One-step sintering construction of electromagnetic synergistic Co7Fe3/Co@CBC nanocomposites for enhanced microwave absorption properties.

Based on the synergistic modulation of electromagnetic parameters and microstructure design, multidimensional porous magnetic carbon-based nanocomposites have become ideal materials with efficient absorption properties. What's more, a carbon-magnetic alloy composite is a commonly used and efficient microwave absorber. In this paper, Co7Fe3/Co@CBC (CFCC) nanocomposites with strong magnetism, a three-phase composition, and a three-dimensional porous structure were synthesized by reducing Fe2+ and Co2+ using chestnut-shell biomass carbon (CBC). Biomass carbon with a higher specific surface area provides numerous active sites for Co7Fe3 nanosheets and Co nanospheres to form three-dimensional ping-pong chrysanthemum-like nanocomposites, which generate rich heterogeneous interfaces and conductive network structures. By adjusting the amount of added biomass, the electromagnetic parameters can be effectively regulated to achieve efficient microwave absorption properties. When the amount of biomass added varies within the range of 1.0 to 2.5 g, all samples exhibit a favorable effective absorption bandwidth (EAB) of over 5.88 GHz. In particular, the CFCC-2.0 composite exhibits optimal microwave absorption properties, with a minimum reflection loss (RLmin) value of -59.25 dB and an EAB of 6.34 GHz at a thickness of 2.8 mm. The simulation and modeling analysis results of radar cross section (RCS) further confirm the exceptional attenuation capability of composite materials at multiple incident angles. The exceptional microwave absorption properties and stability of EAB for the Co7Fe3/Co@CBC nanocomposite make it a promising candidate in the field of absorbing materials. This work also provides some feasible ideas for designing stable broadband wave-absorbing materials.

Research on two-class and four-class action recognition based on EEG signals.

BMI has attracted widespread attention in the past decade, which has greatly improved the living conditions of patients with motor disorders. The application of EEG signals in lower limb rehabilitation robots and human exoskeleton has also been gradually applied by researchers. Therefore, the recognition of EEG signals is of great significance. In this paper, a CNN-LSTM neural network model is designed to study the two-class and four-class motion recognition of EEG signals. In this paper, a brain-computer interface experimental scheme is designed. Combining the characteristics of EEG signals, the time-frequency characteristics of EEG signals and event-related potential phenomena are analyzed, and the ERD/ERS characteristics are obtained. Pre-process EEG signals, and propose a CNN-LSTM neural network model to classify the collected binary and four-class EEG signals. The experimental results show that the CNN-LSTM neural network model has a good effect, and its average accuracy and kappa coefficient are higher than the other two classification algorithms, which also shows that the classification algorithm selected in this paper has a good classification effect.