Polyarylester thin films with narrowed pore size distribution via metal-phenolic network modulated interfacial polymerization for precise separation

2022 ◽  
pp. 120263
Author(s):  
Anqi Tang ◽  
Weilin Feng ◽  
Chuanjie Fang ◽  
Jiaqi Li ◽  
Xing Yang ◽  
...  
Keyword(s):  
Thin Films ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Interfacial Polymerization

Properties of nanoporous organosilicate hybrid thin films with a bimodal pore size distribution

2005 ◽  
Vol 54 (5) ◽  
pp. 772-779 ◽  
Author(s):  
J Hyeon-Lee ◽  
J Rhee ◽  
JH Yim ◽  
HD Jeong ◽  
DW Gidley
Keyword(s):  
Thin Films ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Bimodal Pore Size Distribution ◽  
Hybrid Thin Films

scholarly journals Porogen Effect of Solvents on Pore Size Distribution of Solvent-Casted Polycaprolactone Thin Films

2018 ◽  
Vol 1 (4) ◽  
Author(s):  
Fariba Safaei ◽  
Shahla Khalili ◽  
Saied Nouri Khorasani ◽  
Laleh Ghasemi-Mobarakeh ◽  
Rasoul Esmaeely Neisiany
Keyword(s):  
Thin Films ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Methylene Chloride ◽  
Size Distributions ◽  
Solvent Casting ◽  
Casting Method ◽  
Effect Of Solvents ◽  
Pore Size Distributions

In this study, the effect of porogenic solvents on pore size distribution of the polycaprolactone (PCL) thin films was investigated. Five thin PCL films were prepared using the solvent-casting method. Chloroform, Methylene Chloride (MC) and three different compositions of MC/ Dimethylformamide (DMF) (80/20, 50/50 and 20/80) were used as solvents. Scanning Electron Microscopy (SEM) investigations were employed to study morphology and consequently the pore size distribution of the prepared films. The PCL films made by chloroform and MC as a solvent were completely non-porous. Whereas the other films (made by a combination of MC and DMF) showed both uni-modal and bi-modal pore size distributions.

scholarly journals Determination of pore-size distribution in low-dielectric thin films

2000 ◽  
Vol 76 (10) ◽  
pp. 1282-1284 ◽  
Author(s):  
D. W. Gidley ◽  
W. E. Frieze ◽  
T. L. Dull ◽  
J. Sun ◽  
A. F. Yee ◽  
...  
Keyword(s):  
Thin Films ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Dielectric Thin Films ◽  
Low Dielectric

Mesostructured alumina as powders and thin films

2014 ◽  
Vol 2 (25) ◽  
pp. 9727-9735 ◽  
Author(s):  
Georg J. B. Voss ◽  
Elvia A. Chavez Panduro ◽  
Anette Midttveit ◽  
Jostein B. Fløystad ◽  
Kristin Høydalsvik ◽  
...  
Keyword(s):  
Thin Films ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Narrow Pore Size Distribution

Synthesis and characterisation of tailored mesostructured alumina as thin films and powders with a narrow pore size distribution.

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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