CO2 adsorption on APTES functionalized mesocellular foams obtained from mesoporous silicas
Source abstract
Abstract
Abstract The CO 2 adsorption capacity of different APTES-grafted mesoporous silicas of SBA-15 type has been investigated and the influence of support textural properties and the role of the presence of silanol groups on the adsorption capacity are analyzed. Four adsorbents based on SBA-15 were prepared using tetraethyl orthosilicate (TEOS) as silicon source, with and without the addition of trimethyl-benzene (TMB) and n -heptane as swelling agents, and adding in some cases ammonium fluoride as a solubility enhancer. 3-(triethoxysilyl)propylamine (APTES) was then used as grafting agent by reaction with free silanol groups on the silica surface so as to provide pending amino groups for CO 2 capture. The adsorption behavior for all supports was adequately described by a Freundlich model, whereas for the APTES-grafted silica, a dual-site Langmuir model was applied, which allowed us to quantify and qualify two different adsorption sites. The addition of n -heptane as swelling agent led to pore sizes beyond 10 nm and improved significantly the grafting efficiency, leading to higher CO 2 uptakes as compared to the starting supports. At 1 bar and 25 °C and anhydrous conditions, CO 2 uptakes of 2.4 mmol g −1 or 0.64 mol CO 2 per mol N were achieved (which reveals a significant contribution of physisorption). This sample could be successfully regenerated at 100 °C, maintaining a constant capacity for 3 adsorption–desorption cycles. At 0.15 bar and 60 °C, anhydrous conditions, CO 2 uptake reaches 1.5 mmol g −1 . This value may be theoretically doubled in the presence of humidity, and there is room for further improvement if supports with the same pore size (13 nm) and higher surface areas (e.g. 1000 m 2 /g) are successfully synthesized.
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Research context
OpenAlex topics: Carbon Dioxide Capture Technologies · Phase Equilibria and Thermodynamics · Membrane Separation and Gas Transport
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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