Discharge precipitate's impact in Li-air battery: Comparison of experiment and model predictions

This paper presents a fundamental study on the precipitate formation/morphology and impact of discharge precipitates in Li-air batteries and compares the voltage loss with two Li-air battery models, namely a film-resistor model and surface coverage model. Toray carbon cloth is selected as cathode, which serves as large-porosity electrodes with an approximately planar reaction surface. Imaging analysis shows film formation of precipitates is observed in all the experiments. In addition, toroidal and aggregate morphologies are present under lower currents as well. Specially, toroidal or partially toroidal deposit is observed for 0.06 A/cm2. Aggregates, which consist of small particles with grain boundaries, are shown for 0.03 A/cm2. We found that the film-resistor model is unable to predict the discharge voltage behaviors under the two lower currents due to the presence of the deposit morphologies other than the film formation. The coverage model's prediction shows acceptable agreement with the experimental data because the model accounts for impacts of various morphologies of precipitates

New families of weighted sum formulas for multiple zeta values

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Diffusion Strategies Outperform Consensus Strategies for Distributed Estimation over Adaptive Networks

Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes interact with each other on a local level and diffuse information across the network to solve estimation and inference tasks in a distributed manner. In this work, we compare the mean-square performance of two main strategies for distributed estimation over networks: consensus strategies and diffusion strategies. The analysis in the paper confirms that under constant step-sizes, diffusion strategies allow information to diffuse more thoroughly through the network and this property has a favorable effect on the evolution of the network: diffusion networks are shown to converge faster and reach lower mean-square deviation than consensus networks, and their mean-square stability is insensitive to the choice of the combination weights. In contrast, and surprisingly, it is shown that consensus networks can become unstable even if all the individual nodes are stable and able to solve the estimation task on their own. When this occurs, cooperation over the network leads to a catastrophic failure of the estimation task. This phenomenon does not occur for diffusion networks: we show that stability of the individual nodes always ensures stability of the diffusion network irrespective of the combination topology. Simulation results support the theoretical findings.Comment: 37 pages, 7 figures, To appear in IEEE Transactions on Signal Processing, 201