Palladium-based alloy materials as cathodes for the reduction of oxygen are regarded as potential substitutes for platinum-based catalysts in fuel cells. In this work, we present the scientometric analysis and...

Aqueous zinc-ion batteries have drawn increasing attention due to the intrinsic safety, costeffectiveness and high energy density. However, parasitic reactions and non-uniform dendrite growth on the Zn anode side impede their application. Herein, a multifunctional additive, ammonium dihydrogen phosphate (NHP), is introduced to regulate uniform zinc deposition and to suppress side reactions. The results show that the NH 4 + tends to be preferably absorbed on the Zn surface to form a "shielding effect" and blocks the direct contact of water with Zn. Moreover, NH 4 + and (H 2 PO 4 ) À jointly maintain pH values of the electrode-electrolyte interface. Consequently, the NHP additive enables highly reversible Zn plating/stripping behaviors in Zn//Zn and Zn//Cu cells. Furthermore, the electrochemical performances of Zn//MnO 2 full cells and Zn//active carbon (AC) capacitors are improved. This work provides an efficient and general strategy for modifying Zn plating/stripping behaviors and suppressing side reactions in mild aqueous electrolyte.

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The benign epilepsy with spinous waves in the 1 central temporal region (BECT) is the one of the most common 2 epileptic syndromes in children, that seriously threaten the ner-3 vous system development of children. The most obvious feature of BECT is the existence of a large number of electroencephalogram 5 (EEG) spikes in the Rolandic area during the interictal period, 6 that is an important basis to assist neurologists in BECT diag-7 nosis. With this regard, the paper proposes a novel BECT spike 8 detection algorithm based on time domain EEG sequence features 9 and the long short-term memory (LSTM) neural network. Three time domain sequence features, that can obviously characterize the spikes of BECT, are extracted for EEG representation. The 12 synthetic minority oversampling technique (SMOTE) is applied to 13 address the spike imbalance issue in EEGs, and the bi-directional 14 LSTM (BiLSTM) is trained for spike detection. The algorithm is 15 evaluated using the EEG data of 15 BECT patients recorded from 16 the Children's Hospital, Zhejiang University School of Medicine 17 (CHZU). The experiment shows that the proposed algorithm can obtained an average of 88.54% F1 score, 92.04% sensitivity, and 19 85.75% precision, that generally outperforms several state-of-the-20 art spike detection methods.

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