Issue 48, 2020, Issue in Progress

Model-based research toward design of innovative materials: molecular weight prediction of bridged polysilsesquioxanes

Abstract

Toward the design and manipulation of innovative materials, we propose a new concept called “model-based research (MBR)”. In MBR, measurable physical and chemical properties of materials are mathematically modelled by explanatory parameters obtained by computer simulation from an atomistic point of view. To demonstrate the potential of MBR, we modelled the molecular weights of a series of polysilsesquioxanes with respect to the H2O/silane molar ratio employed for the polymerization of monomers bis(triethoxysilyl)methane, ethane, ethylene, and acetylene (BTES-M, -E1, -E2, and -E3), as an example. The equation y = axn well reproduced the behaviour of the molecular weights of the BTES series, in which a and n were obtained using the calculated molecular parameters for monomers as the explanatory parameters. Detailed understanding and discussion were theoretically possible on the basis of the mathematical model. We predicted the molecular weights of polymers that would be obtained from monomers BTES-P and BTES-Ph with C3H6 and C6H4 as the spacer, respectively, using the mathematical model. Experimental validation of these polymers clearly showed the possibility of qualitative categorization. Our proposed concept, MBR, is a powerful tool to analyse materials science toward innovative materials design.

Graphical abstract: Model-based research toward design of innovative materials: molecular weight prediction of bridged polysilsesquioxanes

Supplementary files

Article information

Article type
Paper
Submitted
31 Mar 2020
Accepted
10 Jul 2020
First published
03 Aug 2020
This article is Open Access
Creative Commons BY license

RSC Adv., 2020,10, 28595-28602

Model-based research toward design of innovative materials: molecular weight prediction of bridged polysilsesquioxanes

T. Ishimoto, S. Tsukada, S. Wakitani, K. Sato, D. Saito, Y. Nakanishi, S. Takase, T. Hamada, J. Ohshita and H. Kai, RSC Adv., 2020, 10, 28595 DOI: 10.1039/D0RA02909B

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