LAUSR

Abstract Knowledge of nanoparticle size, shape and morphology and of their in-situ transformations is crucial for establishing structure-properties relationship in nanosized materials that find applications, e.g., in plasmonic devices and heterogenous catalysis. Here we demonstrate that this information can be extracted reliably from in-situ X-ray absorption near edge structure (XANES) data, by combining ab-initio XANES simulations and machine learning (artificial neural network (NN)) approaches. Here we use NN-XANES method to extract information about the size, shape and interatomic distances in silver clusters, and to monitor their changes during the temperature-controlled particle aggregation.