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The wavelet-domain de-noising technique has many applications in terahertz time-domain spectroscopy (THz-TDS). However, it requires a complex procedure for the selection of the optimal wavelet basis and threshold, which varies for different materials. Inappropriate selections can lead to de-noising failure. Here, we propose the Mean Estimation Empirical Mode Decomposition (ME-EMD) de-noising method for THz-TDS. First, the THz-TDS signal and the collected reference noise are decomposed into the intrinsic mode functions (IMFs); second, the maximum and mean absolute values of the noise IMF amplitudes are calculated and defined as the adaptive threshold and adaptive estimated noise value, respectively; finally, these thresholds and estimated noise values are utilized to filter the noise from the signal IMFs and reconstruct the THz-TDS signal. We also calculate the signal-to-noise ratio (SNR) and mean square error (MSE) for the ME-EMD method, the "db7" wavelet basis, and the "sym8" wavelet basis after de-noising in both the simulation and the real sample experiments. Both theoretical analysis and experimental results demonstrated that the new ME-EMD method is a simple, effective, and high-stability de-noising tool for THz-TDS pulses. The measured refractive index curves are compared before and after de-noising and demonstrated that the de-noising process is necessary and useful for measuring the optical constants of a sample.

We propose a THz nondestructive analysis method based on multiple echoes and feature fusion. Conventionally, it is difficult to identify the debonding defects of the glue layer (II) due to the thin adhesive layer. To this end, a THz propagation model is established, and a quantitative method for determining the thickness of debonding defects based on multiple echoes is presented. The measurement error for preset defect thickness of 500 µm was 4%. Further, for determining the area of debonding defects, a feature fusion imaging algorithm is proposed to realize the lateral recognition of defects and quantitative analysis is used to improve the recognition ability of defects.

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Lithium-oxygen batteries are considered to be one of the most promising next-generation energy storage devices due to their ultra-high theoretical specific capacity and relatively simple structure. Herein, Co-NC@CNTs with 3D...