The Dynamic Change of the Pore Size Distribution in Porous Electrodes of Lithium-Oxygen Batteries during Discharging

2015 ◽  
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Dynamic Change ◽  
Porous Electrodes ◽  
Lithium Oxygen Batteries

The effect of pore size distribution on the frequency dispersion of porous electrodes

2000 ◽  
Vol 45 (14) ◽  
pp. 2241-2257 ◽  
Author(s):  
Hyun-Kon Song ◽  
Hee-Young Hwang ◽  
Kun-Hong Lee ◽  
Le H. Dao
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Frequency Dispersion ◽  
Porous Electrodes

scholarly journals Electrochemical Porosimetry

2001 ◽  
Vol 699 ◽  
Author(s):  
Hyun-Kon Song ◽  
Kun-Hong Lee
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Electrochemical Impedance ◽  
Porous Electrode ◽  
Porous Electrodes ◽  
Geometric Information ◽  
Impedance Data ◽  
Pore Length ◽  
Electrochemical Devices

AbstractWe have developed the electrochemical porosimetry analyzing microstructures of porous electrodes, which can give geometric information most meaningful in electrochemical systems. The methodology is based on the transmission line model with pore size distribution (TLM-PSD) that relates electrochemical impedance data with microstructural information. Pore length (lp), as well as pore size distribution, can be obtained by fitting the TLM-PSD to the experimental impedance data of a porous electrode. This geometric information was validated for the microporous, mesoporous and macroporous samples by comparing with the data obtained from conventional porosimetry. It was also shown that the electrochemical porosimetry could be used as a nondestructive probe to investigate the construction of electrochemical devices.

scholarly journals Design of Porous Carbons for Supercapacitor Applications for Different Organic Solvent-Electrolytes

2021 ◽  
Vol 7 (1) ◽  
pp. 15
Author(s):  
Joshua Bates ◽  
Foivos Markoulidis ◽  
Constantina Lekakou ◽  
Giuliano M. Laudone
Keyword(s):  
Activated Carbon ◽  
Ion Transport ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Porous Electrode ◽  
Specific Capacity ◽  
Lithium Ion ◽  
Specific Area ◽  
Porous Electrodes

The challenge of optimizing the pore size distribution of porous electrodes for different electrolytes is encountered in supercapacitors, lithium-ion capacitors and hybridized battery-supercapacitor devices. A volume-averaged continuum model of ion transport, taking into account the pore size distribution, is employed for the design of porous electrodes for electrochemical double-layer capacitors (EDLCs) in this study. After validation against experimental data, computer simulations investigate two types of porous electrodes, an activated carbon coating and an activated carbon fabric, and three electrolytes: 1.5 M TEABF4 in acetonitrile (AN), 1.5 M TEABF4 in propylene carbonate (PC), and 1 M LiPF6 in ethylene carbonate:ethyl methyl carbonate (EC:EMC) 1:1 v/v. The design exercise concluded that it is important that the porous electrode has a large specific area in terms of micropores larger than the largest desolvated ion, to achieve high specific capacity, and a good proportion of mesopores larger than the largest solvated ion to ensure fast ion transport and accessibility of the micropores.

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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