Development of an automated gas adsorption apparatus for the characterization of the surface area, pore size distribution, and density of powdered materials

2001 ◽  
Vol 72 (7) ◽  
pp. 3038-3045 ◽  
Author(s):  
Alexander Badalyan ◽  
Phillip Pendleton ◽  
Hua Wu
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
Gas Adsorption

Synthesis and Characterization of Dense Mesoporous Alumina

2014 ◽  
Vol 616 ◽  
pp. 252-257
Author(s):  
Atsushi Nakahira ◽  
Hironobu Nishimoto ◽  
Yukitaka Hamada ◽  
Yuki Yamasaki
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
High Specific Surface Area ◽  
Mesoporous Alumina ◽  
Bet Surface Area ◽  
Synthesis And Characterization ◽  
Narrow Pore Size Distribution

Dense mesoporous alumina bulks were successfully synthesized by a hydrothermal hot-pressing (HHP) method for mesoporous alumina powders prepared as starting material with a high BET surface area and narrow pore size distribution. As a result, mesoporous alumina HHP bulks had high density with uniformity pore size distribution and a high specific surface area. Their microstructural features for dense mesoporous alumina bulks were observed by SEM. The characterization of mesopores was examined.

Effects of the Physical Structure of Alumina/Copper Oxide Solids On Their Reactivity for SO2 Removal From Flue Gases

1994 ◽  
Vol 344 ◽  
Author(s):  
Laurent A. Dall'aglio ◽  
Stratis V. Sotirchos
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
Flue Gas ◽  
Gas Adsorption ◽  
Mercury Porosimetry ◽  
Physical Structure ◽  
Bimodal Pore Size Distribution ◽  
Effect Of Copper

AbstractCuO/Al2O3 sorbents based on three aluminas of different pore structure and surface area around 125 m2/g were prepared. Two of the aluminas exhibited bimodal pore size distribution, while the third had narrow unimodal distribution. The effect of copper loading on the physical characteristics of the aluminas (pore size distribution and surface area) was examined using mercury porosimetry and gas adsorption. The reactivity of the sorbents towards SO2 was investigated by carrying out thermogravimetric experiments using simulated flue gas.

Method and Instrumentation for Nondestructive Characterization of Surface Area and Pore Size Distribution of Thin Films in Their Deposited State

1999 ◽  
Vol 591 ◽  
Author(s):  
E. S. Boltz ◽  
Y. H. Spooner ◽  
S. G. Albanna ◽  
D. J. Taylor ◽  
A. R. Stallings ◽  
...  
Keyword(s):  
Thin Film ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
Small Sample ◽  
High Fidelity ◽  
Small Sample Sizes ◽  
Nondestructive Characterization

ABSTRACTTPL has developed a method and instrumentation system for characterizing the surface area and pore size distribution of a thin-film or coating in its as-deposited state. Errors associated with destructive removal of the film are avoided and small sample sizes can be utilized with high-fidelity results.

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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