Coupling Structural and Adsorption Properties of Metal–Organic Frameworks: From Pore Size Distribution to Pore Type Distribution

2020 ◽  
Vol 12 (13) ◽  
pp. 15595-15605 ◽  
Author(s):  
Silvio Dantas ◽  
Alexander V. Neimark
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Metal Organic Frameworks ◽  
Adsorption Properties ◽  
Type Distribution ◽  
Metal Organic

Are you using the right probe molecules for assessing the textural properties of metal–organic frameworks?

2022 ◽  
Author(s):  
Timur Islamoglu ◽  
Karam B. Idrees ◽  
Florencia A. Son ◽  
Zhijie Chen ◽  
Seung-Joon Lee ◽  
...  
Keyword(s):  
Pore Size Distribution ◽  
Porous Materials ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
Pore Volume ◽  
Metal Organic Frameworks ◽  
Textural Properties ◽  
Metal Organic ◽  
The Right

Textural properties—such as the surface area, pore size distribution, and pore volume—are at the forefront of characterization for porous materials.

scholarly journals Reference diffraction patterns, microstructure, and pore-size distribution for the copper (II) benzene-1,3,5-tricarboxylate metal organic framework (Cu-BTC) compounds

2014 ◽  
Vol 30 (1) ◽  
pp. 2-13 ◽  
Author(s):  
W. Wong-Ng ◽  
J.A. Kaduk ◽  
D.L. Siderius ◽  
A.L. Allen ◽  
L. Espinal ◽  
...  
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Powder Diffraction ◽  
Small Angle ◽  
Metal Organic Framework ◽  
X Ray ◽  
Metal Organic ◽  
Diffraction Patterns ◽  
Organic Framework

Cu-paddle-wheel-based Cu3(BTC)2 (nicknamed Cu-BTC, where BTC ≡ benzene 1,3,5-tricarboxylate) is a metal organic framework (MOF) compound that adopts a zeolite-like topology. We have determined the pore-size distribution using the Gelb and Gubbins technique, the microstructure using small-angle neutron scattering and (ultra) small-angle X-ray scattering (USAXS\SAXS) techniques, and X-ray powder diffraction reference patterns for both dehydrated d-Cu-BTC [Cu3(C9H3O6)2] and hydrated h-Cu-BTC [Cu3(C9H3O6)2(H2O)6.96] using the Rietveld refinement technique. Both samples were confirmed to be cubic Fm$\bar 3$m (no. 225), with lattice parameters of a = 26.279 19(3) Å, V = 18 148.31(6) Å3 for d-Cu-BTC, and a = 26.3103(11) Å, and V = 18 213(2) Å3 for h-Cu-BTC. The structure of d-Cu-BTC contains three main pores of which the diameters are approximately, in decreasing order, 12.6, 10.6, and 5.0 Å. The free volume for d-Cu-BTC is approximately (71.85 ± 0.05)% of the total volume and is reduced to approximately (61.33 ± 0.03)% for the h-Cu-BTC structure. The d-Cu-BTC phase undergoes microstructural changes when exposed to moisture in air. The reference X-ray powder patterns for these two materials have been determined for inclusion in the Powder Diffraction File.

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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