Establishing a Quantitative Structure-Property Relationshipfor Porous Carbons in CO2 Capture Applications
11,2,3
Muhammad Irfan Maulana Kusdhany , Stephen M. Lyth
1Department of Automotive Science; 2International Institute for Carbon-Neutral Energy Research (I2CNER);
3Platform of Inter/Transdisciplinary Energy Research (Q-PIT)(a)
COEMISSIONSBY
SECTOR
2capture is an important technology for climate change

CO2mitigation.

EnergyClimate change is caused by anthropogenic CO2 emissions, most of which originate from the energy
and industrial sector [1]. Thus, one of the main ways to mitigate climate change is by preventing
emissions from those sectors. To do this, we can capture CO2 from the exhaust gases of conventional
power plants and factories.

RoadTransport
OtherTransport
Industry

Porous carbons can make CO2 capture cheaper and easier.
Typically, CO2 is captured by absorbing them on liquid amines, but that method has low working
capacity (<0.45 mmol/g) [2] and uses a large amount of heat for desorption, which also leads to thedegradation of the amines. One solution is to use physical adsorption on high surface area porous
carbons instead. This method has the advantages of needing less energy for desorption, being morestable, and having larger capture capacities (>3 mmol/g).

The Research Challenge
Determining the variables that make a carbon
material suitable for CO2 capture is hard:- Low sample size per study- Non-random samples- Multicollinearity within study

Adsorbed CO2(mmol/g)

N-doped Carbon Foam (NCF)• >2600 m2/g
• Micropores only
• Nitrogen-doped (8 at%)864
20273K

298K

Here is our best model-variables
combination so far:Model based on Dubinin-Astakhov
Micropore Filling Equation [4]𝑄𝑎𝑑𝑠 ~ 𝐿𝑜𝑔𝑛𝑜𝑟𝑚𝑎𝑙(𝜇, 𝜎)

CFVery different properties but the end results look thesame. Did NCF’s doping help it compensate for thelower surface area, or was it the pore size
distribution?
How do we know what variables are important?

𝜇 = log 𝑄𝑚𝑎𝑥 − 𝐴/𝐸 𝑛

Data Collection
To date, we collected a dataset from:• 19 studies• 74 carbon samples• ~3900 data points at different pressures
The input variables: chemical composition (wt%), pore size
distribution, pore volume, and surface area.

The model only uses linear regression, so the accuracy is notperfect yet.

Future Work
Improve accuracy using non-parametricfittinglog 𝑄𝑚𝑎𝑥 = ෍ 𝑥𝑖 𝛼𝑖log 𝐸 = ෍ 𝑦𝑖 𝛽𝑖log 𝑛 = ෍ 𝑧𝑖 𝛾𝑖

••••
•••
•

•
The output variable: excess CO2 adsorbed amount, inmmol/g.

Figure 3. A parity plot comparing model predictions andreality in leave-one-study-out cross validation.

𝑃𝐴 = 𝑅𝑇 log
𝑃0No clarity on variable importance →No rational design →Trial-and-error → Slow research!

Establish a clear, quantitative structureproperty relationship.

R2 = 0.73

Try different isotherm equations (equations thatdescribe adsorbed amounts of CO2 at differentpressures)
Try different combinations of the input variables
to predict the parameters of those equations
using a generalized Bayesian linear model

Figure 2. A bar plot
comparing the CO2
capture capacity of
NCF and CF at 100
kPa & two different
temperatures.

Our solution? Gather a vast amount of data from thescientific literature and develop models that can predict
the CO2 adsorption isotherm of each material based on
their porosity and chemical composition using statistics.
In other words:

Figure 1. (a) A pie chart based on data from the International Energy
Agency [1] showing CO2 emissions by sector. (b) Schematic illustration ofCO2 adsorption on carbon

We are at the stage of model andvariable selection.

•Carbon Foam (CF)• >3400 m2/g
• Micro-mesoporous• No doping

ResidentialResults and Discussion•

For example, in our previous work [3]:NCF(b)

Qads is the amount of CO2 adsorbed on the carbon
R is the universal gas constant
T is temperature
P is pressure and P0 is the saturation pressure ofCO2 at temperature T
Qmax is the maximum adsorption capacity of thecarbon materials
α, β, and γ are linear regression coefficientsxi includes N wt% & O wt% and their squares,
surface area divided by pore size, and temperature
yi includes N wt% & O wt% and their squares,
percentage of ultramicropores and micropores,
and temperature
zi includes percentage of ultramicropores andmicropores, and temperature

Figure 4. Non-parametric models like Gaussian Process
regression can automatically find a best fit for a non-linearrelationship. (Image Source: Yunyoungmok, CC BY-SA 3.0,via Wikimedia Commons)

References
1. Foster, V., & Bedrosyan, D. (2014, February 24). Understanding CO2
Emissions from the Global Energy Sector. Live Wire Knowledge Note Series;
World Bank Group.
http://documents.worldbank.org/curated/en/873091468155720710/Under
standing-CO2-emissions-from-the-global-energy-sector
2. Siegelman, R. L., Milner, P. J., Kim, E. J., Weston, S. C., & Long, J. R. (2019).
Challenges and opportunities for adsorption-based CO 2 capture fromnatural gas combined cycle emissions. Energy & Environmental Science,
12(7), 2161–2173. https://doi.org/10.1039/C9EE00505F
3. Kusdhany, M.I.M., Ma, Z., Mufundirwa, A., Li, H-W., Sasaki, K., Hayashi, A., &Lyth, S.M. (2021) Remarkable Hydrogen and Carbon Dioxide Uptake on
Scalable and Inexpensive Microporous Carbon Foams. International Journalof Hydrogen Energy (accepted)
4. Rouquerol, J., Rouquerol, F., Llewellyn, P., Maurin, G., & Sing, K. (2012).
Adsorption by Powders and Porous Solids, 2nd Edition.