In this paper, cyclic voltammetry and square wave voltammetry are applied to characterize the cathode processes of neodymium ions on a W electrode in LiF-NdF3 melts with or without the metal Nd. The results indicate that neodymium ions in the LiF-NdF3 (2 wt%) melt are reduced in two steps, i.e. Nd(3+) → Nd(2+) and Nd(2+) → Nd(0), corresponding to starting reduction potentials of 0.35 V vs. Li(+)/Li and 0.1 V vs. Li(+)/Li, respectively. The Nd(3+) → Nd(2+) process is controlled by mass transfer and the Nd(2+) → Nd(0) process is controlled by both an interfacial step and mass transfer. But in the LiF-NdF3 melt with excess metal Nd equilibrium, the kinetics of the above two processes are controlled by mass transfer. After potentiostatic electrolysis at 0.35 V in the LiF-NdF3-Nd2O3 melt NdF2 is formed on the Mo cathode, and metallic Nd is obtained by potentiostatic electrolysis at 0.1 V in the LiF-NdF3-Nd2O3-Nd melt, which validates the above electrochemical reduction results.
The high utilization of renewable energy to manage climate change and provide green energy requires short-term photovoltaic (PV) power forecasting. In this paper, a novel forecasting strategy that combines a convolutional neural network (CNN) and a salp swarm algorithm (SSA) is proposed to forecast PV power output. First, the historical PV power data and associated weather information are classified into five weather types, such as rainy, heavy cloudy, cloudy, light cloudy and sunny. The CNN classification is then used to determine the prediction for the next day’s weather type. Five models of CNN regression are established to accommodate the prediction for different weather types. Each CNN regression is optimized using a salp swarm algorithm (SSA) to tune the best parameter. To evaluate the performance of the proposed method, comparisons were made to the SSA based support vector machine (SVM-SSA) and long short-term memory neural network (LSTM-SSA) methods. The proposed method was tested on a PV power generation system with a 500 kWp capacity located in south Taiwan. The results showed that the proposed CNN-SSA could accommodate the actual generation pattern better than the SVM-SSA and LSTM-SSA methods.
Layered lithium-rich
transition-metal oxides (LRMs) have been considered
as the most promising next-generation cathode materials for lithium-ion
batteries. However, capacity fading, poor rate performance, and large
voltage decays during cycles hinder their commercial application.
Herein, a spinel membrane (SM) was first in situ constructed on the
surface of the octahedral single crystal Li1.22Mn0.55Ni0.115Co0.115O2 (O-LRM) to form
the O-LRM@SM composite with superior structural stability. The synergetic
effects between the single crystal and spinel membrane are the origins
of the enhancement of performance. On the one hand, the single crystal
avoids the generation of inactive Li2MnO3-like
phase domains, which is the main reason for capacity fading. On the
other hand, the spinel membrane not only prevents the side reactions
between the electrolyte and cathode materials but also increases the
diffusion kinetics of lithium ions and inhibits the phase transformation
on the electrode surface. Based on the beneficial structure, the O-LRM@SM
electrode delivers a high discharge specific capacity and energy density
(245.6 mA h g–1 and 852.1 W h kg–1 at 0.5 C), low voltage decay (0.38 V for 200 cycle), excellent rate
performance, and cycle stability.
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