Bismuth oxychloride (BiOCl)-coated electroactive film was fabricated for the separation of cesium ions based on a potential-triggered method. The effects of applied potential, conductive carbon black (Super P Li), and different exposed facets on the adsorption of Cs + ions were investigated. Experiments revealed that the adsorption efficiency gradually improved with increasing the applied potential to −0.7 V. The introduction of Super P Li improved the adsorption rate and increased the separation efficiency. The adsorption behavior followed pseudo-second-order kinetics. Additionally, the electroactive film showed adsorption selectivity for Cs + in preference to Li + , Na + , and Rb + ; seven consecutive adsorption−desorption experiments indicated that the electroactive film is sensitive to potential. The BiOCl nanosheets with exposed (001) facets exhibited a higher adsorption ratio for Cs + removal than the counterpart with exposed (101) facets. It was also found that the separation performance of Cs + ions was best in the neutral condition. It is expected that this BiOCl material can be used as a promising electroactive material for the removal of Cs + ions from contaminated water in the future.
In this paper, we propose a Boosting Tail Neural Network (BTNN) for improving the performance of Realtime Custom Keyword Spotting (RCKS) that is still an industrial challenge for demanding powerful classification ability with limited computation resources. Inspired by Brain Science that a brain is only partly activated for a nerve simulation and numerous machine learning algorithms are developed to use a batch of weak classifiers to resolve arduous problems, which are often proved to be effective. We show that this method is helpful to the RCKS problem. The proposed approach achieve better performances in terms of wakeup rate and false alarm.In our experiments compared with those traditional algorithms that use only one strong classifier, it gets 18% relative improvement. We also point out that this approach may be promising in future ASR exploration.
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