  Electron microscopy, while reliable, is an expensive, slow, and inefficient
technique for thorough size distribution characterization of both mono- and
polydisperse colloidal nanoparticles. If rapid in-situ characterization of
colloid samples is to be achieved, a different approach, based on fast, widely
accessible, and inexpensive optical measurements such as UV-Vis spectroscopy in
combination with spectra interpretation related to Mie scattering theory, is
needed. In this article, we present a tandem deep neural network (DNN) for the
size distribution and concentration prediction of close to spherical silver
colloidal nanoparticle batches synthesized via the seeded growth method. The
first DNN identified the dipole component of the localized surface plasmon
resonance and the second one determined the size distribution from the isolated
spectral component. The training data was engineered to be bias-free and
generated numerically. High prediction accuracy with root mean square
percentage error of mean size down to 1.2% was achieved, spanning the entire
prediction range from 1 nm up to 150 nm in radius, suggesting the possible
extension limits of the effective medium theory used for simulating the
spectra. The DNN-predicted nanoparticle concentrations also were very close to
the ones expected based on synthesis precursor contents as well as measured by
atomic absorption spectroscopy.
