Mesoporous Perovskite Titanates via Hydrothermal Conversion

2022 ◽  
Author(s):  
Tianyu Li ◽  
Tianyu Li ◽  
Efrain E Rodriguez
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Hydrothermal Conversion

We demonstrate the successful hydrothermal conversion of mesoporous TiO2 to mesoporous perovskite SrTiO3. This method allows for control of pore size distribution and can be readily applied for the preparation...

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.

scholarly journals Construction of pore structure and lithology of digital rock physics based on laboratory experiments

2021 ◽  
Vol 11 (5) ◽  
pp. 2113-2125
Author(s):  
Chenzhi Huang ◽  
Xingde Zhang ◽  
Shuang Liu ◽  
Nianyin Li ◽  
Jia Kang ◽  
...  
Keyword(s):  
Pore Structure ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Laboratory Experiments ◽  
Rock Physics ◽  
Reconstruction Method ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
The Core

AbstractThe development and stimulation of oil and gas fields are inseparable from the experimental analysis of reservoir rocks. Large number of experiments, poor reservoir properties and thin reservoir thickness will lead to insufficient number of cores, which restricts the experimental evaluation effect of cores. Digital rock physics (DRP) can solve these problems well. This paper presents a rapid, simple, and practical method to establish the pore structure and lithology of DRP based on laboratory experiments. First, a core is scanned by computed tomography (CT) scanning technology, and filtering back-projection reconstruction method is used to test the core visualization. Subsequently, three-dimensional median filtering technology is used to eliminate noise signals after scanning, and the maximum interclass variance method is used to segment the rock skeleton and pore. Based on X-ray diffraction technology, the distribution of minerals in the rock core is studied by combining the processed CT scan data. The core pore size distribution is analyzed by the mercury intrusion method, and the core pore size distribution with spatial correlation is constructed by the kriging interpolation method. Based on the analysis of the core particle-size distribution by the screening method, the shape of the rock particle is assumed to be a more practical irregular polyhedron; considering this shape and the mineral distribution, the DRP pore structure and lithology are finally established. The DRP porosity calculated by MATLAB software is 32.4%, and the core porosity measured in a nuclear magnetic resonance experiment is 29.9%; thus, the accuracy of the model is validated. Further, the method of simulating the process of physical and chemical changes by using the digital core is proposed for further study.

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