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Selectivity of MOFs and Silica Nanoparticles in CO(2) Capture from Flue Gases

Until reaching climate neutrality by attaining the EU 2050 level, the current levels of CO(2) must be mitigated through the research and development of resilient technologies. This research explored potential approaches to lower CO(2) emissions resulting from combustion fossil fuels in power plant f...

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Detalles Bibliográficos
Autores principales: Bucura, Felicia, Spiridon, Stefan-Ionut, Ionete, Roxana Elena, Marin, Florian, Zaharioiu, Anca Maria, Armeanu, Adrian, Badea, Silviu-Laurentiu, Botoran, Oana Romina, Ionete, Eusebiu Ilarian, Niculescu, Violeta-Carolina, Constantinescu, Marius
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574321/
https://www.ncbi.nlm.nih.gov/pubmed/37836278
http://dx.doi.org/10.3390/nano13192637
Descripción
Sumario:Until reaching climate neutrality by attaining the EU 2050 level, the current levels of CO(2) must be mitigated through the research and development of resilient technologies. This research explored potential approaches to lower CO(2) emissions resulting from combustion fossil fuels in power plant furnaces. Different nanomaterials (MOFs versus silica nanoparticles) were used in this context to compare their effectiveness to mitigate GHG emissions. Porous materials known as metal–organic frameworks (MOFs) are frequently employed in sustainable CO(2) management for selective adsorption and separation. Understanding the underlying mechanism is difficult due to their textural characteristics, the presence of functional groups and the variation in technological parameters (temperature and pressure) during CO(2)-selective adsorption. A silica-based nanomaterial was also employed in comparison. To systematically map CO(2) adsorption as a function of the textural and compositional features of the nanomaterials and the process parameters set to a column-reactor system (CRS), 160 data points were collected for the current investigation. Different scenarios, as a function of P (bar) or as a function of T (K), were designed based on assumptions, 1 and 5 vs. 1–10 (bar) and 313.15 and 373.15 vs. 313.15–423.15 (K), where the regression analyses through Pearson coefficients of 0.92–0.95, coefficients of determination of 0.87–0.90 and p-values < 0.05, on predictive and on-site laboratory data, confirmed the performances of the CRS.