Autonomous experimental platforms for high-throughput laser testing of energetic materials
Energetic materials store large amounts of chemical energy that can be rapidly released and directed in applications such as propellants, explosives, and pyrotechnics. Traditional experimental methods provide insight into bulk powder behavior; however, averaging effects in heterogeneous particle systems often obscure the fundamental mechanisms controlling energy release at the micro- and nanoscale. This dissertation resolves this limitation by developing automated and fully autonomous experimental platforms for high-throughput characterization of individual particle behavior. An initial automated framework was developed to investigate laser-induced reactions in aluminum microparticles using a combination of scanning electron microscopy and custom optical microscopy. This system achieved over a 100x increase in experimental throughput and enabled the determination of reaction thresholds as a function of particle size. Building upon this foundation, a fully autonomous optical platform was designed and implemented, integrating custom optics, computer-visionbased particle detection, machine-learning-based outcome classification, and precise motion control with synchronized laser excitation. This system enables the identification, targeting, and testing of individual particles without human intervention, permitting rapid data collection and statistical characterization of reaction behavior. A thermomechanical model was developed to interpret these results and investigate the roles of energy deposition, heat transfer, and material properties in governing particle reactivity. Together, these experimental and modeling efforts provide insight into laser-driven particle spallation and demonstrate how automated, data-driven experimentation can accelerate materials research.