![]() These previous studies (34,36) had revealed that three-dimensional interconnected single-wall carbon nanotube networks and pillared graphene nanostructures, consisting of parallel graphene layers stabilized by carbon nanotubes placed vertically to the graphene planes, exhibit a significant SF 6 uptake and high adsorption selectivity for SF 6 over N 2, in comparison with the best performing materials in the literature. ![]() In this respect, we decided to further explore the CF 4 capture and the separation of SF 6–CF 4 and CF 4–N 2 fluid mixtures using some selected carbon-based nanoporous materials and metal–organic frameworks (MOFs), which have been found in our previous studies (34−36) to be very efficient for SF 6 capture and SF 6–N 2 fluid mixture separation. However, only some limited studies in the literature are devoted to CF 4 capture using nanoporous adsorbents. Among them, physical adsorption using efficient nanoporous adsorbents (6,7,19−33) is considered as the most competitive technology for capturing CF 4 due to its low energy consumption, low cost, and easy management. To achieve an efficient CF 4 capture, several technologies based on thermal decomposition, plasma treatment, absorption, adsorption, cryogenic recovery, and membrane separation have been developed. The SIFSIX-2-Cu material exhibits a slightly higher kinetic selectivity at ambient and high pressures. The results obtained have revealed that under near-ambient pressure conditions, the carbon-based nanoporous materials exhibit a higher gravimetric fluid uptake and thermodynamic separation selectivity. The pressure dependence of the thermodynamic and kinetic separation selectivity for the CF 4–SF 6 and CF 4–N 2 fluid mixtures has therefore been investigated, to provide deeper insights into the molecular scale phenomena taking place in the investigated nanoporous materials. The selection of these materials was based on their previously reported efficiency to separate fluid SF 6–N 2 mixtures. The selected materials under study were the three-dimensional carbon nanotube networks, pillared graphene using carbon nanotube pillars, and the SIFSIX-2-Cu metal–organic framework. See handout for trends through periodic tableĮlectronegativities Greater than 1.7 ionic bonds 0.3 1.7 polar covalent bonds 0 0.The adsorption of pure fluid carbon tetrafluoride and the separation of CF 4–SF 6 and CF 4–N 2 fluid mixtures using representative nanoporous materials have been investigated by employing Monte Carlo and molecular dynamics simulation techniques. One Other Note on BondingElectronegativity determines bonding which contributes to the bond angleGreater than 1.7 ionic bonds0.3 1.7 polar covalent bonds0 0.3 covalent Group PracticeMolecular Geometry Construction Game RevisitedĮ. Orbital HybridizationFor Example ~ Methane (CH4)C = 1s22s22p2H = 1s1 (and there are four H atoms)C re-configures its one 2s and three 2p orbitals into four sp3 orbitals, which overlap the 1s orbitals of the 4 hydrogen atoms Orbital HybridizationVSEPR Theory works well when accounting for molecular shapes, but doesnt help describing the types of bonds formed.In hybridization, several atomic orbitals mix to form the same total number of equivalent hybrid orbitalsYouTube videoĭ. Common Molecular Shapes 8Įxampleslinear: BeH2, CO2, MgF2, I3 bent (angular): SO2, H2O, H2S, SF2 square planar: XeF4, IF4- trigonal planar: SO3, BF3 square pyramidal: IF5, BrF5 trigonal pyramidal: NH3, PF3, AsCl3 trigonal bipyramidal: PF5, PCl5, AsF5 tetrahedral: CH4, CF4, SO42- octahedral: SF6, PF6-, SiF62- seesaw: SF4 T-shaped: ClF3ĭ. Common Molecular Shapes 7Ħ total6 bond0 loneOCTAHEDRAL90C. Common Molecular Shapes 6ĥ total5 bond0 loneTRIGONAL BIPYRAMIDAL120/90C. PF34 total3 bond1 loneTRIGONAL PYRAMIDAL107ExamplesĤ total2 bond2 loneBENT104.5C. Common Molecular Shapes 4Ĥ total3 bond1 loneTRIGONAL PYRAMIDAL107C. Common Molecular Shapes 3Ĥ total4 bond0 loneTETRAHEDRAL109.5C. Common Molecular Shapes 23 total2 bond1 loneBENTģ total3 bond0 loneTRIGONAL PLANAR120C. Common Molecular Shapes 12 total2 bond0 loneLINEAR180 Shape is determined by the # of bonding pairs and lone pairs.B. VSEPR TheoryLone pairs reduce the bond angle between atoms.ġ. Electron Pair repulsion - electron pairs try to get as far away as possible.7. Predicts three dimensional geometry of molecules.4. Electron pairs orient themselves in order to minimize repulsive forces. ![]() Valence Shell Electron Pair Repulsion Theory gives us a three-dimensional picture of atomic bonding that the Electron Dot Structure does not.2. TLW predict molecular structure for molecules using Valence Shell Electron Pair Repulsion (VSEPR) Theory (TEKS 7.E)Ī.
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