  Soot is an important material with impacts that depend on particle
morphology. Transmission electron microscopy (TEM) represents one of the most
direct routes to qualitatively assess particle characteristics. However,
producing quantitative information requires robust image processing tools,
which is complicated by the low image contrast and complex aggregated
morphologies characteristic of soot. The current work presents a new
convolutional neural network explicitly trained to characterize soot, using
pre-classified images of particles from a natural gas engine; a laboratory gas
flare; and a marine engine. The results are compared against other existing
classifiers before considering the effect that the classifiers have on
automated primary particle size methods. Estimates of the overall uncertainties
between fully automated approaches of aggregate characterization range from 25%
in d_{p,100} to 85% in D_{TEM}. A consistent correlation is observed between
projected-area equivalent diameter and primary particle size across all of the
techniques.
