A Monte Carlo Method for Simulating Drying Temperature of the Gas-Sensing Material Based on Polyacrylonitrile

Article Preview

Abstract:

The calculations of the dimer, trimer and tetramer molecules of polyacrylonitrile (PAN) using the quantum-chemical method are carried out. The computer simulation of formation of a polymer PAN chain by the Monte-Carlo method taken with the Metropolis and Wang-Landau algorithm is done. Technology of fabrication of gas-sensing material based on metal-containing PAN is developed. Electroconductive metal-containing PAN films are fabricated by method of pyrolysis under the influence of incoherent IR-radiation. Their sensing properties are studied.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

145-149

Citation:

Online since:

January 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] T.V. Semenistaya, V.V. Petrov, P. Lu, Nanocomposite of Ag-polyacrylonitryle as a selective chlorine sensor, Advanced Materials Research. 804 (2013) 135-140.

DOI: 10.4028/www.scientific.net/amr.804.135

Google Scholar

[2] V.V. Petrov, N.K. Plugotarenko, T.V. Semenistaya, Self-organization in the thin gas-sensitive Ag-containing polyacrylonitrile films, Chaotic Modeling and Simulation. 4 (2013) 609-614.

Google Scholar

[3] A. N. Korolev, T. V. Semenistaya, I. S. Al-Hadrami, T. P. Loginova, M. Bruns, Nanokompozitnyie plenki medsoderzhaschego poliakrilonirila: sostav, struktura, morfologiya poverhnosti, Perspektivnyie materialyi. 5 (2010) 52-56.

Google Scholar

[4] P. Lu, Yu.A. Gorbatenko, T.V. Semenistaya, E.V. Vorobev, A.N. Korolev, Poluchenie chuvstvitelnyih elementov sensorov gazov na osnove plenok poliakrilonitrila i serebrosoderzhaschego poliakrilonitrila i opredelenie ih harakteristik, Nano- i mikrosistemnaya tehnika. 9 (2011).

Google Scholar

[5] L.M. Zemtsov, G.P. Karpacheva, Chemical conversion of polyacrylonitril under uncogerent IR-irradiation, High-molec. comp. 6(36) (1994) 919-924.

Google Scholar

[6] Y. Lba, Extended ensemble Monte Carlo, Int. J. Mod. Phys. 12 (2001) 623-656.

Google Scholar

[7] A. Mitsutake, Y. Sugita, Y. Okamoto, Generalized-ensemble algorithms for molecular simulations of biopolymers, Biopolymers. 60 (2001) 96-123.

DOI: 10.1002/1097-0282(2001)60:2<96::aid-bip1007>3.0.co;2-f

Google Scholar

[8] J. Lee, New Monte Carlo algorithm: Entropic sampling, Phys. Rev. Lett. 71 (1993) 211-214.

DOI: 10.1103/physrevlett.71.211

Google Scholar

[9] N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys. 21 (1953) 1087-1092.

DOI: 10.1063/1.1699114

Google Scholar

[10] F. Wang, D.P. Landau, Efficient, multiple-range random walk algorithm to calculate the density of states, Phys. Rev. Lett. 86 (2001) 2050-(2053).

DOI: 10.1103/physrevlett.86.2050

Google Scholar