Textured surface boron-doped zinc oxide (BZO) thin films were fabricated by metal organic chemical vapor deposition as transparent conductive oxide (TCO) for solar cells. The surface microstructure was characterized by X-ray diffraction spectrum and scanning electron microscope. The optical transmittance was shown by optical transmittance microscope and the electrical properties were tested by Hall measurements. The thickness of the BZO film has crucial impact on the surface morphology, optical transmittance, and resistivity. The electrical and optical properties as well as surface microstructure varied inconsistently with the increase of the film thickness. The grain size and the surface roughness increased with the increase of the film thickness. The conductivity increased from 0.96×10³ to 6.94×10³ S/cm while the optical transmittance decreased from above 85% to nearly 80% with the increase of film thickness from 195 to 1021 nm. The BZO films deposited as both front and back transparent electrodes were applied to the bifacial ptype a-Si:H/i-type a-Si:H/n-type c-Si/i-type a-Si:H/n⁺-type a-Si:H heterojunction solar cells to obtain the optimized parameter of thickness. The highest efficiency of all the samples was 17.8% obtained with the BZO film thickness of 829 nm. Meanwhile, the fill factor was 0.676, the opencircuit voltage was 0.63 Vand the short-circuit density was 41.79 mA/cm². The properties of the solar cells changing with the thickness were also investigated.

Traditional lung electrical impedance imaging devices require the placement of measurement electrodes around the chest, which is inconvenient for clinical application in patients in a supine position. To address this issue, this study proposes a Virtual Electrode-based Level Set combined with GREIT algorithm (VESL-GREIT) for pulmonary impedance tomography. It utilizes measurement data from electrodes placed on only one side of the chest and generates full-electrode measurement data using a deep learning network. The improved GREIT algorithm is then employed for image reconstruction. Acting as a "gray box," VESL-GREIT not only takes advantage of prior information about the lungs but also enhances the robustness and generalizability of the algorithm. Simulation experiments demonstrate that with a measurement signal-to-noise ratio (SNR) above 20 dB, the imaging relative error (RE) and structure correlation coefficient (CC) change slowly, indicating high robustness of the algorithm. Physical model experiments show that compared to traditional dynamic electrical impedance imaging algorithms, the proposed algorithm achieves a relative error of 0.167 and a structure correlation coefficient of 0.886, enabling more accurate characterization of lung ventilation status. This research contributes to promoting the clinical application and dissemination of lung electrical impedance imaging monitoring methods and provides a new approach for open dynamic electrical impedance imaging.

The lung is one of the most vital organs in the human body, and its condition is closely correlated with overall health. Electrical impedance tomography (EIT), as a biomedical imaging technique, often produces low-quality reconstructed images due to its inherent ill-posedness in solving the inverse problem. To address this issue, this paper proposes a soft-threshold region segmentation algorithm with a relaxation factor. This algorithm segments the reconstructed lung images into internal regions, edge regions, and background regions, resulting in clearer boundaries in the reconstructed images. This facilitates the intuitive identification of regions of interest by healthcare professionals. Additionally, this segmentation algorithm is suitably combined with a dimension-reduced Tikhonov regularization algorithm. By utilizing the joint capabilities of these algorithms, the partition points belonging to the background region can be excluded from the sought grayscale vector, thereby improving the ill-posedness of the image reconstruction process and enhancing the quality of image reconstruction. Finally, a 16-electrode human lung EIT simulation model is established for the thoracic region and verified through simulation. Experimental validation is conducted using a human lung tank simulation platform to further demonstrate the effectiveness of the proposed method.