Computational screening of amino-functionalized molecules for direct air capture of CO2

Direct air capture (DAC) of CO2 is a promising strategy for mitigating global carbon emissions by removing CO2 from the atmosphere. A critical factor in enhancing the efficiency of DAC is the design of functionalized materials with strong CO2 binding capabilities. This study screens a variety of amino-functionalized molecules, utilizing MP2 and density functional theory calculations, to identify promising candidates for CO2 capture under dry and humid conditions. The analysis determined the most stable configurations of CO2 and water with 15 amino-functionalized molecules. Amino acids such as arginine, 7-azaindole, 1,5,7-triazabicyclo-[4.4.0]dec-5-ene, and melamine demonstrated the strongest CO2 binding energies, ranging from -17 to -19 kJ/mol. This is the result of both Lewis acid-base interactions between the electron-deficient carbon of CO2 and a N atom and hydrogen bonding. Generally, all of the amino groups exhibited a stronger binding affinity with water, attributed to the formation of stable hydrogen bonds between an electron-rich N atom and the hydrogen atoms of water. To guide the design of porous host structures incorporating these molecules as functional groups, the study was extended to hypothetical systems where multiple functional groups can essentially "sandwich" CO2, promoting simultaneous binding. In these scenarios, the repulsion between functional molecules emerged as a critical factor increasing the overall CO2 binding energy to ca. -30 to -40 kJ/mol. This analysis enabled the identification of optimal pore sizes for the design of functionalized frameworks to maximize the CO2 capture efficiency.

Publication Details

Journal
White Rose Research Online (University of Leeds, The University of Sheffield, University of York)
Published
2025-10-02
Primary Topic
Carbon Dioxide Capture Technologies
Type
article
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article

Computational screening of amino-functionalized molecules for direct air capture of CO2

White Rose Research Online (University of Leeds, The University of Sheffield, University of York)
Carbon Dioxide Capture Technologies
article

Computational screening of amino-functionalized molecules for direct air capture of CO2

article en

Abstract

Direct air capture (DAC) of CO2 is a promising strategy for mitigating global carbon emissions by removing CO2 from the atmosphere. A critical factor in enhancing the efficiency of DAC is the design of functionalized materials with strong CO2 binding capabilities. This study screens a variety of amino-functionalized molecules, utilizing MP2 and density functional theory calculations, to identify promising candidates for CO2 capture under dry and humid conditions. The analysis determined the most stable configurations of CO2 and water with 15 amino-functionalized molecules. Amino acids such as arginine, 7-azaindole, 1,5,7-triazabicyclo-[4.4.0]dec-5-ene, and melamine demonstrated the strongest CO2 binding energies, ranging from -17 to -19 kJ/mol. This is the result of both Lewis acid-base interactions between the electron-deficient carbon of CO2 and a N atom and hydrogen bonding. Generally, all of the amino groups exhibited a stronger binding affinity with water, attributed to the formation of stable hydrogen bonds between an electron-rich N atom and the hydrogen atoms of water. To guide the design of porous host structures incorporating these molecules as functional groups, the study was extended to hypothetical systems where multiple functional groups can essentially "sandwich" CO2, promoting simultaneous binding. In these scenarios, the repulsion between functional molecules emerged as a critical factor increasing the overall CO2 binding energy to ca. -30 to -40 kJ/mol. This analysis enabled the identification of optimal pore sizes for the design of functionalized frameworks to maximize the CO2 capture efficiency.

White Rose Research Online (University of Leeds, The University of Sheffield, University of York)
Openalex Percentile: Top 47%
Carbon Dioxide Capture Technologies
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