scholarly journals Relationship between pore-size distribution and flexibility of adsorbent materials: statistical mechanics and future material characterization techniques

Adsorption ◽  
2017 ◽  
Vol 23 (4) ◽  
pp. 593-602 ◽  
Author(s):  
Daniel. W. Siderius ◽  
Nathan. A. Mahynski ◽  
Vincent K. Shen
Keyword(s):  
Statistical Mechanics ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Material Characterization ◽  
Characterization Techniques ◽  
Adsorbent Materials

New insights on estimating pore size distribution of latex particles: Statistical mechanics approach and modeling

2016 ◽  
Vol 224 ◽  
pp. 360-371 ◽  
Author(s):  
Manel Bergaoui ◽  
Mohamed Khalfaoui ◽  
Jhonny Villarroel-Rocha ◽  
Deicy Barrera ◽  
Shaheen Al-Muhtaseb ◽  
...  
Keyword(s):  
Statistical Mechanics ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Latex Particles

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.

scholarly journals Air filtration improvement of konjac glucomannan-based aerogel air filters through physical structure design

2021 ◽  
Author(s):  
Hong Qian ◽  
Ying Fang ◽  
Kao Wu ◽  
Hao Wang ◽  
Bin Li ◽  
...  
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
Physical Structure ◽  
Structure Design ◽  
Air Filtration ◽  
Air Filters ◽  
Air Filter ◽  
Purification Time

Abstract This study presents two methods to improve the air filtration performance of konjac glucomannan (KGM)-based aerogel air filters through physical structure design by changing the pore-size distribution and the surface area, using an air purifier. Results indicated that KGM-based aerogels had a comparable filtration effect with the commercial air filter with a longer purification time. This purification time could be shortened by over 50%, by changing the pore-size distribution from large size to small size or increase the surface area with the fold structure. This should boost the development of polysaccharide-based aerogel used as the air filter.

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