Statistical Model of a Thin Film Formation

E.L. Kuleshov; Vladimir S. Plotnikov; Evgeny Vladislavovich Pustovalov; T.S. Ostachenova

doi:10.4028/www.scientific.net/KEM.887.597

Paper Titles

Methodology for Calculating Heat Loss in the Study of Friction Stir Welding
p.575

Investigation of Surface-Functionalized CNT-Based Array for Detection of Acetone Vapors
p.581

Evaluation of Pozzolanic Activity of Metakaolin Chemometric Method According to the UV-VIS-NIR Spectroscopy
p.586

Effect of Initial Conditions and Numerical Investigation of Instability Developed during Synthesis of TiC via Vortex Combustion at Moderate Temperatures. Model of Vortex Combustion
p.591

Statistical Model of a Thin Film Formation
p.597

Finding the Specific Surface Area of an Organomineral Sorbent Produced by Electroplasma Treatment of Coal Flotation Waste
p.603

Generalized Susceptibility of Mixed Dislocation in Ferroelastics near Structural Phase Transition
p.610

The Influence of Technological and Operational Factors on the Durability of the Shafts of Air-Cooler Unit Electric Motors
p.619

Calculated Model of Endurance Limit Estimation Based on Structural-Strain Analysis of the Critical Condition of Ferrite-Perlite Steels with Macrocracks in Ship Structures
p.627

HomeKey Engineering MaterialsKey Engineering Materials Vol. 887Statistical Model of a Thin Film Formation

Statistical Model of a Thin Film Formation

Abstract:

This paper presents a model of a thin film formation process of an amorphous alloy as a sequential procedure when a conditional unit of substance is randomly thrown onto a substrate at each next step. The islands of a precipitant are generated on the substrate with an increase of number of steps (density defects of substance). We determine the probability distribution of an island area, which shows the maximum informational entropy. An algorithm for computing estimates of parameters of this distribution is obtained. The results of processing experimental data are presented. We demonstrate that the proposed distribution is more consistent with the experimental data than the Pareto distribution.

You might also be interested in these eBooks

FarEastCon - Materials and Construction III

View Preview

Info:

Periodical:

Key Engineering Materials (Volume 887)

Pages:

597-602

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.887.597

Citation:

Cite this paper

Online since:

May 2021

Authors:

E.L. Kuleshov, Vladimir S. Plotnikov, Evgeny Vladislavovich Pustovalov, T.S. Ostachenova

Keywords:

Amorphous Alloy, Density Defects, Electron Microscope Image, Estimates of Parameters, Information Entropy, Pareto Distribution

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] A.M. Glezer, N.A. Shurygina, Amorphous-Nanocrystalline Alloys, FIZXMATLIT, Moscow, (2013).

Google Scholar

[2] I.N. Bronshtein, K.A. Semendyayev, A Guide Book to Mathematics: Fundamental Formulas, Tables, Graphs, Methods, Springer, (2012).

Google Scholar

[3] V.S. Korolyuk, N.I. Portenko, A.V. Skorohod, A.F. Trubin, Guidebook for probability theory and mathematical statistics, Nauka, Moscow, (1985).

Google Scholar

[4] O.V. Voitenko, E.B. Modin, I.S. Smirnov, E.V. Pustovalov, B.N. Grudin, V.S. Plotnikov, L.B. Sosnovskaya, S.S. Grabchikov, Electron tomography and morphological analysis of the structure of multicomponent amorphous and nanocrystalline alloys, Bulletin of the Russian Academy of Sciences: Physics. v. 76, № 9 (2012) 999-1001.

DOI: 10.3103/s1062873812090249

Google Scholar

[5] W.S. Rasband, ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, http://rsb.info.nih.gov/ij/, 1997-2008.

Google Scholar

[6] M.D. Abramoff, P.J. Magelhaes, S.J. Ram, Image Processing with ImageJ, Biophotonics International. v. 11(7) (2004) 36-42.

Google Scholar