The high-pressure cold spraying technology was adopted to deposit silicon particles on the copper substrates for preparing the silicon-based electrode of lithium-ion batteries. The deposition of silicon powder was accomplished at two spray process parameter sets by impacting the particle stream or individual particle. The microstructure and the morphology of the silicon coatings were characterised by scanning electron microscopy. A single-silicon particle represents three different deposition behaviours. The mass gains of as-sprayed samples are negative over multiple spray passes, but the thin silicon coating with the particular covered structures is formed. Increasing the number of spray pass cannot cause the obvious increase in the coating thickness. The interfacial morphology of the silicon coatings indicates that, except for the plastic deformation of the copper substrate, both the mechanical interlock effect among the deposited silicon particles and even the chemical binding contributing to the effective bond of silicon particles to the substrate.

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Accurate 4D trajectory prediction plays an important role in the sustainable management of future air traffic. Aiming at the problems of inadequate feature utilization, unbalanced overall prediction (OP) result, and weak real-time response in 4D trajectory prediction by machine learning, a fractal dimension feature-prediction (FDFP) model is proposed, starting from the airborne quick access recorder (QAR) trajectory data. Firstly, the trajectory features are classified and transformed according to the aircraft operation characteristics. Then, the long short-term memory (LSTM) network is used to construct the prediction model by fractional dimensions; based on the fractal dimension feature (FDF), the different combinations of influencing factors are selected as the feature matrix, and the optimal prediction model of each dimension is obtained. Finally, 671 city pair trajectory data are used to conduct simulation experiments to verify the accuracy and effectiveness of the model. The experimental results show that the FDFP model performs well, with the mean absolute error (MAE) of longitude and latitude both less than 0.0015°, and the MAE of altitude less than 3 m. Compared with the OP model, the MAE of the FDFP model in these three dimensions decreased by 92%, 81% and 79%, respectively. Compared with experiments without feature transformation, the MAE of the FDFP model is reduced by 75%, 82%, and 69%, respectively. Each prediction of the model takes about 30 ms, which satisfies the real-time prediction conditions and can provide a reference for air traffic operation assessment.