There
is great interest in controlling the spatial dispersion of
inorganic nanoparticles (NPs) in an organic polymer matrix because
this centrally underpins the property enhancements obtained from these
hybrid materials. Currently, qualitative information on NP spatial
distribution is obtained by visual inspection of transmission electron
microscopy (TEM) images. Quantitative information is only indirectly
obtained through the use of scattering probes such as small-angle
X-ray/neutron scattering (SAXS/SANS). While the main challenge, that
scattering probes operate in reciprocal space, can be remedied by
Fourier inverting the data into real space, a much harder issue deconvolves
the contribution of the particle form factor (which is affected by
the details of the NP size and shape) from the structure factor that
contains information on NP spatial distribution. These problems become
acute when we deal with the popular topic of NPs grafted with polymer
chains because the polymeric corona and hence the particle form factor
becomes context-dependent and is hard to quantify. To make progress,
we develop and apply a deep-learning-based image analysis method to
quantify the distribution of spherical NPs in a polymer matrix directly
from their real-space TEM images. A dataset of NP detection (DOPAD)
is built by manually labeling particle positions on experimental TEM
images of diverse polymer composite systems. A convolutional neural
network object detection model is then trained on DOPAD. Together
with sliding-window and merging algorithms, an automated pipeline
is established, which takes a large TEM image as input and extracts
NP locations and sizes. We validate the structural information resulting
from this method against SAXS-derived structural information for NPs
ordered by polymer crystallization and then use it to distinguish
between different states of the assembly of polymer-grafted NPs in
a polymer matrix achieved by using their surfactancy. We show that
this data-rich protocol allows us to draw critical facets of experimental
behavior which have previously not been accessible. The DOPAD dataset,
Python source code, and trained model are shared on GitHub at .