Aerosol Synthesis and Reactivity of Thin Oxide Shell Aluminum Nanoparticles via Fluorocarboxylic Acid Functional Coating

There is currently a need for low oxide content nanoaluminum as a component in high‐energy‐density fuels. A gas‐phase passivation coating of perfluoropentanoic acid on in situ generated bare nanoaluminum accomplished in an aerosol stream and resulting in an air‐stable product is demonstrated. Transmission electron microscopy inspection demonstrates a 1–2 nm coating layer, and thermogravimetric analysis reveals an 80% active fuel content, an increase of 17% from untreated product. X‐ray photoelectron spectroscopy confirms both the presence of the fluorocarboxylic acid on the aluminum surface and the thinner coating layer compared with the untreated case. A bridge bonding coordination between the carboxylate group and aluminum is indicated via Fourier transform infrared spectroscopy. The coated product demonstrates reduced ignition temperature in thermite combinations for temperature‐jump fine wire combustion tests and X‐ray photoelectron spectroscopy verifies formation of AlF₃ in burned product.     

Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning

Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a content centric network. Power control and optimal scheduling can significantly improve the wireless multicast network’s performance under fading. However, the model-based approaches for power control and scheduling studied earlier are not scalable to large state spaces or changing system dynamics. In this paper, we use deep reinforcement learning, where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learned for reasonably large systems via this approach. Further, we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-time scale approach to simultaneously learn the optimal queuing strategy along with power control. We demonstrate the scalability, tracking and cross-layer optimization capabilities of our algorithms via simulations. The proposed multi-time scale approach can be used in general large state-space dynamical systems with multiple objectives and constraints, and may be of independent interest.