Nanoscale Topography: A Tool to Enhance Pore Order and Pore Size Distribution in Anodic Aluminum Oxide

2011 ◽  
Vol 115 (17) ◽  
pp. 8567-8572 ◽  
Author(s):  
D. C. Leitao ◽  
A. Apolinario ◽  
C. T. Sousa ◽  
J. Ventura ◽  
J. B. Sousa ◽  
...  
Keyword(s):  
Aluminum Oxide ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Anodic Aluminum Oxide ◽  
Anodic Aluminum ◽  
Nanoscale Topography

Hydraulic conductivity of clay mixed with nanomaterials

2015 ◽  
Vol 52 (6) ◽  
pp. 808-811 ◽  
Author(s):  
C.W.W. Ng ◽  
J.L. Coo
Keyword(s):  
Hydraulic Conductivity ◽  
Aluminum Oxide ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Mercury Intrusion Porosimetry ◽  
Flow Paths ◽  
Mercury Intrusion ◽  
Flexible Wall ◽  
Conductivity Behavior

The focus of this note is to investigate the hydraulic conductivity behavior of clay mixed with nanomaterials. Two different nanomaterials — namely, gamma-aluminum oxide powder (γ-Al2O3) and nano-copper oxide (CuO) — were selected and mixed with clay at different percentages (i.e., 2%, 4%, and 6%). Hydraulic conductivity tests were carried out in a flexible wall permeameter following the ASTM D5084 standard. Mercury intrusion porosimetry (MIP) tests were also carried out to determine the pore-size distribution. At 2% of γ-Al2O3 and nano-CuO, the hydraulic conductivity of clay decreased 30% and 45%, respectively. As the proportion of the nanomaterial increases, the reduction of hydraulic conductivity becomes less prominent as flow paths devoid of nanomaterials are unlikely. Reduction of hydraulic conductivity is due to the pores of clay being clogged by the nanomaterial. Pore-size distribution curves show that the largest pore size reduced by 20% when clay was mixed with 4% nano-CuO.

Anodic Aluminum Oxide (AAO) Membranes for Neurite Outgrowth

2012 ◽  
Vol 1498 ◽  
pp. 97-102 ◽  
Author(s):  
Meghan E. Casey ◽  
Anthony P. Ventura ◽  
Wojciech Z. Misiolek ◽  
Sabrina Jedlicka
Keyword(s):  
Aluminum Oxide ◽  
Pore Size ◽  
Neurite Outgrowth ◽  
Anodic Aluminum Oxide ◽  
Cellular Growth ◽  
Anodic Aluminum ◽  
Cell Counts ◽  
Size And Morphology ◽  
Imagej Software

ABSTRACTAnodic aluminum oxide (AAO) membranes were fabricated in a mild two-step anodization procedure. The voltage was varied during both anodization steps to control the pore size and morphology of the AAO membranes. Pore sizes ranged from 34 nm to 117 nm. Characterization of the pore structure was performed by scanning electron microscopy (SEM). To assess the potential of the AAO membranes as a neuronal differentiation platform, C17.2 neural stem cells (NSCs), an immortalized and multipotent cell line, were used. The NSCs were forced to differentiate via serum-withdrawal. Cellular growth was characterized by immunocytochemistry (ICC) and SEM. ImageJ software was used to obtain phenotypic cell counts and neurite outgrowth lengths. Results indicate a highly tunable correlation between AAO nanopore sizes and differentiated cell populations. By selecting AAO membranes with specific pore size ranges, control of neuronal network density and neurite outgrowth length was achieved.

Anodic aluminum oxide with fine pore size control for selective and effective particulate matter filtering

2016 ◽  
Vol 3 (7) ◽  
pp. 074004 ◽  
Author(s):  
Su Zhang ◽  
Yang Wang ◽  
Yingling Tan ◽  
Jianfeng Zhu ◽  
Kai Liu ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Aluminum Oxide ◽  
Pore Size ◽  
Anodic Aluminum Oxide ◽  
Size Control ◽  
Anodic Aluminum ◽  
Pore Size Control

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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