Plasma-Engineering of Oxygen Vacancies on NiCo2O4 Nanowires with Enhanced Bifunctional Electrocatalytic Performance for Rechargeable Zinc-air Battery.

Designing an efficient, durable, and inexpensive bifunctional electrocatalyst toward oxygen evolution reactions (OER) and oxygen reduction reactions (ORR) remains a significant challenge for the development of rechargeable zinc-air batteries (ZABs). The generation of oxygen vacancies plays a vital role in modifying the surface properties of transition-metal-oxides (TMOs) and thus optimizing their electrocatalytic performances. Herein, a H2/Ar plasma is employed to generate abundant oxygen vacancies at the surfaces of NiCo2O4 nanowires. Compared with the Ar plasma, the H2/Ar plasma generated more oxygen vacancies at the catalyst surface owing to the synergic effect of the Ar-related ions and H-radicals in the plasma. As a result, the NiCo2O4 catalyst treated for 7.5 min in H2/Ar plasma exhibited the best bifunctional electrocatalytic activities and its gap potential between Ej = 10 for OER and E1/2 for ORR is even smaller than that of the noble-metal-based catalyst. In situ electrochemical experiments are also conducted to reveal the proposed mechanisms for the enhanced electrocatalytic performance. The rechargeable ZABs, when equipped with cathodes utilizing the aforementioned catalyst, achieved an outstanding charge-discharge gap, as well as superior cycling stability, outperforming batteries employing noble-metal catalyst counterparts.

Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network.

The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly attractive option. For such implantable medical devices, efficient and low energy consumption, small size, and efficient processing architectures are essential. To meet these requirements, epileptic seizure detection by analysis and classification of brain signals with a convolutional neural network (CNN) is an attractive approach. This work presents a CNN for epileptic seizure detection capable of running on an ultra-low-power microprocessor. The CNN is implemented and optimized in MATLAB. In addition, the CNN is also implemented on a GAP8 microprocessor with RISC-V architecture. The training, optimization, and evaluation of the proposed CNN are based on the CHB-MIT dataset. The CNN reaches a median sensitivity of 90% and a very high specificity over 99% corresponding to a median false positive rate of 6.8 s per hour. After implementation of the CNN on the microcontroller, a sensitivity of 85% is reached. The classification of 1 s of EEG data takes t=35 ms and consumes an average power of P≈140 µW. The proposed detector outperforms related approaches in terms of power consumption by a factor of 6. The universal applicability of the proposed CNN based detector is verified with recording of epileptic rats. This results enable the design of future medical devices for epilepsy treatment.