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Title: Modeling the self-assembly of ordered nanoporous materials

Abstract

This report describes progress on a collaborative project on the multiscale modeling of the assembly processes in the synthesis of nanoporous materials. Such materials are of enormous importance in modern technology with application in the chemical process industries, biomedicine and biotechnology as well as microelectronics. The project focuses on two important classes of materials: i) microporous crystalline materials, such as zeolites, and ii) ordered mesoporous materials. In the first case the pores are part of the crystalline structure, while in the second the structures are amorphous on the atomistic length scale but where surfactant templating gives rise to order on the length scale of 2 - 20 nm. We have developed a modeling framework that encompasses both these kinds of materials. Our models focus on the assembly of corner sharing silica tetrahedra in the presence of structure directing agents. We emphasize a balance between sufficient realism in the models and computational tractibility given the complex many-body phenomena. We use both on-lattice and off-lattice models and the primary computational tools are Monte Carlo simulations with sampling techniques and ensembles appropriate to specific situations. Our modeling approach is the first to capture silica polymerization, nanopore crystallization, and mesopore formation through computer-simulated selfmore » assembly.« less

Authors:
 [1];  [1]
  1. Univ. of Massachusetts, Amherst, MA (United States)
Publication Date:
Research Org.:
Univ. of Massachusetts, Amherst, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division
OSTI Identifier:
1408278
Report Number(s):
DOE/ER-/46466-7
DOE Contract Number:
FG02-07ER46466
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Monson, Peter, and Auerbach, Scott. Modeling the self-assembly of ordered nanoporous materials. United States: N. p., 2017. Web. doi:10.2172/1408278.
Monson, Peter, & Auerbach, Scott. Modeling the self-assembly of ordered nanoporous materials. United States. doi:10.2172/1408278.
Monson, Peter, and Auerbach, Scott. Mon . "Modeling the self-assembly of ordered nanoporous materials". United States. doi:10.2172/1408278. https://www.osti.gov/servlets/purl/1408278.
@article{osti_1408278,
title = {Modeling the self-assembly of ordered nanoporous materials},
author = {Monson, Peter and Auerbach, Scott},
abstractNote = {This report describes progress on a collaborative project on the multiscale modeling of the assembly processes in the synthesis of nanoporous materials. Such materials are of enormous importance in modern technology with application in the chemical process industries, biomedicine and biotechnology as well as microelectronics. The project focuses on two important classes of materials: i) microporous crystalline materials, such as zeolites, and ii) ordered mesoporous materials. In the first case the pores are part of the crystalline structure, while in the second the structures are amorphous on the atomistic length scale but where surfactant templating gives rise to order on the length scale of 2 - 20 nm. We have developed a modeling framework that encompasses both these kinds of materials. Our models focus on the assembly of corner sharing silica tetrahedra in the presence of structure directing agents. We emphasize a balance between sufficient realism in the models and computational tractibility given the complex many-body phenomena. We use both on-lattice and off-lattice models and the primary computational tools are Monte Carlo simulations with sampling techniques and ensembles appropriate to specific situations. Our modeling approach is the first to capture silica polymerization, nanopore crystallization, and mesopore formation through computer-simulated self assembly.},
doi = {10.2172/1408278},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Nov 13 00:00:00 EST 2017},
month = {Mon Nov 13 00:00:00 EST 2017}
}

Technical Report:

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