Pore Size Distribution of Porous Glasses:  A Test of the Independent Pore Model

Langmuir ◽  
2003 ◽  
Vol 19 (20) ◽  
pp. 8592-8604 ◽  
Author(s):  
Susana Figueroa-Gerstenmaier ◽  
Josep Bonet Avalos ◽  
Lev D. Gelb ◽  
Keith E. Gubbins ◽  
Lourdes F. Vega
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Porous Glasses ◽  
Pore Model

Mechanical Properties and Pore Size Distribution in Athermal Porous Glasses

Soft Matter ◽  
2021 ◽  
Author(s):  
Sucharita Niyogi ◽  
Bhaskar Sen Gupta
Keyword(s):  
Mechanical Properties ◽  
Molecular Dynamics ◽  
Pore Structure ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Porous Glass ◽  
Three Dimensional ◽  
Dynamics Model ◽  
Porous Glasses

In this paper, we study the mechanical properties and pore structure in a three-dimensional molecular dynamics model of porous glass under athermal quasistatic shear. The vitreous samples are prepared by...

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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