bash
#SBATCH --job-name=test
#SBATCH --partition=batch
#SBATCH --account=free
#SBATCH --time=06:00:00
#SBATCH --nodes=3
#SBATCH --ntasks-per-node=16
#mail alert end of execution
#SBATCH --mail-type=END
#send mail to this address
#SBATCH --mail-user=al966@bath.ac.uk
module purge
module load group ce-molsim stack
module load raspa/2.0.2 taskfarmer
cd /home/o/al966/scratch/H2/Isotherm
mpiexec -n 16 taskfarmer -f task.finder2 -v -r
B-2.2 Scripts for making COF directories and basic simulation input files
for hippo in *cif
do
echo $hippo
foo="COF${hippo%.cif}" #this is our folder name
echo $foo ; mkdir $foo #actually make the directory
cp $hippo $foo #copy this structure into its own folder
done
for pig in *cif
do
echo $pig
done > COFSfilelist
while read alfred
do
echo $alfred
foo="COF${alfred%.cif}" #folder name
boo="run${alfred%.cif}" #command filename
echo $foo
cat simulation | sed -e "s/CORONA/${alfred%.cif}/g" > $boo
mv $boo $foo
done < cofsfilelist
B-2.3 Script for copying of all basic files into COF directories
for i in {1..309..1}
do
find -name "COF${i}" -exec cp H2.def /"COF${i}" {} \;
done
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B-2.4 Script for editing the specific void fraction for the simulation input file of each COF
for i in {1..309..1}
do
sed -n "${i}p" VoidFrac.txt
#echo $(sed -n "${i}p" VoidFrac.txt)
find . -name "run${i}" -exec sed -i "s/VIRUS/$(sed -n "${i}p" VoidFrac.txt)/g" {} \;
done
B-2.5 Script for editing the specific unit cell size for the simulation input file of each COF
for i in {1..309..1}
do
sed -n "${i}p" Size_Cell.txt
#echo $(sed -n "${i}p" Size_Cell.txt)
find . -name "run${i}" -exec sed -i "s/1 1 1/$(sed -n "${i}p" Size_Cell.txt)/g" {} \;
done
B-2.6 Script for outputting and formatting results
grep -A 8 'Average Density:' System_0/* > file1
sed -n '9~10p' file1 > file2
sed 's .\{7\} ' file2 > file3
sort -V file3 > Average_Density.txt
rm file1
rm file2
rm file3
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Appendix C – Additional simulation results
C-1 Adsorption isotherm for COF-103 at 185 K.
C-2 Adsorption isotherm for COF-103 at 298 K.
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C-3 Screening of all 309 COFs at 77 K and 1.5 bar
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