Fabric, pore size distribution, and permeability of sandy soils

AbstractWater storage and flow in soils are highly dependent on soil structure, which strongly determines soil porosity. However pore size distribution can be derived from soil water retention curve (SWRC). Structural characteristics of cultivated arable fields (693 soil profiles, 1773 samples) and soils covered by treated forest stands (137 soil profiles, 405 samples) were selected from the MARTHA Hungarian soil physical database, and evaluated for expressing organic matter effects on soil structure and water retention. For this purpose the normalized pore size distribution curves were determined for the selected soils, plus the modal suction (MS) corresponding to the most frequent pore size class of the soil. Skewness of soils’ pore size distribution curves are found different. The quasi-normal distribution of sandy soils are transformed into distorted in clayey soils. A general growing trend of MS with the ever finer soil texture was shown. Sandy soils have the lowest average MS values, i.e. the highest most frequent equivalent pore diameter. Silty clay and clay soil textures are characterized by the highest MS values. A slight effect of land use and organic matter content is also observable in different MS values of soils under forest vegetation (’forest’) and cultivated arable land (‘plough fields’). MS values of the two land uses were compared statistically. The results of the analyses show that certain soil group’s MS are significantly different under forest vegetation and cultivation. However this difference can be explained only partly and indirectly by the organic matter of different plant coverage in the land use types.

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Fabric, Pore Size Distribution, and Permeability of Sandy Soils

Journal of Geotechnical Engineering ◽

10.1061/(asce)0733-9410(1986)112:9(855) ◽

1986 ◽

Vol 112 (9) ◽

pp. 855-868 ◽

Cited By ~ 62

Author(s):

Charng H. Juang ◽

Robert D. Holtz

Keyword(s):

Pore Size Distribution ◽

Pore Size ◽

Size Distribution ◽

Sandy Soils

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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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