Tailoring the interface of an immiscible polymer blend by a mutually miscible homopolymer grafted onto graphene oxide: outstanding mechanical properties

A unique strategy was adopted here to improve the compatibility between the components of an immiscible polymer blend and strengthen the interface. PMMA, a mutually miscible polymer to both PVDF and ABS, improved the compatibility between the phases by localizing at the blends interface. This was supported by the core-shell formation with PMMA as the shell and ABS as the core as observed from the SEM micrographs. This phenomenon was strongly contingent on the concentration of PMMA in the blends. This strategy was further extended to localize graphene oxide (GO) sheets at the blends interface by chemically coupling it to PMMA (PMMA-g-GO). A dramatic increment of ca. 84% in the Young's modulus and ca. 124% in the yield strength was observed in the presence of PMMA-g-GO with respect to the neat blends. A simultaneous increment in both the strength and the modulus was observed in the presence of PMMA-g-GO whereas, only addition of GO resulted in a moderate improvement in the yield strength. This study reveals that a mutually miscible polymer can render compatibility between the immiscible pair and can improve the stress transfer at the interface.

Energy Sharing for Multiple Sensor Nodes With Finite Buffers

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the epsilon-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.