Theoretical Study on Catalytic Capture and Fixation of Carbon Dioxide by Metal–Organic Frameworks (MOFs)

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Metal-Organic Frameworks (MOFs) as Catalysts
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Abstract

Over the years, the production of carbon dioxide (CO2) has been on the rise owing to industrialization, and globalization. The presence of excess CO2 in the atmosphere has been causing a lot of problems lately; the major one being global warming due to its severe impact on the ozone layer. In the last few decades, the main problem bogging scientists and environmentalists is how to capture and store excessive CO2 and stop it from entering the carbon cycle. Further, since CO2 is made of essential elements like carbon and oxygen, it would be highly economical and environment-friendly, if somehow this could be converted to some other form that could be used as a fuel. The tricky part is to capture CO2 gas and for that, various methods have been employed, adsorption being quite efficient and inexpensive among them. There are many adsorbents in use for capturing CO2, but metal–organic frameworks (MOFs) have piqued the interest of scientists owing to their valuable properties like high surface area, resilience to water and chemicals, economic viability, and environment-friendliness. MOFs have also been used as a catalyst to reduce CO2 into other energy-rich compounds. To understand the mechanism of action of MOF for CO2 adsorption, molecular modeling and simulation techniques have been in use. One major advantage of MOFs is that it has organic ligands connecting metal ions, wherein functionalities of the organic groups can be changed to increase its catalytic and adsorption power. Such a feature of changing metal ions and organic functionalities to develop better MOFs can easily be studied by using computational methods along with experiments. This chapter addresses various computational techniques used to study MOFs which could not only capture CO2 but reduce them to form valuable energy-rich substances.

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Abbreviations

MOFs:

Metal–Organic Frameworks

Ppm:

Parts per million

CCS:

CO2 Capture and Storage

BDC:

1,4-Benzenedicarboxylate

BTT:

1,2,5-Benzenetristetrazolate

BPZ:

Bipyrazole

BTP:

Benzenetripyrazolate

MIL:

Matérial Institut Lavoisier

DFT:

Density Functional Theory

PDFT:

Periodic Density Functional Theory

MD:

Molecular Dynamics

GCMC:

Grand Canonical Monte Carlo

OX:

Oxalate

HATZ:

3-Amino-1,2,4- triazole

PCN:

Porous Coordination Network

TZC:

Tetrazole-5-carboxylate

DPP:

1,3-Di(4-pyridyl) propane

NJU-Bai3:

Nan**g University Bai group

BTB:

1, 3, 5—Tris (4-carboxyphenyl)benzene

TATB:

4,4′,4″-S-Triazine-2,4,6-triyl-tribenzoic acid

NH2-BDC:

2-Aminoterephthalic acid

dpNDI:

N,N′-di(4-pyridyl)-1,4,5,8-naphthalenediimide

BPDA:

N,N′-bis(4-pyridinyl)-1,4-benzenedicarboxamide

DMA:

N,N-di-methylacetamide

TEPA:

Tetraethylenepentamine

DOPDC:

4,4′-Dioxidobiphenyl-3,3′-dicarboxylate

H2SBPDC:

2,2′-Sulfone-4,4′-biphenyldicarboxylate

BTC:

1,3,5-Benzenetricarboxylate

TZPA:

5-(4-(Tetrazol-5-yl)phenyl)isophthalate

BTDC:

2,2′-Bithiophene-5,5′-dicarboxylate)

BFDC:

2,2′-Bifuran-5,5′-dicarboxylate

FDCA:

9-Fluorenone-2,7-dicarboxylate

DTDAO:

Dibenzo[b,d]thiophene-3,7-dicarboxylate 5,5-dioxide

DOBDC:

2,5-Dioxido-1,4-benzenedicarboxylate

H2CBPTZ:

3-(4-Carboxylbenzene)-5-(2-pyrazinyl)-1H-1,2,4-triazole

TDM:

Tetrakis[(3,5-dicarboxyphenyl)oxamethyl]methane

BTTA:

2,5-Di(1H-1,2,4-triazol-1-yl)terephthalate

DABCO:

1,4-Diazabicyclo[2.2.2]octane

MTV-MOFs:

Multivariate metal–organic frameworks

NH2-BDC:

2-Aminobenzenedicarboxylic acid

BPHZ:

1,2-Bis(4-[pirydyl-methylene)hydrazine

NDPA:

5,5′-(Naphthalene-2,7-diyl)diisophthalate

H2BDIM:

1,5-Dihydrobenzo[1,2-d:4,5-d′]diimidazole

CoRE-MOFs:

Computation-ready, Experimental MOFs

ML:

Machine Learning

ANNs:

Artificial neural networks

EMD:

Equilibrium molecular dynamics

GGA:

Generalized Gradient Approximation

QM/MM:

Quantum Mechanics/Molecular Mechanics

UFF:

Universal Force Field

MMSV:

Morse–Morse–spline–van der Waals

HF:

Hartree-Fock

GMC:

Gibbs Ensemble Monte Carlo

DPDS:

4,4′-Dipyridyldisulfide

IRMOFs:

Isoreticular metal–organic frameworks

STP:

Standard Temperature And Pressure

ZIFs:

Zeolitic Imidazolate Frameworks

CBMC:

Configurational-Bias Monte Carlo

ABDC:

2-Amino-1,4-benzenedicarboxylic acid

H2Me2BPZ:

3,3′-Dimethyl-1H,1′H-4,4′-bipyrazole

DHBDC:

2,5-Dihydroxybenzenedicarboxylate

BPTC:

3,3′,5,5′-Biphenyltetracarboxylate

CCs:

Cyclic carbonates

DMA:

Dimethylamine

NH3-TPD:

Temperature-programmed Desorption

TBAB:

Tetra-butylammonium bromide

HOMO:

Highest Occupied Molecular Orbital

LUMO:

Lowest Unoccupied Molecular Orbital

BPY:

4,4′-Bipyridine

NPs:

Nanoparticles

POM:

Polyoxometalate

M-PMOF:

Polyoxometalate-Metalloporphyrin Organic Frameworks

H2BBTA:

1H,5H-benzo(1,2-d:4,5-d′)bistriazole

PAN:

Polyacrylonitrile

OD Cu/C:

Oxide-derived Cu/carbon

TPSS:

Tao-Perdew-Staroverov-Scuseria

ATA:

2-Aminoterephthalic acid

PHEN:

1,10 Phenanthroline

CPTPY:

Bis(4′-(4-carboxyphenyl)-terpyridine

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Issar, U., Arora, R. (2022). Theoretical Study on Catalytic Capture and Fixation of Carbon Dioxide by Metal–Organic Frameworks (MOFs). In: Gulati, S. (eds) Metal-Organic Frameworks (MOFs) as Catalysts. Springer, Singapore. https://doi.org/10.1007/978-981-16-7959-9_9

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