Machine-Learning-Enabled Quantification of Metal-Based Nanoparticle Sizes Using Linear Sweep Voltammetry

Metal-based nanoparticles serve as critical catalysts in reactions, such as hydrogen evolution and oxygen reduction, with their catalytic activity strongly influenced by particle size due to the higher density of active sites in smaller particles. Current techniques for nanoparticle size characterization often rely on costly and complex instrumentation, limiting their efficiency. Here, we present “LSV2NP”, an innovative machine learning approach leveraging the gradient boosting regression (GBR) model to predict nanoparticle sizes with the help of the selected overpotential data from a linear sweep voltammetry (LSV) test. Utilizing an extensive database of nanoparticle compositions and electrochemical properties, LSV2NP achieves accurate size predictions through data-driven insights. Validation with commercial Pt/C and PtCo/C catalysts confirms the model’s high predictive accuracy. Furthermore, we demonstrate the characterization capabilities of LSV2NP following a 15 h chronoamperometry test on Pt/C. A subsequent LSV measurement, analyzed via LSV2NP, provided particle size estimates consistent with transmission electron microscopy observations, revealing aggregated particle sizes. LSV2NP offers a powerful tool for catalyst development and the broader study of nanomaterials, streamlining nanoparticle size characterization.