skip to main content

DOE PAGESDOE PAGES

This content will become publicly available on February 26, 2019

Title: NMR Methodologies for the Detection and Quantification of Nanostructural Defects in Silicone Networks

Here, we present and discuss a sensitive spectroscopic means of detecting and quantifying network defects within a series of polysiloxane elastomers through a novel application of 19F solution state nuclear magnetic resonance (NMR). Polysiloxanes are the most utilized non-carbon polymeric material today. Their final network structure is complex, hierarchical, and often ill-defined due to modification. Characterization of these materials with respect to starting and age-dependent network structure is obfuscated by the intractable nature of polysiloxane network elastomers. We report a synthetic strategy for selectively tagging chain-end silanols with an organofluorine compound, which may then be conveniently and quantitatively measured as a function of structure and environment by means of 19F NMR. This study represents a new and sensitive means of directly quantifying aspects of network architecture in polysiloxane materials and has the potential to be a powerful new tool for the spectroscopic assessment of structural dynamic response in polysiloxane networks.
Authors:
 [1] ;  [1] ;  [1] ;  [1] ; ORCiD logo [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Report Number(s):
LLNL-JRNL-737499
Journal ID: ISSN 0024-9297; 890349
Grant/Contract Number:
AC52-07NA27344
Type:
Accepted Manuscript
Journal Name:
Macromolecules
Additional Journal Information:
Journal Volume: 51; Journal Issue: 5; Journal ID: ISSN 0024-9297
Publisher:
American Chemical Society
Research Org:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
OSTI Identifier:
1460072

Rodriguez, Jennifer N., Alviso, Cynthia T., Fox, Christina A., Maxwell, Robert S., and Lewicki, James P.. NMR Methodologies for the Detection and Quantification of Nanostructural Defects in Silicone Networks. United States: N. p., Web. doi:10.1021/acs.macromol.7b02197.
Rodriguez, Jennifer N., Alviso, Cynthia T., Fox, Christina A., Maxwell, Robert S., & Lewicki, James P.. NMR Methodologies for the Detection and Quantification of Nanostructural Defects in Silicone Networks. United States. doi:10.1021/acs.macromol.7b02197.
Rodriguez, Jennifer N., Alviso, Cynthia T., Fox, Christina A., Maxwell, Robert S., and Lewicki, James P.. 2018. "NMR Methodologies for the Detection and Quantification of Nanostructural Defects in Silicone Networks". United States. doi:10.1021/acs.macromol.7b02197.
@article{osti_1460072,
title = {NMR Methodologies for the Detection and Quantification of Nanostructural Defects in Silicone Networks},
author = {Rodriguez, Jennifer N. and Alviso, Cynthia T. and Fox, Christina A. and Maxwell, Robert S. and Lewicki, James P.},
abstractNote = {Here, we present and discuss a sensitive spectroscopic means of detecting and quantifying network defects within a series of polysiloxane elastomers through a novel application of 19F solution state nuclear magnetic resonance (NMR). Polysiloxanes are the most utilized non-carbon polymeric material today. Their final network structure is complex, hierarchical, and often ill-defined due to modification. Characterization of these materials with respect to starting and age-dependent network structure is obfuscated by the intractable nature of polysiloxane network elastomers. We report a synthetic strategy for selectively tagging chain-end silanols with an organofluorine compound, which may then be conveniently and quantitatively measured as a function of structure and environment by means of 19F NMR. This study represents a new and sensitive means of directly quantifying aspects of network architecture in polysiloxane materials and has the potential to be a powerful new tool for the spectroscopic assessment of structural dynamic response in polysiloxane networks.},
doi = {10.1021/acs.macromol.7b02197},
journal = {Macromolecules},
number = 5,
volume = 51,
place = {United States},
year = {2018},
month = {2}
}