Owing to its high penetrability with dielectric materials, terahertz time domain spectroscopy (THz-TDS) is a promising nondestructive measurement technology. The coating thickness deviation and defect of thermal barrier coatings (TBC) will affect its thermal insulation performance and lifetime. In this work, THz-TDS was applied to measure the coating thickness distribution of TBC. The refractive index was obtained by THz-TDS transmission mode. To avoid the normal incidence THz signal loss, the THz signal was reflected from the TBC with a 10° incident angle, which also made the measurement result insensitive to the unevenness and tilt of the TBC sample. In the experiment, the yttria-stabilized zirconia (YSZ) TBC was measured by THz-TDS to estimate the thickness distribution. To validate the thickness measurements, metallography was introduced to correlate the TBC thickness result. The measurement deviation was within 12.1 µm, i.e., 3.45% for the THz-TDS and metallography result. A piece of turbine blade was measured by THz-TDS and a eddy current test. The maximum deviation was 8.48 µm, i.e., 2.36% of these two methods. Unlike the eddy current test, the THz-TDS thickness result was not affected by the cooling holes. The effectiveness of the nondestructive thickness measurement of TBC for turbine blades by THz-TDS was verified.

This study aimed to explore the innovative application of terahertz (THz) time-domain technique combined with machine learning approach to predict the porosity of thermal barrier coatings (TBCs). To achieve accurate and nondestructive porosity measurement, reflection THz time-domain spectroscopy was applied to detect TBC samples with different porosities ranging from 1.91% to 18.21%. Through a principal component analysis (PCA), the key features of the data were retained, which reduced the 25 000 high-dimensional THz time-domain data to 9 dimensions. Hybrid machine learning models were established with THz time-domain data as input to accurately predict the porosity, which was based on backpropagation (BP) neural networks and support vector machines (SVMs). Reducing the dimensionality of the data could significantly improve the PCA-SVM prediction accuracy of the hybrid machine learning model. The mean absolute error (MAE), mean absolute percentage error (MAPE), and root-mean-squared error (RMSE) of PCA-SVM were minimal at 0.2555, 19.62%, and 2.1944, respectively, while the regression coefficient was 0.9186. The hybrid PCA-SVM model could accurately predict the porosity of TBCs.

Topological photonics provides a robust framework for controlling light, with edge states offering immunity to disorder. However, utilizing this stability for practical sensing remains a challenge, as conventional high-sensitivity photonic crystal sensors suffer common imperfections. Here, we numerically design a topological photonic crystal sensor for THz refractive-index detection. The silicon-based structural slab is engineered, showing a wide photonic bandgap (PBG). By breaking the inversion symmetry, two structures with trivial and nontrivial topological phases are constructed. Due to their opposite valley Chern numbers, the topologically protected edge states can be formed at the interface. Such edge states were further integrated with the resonant cavities, so that the resonant frequencies were observed in transmittance property, forming a stable cavity–edge states coupling channel for refractive-index sensing. Numerical results demonstrate strong suppression of transmission distortion induced by boundary defects, verifying favorable topological robustness of the proposed architecture. The structure exhibits linear refractive-index response with a simulated sensitivity of 1.9 THz/RIU, outperforming conventional photonic crystal sensors in numerical comparison. This work merges topological stability of edge states with high sensitivity response of resonant frequency, offering a theoretical candidate for robust biosensing and chemical detection.

Food security serves as a vital cornerstone for social stability. As one of the most important staple crops globally, the quality and geographical origin of rice are directly associated with consumer health. Traditional methods for classifying rice by origin and variety rely on sensory evaluation and manual inspection, which are subject to uncertainty and human error. To address this, this paper proposes a method for classifying rice by origin and variety based on terahertz time-domain spectroscopy. Terahertz technology features the advantages of non-destructive, high-sensitivity and non-contact detection, making it well-suited for food detection. This study employs terahertz time-domain spectroscopy combined with machine learning modeling methods, using 20 types of rice as the subject of investigation, with a focus on modeling and analyzing four representative samples. Refractive index and absorption coefficient were extracted through preprocessing methods including Savitzky–Golay convolution smoothing, wavelet denoising and moving average smoothing. Modeling, classification, and detection were implemented using principal component analysis, partial least squares discriminant analysis, and least-squares support vector machine. The experimental results indicate that principal component analysis (PCA) alone performs poorly in classification tasks. However, a classification model combining PCA for dimensionality reduction with a least-squares support vector machine (SVM), following Savitzky–Golay smoothing, demonstrated the best performance, achieving a prediction accuracy of 93.3%. In an extended test involving 20 samples, the model achieved an identification accuracy of 89.6%. Quantitative metrics demonstrate the feasibility of using terahertz technology combined with optimized machine learning algorithms for classifying rice by origin and variety.

Aiming to address the problems of low accuracy in coal–rock identification during coal mining, which lead to energy waste and safety hazards, a high-precision coal–rock medium identification method combining terahertz time-domain spectroscopy technology and multiple machine learning algorithms is proposed. By preparing coal–rock samples with a gradient change in coal content, terahertz time-domain spectroscopy data of coal–rock mixed media are collected, and optical parameters such as the refractive index and absorption coefficient are extracted. Principal component analysis is used to reduce the dimensionality of the terahertz data, and machine learning algorithms such as support vector machine, least squares support vector machine, artificial neural networks, and random forests are adopted for classification and identification. The study found that terahertz waves are more sensitive to coal–rock media in the 0.7–1.3 THz frequency band, and that the refractive index and absorption coefficient of coal–rock mixed media are significantly positively correlated with coal content within the range of 0–30%. After feature extraction and K-fold cross-validation, the random forest model achieved a coal–rock classification accuracy of over 96% on the test set, significantly outperforming other comparison algorithms. The research verifies the efficiency and practicality of terahertz technology combined with multiple machine learning algorithms in coal–rock identification, providing a new method for fields such as mineral separation. This method has, to a certain extent, broken through the accuracy bottleneck of traditional coal–rock identification technologies within its applicable range, providing a new solution for real-time detection of coal–rock interfaces and is expected to further reduce the risks of ineffective mining and roof accidents in the future.