Polypropylene (PP) is a widely used common type of polymer with excellent chemical and mechanical properties. Thermal aging is a main culprit in degrading its lifetime and effectiveness. In this work, terahertz dielectric frequency response of PP during the process of thermal aging is monitored and analyzed to understand the underlying mechanism, and to qualify terahertz (THz) dielectric spectroscopy as a useful tool for time-stamping and benchmarking thermal aging of polymer materials. Using an improved THz material parameter extraction algorithm for thin films, the real and imaginary parts of the dielectric permittivity of standard PP test coupons were obtained at various stages of the thermal aging process. In agreement with theoretical analysis based on a molecular-level understanding of the thermal aging dynamics, the experimental results indicated that THz wave transmission loss of PP correlated quantitatively with the degree of its thermal aging.

Multilayer composite materials often develop internal defects at varying depths due to manufacturing and environmental factors. Traditional planar scanning methods lack the ability to pinpoint defect locations in depth. This study proposes a terahertz time-domain spectroscopy (THz-TDS)-based defect detection method using continuous wavelet transform (CWT) to convert spectral signals into time-frequency images. These are analyzed by the ResNet18 model combined with a support vector machine (SVM) classifier. Comparative experiments with four classical deep learning models and three classifiers show that the Residual Network with 18 layers (ResNet18-SVM) approach achieves the highest accuracy of 98.56%, effectively identifying three types of defects. The results demonstrate the method’s strong feature extraction, depth resolution, and its potential for nondestructive evaluation of multilayer structures.

The limited accuracy of detecting small objects with current AI algorithm is becoming prominent under background of security check using low-resolved terahertz images. To solve this problem, a modified model based on YOLOv8 architecture -- YOLOv8-SAB is built to achieve satisfactory detection of suspicious small objects. Specifically, the model integrates Shuffle attention mechanism into YOLOv8, capturing Channel dependence and spatial pixel relationship through Channel Shuffle operation and attention mechanism, thus improving the detection effect of small objects. In addition, we also use BiFPN (Bi-directional Feature Pyramid Network) in feature fusion to overcome low resolution of terahertz images, and employ SIoU (SCYLLA IoU) as a friendly optimization object. Experimental results indicate that the inference speed of YOLOv8-SAB reaches 1.7ms to meet the fast detection requirement in real task. YOLOv8-SAB not only shows great potential in small object detection, but also has excellent detection ability for large objects. According to the self-made data set, mAP0.5 and mAP0.5-0.95 for small objects reach 0.93 and 0.716, respectively. As a comparison, mAP0.5 and MAP0.5-0.95 for large objects reach 0.98 and 0.84, respectively. This method plays an important role in the designing and deployment of intelligent real-time terahertz object detection system for security check.

Baicalin concentration is concerned in manufacture of scutellaria spray drying powder as a traditional Chinese medicine, and the quality control based on high‐performance liquid chromatography is inconvenience. In this study, terahertz time domain spectroscopy was employed to achieve quality control of scutellaria spray drying powder; however, an acute difficulty was found that terahertz spectra overlapped due to the disturbance in both content matrix and measurement error. In this study, similar terahertz spectra of scutellaria spray drying powder were classified with the help of Gaussian mixture model and built a classifier based on probability feature instead of spectral features conventionally employed in previous investigations. To explore the feasibility of GMM, principal component analysis was given, indicating that it is possible to train GMM with original features and proper principal components. Probable advantage of training GMM based on PCA feature was discussed and so it was with the capacity of the model to identify the linear combined spectra by comparing the performance of GMM and a decision tree model. Above all, the reason why GMM shows potential in the analysis of TCM terahertz spectra was illustrated by comparing the thought of discriminative model and generative model. This study implied that generative model may have natural advantage of overcoming the inherent disturbance of terahertz spectroscopy, which would be promising in future studies.

Free space measurement is widely employed to characterise the permittivity of flat materials. However, the requirement of shape and dimension can hardly be satisfied in measurement of materials employed in semiconductor field. In this study, permittivity of SiO ₂ samples with various thicknesses and small section were characterised in an improved light path. The proposed light path configuration consisted of two pairs of discrete concave lenses and general horn antennas attached to frequency extenders which were connected to a vector network analyser. Reliability and far field approximation were verified by optical simulation and real tests. In addition, the performance of NRW algorithm and NIST algorithm were compared, indicating that the permittivity of SiO ₂ glass ranged from 3.15 to 3.8 in WR10 band (75–110 GHz) and that NRW took no advantage over reducing thickness dependence of permittivity compared to NIST algorithm. Moreover, the NIST model was compatible to the proposed light path configuration for characterising small thin materials with arbitrary thickness. This study may guide characterisation of dielectric property for cases that large cross section of material cannot be satisfied.