Distribution of Pore Sizes in White Concrete

1994 ◽  
Vol 370 ◽  
Author(s):  
G. Best ◽  
A. Cross ◽  
H. Peemoeller ◽  
M.M. Pintar
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Discrete Distribution ◽  
Nmr Analysis ◽  
Hydration Time ◽  
Good Correspondence ◽  
Pore Sizes ◽  
White Cement ◽  
Proton Magnetization

AbstractNMR is used to study the evolution of the discrete distribution of proton magnetization fractions in hydrating synthetic white cement. The results at 100 hours hydration time are modelled using a trimodel pore size distribution. A good correspondence is found between the NMR analysis and SANS results reported in the literature.

Porosity and pore size distribution of shales: a case study of the Carynginia Formation, Perth Basin, Western Australia

2017 ◽  
Vol 57 (2) ◽  
pp. 660
Author(s):  
M. Nadia Testamanti ◽  
Reza Rezaee ◽  
Jie Zou
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Gas Adsorption ◽  
Pore Network ◽  
Pore Sizes ◽  
Wide Range ◽  
Pore Characterisation ◽  
Shale Reservoirs ◽  
Mercury Injection Capillary Pressure

The evaluation of the gas storage potential of shale reservoirs requires a good understanding of their pore network. Each of the laboratory techniques used for pore characterisation can be applied to a specific range of pore sizes; but if the lithology of the rock is known, usually one suitable method can be selected to investigate its pore system. Shales do not fall under any particular lithological classification and can have a wide range of minerals present, so a combination of at least two methods is typically recommended for a better understanding of their pore network. In the laboratory, the Low-Pressure Nitrogen Gas Adsorption (LP-N2-GA) technique is typically used to examine micropores and mesopores, and Mercury Injection Capillary Pressure (MICP) tests can identify pore throats larger than 3 nm. In contrast, a wider range of pore sizes in rock can be screened with Nuclear Magnetic Resonance (NMR), either in laboratory measurements made on cores or through well logging, provided that the pores are saturated with a fluid. The pore network of a set of shale core samples from the Carynginia Formation was investigated using a combination of laboratory methods. The cores were studied using the NMR, LP-N2-GA and MICP techniques, and the experimental porosity and pore size distribution results are presented. When NMR results were calibrated with MICP or LP-N2-GA measurements, then the pore size distribution of the shale samples studied could be estimated.

Two sides of the same inverse problem, in identifying the pore size distribution based on experiments with non-Newtonian fluids

2021 ◽  
Author(s):  
Martin Lanzendörfer
Keyword(s):  
Inverse Problem ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Forward Problem ◽  
Size Distributions ◽  
Pore Sizes ◽  
Pore Size Distributions ◽  
Flow Through ◽  
Effective Pore Size

<p>Following the capillary bundle concept, i.e. idealizing the flow in a saturated porous media in a given direction as the Hagen-Poiseuille flow through a number of tubular capillaries, one can very easily solve what we would call the <em>forward problem</em>: Given the number and geometry of the capillaries (in particular, given the pore size distribution), the rheology of the fluid and the hydraulic gradient, to determine the resulting flux. With a Newtonian fluid, the flux would follow the linear Darcy law and the porous media would then be represented by one constant only (the permeability), while materials with very different pore size distributions can have identical permeability. With a non-Newtonian fluid, however, the flux resulting from the forward problem (while still easy to solve) depends in a more complicated nonlinear way upon the pore sizes. This has allowed researchers to try to solve the much more complicated <em>inverse problem</em>: Given the fluxes corresponding to a set of non-Newtonian rheologies and/or hydraulic gradients, to identify the geometry of the capillaries (say, the effective pore size distribution).</p><p>The potential applications are many. However, the inverse problem is, as they usually are, much more complicated. We will try to comment on some of the challenges that hinder our way forward. Some sets of experimental data may not reveal any information about the pore sizes. Some data may lead to numerically ill-posed problems. Different effective pore size distributions correspond to the same data set. Some resulting pore sizes may be misleading. We do not know how the measurement error affects the inverse problem results. How to plan an optimal set of experiments? Not speaking about the important question, how are the observed effective pore sizes related to other notions of pore size distribution.</p><p>All of the above issues can be addressed (at least initially) with artificial data, obtained e.g. by solving the forward problem numerically or by computing the flow through other idealized pore geometries. Apart from illustrating the above issues, we focus on <em>two distinct aspects of the inverse problem</em>, that should be regarded separately. First: given the forward problem with <em>N</em> distinct pore sizes, how do different algorithms and/or different sets of experiments perform in identifying them? Second: given the forward problem with a smooth continuous pore size distribution (or, with the number of pore sizes greater than <em>N</em>), how should an optimal representation by <em>N</em> effective pore sizes be defined, regardless of the method necessary to find them?</p>

scholarly journals Porosity of silicon carbide ceramics

2002 ◽  
Vol 34 (2) ◽  
pp. 143-156 ◽  
Author(s):  
A.I. Slutsker ◽  
V.I. Betekhtin ◽  
A.B. Sinani ◽  
A.G. Kadomtsev ◽  
S.S. Ordanyan
Keyword(s):  
Silicon Carbide ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Size Distribution Function ◽  
Average Pore ◽  
Pore Sizes ◽  
X Ray Scattering ◽  
Carbide Ceramics ◽  
Silicon Carbide Ceramics

A set of ceramic samples based on silicon carbide with substantially varying integral porosity (from 1 to 10 %) was studied. The pore size distribution was examined by optical microscopy (for pores ranging from 10 mm to hundreds of microns in size), scanning electron microscopy (0.5 - 10 mm), and small-angle X-ray scattering (0.04 - 0.20 mm). The obtained pore size distribution function was used to estimate the integral porosity of ceramics, which was compared with the estimates of porosity obtained by the dilatometric technique. Relative contributions of large, small, and fine pores into the integral porosity of ceramics were found. The average pore sizes and average distances between them were estimated for each range of pore sizes.

scholarly journals The use of gel chromatography for the determination of sizes and relative molecular masses of proteins. Interpretation of calibration curves in terms of gel-pore-size distribution

1987 ◽  
Vol 243 (2) ◽  
pp. 399-404 ◽  
Author(s):  
M le Maire ◽  
A Ghazi ◽  
J V Møller ◽  
L P Aggerbeck
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Linear Relationship ◽  
Size Distribution ◽  
Gel Chromatography ◽  
Linear Calibration ◽  
Pore Sizes ◽  
Calibration Curves ◽  
Wide Range

The separation of proteins by gel-exclusion chromatography has been explained in terms of partitioning of the macromolecules within the gel by a distribution of pores of various radii. The assumption that the distribution of pore sizes is Gaussian has led to the prediction of a linear relationship between the molecular Stokes radius (RS) of the protein and the function erf-1 (1-KD), where KD is the partition coefficient [Ackers (1967) J. Biol. Chem. 242, 3237-3238]. Since careful calibrations of classical (agarose and dextran) gels and h.p.l.c. gels have shown that such a linear relationship is not verified experimentally over a wide range of native protein sizes, we have reinvestigated the model of Ackers (above reference). We show that Ackers' (above reference) derivation is not valid except for a particular Gaussian distribution of pore sizes centred at the origin. Relaxation of this restriction to allow for other types of Gaussian distributions cannot account for the non-linear calibration curves that we have obtained. Instead we show that the pore-size distribution can be calculated from the experimentally determined function KD = f(RS) and that this distribution is bimodal (non-Gaussian). One distribution is centred below 2 nm, whereas the mean value of the second one is around 6-8 nm. The minimum in this bimodal distribution corresponds, for some gels, to a region of poor resolution, which needs to be appreciated for the proper use of gel chromatography in the determination of molecular size.

The Application of Freezing-Melting Hysteresis in Hardened White Cement Paste

2013 ◽  
Vol 539 ◽  
pp. 10-13
Author(s):  
Zhong Ping Wang ◽  
Tao Wang ◽  
Long Zhou
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Cement Paste ◽  
Molecular Sieves ◽  
Mesoporous Molecular Sieves ◽  
White Cement ◽  
Nuclear Magnetic

In this paper, the freezing-melting hysteresis was first obtained in two mesoporous molecular sieves with similar pore size to analyze the reason lead to the different results in nuclear magnetic resonance cryoporometry (NMR-C) experiment between them. Then the hardened white cement paste was explored by the same method. The primary results shows that the freezing branch can offer more details to the pore size distribution (PSD) obtained from the melting branch, which will probably improve the accuracy of PSD.

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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