Effect of Pore Size Distribution on Capillary Condensation in Nanoporous Media

Langmuir ◽  
2021 ◽  
Vol 37 (7) ◽  
pp. 2276-2288
Author(s):  
Elizabeth Barsotti ◽  
Mohammad Piri
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Capillary Condensation ◽  
Nanoporous Media

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.

Effect of Pore-Size Distribution on Phase Transition of Hydrocarbon Mixtures in Nanoporous Media

SPE Journal ◽  
2016 ◽  
Vol 21 (06) ◽  
pp. 1981-1995 ◽  
Author(s):  
Lei Wang ◽  
Xiaolong Yin ◽  
Keith B. Neeves ◽  
Erdal Ozkan
Keyword(s):  
Phase Transition ◽  
Phase Change ◽  
Pore Size Distribution ◽  
Phase Behavior ◽  
Pore Size ◽  
Capillary Pressure ◽  
Size Distribution ◽  
Hydrocarbon Mixtures ◽  
Nanoporous Media ◽  
Light Oil

Summary Pore sizes of many shale-oil and tight gas reservoirs are in the range of nanometers. In these pores, capillary pressure and surface forces can make the phase behavior of hydrocarbon mixtures different from that characterized in pressure/volume/temperature (PVT) cells. Many existing phase-behavior models use a single pore size to describe the effect of confinement on phase behavior. To follow up with our earlier theoretical studies and experimental observations, this research investigates the effect of pore-size distribution. By use of a vapor/liquid equilibrium model that considers the effect of capillary pressure, we present a procedure to simulate the sequence of phase changes in a porous medium caused by a pore-size distribution. This procedure is used to simulate depressurizations of a light oil and a retrograde gas confined inside nanoporous media, the pore-size distributions of which are characteristic of tight reservoirs. The fluid compositions are representative of typical reservoir fluids. Predictions of the model show that phase transition in nanoporous medium with pore-size distribution is not described by a single phase boundary. The initial phase change in the large pores alters the composition of the remaining fluid, and, in turn, suppresses the next phase change. For the two cases studied, models with and without capillary pressure gave similar predictions. For light oil, capillary pressure still noticeably increased the level of supersaturation, and the critical gas saturation had a strong influence on the properties of produced fluids. For retrograde gas, the effect of capillary pressure was insignificant because of the low interfacial tension (IFT). Despite the choice of fluids, calculations indicate that the smallest pores are probably always occupied by hydrocarbon liquid during depressurization.

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.

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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