Simple Method to Relate Experimental Pore Size Distribution and Discharge Capacity in Cathodes for Li/O2 Batteries

A simple method was used to prepare a nonwoven fabric of intimately co-mingled Nylon-6 and Polyethylene oxide (PEO) electrospun fibers by spinning fibers onto a rotating collector. Electrospinning parameters for each polymer were independent. Fiber mixture and distribution was uniform throughout the depth of the fabric. Porosity and pore size distribution of the materials were measured before and after a washing treatment. The PEO component was removed during the washing step to create increased pore size in the remaining fabric. This study indicates a simple method to create nanofiber nonwovens of multiple dissimilar polymers and provides a strategy for controlling pore size distribution independently from fiber formation.

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Lithium Intercalation Properties of Porous Anatase TiO2 with Bimodal Pore Size Distribution

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.350.187 ◽

2007 ◽

Vol 350 ◽

pp. 187-190

Author(s):

Shinya Suzuki ◽

Akira Ogawa ◽

Masaru Miyayama

Keyword(s):

Pore Size Distribution ◽

Pore Size ◽

Size Distribution ◽

Discharge Capacity ◽

Rate Capability ◽

Anatase Tio2 ◽

High Rate ◽

Lithium Intercalation ◽

Tio2 Electrode ◽

Bimodal Pore Size Distribution

The lithium intercalation properties of anatase TiO2 electrodes with a bimodal pore size distribution were examined. Porous anatase TiO2 was prepared by hydrolyzing titanium tetrabutoxide using polyoxietylene cetyl ether as a surfactant and its subsequent calcination at 500°C for 4 hours. A porous anatase TiO2 electrode with a relatively large pore volume and 50-nm-diamater pores showed a discharge capacity of approximately 150 mAh g-1. It maintained a relatively large discharge capacity of 125 mAh g-1 at a current density of 1 A g-1, and exhibited a good high-rate capability.

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pyGAPS: A Python-Based Framework for Adsorption Isotherm Processing and Material Characterisation

10.26434/chemrxiv.7970402.v1 ◽

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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