Pore size distribution of silica gel by employing different methods of calculation

1978 ◽  
Vol 256 (6) ◽  
pp. 563-572 ◽  
Author(s):  
B. S. Girgis
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Silica Gel ◽  
Methods Of Calculation

scholarly journals Mathematical modeling and simulation of gel drying with supercritical carbon dioxide

2005 ◽  
Vol 70 (1) ◽  
pp. 125-136 ◽  
Author(s):  
Aleksandar Orlovic ◽  
Stojan Petrovic ◽  
Dejan Skala
Keyword(s):  
Experimental Data ◽  
Carbon Dioxide ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Silica Gel ◽  
Particle Diameter ◽  
Effective Diffusivity ◽  
Supercritical Drying ◽  
Drying Time

Mathematical models of alumina/silica gel supercritical drying with carbon dioxide were studied using supercritical drying experimental data. An alumina/silica gel with zinc chloride was synthesized and dried with superciritical carbon dioxide, and its weight change was monitored as a function of drying time. The pore size distribution of the obtained aerogel was determined using the BET method and nitrogen adsorption/ desorption. The mathematical model of the supercritical drying of the wet gel was represented as unsteady and one-dimensional diffusion of solvent through the aerogel pores filled with supercitical carbon dioxide. Parallel pore model and pores in series model were developed on the basis of the measured porous structure of the aerogel. It was found that these models which use different effective diffusivity value for each pore size were in much better agreement with the experimental data than models which use an overall effective diffusivity. The local effective diffusivity coefficients were calculated using different tortuosity values for each pore size, and they were distributed according to the pore size distribution data. Model simulations of the superciritical drying with carbon dioxide confirmed that the drying temperature and gel particle diameter have a significant influence on the drying time.

Control of pore size distribution of silica gel through sol-gel process using inorganic salts and surfactants as additives

1992 ◽  
Vol 27 (6) ◽  
pp. 1567-1574 ◽  
Author(s):  
Tadahiro Murakata ◽  
Shimio Sato ◽  
Takashi Ohgawara ◽  
Tetushi Watanabe ◽  
Tohru Suzuki
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Silica Gel ◽  
Sol Gel ◽  
Inorganic Salts ◽  
Sol Gel Process

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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