  Wide-angle light scattering (WALS) offers the possibility of a highly
temporally and spatially resolved measurement of droplets in spray-based
methods for nanoparticle synthesis. The size of these droplets is a critical
variable affecting the final properties of synthesized materials such as
hetero-aggregates. However, conventional methods for determining droplet sizes
from WALS image data are labor-intensive and may introduce biases, particularly
when applied to complex systems like spray flame synthesis (SFS). To address
these challenges, we introduce a fully automatic machine learning-based
approach that employs convolutional neural networks (CNNs) in order to
streamline the droplet sizing process. This CNN-based methodology offers
further advantages: it requires few manual labels and can utilize transfer
learning, making it a promising alternative to conventional methods,
specifically with respect to efficiency. To evaluate the performance of our
machine learning models, we consider WALS data from an ethanol spray flame
process at various heights above the burner surface (HABs), where the models
are trained and cross-validated on a large dataset comprising nearly 35000 WALS
images.
