3D interdigitated microbattery architectures (3D-IMA) are fabricated by printing concentrated lithium oxide-based inks. The microbatteries are composed of interdigitated, high-aspect ratio cathode and anode structures. Our 3D-IMA, which exhibit high areal energy and power densities, may find potential application in autonomously powered microdevices.

The development of highly active and stable oxygen evolution reaction (OER) electrocatalysts is crucial for improving the efficiency of water splitting and metal-air battery devices. Herein, an efficient strategy is demonstrated for making the oxygen vacancies dominated cobalt-nickel sulfide interface porous nanowires (NiS /CoS -O NWs) for boosting OER catalysis through in situ electrochemical reaction of NiS /CoS interface NWs. Because of the abundant oxygen vacancies and interface porous nanowires structure, they can catalyze the OER efficiently with a low overpotential of 235 mV at j = 10 mA cm and remarkable long-term stability in 1.0 m KOH. The home-made rechargeable portable Zn-air batteries by using NiS /CoS -O NWs as the air-cathode display a very high open-circuit voltage of 1.49 V, which can maintain for more than 30 h. Most importantly, a highly efficient self-driven water splitting device is designed with NiS /CoS -O NWs as both anode and cathode, powered by two-series-connected NiS /CoS -O NWs-based portable Zn-air batteries. The present work opens a new way for designing oxygen vacancies dominated interface nanowires as highly efficient multifunctional electrocatalysts for electrochemical reactions and renewable energy devices.

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In recent years, vision-aided inertial odometry for state estimation has matured significantly. However, we still encounter challenges in terms of improving the computational efficiency and robustness of the underlying algorithms for applications in autonomous flight with micro aerial vehicles in which it is difficult to use high quality sensors and powerful processors because of constraints on size and weight. In this paper, we present a filter-based stereo visual inertial odometry that uses the Multi-State Constraint Kalman Filter (MSCKF) [1]. Previous work on stereo visual inertial odometry has resulted in solutions that are computationally expensive. We demonstrate that our Stereo Multi-State Constraint Kalman Filter (S-MSCKF) is comparable to state-of-art monocular solutions in terms of computational cost, while providing significantly greater robustness. We evaluate our S-MSCKF algorithm and compare it with state-of-art methods including OKVIS, ROVIO, and VINS-MONO on both the EuRoC dataset, and our own experimental datasets demonstrating fast autonomous flight with maximum speed of 17.5m/s in indoor and outdoor environments. Our implementation of the S-MSCKF is available at https://github.com/KumarRobotics/msckf vio.