Untangling
the Condensation Network of Organosiloxanes
on Nanoparticles using 2D <sup>29</sup>Si–<sup>29</sup>Si Solid-State
NMR Enhanced by Dynamic Nuclear Polarization
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Abstract
Silica (SiO<sub>2</sub>) 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 <sup>29</sup>Si solid-state NMR could be employed. However, owing to the
sensitivity limitations associated with the low natural abundance
(4.7%) of <sup>29</sup>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 ∼625 compared to conventional
solid-state NMR. This allowed the acquisition of previously infeasible
data. Using both through-space and through-bond 2D <sup>29</sup>Si–<sup>29</sup>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 <sup>2</sup><i>J</i><sup>Si–O–Si</sup>-couplings (13.8 ± 1.4 Hz for surface silica) and interatomic
distances (3.04 ± 0.08 Å 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 <sup>29</sup>Si–<sup>29</sup>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