The tunable CO₂ laser is a common pump source for optically pumped terahertz lasers. In this paper, based on a sealed-off CO₂ laser tube and a grating-tuned cavity, we reported an efficient, high-power, low-cost, simple structure and tunable continuous-wave CO₂ laser. A sealed-off CO₂ laser tube was designed and customized, and its plasma discharge characteristics were experimentally analyzed. The influence of output coupler transmittance and discharge current on the laser’s tunability and output power was systematically studied using grating tuning. A total of 78 spectral lines were achieved within the wavelength range of 9.17–10.86 μm. The maximum output power of 55.5 W was recorded on the 10P20 line when the output coupler transmittance was approximately 22%. The laser exhibited a beam spot radius of about 3.8 mm and a beam quality factor (M²) of 1.74.

The paper proposes and verifies a small-angle measurement method based on the defect spot mode of the position-sensitive detector (PSD). With the output characteristics of the PSD in the defect spot mode and the size transformation properties of a focused beam, the measurement sensitivity can be significantly improved. Calibration experiments with the piezoelectric transducer (PZT) indicate that compared with the current PSD-based autocollimation method, the proposed method can improve the sensitivity of small-angle measurement by 57 times, and the measurement sensitivity of the proposed method can be further improved by optimizing the system parameters, while the proposed method has the advantages of a simple system and high real-time performance. Therefore, the proposed method is expected to be used in high-precision motion error detection, as well as in shape and position measurement.

Accurate online inspection of train wheelset tread defects is challenging owing to the variety and position uncertainty of defects. This study develops an improved YOLOv7 model capable of inspecting various wheelset tread defects with high accuracy and low computation complexity. This model comprises GSConv, a small target enhancement (STE) module, and StyleGAN3. GSConv significantly reduces the model volume while maintaining the feature expression ability, achieving a lightweight structure. The STE module enhances the fusion of shallow features and distribution of attention weights, significantly improving the sensitivity to small-sized defects and positioning robustness. StyleGAN3 enhances small samples by addressing inhomogeneity, thereby generating high-quality defect samples; it overcomes the limitations of traditional amplification methods regarding texture authenticity and morphological diversity, systematically improving the model’s generalization ability under sample scarcity conditions. The model achieves 1.6%, 10.7%, 48.63% and 37.97% higher mean average precision values than YOLOv7, YOLOv5, SSD, and Faster R-CNN, respectively, and the model parameter size is reduced by 73.91, 94.69, 122.11, and 154.91 MB, respectively. Hence, the proposed YOLOv7-STE model outperforms traditional models. Moreover, it demonstrates satisfactory performance in detecting small target defects in different samples, highlighting its potential applicability in online wheel tread defect inspection.

Train wheelsets are key components of high-speed trains and are prone to failure during long-term operation under harsh conditions. Accurate and comprehensive online inspection of defects in wheelset treads are difficult because of their variety and position uncertainty. Accurate detection and location of small targets are highly challenging. This study developed an improved YOLOv7 model with better accuracy and lower power computation. The model was applied to the inspection of various wheelset-tread defects. GSConv was used to reduce the model volume, while a small target enhancement module addressed low pixels and difficulty in distinguishing small targets. This improved the classification accuracy for damage feature recognition. The captured images of the wheelset tread were preprocessed using StyleGAN3 to augment small samples and address non-uniformities. The experimental results showed that the proposed model exhibited a remarkably better overall performance than conventional models, particularly in terms of mean average precision. The public RSDDs dataset of small rail defects was selected to verify the robustness of the proposed algorithm. Compared with the traditional network model, the improved model proposed herein showed good performance for small target defects in different samples, highlighting its potential applicability in online inspection of wheel tread defects.

Silicon heterojunction (HJT) solar cells exhibit outstanding passivation performance and low temperature coefficient, nevertheless traditional doped a-Si:H contacts suffer severe parasitic absorption, poor thermal stability and high cost, limiting efficiency promotion. P-type wide-bandgap Nickel oxide (NiO _x ) serves as a promising hole-selective alternative, owing to its suitable band alignment with crystalline silicon and excellent electron-blocking capacity. Existing NiO _x -based HJT research has obvious bottlenecks: Most existing numerical simulations only optimize the parameter of NiO _x in isolation, while ignoring the synergistic coupling effect between multi-layer optical-electrical parameters; Moreover, nearly all reported NiO _x heterojunction devices still employ narrow-bandgap a-Si:H front window layers, which causes serious short-wavelength parasitic absorption and limits short-circuit current density. Few systematic simulation studies have combined NiO _x band engineering, acceptor doping tuning, wide-bandgap n-type micro-crystalline silicon oxide ( μ c–SiO _x (n)) window replacement and n-type silicon substrate doping optimization to explore the upper efficiency limit of this novel structure. In this work, SCAPS-1D numerical simulation is adopted to explore the potential application of NiO _x rear hole-selective contacts in n-type HJT cells. The independent and synergistic impacts of NiO _x bandgap, NiO _x acceptor concentration, μ c-SiO _x electron affinity and n-c-Si doping are fully analyzed. The globally optimized parameter set is determined: NiO _x bandgap = 3.8 eV, NiO _x acceptor doping = 1 × 10 ¹⁷ cm ⁻³ , n-type c-Si substrate doping = 1 × 10 ¹⁷ cm ⁻³ , with the optimal μ c-SiO _x electron affinity at 3.76 eV. Under multi-parameter co-optimization, the optimized device achieves a photoelectric conversion efficiency of 25.40% ( V _OC = 0.7995 V, J _SC =37.27 mA cm ⁻² , FF = 85.23%), under idealized simulation assumptions. This work establishes a full multi-dimensional optimization scheme missing in previous research, and offers theoretical guidance and structural design principles for low-cost, stable next-generation silicon heterojunction photovoltaic devices.