Experimental Investigation of the Effect of Pore Size Distribution on Nano-particle Capture Efficiency Within Ceramic Particulate Filters

2021 ◽  
Vol 7 (1) ◽  
pp. 26-40
Author(s):  
Sandeep Viswanathan ◽  
Mark L Stewart ◽  
David A Rothamer
2018 ◽  
Vol 338 ◽  
pp. 15-26 ◽  
Author(s):  
Jian Gong ◽  
Mark L. Stewart ◽  
Alla Zelenyuk ◽  
Andrea Strzelec ◽  
Sandeep Viswanathan ◽  
...  

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3608 ◽  
Author(s):  
Fang Liu ◽  
Tonghuan Zhang ◽  
Tao Luo ◽  
Mengzhen Zhou ◽  
Weiwei Ma ◽  
...  

The objective of this manuscript is to study the effects of nano-particle addition on the durability and internal deterioration of concrete subject to freezing and thawing cycles (FTCs). Fifteen nm of SiO2, 30 nm of SiO2, and 30 nm of TiO2 were added to concrete to prepare specimens with different contents. All the specimens were subjected to FTCs from 0 to 75. The mass of each specimen was measured once the FTCs reached 25, 50, and 75. Then the freezing and thawing resistance of the concrete was evaluated by computing the mass loss ratio. The pore fluid size distribution of the concrete was detected using nuclear magnetic resonance (NMR). The deterioration of the concrete subjected to FTCs was detected by industrial computed tomography (CT). The effect of different nano-particle sizes, different contents of nano-particles, and different types of nano-particles on the freezing and thawing resistance, the pore size, distribution, and the deterioration of the concrete were analyzed. The effects of FTCs on the pore size distribution and the internal deterioration of concrete were also studied. Compared to 30 nm-Nono-SiO2 (NS), 15 nm-NS had a better effect in improving the internal structure for concrete, and 30 nm-Nano-TiO2 (NT) also had a better effect in preventing pore and crack expansion.


2019 ◽  
Author(s):  
Paul Iacomi ◽  
Philip L. Llewellyn

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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