Mechanical Properties and Pore Size Distribution in Athermal Porous Glasses

Soft Matter ◽  
2021 ◽  
Author(s):  
Sucharita Niyogi ◽  
Bhaskar Sen Gupta
Keyword(s):  
Mechanical Properties ◽  
Molecular Dynamics ◽  
Pore Structure ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Porous Glass ◽  
Three Dimensional ◽  
Dynamics Model ◽  
Porous Glasses

In this paper, we study the mechanical properties and pore structure in a three-dimensional molecular dynamics model of porous glass under athermal quasistatic shear. The vitreous samples are prepared by...

scholarly journals Experimental investigation on pore size distribution and drying kinetics during lyophilization of sugar solutions

2018 ◽  
Author(s):  
Petra Foerst ◽  
M. Lechner ◽  
N. Vorhauer ◽  
H. Schuchmann ◽  
E. Tsotsas
Keyword(s):  
Pore Structure ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Freeze Drying ◽  
Three Dimensional ◽  
Drying Kinetics ◽  
Process Efficiency ◽  
Freeze Dried ◽  
Pore Size Distributions

The pore structure is a decisive factor for the process efficiency and product quality of freeze dried products. In this work the two-dimensional ice crystal structure was investigated for maltodextrin solutions with different concentrations by a freeze drying microscope. The resulting drying kinetics was investigated for different pore structures. Additionally the three-dimensional pore structure of the freeze dried samples was measured by µ-computed tomography and the pore size distribution was quantified by image analysis techniques. The two- and three-dimensional pore size distributions were compared and linked to the drying kinetics.Keywords: pore size distribution; freeze drying; maltodextrin solution; freeze drying microscope   

scholarly journals Construction of pore structure and lithology of digital rock physics based on laboratory experiments

2021 ◽  
Vol 11 (5) ◽  
pp. 2113-2125
Author(s):  
Chenzhi Huang ◽  
Xingde Zhang ◽  
Shuang Liu ◽  
Nianyin Li ◽  
Jia Kang ◽  
...  
Keyword(s):  
Pore Structure ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Laboratory Experiments ◽  
Rock Physics ◽  
Reconstruction Method ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
The Core

AbstractThe development and stimulation of oil and gas fields are inseparable from the experimental analysis of reservoir rocks. Large number of experiments, poor reservoir properties and thin reservoir thickness will lead to insufficient number of cores, which restricts the experimental evaluation effect of cores. Digital rock physics (DRP) can solve these problems well. This paper presents a rapid, simple, and practical method to establish the pore structure and lithology of DRP based on laboratory experiments. First, a core is scanned by computed tomography (CT) scanning technology, and filtering back-projection reconstruction method is used to test the core visualization. Subsequently, three-dimensional median filtering technology is used to eliminate noise signals after scanning, and the maximum interclass variance method is used to segment the rock skeleton and pore. Based on X-ray diffraction technology, the distribution of minerals in the rock core is studied by combining the processed CT scan data. The core pore size distribution is analyzed by the mercury intrusion method, and the core pore size distribution with spatial correlation is constructed by the kriging interpolation method. Based on the analysis of the core particle-size distribution by the screening method, the shape of the rock particle is assumed to be a more practical irregular polyhedron; considering this shape and the mineral distribution, the DRP pore structure and lithology are finally established. The DRP porosity calculated by MATLAB software is 32.4%, and the core porosity measured in a nuclear magnetic resonance experiment is 29.9%; thus, the accuracy of the model is validated. Further, the method of simulating the process of physical and chemical changes by using the digital core is proposed for further study.

scholarly journals Ink-bottle Effect and Pore Size Distribution of Cementitious Materials Identified by Pressurization–Depressurization Cycling Mercury Intrusion Porosimetry

Materials ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1454 ◽  
Author(s):  
Yong Zhang ◽  
Bin Yang ◽  
Zhengxian Yang ◽  
Guang Ye
Keyword(s):  
Pore Structure ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Mercury Intrusion Porosimetry ◽  
Cementitious Materials ◽  
Limestone Powder ◽  
Accurate Estimation ◽  
Mercury Intrusion ◽  
Pore Size Distributions

Capturing the long-term performance of concrete must be underpinned by a detailed understanding of the pore structure. Mercury intrusion porosimetry (MIP) is a widely used technique for pore structure characterization. However, it has been proven inappropriate to measure the pore size distribution of cementitious materials due to the ink-bottle effect. MIP with cyclic pressurization–depressurization can overcome the ink-bottle effect and enables a distinction between large (ink-bottle) pores and small (throat) pores. In this paper, pressurization–depressurization cycling mercury intrusion porosimetry (PDC-MIP) is adopted to characterize the pore structure in a range of cementitious pastes cured from 28 to 370 days. The results indicate that PDC-MIP provides a more accurate estimation of the pore size distribution in cementitious pastes than the standard MIP. Bimodal pore size distributions can be obtained by performing PDC-MIP measurements on cementitious pastes, regardless of the age. Water–binder ratio, fly ash and limestone powder have considerable influences on the formation of capillary pores ranging from 0.01 to 0.5 µm.

Pore Size Distribution of Porous Glasses:  A Test of the Independent Pore Model

Langmuir ◽  
2003 ◽  
Vol 19 (20) ◽  
pp. 8592-8604 ◽  
Author(s):  
Susana Figueroa-Gerstenmaier ◽  
Josep Bonet Avalos ◽  
Lev D. Gelb ◽  
Keith E. Gubbins ◽  
Lourdes F. Vega
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Porous Glasses ◽  
Pore Model

scholarly journals Comprehensive characterization of nano-pore system for Chang 7 shale oil reservoir in Ordos Basin

2020 ◽  
pp. 014459872097067
Author(s):  
Hui Gao ◽  
Jie Cao ◽  
Chen Wang ◽  
Teng Li ◽  
Mengqing He ◽  
...  
Keyword(s):  
Pore Structure ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Ordos Basin ◽  
Oil Reservoir ◽  
Shale Oil ◽  
Size Distributions ◽  
Group I ◽  
Group Ii

Detailed study on the pore structure of shale oil reservoir is significantly for the exploration and development, and the conventional single pore structure measurement method cannot accurately describe the pore structure characteristics of the shale oil reservoir. In this paper, the Field Emission Scanning Electron Microscope (FESEM), low-pressure nitrogen adsorption (LP-N2A) and mercury injection porosimetry (MIP) techniques are used to comprehensive evaluate the pore structure of Chang 7 shale oil reservoir. The FESEM results show that inter pores, inner pores, organic pores and micro-cracks are developed in Chang 7 shale oil reservoir, and the pore structure can be divided into two groups from the LP-N2A and MIP. A new pore structure comprehensive evaluation method was promoted according to the connection points from the pore sizes distribution curves of LP-N2A and MIP. With this comprehensive analysis of the pore size distribution, the pore size distribution of various shale samples feature as triple-peak pattern. Due to the heterogeneity of the shale oil samples, the corresponding pore apertures of the connection points are various, and the overall pore size distribution of shale oil reservoir samples can also be divided into two types. In Group I, the size distributions exhibited a bimodal feature in a narrow range from 1.71 to 100 nm. The trimodal feature of size distributions was captured in Group II with the pore diameter ranges from 1.71 to 1426.8 nm. Group I features smaller sorting coefficient and good pore connectivity. However, the trimodal corresponds to the complex pore structure and larger sorting coefficient for Group II.

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