Monday, October 20, 2014

Untangling the Condensation Network of Organosiloxanes on Nanoparticles using 2D (29)Si-(29)Si Solid-State NMR Enhanced by Dynamic Nuclear Polarization


Lee, D., et al., Untangling the Condensation Network of Organosiloxanes on Nanoparticles using 2D (29)Si-(29)Si Solid-State NMR Enhanced by Dynamic Nuclear Polarization. J Am Chem Soc, 2014. 136(39): p. 13781-8.


Silica (SiO2) nanoparticles (NPs) were functionalized by silanization to produce a surface covered with organosiloxanes. Information about the surface coverage and the nature, if any, of organosiloxane polymerization, whether parallel or perpendicular to the surface, is highly desired. To this extent, two-dimensional homonuclear (29)Si solid-state NMR could be employed. However, owing to the sensitivity limitations associated with the low natural abundance (4.7%) of (29)Si and the difficulty and expense of isotopic labeling here, this technique would usually be deemed impracticable. Nevertheless, we show that recent developments in the field of dynamic nuclear polarization under magic angle spinning (MAS-DNP) could be used to dramatically increase the sensitivity of the NMR experiments, resulting in a timesaving factor of approximately 625 compared to conventional solid-state NMR. This allowed the acquisition of previously infeasible data. Using both through-space and through-bond 2D (29)Si-(29)Si correlation experiments, it is shown that the required reaction conditions favor lateral polymerization and domain growth. Moreover, the natural abundance correlation experiments permitted the estimation of (2)J(Si-O-Si)-couplings (13.8 +/- 1.4 Hz for surface silica) and interatomic distances (3.04 +/- 0.08 A for surface silica) since complications associated with many-spin systems and also sensitivity were avoided. The work detailed herein not only demonstrates the possibility of using MAS-DNP to greatly facilitate the acquisition of 2D (29)Si-(29)Si correlation spectra but also shows that this technique can be used in a routine fashion to characterize surface grafting networks and gain structural constraints, which can be related to a system's chemical and physical properties.