The application of deep ultraviolet detection (DUV) in military and civil fields has increasingly attracted the attention of researchers’ attention. Compared with the disadvantages of organic materials, such as complex molecular structure and poor stability, inorganic materials are widely used in the field of DUV detection because of their good stability, controllable growth, and other characteristics. Rapid advances in preparing high-quality ultrawide-bandgap (UWBG) semiconductors have enabled the realization of various high-performance DUV photodetectors with different geometries, which provide an avenue for circumventing numerous disadvantages in traditional detectors. Herein, the development history and types of DUV detectors are briefly introduced. Typical UWBG detection materials and their preparation methods, as well as their research and application status in the field of DUV detection, are emphatically summarized and reviewed, including III-nitride semiconductors, gallium oxide, diamond, etc. Finally, problems pertaining to DUV detection materials, such as the growth of materials, the performance of devices, and their future development, are also discussed.

In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time–frequency attention convolutional neural network (TFA-CNN). A time–frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model’s robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R²) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time–frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system.