scholarly journals Well-controlled foam-based solid coatings

Soft Matter ◽  
2019 ◽  
Vol 15 (25) ◽  
pp. 5084-5093 ◽  
Author(s):  
A. Mouquet ◽  
Y. Khidas ◽  
T. Saison ◽  
J.-Y. Faou ◽  
O. Pitois
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Efficient Method ◽  
Open Cell ◽  
Open Cell Foam ◽  
Cell Foam

An efficient method is presented for producing open-cell foam coatings having tunable pore size distribution, tunable thickness, and tunable bulk and surface porosities.

scholarly journals Influence of Pore Size Variation on Thermal Conductivity of Open-Porous Foams

Materials ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2017 ◽  
Author(s):  
Jakub Skibinski ◽  
Karol Cwieka ◽  
Samih Haj Ibrahim ◽  
Tomasz Wejrzanowski
Keyword(s):  
Thermal Conductivity ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Structural Parameters ◽  
Size Variation ◽  
Design Procedure ◽  
Open Cell ◽  
Open Cell Foam ◽  
Analytical Formulas

This study addresses the influence of pore size variation on the effective thermal conductivity of open-cell foam structures. Numerical design procedure which renders it possible to control chosen structural parameters has been developed based on characterization of commercially available open-cell copper foams. Open-porous materials with various pore size distribution were numerically designed using the Laguerre–Voronoi Tessellations procedure. Heat transfer through an isolated structure was simulated with the finite element method. The results reveal that thermal conductivity is strongly related to porosity, which is in agreement with the literature. The influence of pore size distribution has also been observed and compared with analytical formulas proposed in the literature.

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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