High-throughput reaction of individual aluminum microparticles via experimental automation

Aluminum nano- and micro-scale particles are attractive fuel for energetic reactions due to their high energy density, natural abundance, environmental stability, and potential for rapid energy release. However, the mechanisms governing the liberation of the metallic Al core from its native alumina shell remain poorly understood. To address this challenge, we developed an automated experimentation pipeline that first utilizes machine learning-based particle localization and characterization using scanning electron microscopy and then reacts Al particles with a separate autonomous optical microscope-based setup that employs focused pulsed laser irradiation for localized heating. With a human out of the experimentation loop, we achieved more than a 100× improvement in experimental throughput compared with manual operation. As a demonstration of autonomy, the system mapped the laser energy threshold required to induce Al particle explosions as a function of particle diameter (0.4–2.5 μm). These findings represent a critical first step toward autonomous exploration of more complex energetic reactions, in which advanced artificial intelligence-based planning dynamically selects experiments to accelerate mechanistic discovery.