Porous alumina aerogel with tunable pore structure for facile, ultrasensitive, and reproducible SERS platform

2019 ◽  
Vol 50 (10) ◽  
pp. 1429-1437
Author(s):  
Wenbing Zou ◽  
Xiaodong Wang ◽  
Yongqiang Yang ◽  
Yu Wu ◽  
Guoqing Zu ◽  
...  
Keyword(s):  
Pore Structure ◽  
Porous Alumina ◽  
Alumina Aerogel

Tailored ordered porous alumina with well-defined and uniform pore-structure

2013 ◽  
Vol 223 ◽  
pp. 670-677 ◽  
Author(s):  
Zheng Zhou ◽  
Sheng-Li Chen ◽  
Derun Hua ◽  
Zhi-Gang Wang ◽  
Ai-Cheng Chen ◽  
...  
Keyword(s):  
Pore Structure ◽  
Porous Alumina ◽  
Ordered Porous

Structural characterization of hierarchically porous alumina aerogel and xerogel monoliths

2009 ◽  
Vol 338 (2) ◽  
pp. 506-513 ◽  
Author(s):  
Yasuaki Tokudome ◽  
Kazuki Nakanishi ◽  
Kazuyoshi Kanamori ◽  
Koji Fujita ◽  
Hirofumi Akamatsu ◽  
...  
Keyword(s):  
Structural Characterization ◽  
Porous Alumina ◽  
Hierarchically Porous ◽  
Alumina Aerogel

Effect of pore structure on gas permeability constants of porous alumina

2019 ◽  
Vol 45 (5) ◽  
pp. 5231-5239 ◽  
Author(s):  
Jae Kim ◽  
Jang-Hoon Ha ◽  
Jongman Lee ◽  
In-Hyuck Song
Keyword(s):  
Pore Structure ◽  
Gas Permeability ◽  
Porous Alumina ◽  
Permeability Constants

Enhanced mechanical properties and decreased thermal conductivity of porous alumina ceramics by optimizing pore structure

2018 ◽  
Vol 44 (11) ◽  
pp. 13240-13246 ◽  
Author(s):  
Jingjing Liu ◽  
Bo Ren ◽  
Tianbin Zhu ◽  
Shu Yan ◽  
Xiaoyan Zhang ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Thermal Conductivity ◽  
Pore Structure ◽  
Porous Alumina ◽  
Alumina Ceramics

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

Author response for "Effect of lithofacies on pore structure of the Cambrian organic‐rich shale in northern Guizhou, China"

2020 ◽  
Author(s):  
Peng Xia ◽  
Hongnan Li ◽  
Yong Fu ◽  
Wenlang Qiao ◽  
Chuan Guo ◽  
...  
Keyword(s):  
Pore Structure ◽  
Author Response
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