Combining size distribution and shape of plastic and oxide particles to evaluate physicochemical interactions:...

Particles naturally have size distributions and shapes, but these are overlooked in the physicochemical theory used to estimate interaction energies for particle aggregation or attachment. Consequently, the objectives of this research were to implement size distribution and shape in physicochemical interactions, and to use machine learning (ML) to investigate physicochemical parameters to interpret aggregation or attachment. A deep neural network was trained on databases generated for the interactions of spheres, ellipsoids, and cylinders. The primary sizes of particles were measured and then used in a machine learning model to predict interaction profiles considering size distributions. Spherical polystyrene and polymethyl-methacrylate were used in stability and aggregation experiments. Bullet- and fragment-like silica particles were used in attachment experiments. Subsequently, ML predictions were used to interpret the results of the experiments. The size distribution provides an active zone for physicochemical interactions that is absent using the traditional mean particle diameter (one equivalent sphere or ellipsoid). This is relevant because the size distribution increases the estimates of favorable and unfavorable aggregation and attachment. For example, these zones increase as the particle size distribution increases (high polydispersity index). Finally, although the approach is appropriate for spherical, ellipsoidal, and bullet-like particles, it is inappropriate for fragment-like particles, such as microplastics.