Periodic large mesoporous organosilicas from lyotropic liquid crystal polymer templatesElectronic supplementary information (ESI) available: TEM image, MAS NMR and FT-IR spectra, and BJH pore size distribution for PMO materials. See http://www.rsc.org/suppdata/cc/b1/b106938c/

2001 ◽  
pp. 2568-2569 ◽  
Author(s):  
Haoguo Zhu ◽  
Deborah J. Jones ◽  
Jerzy Zajac ◽  
Jacques Rozière ◽  
Roger Dutartre
Keyword(s):  
Liquid Crystal ◽  
Ir Spectra ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Lyotropic Liquid Crystal ◽  
Supplementary Information ◽  
Crystal Polymer ◽  
Ft Ir ◽  
Mesoporous Organosilicas

Preparation of SiC Nanoporous Membrane for Hydrogen Separation at High Temperature

2006 ◽  
Vol 510-511 ◽  
pp. 926-929 ◽  
Author(s):  
Y. Kim ◽  
Soo Ryong Kim ◽  
Kun Hang Cho ◽  
Seong Youl Bae ◽  
Woo Teck Kwon
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Porous Alumina ◽  
Amorphous State ◽  
Hydrogen Separation ◽  
Preceramic Polymer ◽  
Preceramic Polymers ◽  
Coating Method ◽  
Ft Ir

Nanoporous SiC membrane was developed on the porous alumina plate for the hydrogen separation using preceramic polymers such as polyphenylcarbosilane. The prepared preceramic polymers were characterized with FT-IR, TGA, GPC and XRD. Nanoporous SiC membrane was derived from the preceramic polymer using a spin coating method. The SiC membrane spin coated using 20 wt.% of polyphenylcarbosilane solution in cyclohexane does not show any cracks on the surface after heat treatment at 800oC. The average thickness of the SiC membrane is about 1µm. SiC coated porous alumina possesses asymmetric pore size distribution. There are micropores that originated from porous alumina substrate, and nanopores that derived from amorphous state of SiC membranes. The pore size distribution measurement showed that the sample contains 1-3 nm sized nano pores.

pyGAPS: A Python-Based Framework for Adsorption Isotherm Processing and Material Characterisation

2019 ◽  
Author(s):  
Paul Iacomi ◽  
Philip L. Llewellyn
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
Isosteric Heat ◽  
Material Characterisation ◽  
Mixture Adsorption ◽  
Surface Energetics ◽  
Adsorption Processing ◽  
User Friendly

Material characterisation through adsorption is a widely-used laboratory technique. The isotherms obtained through volumetric or gravimetric experiments impart insight through their features but can also be analysed to determine material characteristics such as specific surface area, pore size distribution, surface energetics, or used for predicting mixture adsorption. The pyGAPS (python General Adsorption Processing Suite) framework was developed to address the need for high-throughput processing of such adsorption data, independent of the origin, while also being capable of presenting individual results in a user-friendly manner. It contains many common characterisation methods such as: BET and Langmuir surface area, t and α plots, pore size distribution calculations (BJH, Dollimore-Heal, Horvath-Kawazoe, DFT/NLDFT kernel fitting), isosteric heat calculations, IAST calculations, isotherm modelling and more, as well as the ability to import and store data from Excel, CSV, JSON and sqlite databases. In this work, a description of the capabilities of pyGAPS is presented. The code is then be used in two case studies: a routine characterisation of a UiO-66(Zr) sample and in the processing of an adsorption dataset of a commercial carbon (Takeda 5A) for applications in gas separation.

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