scholarly journals Method for the calculation of effective pore size distribution in molecular sieve carbon.

1983 ◽  
Vol 16 (6) ◽  
pp. 470-475 ◽  
Author(s):  
GÉZA HORVÁTH ◽  
KUNITARO KAWAZOE
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Molecular Sieve ◽  
Effective Pore Size

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>

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.

scholarly journals Air filtration improvement of konjac glucomannan-based aerogel air filters through physical structure design

2021 ◽  
Author(s):  
Hong Qian ◽  
Ying Fang ◽  
Kao Wu ◽  
Hao Wang ◽  
Bin Li ◽  
...  
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
Physical Structure ◽  
Structure Design ◽  
Air Filtration ◽  
Air Filters ◽  
Air Filter ◽  
Purification Time

Abstract This study presents two methods to improve the air filtration performance of konjac glucomannan (KGM)-based aerogel air filters through physical structure design by changing the pore-size distribution and the surface area, using an air purifier. Results indicated that KGM-based aerogels had a comparable filtration effect with the commercial air filter with a longer purification time. This purification time could be shortened by over 50%, by changing the pore-size distribution from large size to small size or increase the surface area with the fold structure. This should boost the development of polysaccharide-based aerogel used as the air filter.

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