Pseudocapacitive electrode materials that involve a Faradaic process are being used in capacitive deionization (CDI). In the present work, manganese dioxide was electrodeposited onto a carbon nanotube-chitosan composite to create a pseudocapacitive electrode (denoted MnO 2 /CNT−CS) for an enhanced desalination performance. The benefits of coupling MnO 2 with the CNT−CS matrix are attributed to the mesoporosity to facilitate ion transport, hydrophilicity to increase the electrochemical contact, and good electrical conductivity to improve the Faradaic chargetransfer of MnO 2 . The electrochemical properties were investigated by electrochemical impedance, galvanostatic charge−discharge and cyclic voltammetry studies. Considering its low specific surface area (89.8 m 2 g −1 ), the MnO 2 /CNT−CS composite electrode exhibited pseudocapacitive-like behaviors with a high specific capacitance (42.3 F g −1 at 5 mV s −1 ) and good rate capability. Furthermore, when the MnO 2 /CNT−CS electrode served as a cathode in batch-mode CDI at 1.2 V, the salt adsorption capacity was 6.01 mg g −1 for 1 mM NaCl, which is higher than that of most MnO 2 -coated carbon electrodes. The improved sorption capacity was ascribed to the intercalation of Na + ions via the Mn redox reaction between +4 and +3 oxidation states. The cycling performance was further tested over 15 charge− discharge cycles. Consequently, the pseudocapacitive MnO 2 /CNT−CS electrode is a promising cathode material for highperformance desalination. These results also suggest a promising approach for using redox-active MnO 2 in CDI.

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A novel approach of fault detection and fault phase selection based on correlation coefficient for transmission line is presented in the paper. Our main object in this work is to calculate and analyze the feature space relativity in current of different fault types with the same structure and parameters in each relaying location. The experimental results show that different fault types have the obvious different correlation coefficient, which can efficiently distinguish normal and abnormal status, and accurately detect and classify various anomaly behaviors not depend on fault conditions (i.e., current transformer saturation, dynamic arcing faults, short-circuit level, and system topology) and select the fault phase quickly and correctly. The simulations results show that the method has the effectiveness and correctness in fault detection as well as faulted phase selection.

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