CO2-Filling Capacity and Selectivity of Carbon Nanopores: Synthesis, Texture, and Pore-Size Distribution from Quenched-Solid Density Functional Theory (QSDFT)

2011 ◽  
Vol 45 (16) ◽  
pp. 7068-7074 ◽  
Author(s):  
Xin Hu ◽  
Maciej Radosz ◽  
Katie A. Cychosz ◽  
Matthias Thommes
Keyword(s):  
Density Functional Theory ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Density Functional ◽  
Functional Theory ◽  
Solid Density ◽  
Filling Capacity

scholarly journals Graphene Oxide: Study of Pore Size Distribution and Surface Chemistry Using Immersion Calorimetry

Nanomaterials ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1492
Author(s):  
Carlos A. Guerrero-Fajardo ◽  
Liliana Giraldo ◽  
Juan Carlos Moreno-Piraján
Keyword(s):  
Density Functional Theory ◽  
Graphene Oxide ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Density Functional ◽  
Local Density ◽  
Donor Number ◽  
Immersion Enthalpy ◽  
Functional Theory

In this work, the textural parameters of graphene oxide (GO) and graphite (Gr) samples were determined. The non-local density functional theory (NLDFT) and quenched solid density functional theory (QSDFT) kernels were used to evaluate the pore size distribution (PSD) by modeling the pores as slit, cylinder and slit-cylinder. The PSD results were compared with the immersion enthalpies obtained using molecules with different kinetic diameter (between 0.272 nm and 1.50 nm). Determination of immersion enthalpy showed to track PSD for GO and graphite (Gr), which was used as a comparison solid. Additionally, the functional groups of Gr and GO were determined by the Boehm method. Donor number (DN) Gutmann was used as criteria to establish the relationship between the immersion enthalpy and the parameter of the probe molecules. It was found that according to the Gutmann DN the immersion enthalpy presented different values that were a function of the chemical groups of the materials. Finally, the experimental and modeling results were critically discussed.

scholarly journals Multimodels computation for adsorption capacity of activated carbon

2017 ◽  
Vol 36 (1-2) ◽  
pp. 508-520 ◽  
Author(s):  
Guodong Wang ◽  
Yun Tian ◽  
Jianchun Jiang ◽  
Jianzhong Wu
Keyword(s):  
Density Functional Theory ◽  
Activated Carbon ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Density Functional ◽  
Mean Field ◽  
Field Approximation ◽  
Mean Field Approximation ◽  
Functional Theory

The pore size distribution of activated carbon is conventionally characterized with nitrogen adsorption measurements at 77 K. The adsorption isotherms are commonly analyzed with a nonlocal density functional theory in combination with a mathematical model for the pore size and geometry. While nonlocal density functional theory is significantly more accurate than the Brunauer–Emmett–Teller theory for gas adsorption, its application to materials characterization is mostly based on a mean-field approximation for van der Waals attractions that is only qualitative in comparison with alternative versions of nonlocal density functional theory or molecular simulations. Toward development of a more reliable theoretical procedure, we compare mean-field approximation-nonlocal density functional theory with three recent versions of non-mean-field methods for gas adsorption at conditions corresponding to experiments for porous materials characterization. The potential applicability of different nonlocal density functional theory methods for pore size distribution predictions is evaluated in terms of the theoretical error bound scale analysis. We find that the weight density approximation is the most reliable for predicting the pore size distribution of amorphous porous materials. In addition to accurate isotherm, weight density approximation yields the theoretical error bound scale for pore size distribution prediction nearly 104 times narrower than that corresponding to mean-field approximation. The new theoretical procedure has been used to analyze the pore size distribution of four activated carbon samples and to predict the adsorption capacities of these materials.

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