FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308388809> ?p ?o ?g. }

• W4308388809 endingPage "122521" @default.
• W4308388809 startingPage "122521" @default.
• W4308388809 abstract "Porous carbon is one of the important CO 2 adsorbents being developed at present. However, interpreting the potential mechanism of CO 2 adsorption by porous carbon is still challenging due to their various functional groups, different structural characteristics and different adsorption conditions (temperature and pressure) during CO 2 adsorption. Here, this study firstly applied machine learning to study the effects of pore structure, chemical properties, and adsorption conditions on CO 2 adsorption performance based on 1594 CO 2 adsorption datasets, and to predict CO 2 adsorption capacity. The results show that the R 2 of the random forest (RF) model is above 0.97 on the training and test data, which has good prediction performance. According to RF analysis results, the nitrogen groups of porous carbon have the greatest impact on CO 2 capture at 0–0.15 bar, while ultra-micropores have the greatest impact on CO 2 capture at 0.15–1 bar. Subsequently, we prepared three kinds of porous carbons with different pore structures and functional groups, and carried out CO 2 adsorption isotherm tests. The results were consistent with the results of machine learning. However, the above results hardly reveal the effect of functional group type and pore size on CO 2 capture. Finally, the relative importance of pore size and functional group on CO 2 adsorption under different pressures was calculated by molecular simulation, and the mechanism of CO 2 adsorption by a single pore size and functional group species was revealed. The results based on the aforementioned machine learning, experimental data and molecular simulation are of great significance for predicting gas adsorption and guiding the development of the carbon-based adsorbents. • CO 2 capture on carbon-based materials was modeled by machine learning. • V u , N and O groups were critical factors for CO 2 capture on porous carbon. • The machine learning results are validated with experimental data. • CO 2 uptake mechanism by pore size and functional group was revealed by GCMC." @default.
• W4308388809 created "2022-11-11" @default.
• W4308388809 creator A5020042795 @default.
• W4308388809 creator A5047299839 @default.
• W4308388809 creator A5064169457 @default.
• W4308388809 creator A5067321928 @default.
• W4308388809 creator A5071088281 @default.
• W4308388809 creator A5077485590 @default.
• W4308388809 creator A5080410205 @default.
• W4308388809 date "2023-02-01" @default.
• W4308388809 modified "2023-10-14" @default.
• W4308388809 title "Insights into CO2 capture in porous carbons from machine learning, experiments and molecular simulation" @default.
• W4308388809 cites W1972427151 @default.
• W4308388809 cites W1976499791 @default.
• W4308388809 cites W2013580875 @default.
• W4308388809 cites W2055738871 @default.
• W4308388809 cites W2057558267 @default.
• W4308388809 cites W2070821099 @default.
• W4308388809 cites W2109846092 @default.
• W4308388809 cites W2128705258 @default.
• W4308388809 cites W2135189837 @default.
• W4308388809 cites W2152063651 @default.
• W4308388809 cites W2260658511 @default.
• W4308388809 cites W2316237314 @default.
• W4308388809 cites W2564937919 @default.
• W4308388809 cites W2737133187 @default.
• W4308388809 cites W2737439951 @default.
• W4308388809 cites W2744795430 @default.
• W4308388809 cites W2757130602 @default.
• W4308388809 cites W2769359172 @default.
• W4308388809 cites W2779278463 @default.
• W4308388809 cites W2789668490 @default.
• W4308388809 cites W2789925315 @default.
• W4308388809 cites W2803496994 @default.
• W4308388809 cites W2885631997 @default.
• W4308388809 cites W2888509132 @default.
• W4308388809 cites W2897341560 @default.
• W4308388809 cites W2898070916 @default.
• W4308388809 cites W2901446822 @default.
• W4308388809 cites W2910754795 @default.
• W4308388809 cites W2911964244 @default.
• W4308388809 cites W2919303270 @default.
• W4308388809 cites W2920941937 @default.
• W4308388809 cites W2921589864 @default.
• W4308388809 cites W2922667433 @default.
• W4308388809 cites W2925521605 @default.
• W4308388809 cites W2949006411 @default.
• W4308388809 cites W2969815508 @default.
• W4308388809 cites W2977074277 @default.
• W4308388809 cites W3000274487 @default.
• W4308388809 cites W3020916316 @default.
• W4308388809 cites W3023669649 @default.
• W4308388809 cites W3024525656 @default.
• W4308388809 cites W3042947162 @default.
• W4308388809 cites W3045645249 @default.
• W4308388809 cites W3047742349 @default.
• W4308388809 cites W3048979979 @default.
• W4308388809 cites W3081602797 @default.
• W4308388809 cites W3082813113 @default.
• W4308388809 cites W3084731547 @default.
• W4308388809 cites W3100701891 @default.
• W4308388809 cites W3102166968 @default.
• W4308388809 cites W3113101293 @default.
• W4308388809 cites W3126942819 @default.
• W4308388809 cites W3128422518 @default.
• W4308388809 cites W3162833253 @default.
• W4308388809 cites W3183981393 @default.
• W4308388809 cites W4206948863 @default.
• W4308388809 cites W4214662119 @default.
• W4308388809 cites W4220955375 @default.
• W4308388809 cites W4283317888 @default.
• W4308388809 doi "https://doi.org/10.1016/j.seppur.2022.122521" @default.
• W4308388809 hasPublicationYear "2023" @default.
• W4308388809 type Work @default.
• W4308388809 citedByCount "9" @default.
• W4308388809 countsByYear W43083888092023 @default.
• W4308388809 crossrefType "journal-article" @default.
• W4308388809 hasAuthorship W4308388809A5020042795 @default.
• W4308388809 hasAuthorship W4308388809A5047299839 @default.
• W4308388809 hasAuthorship W4308388809A5064169457 @default.
• W4308388809 hasAuthorship W4308388809A5067321928 @default.
• W4308388809 hasAuthorship W4308388809A5071088281 @default.
• W4308388809 hasAuthorship W4308388809A5077485590 @default.
• W4308388809 hasAuthorship W4308388809A5080410205 @default.
• W4308388809 hasConcept C127413603 @default.
• W4308388809 hasConcept C178790620 @default.
• W4308388809 hasConcept C185592680 @default.
• W4308388809 hasConcept C192562407 @default.
• W4308388809 hasConcept C41008148 @default.
• W4308388809 hasConcept C42360764 @default.
• W4308388809 hasConcept C6648577 @default.
• W4308388809 hasConceptScore W4308388809C127413603 @default.
• W4308388809 hasConceptScore W4308388809C178790620 @default.
• W4308388809 hasConceptScore W4308388809C185592680 @default.
• W4308388809 hasConceptScore W4308388809C192562407 @default.
• W4308388809 hasConceptScore W4308388809C41008148 @default.
• W4308388809 hasConceptScore W4308388809C42360764 @default.
• W4308388809 hasConceptScore W4308388809C6648577 @default.

• next