A new method on producing high surface area activated carbon: The effect of salt on the surface area and the pore size distribution of activated carbon prepared from pistachio shell

Heteroatom-rich porous-organic-polymers (POPs) comprising of highly cross-linked robust skeletons with high physical and thermal stability, high surface area, and tunable pore size distribution have garnered significant research interests for their...

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Mesoporous Aerogels Synthesized with an Ionic Liquid [Hmim]Br as Template via Ambient Pressure Drying

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.616-618.1864 ◽

2012 ◽

Vol 616-618 ◽

pp. 1864-1868

Author(s):

Hong Yi Dai ◽

Hai Rui Yan ◽

Chun Ling Yu ◽

Ying Huan Fu ◽

Guo Lin Shao ◽

...

Keyword(s):

Ionic Liquid ◽

Pore Size Distribution ◽

Pore Size ◽

Size Distribution ◽

Surface Area ◽

Ambient Pressure ◽

High Surface ◽

Molar Ratio ◽

Average Pore ◽

Average Pore Radius

Mesoporous silica aerogels with a high surface area and narrow pore size distribution were prepared from tetraethylorthosilicate (TEOS) precursor at ambient pressure by using a water miscible ionic liquid (IL) [Hmim]Br as a template. The aerogels were characterized by scanning electron microscopy (SEM) and N2 gas adsorption-desorption isotherms (BET and BJH analyses), and the effect of the IL on gel structure was also studied. The results showed that IL plays an important role in regulating the nanostructure of the aerogels, in particular, pore sizes and their distribution. By increasing the IL/Si molar ratio from 4 to 7, the specific surface area of the resultant aerogel increased from 822.38 to 992.43 m2/g, while the pore volume decreased from 1.568 to 1.031 cm3/g. More importantly, the pore size distribution became narrower with minimum average pore radius centralized at 20 nm as the IL/Si molar ratio of 7. Compared with other IL templating methods previously reported, notable attributes of this method include gelation at a much wider range of the IL/Si molar ratios (up to 7) and the formation of homogeneous porous structure whose size can be up to meso-scale (2 nm - 50 nm).

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Determination of Specific Surface Area of Granular Activated Carbon by Aqueous Phase Adsorption of Phenol and from Pore Size Distribution Measurements

Separation Science and Technology ◽

10.1080/01496399308018028 ◽

1993 ◽

Vol 28 (5) ◽

pp. 1177-1190 ◽

Cited By ~ 14

Author(s):

M. K. N. Yenkie ◽

G. S. Natarajan

Keyword(s):

Activated Carbon ◽

Specific Surface ◽

Pore Size Distribution ◽

Pore Size ◽

Size Distribution ◽

Surface Area ◽

Aqueous Phase ◽

Specific Surface Area ◽

Granular Activated Carbon

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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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