The Effect of Fabric Weave Structure and Component Ratio on Pore Size Distribution

2011 ◽  
Vol 314-316 ◽  
pp. 1537-1541
Author(s):  
Jian Feng Di ◽  
Xiao Xia He ◽  
Hong Jin Qi ◽  
Wen Qin Du
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Cotton Fabric ◽  
Capillary Condensation ◽  
Polyester Fabric ◽  
Component Ratio ◽  
Weave Structure ◽  
Weave Fabric ◽  
The Difference

In order to provide the wetting processing and the design of thermal moisture comfort of fabric with micron-scaled pore size data, this paper reports on an experimental investigation on the pore size distribution of 6 kinds of fabrics with the method of seft-proposed weight-classification method. This paper focuses on the effect of fabric structure and component on the pore size distribution . Histograms reveal the relationship between various factors. For cotton fabric, the peak area of the histogram of 1/2 twill weave fabric (TWF) is wider and higher than that of plain weave fabric (PWF) due to fewer structure points and more loose structure. This leads to wicking rate increase. For the polyester fabric, the difference between the peak area shapes of the TWF and PWF is not obvious. This may arise from that smaller warp/weft density of both the samples inhibited by the change in inter-yarn gap leading to the similarity. For polyester-cotton fabric, with the increase in the ratio of hydrophilic cotton component, pore size range significantly expanded, showing more uniform wicking and capillary condensation.

scholarly journals Comparison of Nitrogen Adsorption and Mercury Penetration Results: II. Pore size Distributions Calculated from Type IV Isotherm Data

1988 ◽  
Vol 5 (3) ◽  
pp. 168-190 ◽  
Author(s):  
Bruce D. Adkins ◽  
Burtron H. Davis
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Nitrogen Adsorption ◽  
Type Iv ◽  
Pore Size Distributions ◽  
Nitrogen Desorption ◽  
The Difference ◽  
Penetration Data ◽  
Packed Sphere

The pore distributions calculated from nitrogen desorption and from mercury penetration data are similar for the four materials utilized in this study. While there are small differences in the distributions calculated using different models (Cohan. Foster or Broekhoff-deBoer) with nitrogen adsorption or desorption isotherm data, all three show reasonable agreement with distributions calculated from mercury penetration data. Frequently practical catalysts have such a broad pore size distribution that neither method alone is adequate to measure the total pore size range. The present results suggest a direct comparison, without recourse to a scaling factor, is appropriate when comparing results from the two methods even though the pore size distribution maximum may vary by at least 50% depending upon the model chosen for the calculation. Better agreement may be obtained between the two experimental techniques by adjusting either the nitrogen adsorption data using a packed sphere model or the mercury penetration data by an earlier reported correction ratio. The difference between the two methods becomes less than 20% when a correction procedure is used; however, further studies are needed to define the range of material shaped that these procedures are applicable to.

A Novel Method for Analyzing Pore Size Distribution of Complex Geometry Shaped Porous Shale

2020 ◽  
Vol 1003 ◽  
pp. 134-143
Author(s):  
Yang Ming ◽  
Lin Mian
Keyword(s):  
Porous Media ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Complex Geometry ◽  
Capillary Condensation ◽  
Relative Pressure ◽  
Differential Function ◽  
Novel Method

This article proposes the differential BJH equation based on the principles of multilayer adsorption and capillary condensation, which was simplified by theoretical investigation and experiments. This work indicates that the differential function of isotherm and the differential function of pore size to relative pressure determine the pore size distribution of porous media. The differential BJH model can be used to explain the source of the false peak in pore size distribution and to calculate the pore size distribution of different shapes of pores in a porous media with a porous structure. It has an excellent application prospect in the characterization of complex pore structure represented by shale.

The Influence of Liquid Properties on Fabric Pore Size Distribution

2011 ◽  
Vol 339 ◽  
pp. 710-713
Author(s):  
Xiao Xia He ◽  
Jian Feng Di ◽  
Wen Qin Du ◽  
Hong Jin Qi
Keyword(s):  
Surface Tension ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Polyester Fabric ◽  
Large Pore ◽  
Large Pore Size

To obtain detailed micron-scaled pore size distribution for cotton-polyester fabric (CPF), this study focus on the influence of wicking liquid properties on size distribution with three kinds of wicking liquid. For three kinds of wicking liquid, the paper reveal that it is difficult to fully characterize the specific pore size distribution with only one wicking liquid. The results illustrate that the surface tension has a significant impact on the pore size distribution as well as mean pore size. It is found that the whole graphics shift towards the large pore size direction and even result in the disappearance of smaller pore size with decreasing the surface tension and with increasing viscosity. The emergence of larger pore size, instead of smaller one, may be due to the wetting expansion by larger viscosity liquid. Meanwhile, it is obvious that the pore in peak area become dominant, accounting for more than 75%.

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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