18 resultados para Pottinger, Eldred, 1811-1843.
Resumo:
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.
Resumo:
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.
Resumo:
Multifrequency atomic force microscopy is a powerful nanoscale imaging and characterization technique that involves excitation of the atomic force microscope (AFM) probe and measurement of its response at multiple frequencies. This paper reports the design, fabrication, and evaluation of AFM probes with a specified set of torsional eigen-frequencies that facilitate enhancement of sensitivity in multifrequency AFM. A general approach is proposed to design the probes, which includes the design of their generic geometry, adoption of a simple lumped-parameter model, guidelines for determination of the initial dimensions, and an iterative scheme to obtain a probe with the specified eigen-frequencies. The proposed approach is employed to design a harmonic probe wherein the second and the third eigen-frequencies are the corresponding harmonics of the first eigen-frequency. The probe is subsequently fabricated and evaluated. The experimentally evaluated eigen-frequencies and associated mode shapes are shown to closely match the theoretical results. Finally, a simulation study is performed to demonstrate significant improvements in sensitivity to the second-and the third-harmonic spectral components of the tip-sample interaction force with the harmonic probe compared to that of a conventional probe.