Measurements of Effective Oxygen Diffusivity, Pore Size Distribution, and Porosity in PEM Fuel Cell Electrodes

2019 ◽  
Vol 19 (17) ◽  
pp. 1-15 ◽  
Author(s):  
Zhiqiang Yu ◽  
Robert Carter
Keyword(s):  
Fuel Cell ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Pem Fuel Cell ◽  
Fuel Cell Electrodes ◽  
Oxygen Diffusivity

Effect of compression on pore size distribution and porosity of PEM fuel cell catalyst layers

2019 ◽  
Vol 44 (41) ◽  
pp. 23396-23405 ◽  
Author(s):  
Ali Malekian ◽  
Sina Salari ◽  
Juergen Stumper ◽  
Ned Djilali ◽  
Majid Bahrami
Keyword(s):  
Fuel Cell ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Pem Fuel Cell ◽  
Fuel Cell Catalyst ◽  
Catalyst Layers ◽  
Pem Fuel Cell Catalyst

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.

Sign in / Sign up

Export Citation Format

Share Document