Abstract
In the multifactorial preparation of porous materials, the simultaneous/se- quential influence of a number of technological variables changes the individual parameters of the texture of the material (surface area, volume, pore size, etc.) to different values and with increase or decrease. Generalized parameters (GPs) combine these changes; new dependencies arise. GPs behave like the dimensionless similarity numbers known in science and technology (Reynolds, etc.). They split the data (phenomena) into series with similar properties, reveal special patterns and structural nuances. New GPs proposed. The average pore size is presented as the product of two GPs: the dimentionless shape factor F and pore width of unknown shape (reciprocal of the volumetric surface). Using F, for example, the SBA-15 dataset (D. Zhao, Science 1998) was split into 3 series of samples differing in synthesis temperatures, unit cell parameters, intra-wall pore volumes, pore lengths, and the ratios of wall thickness to pore size. A surprising phenomenon was discovered one of the copolymers acts in a similar way to high temperatures. The standard deviation (STD, %) of the texture parameter in the series is its serial GP. The surface topography (micropore volume per m2) is proposed; it eliminates fluctuation in material density and has a lower STD than cm3/g. Examples of the use of GPs for silica, carbon, alumina and catalysts are given. A correlation has been shown between the efficiency of some catalytic reactions (adsorption) and GPs. GPs provide new information about materials and open up new research challenges.
Highlights
In the multifactorial preparation of porous materials, the simultaneous/sequential influence of a number of technological variables changes the individual parameters of the texture of the material to different values and with increase or decrease
The purpose of this article is to expand the number of Generalized parameters (GPs) and demostrate the ability of GPs to behave like similarity numbers
It was found that the percentage of micropores effects on the pore shape of the host: high- and low-microporous SBA-15 have factors F = 6233 (≈spherical shape) and 4993 (50% spherical and 50% cylindrical motifs), respectively
Summary
Similarity numbers (Reynolds, Peclet, Bodenstein, etc.), well known in science. The independent generalized variable X [2] covers all process variables (conditions of synthesis and post-synthesis treatment: reagents, temperature, pressure, time, etc.) and can be represented by the number (designation) of the sample, experiment, run, batch. When a set of processing and texture data is sorted by some texture parameter “y” (surface area, pore volume, pore size, etc.) as a key (in Excell or similar program), the X part turns out to be equivalent conditions for obtaining equal or close the values of this parameter; you can choose the most economical X and significantly reduce the dataset [3]
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