Micro-Fabric, Pore-Size Distribution and Water Absorption of Consolidated Porous Limestone

Low-field nuclear magnetic resonance (NMR) technology was used to perform the experiments of transverse relaxation time (T2), pore size distribution, and water absorption rate for wood-plastic composites (WPC) with different contents of added slag powder, exploring the water movement and the dynamic changes of pore size during the moisture absorption process of the material under immersion condition. The experimental results were as follows: (1) According to the T2 of H proton and its inversion pattern, the measured porosity had a relatively small difference from that of the weighing method. (2) The pore size distribution graph showed the following: (i) when the immersion time of composite materials was different, the changing law of volume of pores with different radius was different.; (ii) when the material’s immersion time was greater than 216 h, the pore radius and its distribution characteristics showed large differences; (iii) slag powder changed the pore structure of the WPC but did not change the water absorption characteristics of the wheat straw. (3) The changes of water absorption and expansion rate showed that the slag powder changed the time for the materials’ pores to absorb water until saturation and reduced the water absorption and expansion rate. The measurement results were consistent with changing trend in the pore size obtained by low-field NMR relaxometry.

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Water Absorption Properties of Cement Pastes: Experimental and Modelling Inspections

Advances in Materials Science and Engineering ◽

10.1155/2018/7679131 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Ludovica Casnedi ◽

Ombretta Cocco ◽

Paola Meloni ◽

Giorgio Pia

Keyword(s):

Pore Size Distribution ◽

Water Absorption ◽

Pore Size ◽

Size Distribution ◽

Cement Pastes ◽

Water Absorption Coefficient ◽

Proposed Model ◽

Porous Microstructure ◽

Base Units ◽

Good Agreement

An intermingled fractal units’ model is shown in order to simulate pore microstructures as pore fraction and pore size distribution. This model is aimed at predicting capillary water absorption coefficient and sorptivity values in cement pastes. The results obtained are in good agreement with the experimental ones. For validating this model, a comparison with other procedures has been shown. It is possible to establish that the newly proposed method matches better with the experimental results. That is probably due to the fact that pore size distribution has been considered as a whole. Moreover, even though the proposed model is based on fractal base units, it is able to simulate and predict different properties as well as nonfractal porous microstructure.

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pyGAPS: A Python-Based Framework for Adsorption Isotherm Processing and Material Characterisation

10.26434/chemrxiv.7970402.v1 ◽

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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