This document summarizes a research project aiming to improve the molecular selectivity of graphene through computational modeling and Monte Carlo simulations. The student investigated how doping graphene with additional sites affects its ability to selectively adsorb carbon dioxide over methane and nitrogen at room temperature. Simulation results showed that adding 100 dopant sites and increasing the well depth parameter epsilon improved the selectivity. The next steps are to experimentally test materials that can replicate the optimized doping conditions from the simulations.

1. Improving the Molecular
Selectivity Graphene
ADVISOR: PROF. SILVINA GATICA
MENTOR: DR SIDI MAIGA
R.E.U. INTERN: JORDAN NNABUGWU
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2. Outline
• Motivation
• Molecular interactions
• Project Overview
• Methods
• Monte Carlo
• Data
• Increasing Selectivity Results.
• Dopants and Epsilon
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3. Motivation
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4. Project idea: Motivation
• One solution to this problem is to
trap the pollutant before it gets into
the atmosphere.
• This can be done by having the gases
physically adsorbed on a substrate.
• Adsorption is a phenomenon that
happens when a substrate is
equilibrium with a vapor and the
molecules of the vapor will stick and
form a monolayer on the substrate.
• Adsorbent – Graphene.
• We know that Graphene does not
have good selectivity at room
temperature.
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5. Graphene
• Chemical vapor
deposition is the most
common way of
producing graphene
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6. Molecular interactions
• In a computer simulation we must simulate the
different molecule interactions.
• The main interactions are the VDW forces
however due to the partial charges in some
molecules(CO2) we have to include the
Coulomb force
• Van der Waals interactions
• weak electrodynamic forces between two neutral
atoms or molecules
• caused by quantum the fluctuations of electrons.
• The Lennard Jones potential approximates the
energies of repulsion and attraction between
the nonionic particles of interest.
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7. Lennard Jones potential
• V is the intermolecular potential
between the two atoms or
molecules
• ε is the well depth and a measure
of how strongly the two particles
attract each other.
• σ is the distance at which the
intermolecular potential is zero
• r is the distance between the
particles of interest.
rr is the distance of separation between both particles
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8. Molecular interactions
• Coulomb potential
• we include the coulomb interactions from CO2 because CO2 has a
quadrupole moment.
• To represent the quadruple movement we assign 3 partial charges
to the carbon and oxygen.
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9. Total energy is the coulomb + VdW
• The total potential energy of the system is a sum of the coulomb
potential energy and the Van der Waals potential energy.
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10. Methods
• Computational methods are a great tool to describe systems of
particles.
• Computational analysis is useful for simulating conditions that
are difficult or not possible experimentally.
• We can simulate a hypothetical substrate.
• We will be utilizing the Monte Carlo simulation for this project.
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11. Monte Carlo
• A Monte Carlo simulation is a probabilistic method.
• at each MC step particles are displaced randomly
• In the simulation we compute the average number of
particles on a substrate at given temperature and pressure.
• We plot of the number of particles absorbed as a function
of the pressure and temperature.
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12. Project details
The Simulation box has a dimensions 40A
40A 70A.
The boundary conditions are reflective in Z
and periodic in X and Y.
• This simulates a endless slab of graphene.
• Since we only want adsorption on one side
we will have the graphene sheet at Z = 0A.
• The gases absorbed will be carbon dioxide,
methane and nitrogen.
• Our goal is to have Carbon dioxide
adsorbed and separated from Methane
and Nitrogen at a lower pressure while at
room temperature.
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13. Next steps
• Doping the Graphene to
increase the selectivity.
• Doping is a phenomenon
where you introduce
impurities to an intrinsic
material to induce a desired
quality.
• Increase the number of
dopant sites.
• We are placing dopant sites
upon the Graphene
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14. How we dope it.
• Find a material the increases selectivity at room temperature.
• Finding the epsilon and number of dopant sites that make it work.
• Then find the material that can best mimic the conditions.
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15. Project details
• We will calculate the selectivity of the doped graphene using IAST.
• The Ideal adsorbed solution theory (IAST) predicts the adsorption
properties of the mixture using the adsorption properties of each
single gas.
• The selectivity is the ratio of the molecule concentration on the film
/ molecular concentration in the vapor.
• The uptake pressures are used to calculate the Selectivity.
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16. Calculation of selectivity at room temperature.
• The uptake pressures
• CH4 – 2.38 atm
• CO2 – 1.6241 atm
• Selectivity = 1.4648 at 300k
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0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
1.00E-02 1.00E-01 1.00E+00 1.00E+01 1.00E+02
N/A-2
Pressure (Atm)
CH4 CO2

17. Experiment details
• Increase dopant sites.
• Started at 4 – then jump to 100
• Increasing the epsilon
• 0, 100 ,300
• T= 300K
• We have about 600 carbon atoms in our
graphene sample.
• dopants per C ~ 1/6
• One site for every 4 Angstroms.
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40x40 slab

18. 100 doped sites add to our simulation
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19. Selectivity Results: 100 dopant sites.
Epsilon S = CH4/N2 S = CO2/CH4 S = CO2/N2
0 1.21 1.47 1.77
100 1.61 2.36 3.80
300 2.14 3.80 8.15
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20. Does the quadruple moment have a great effect?
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21. Findings
• Good news
• Concluded that we can increase the selectivity by adding the dopant sites.
• Next Steps
• We will look to implement this property into reality
• Take our data of epsilon and dopants and find a substance close to the
conditions.
• Experimenting with different materials.
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22. Questions
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23. Special Thanks to:
• R.E.U. Program coordinator – Dr. Prabhakar Misra
• Project Advisor – Dr. Silvina Gatica
• Project Mentor – Dr. Sidi Maiga
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