No abstract

Isothermal and non-isothermal differential scanning calorimetry experiments were carried out to study rapidly solidified Al90Fe5Ce5 alloy. Microstructural analysis suggests that icosahedral nanoparticles are homogeneously distributed in the matrix of annealed amorphous Al90Fe5Ce5 alloy. The presence and homogeneous distribution of icosahedral structure units and icosahedral short-range domains appear to be critical for the formation and stability of the amorphous phase.

Hybrid phase Li2NaV2(PO4)3 (H-LNVP) is one of the most promising cathode materials for Li+/Na+ mixed-ion batteries.

No abstract

Highly efficient saturation up-conversion bright visible luminescence was obtained using 1.55 μm laser diode excitation power as low as ∼0.03 W cm−2 in lab-synthesized Y2O3:Er3+ microspheres.

A nanoporous CuO/Cu composite material was prepared using a dealloy method, and exhibited excellent cycling stability when evaluated as an anode for lithium-ion batteries.

No abstract

Wind turbine energy generators operate in a variety of environments and often under harsh operational conditions, which can result in the mechanical failure of wind turbines. In order to ensure the efficient operation of wind turbines, the detection of any abnormality in the mechanics is particularly important. In this paper, a method for detecting abnormalities in the bearings of wind turbine energy generators, based on the cascade deep learning model, is proposed. First, data on the mechanics of wind turbine generators were collected, and the correlation between the data was studied in order to select the parameters related to the bearing temperature. Then, the logical relationship between the observation parameters and the target parameters was established based on a one-dimensional convolutional neural network (CNN) and a long short-term memory (LSTM) network, and the difference between the predicted temperature and the actual temperature was assessed using the root mean square error evaluation model. Finally, a numerical example was used to verify the operational data from a wind farm unit in northwest China. The results show that the CNN-LSTM model proposed in this paper can detect abnormalities earlier in the state of the main bearing than the LSTM model, and the CNN-LSTM model can detect abnormalities in the main bearing that the LSTM network cannot find.