Solar energy is a clean and renewable energy source and solves today’s energy and climate emergency. Near-perfect broadband solar absorbers can offer necessary technical assistance to follow this route and develop an effective solar energy-harvesting system. In this work, the metamaterial perfect absorber operating in the ultraviolet to the near-infrared spectral range was designed, consisting of a periodically aligned titanium (Ti) nanoarray coupled to an optical cavity. Through numerical simulations, the average absorption efficiency of the optimal parameter absorber can reach up to 99.84% in the 200–3000 nm broadband range. We show that the Ti pyramid’s localized surface plasmon resonances, the intrinsic loss of the Ti material, and the coupling of resonance modes between two neighboring pyramids are highly responsible for this broadband perfect absorption effect. Additionally, we demonstrate that the absorber exhibits some excellent features desirable for the practical absorption and harvesting of solar energy, such as precision tolerance, polarization independence, and large angular acceptance.

Terahertz (THz) nondestructive testing (NDT) technology has been increasingly applied to the internal defect detection of composite materials. However, the THz image is affected by background noise and power limitation, leading to poor THz image quality. The recognition rate based on traditional machine vision algorithms is not high. The above methods are usually unable to determine surface defects in a timely and accurate manner. In this paper, we propose a method to detect the internal defects of composite materials by using terahertz images based on a faster region-convolutional neural networks (faster R-CNNs) algorithm. Terahertz images showing internal defects in composite materials are first acquired by a terahertz time-domain spectroscopy system. Then the terahertz images are filtered, the blurred images are removed, and the remaining images are enhanced with data and annotated with image defects to create a dataset consistent with the internal defects of the material. On the basis of the above work, an improved faster R-CNN algorithm is proposed in this paper. The network can detect various defects in THz images by changing the backbone network, optimising the training parameters, and improving the prior box algorithm to improve the detection accuracy and efficiency of the network. By taking the commonly used composite sandwich structure as a representative, a sample with typical defects is designed, and the image data are obtained through the test. Comparing the proposed method with other existing network methods, the former proves to have the advantages of a short training time and high detection accuracy. The results show that the mean average precision (mAP) without data enhancement reached 95.50%, and the mAP with data enhancement reached 98.35% and exceeded the error rate of human eye detection (5%). Compared with the original faster R-CNN algorithm of 84.39% and 85.12%, the improvement is 11.11% and 10.23%, respectively, which demonstrates superb feature extraction capability and reduces the occurrence of network errors and omissions.

Ceramic adhesive structures have been increasingly used in aerospace applications. However, the peaks of the signal on the upper and lower surface of the adhesive layer are difficult to measure directly due to the thin thickness of the adhesive layer and the effect of the attenuation dispersion of the ceramic layer. Thus, the existing non-destructive testing techniques have been ineffective in detecting adhesive quality. In this paper, the thickness of the adhesive layer is measured using terahertz time-domain spectroscopy. A sparse deconvolution method is proposed for the terahertz time-domain spectral signal of ceramic adhesive structures with different adhesive layer thicknesses. The results show that the methods proposed in this paper can realize the separation of reflection signals for glue layers with a thickness of 0.20 mm. By comparing with a wavelet denoising method and a modified covariance method (AR/MCM), the effectiveness of the sparse deconvolution method in estimating the thickness of the glue layer is demonstrated. This work will provide the theoretical and experimental basis for using terahertz time-domain spectroscopy to detect the homogeneity of ceramic adhesive structures.