Towards Automatic Characterization of Nano-Energetic Material Response to Directed Energy

Aluminum nanoparticles (Al-NPs) have garnered interest from researchers due to their low cost, naturally occurring passivating aluminum-oxide shell, and high energy density. The passivating oxide shell limits the achievable rate of reaction, thus restricting the possible applications for this material. Various proposed reaction mechanisms describe the escape of aluminum from the passivation shell. The first and most common is thermally driven diffusion which describes when a cluster of particles is irradiated, the temperature increase causes particles near sinter together. The melt dispersion mechanism proposes that volumetric expansion during rapid heating overcomes the yield stress of the shell, leading to shell rupture and spallation of the particle. Melt dispersion remains largely unobserved experimentally, and therefore not fully understood. However, if this mechanism was well understood and triggerable, the application space for aluminum nanoparticles would expand significantly.<br/>To better understand these reaction mechanisms, single particle experimentation has been performed using targeted photothermal heating with a custom optical microscope setup equipped with a microscope camera, piezoelectric nano positioner, and diode laser. Experiments have been performed by irradiating particles or particle clusters while capturing optical and SEM images before and after irradiation. However, because particle composition, particle diameter, heating rate, and packing density are all contributing factors to reaction outcomes, the parameter space has become overwhelmingly large to explore using traditional experimental techniques. To combat this, automation has been employed to accelerate the experimental throughput allowing for more efficient and effective exploration of this parameter space. Automation was achieved by coordinating the microscope setup using a control computer and API calls to carry out experimentation without the need for human intervention. Likewise, computer vision techniques have been used to measure distinctive features from images captured before and after directed energy excitation of nanoenergetic particles. These features are used to describe the reactions in the pursuit of creating an automated nanoenergetic material reaction characterization model. Combined, these endeavors can autonomously collect and analyze experimental data to better inform researchers and physical models.