In this paper, silicon solar cells with Ag nanoparticles deposited on a SiO₂ spacer were studied concentrating on the influence of the surface plasmon and the antireflection film. We experimentally found that the photocurrent conversion efficiency of the solar cell decorated by random arrays of self-assembled Ag nanoparticles increases firstly and decreases afterwards with increasing spacer thickness. Further investigations on the external quantum efficiency (EQE) illustrated this trend more clearly. It was also found that the effect of the surface plasmon on light absorption dominates over that of the antireflection film at the resonance wavelength which is an important factor determining the light trapping. Moreover, surface plasmon is determined by both the Si substrate and the SiO₂ spacer. For self-assembled Ag particles on the surface of the solar cells in our experiments, appropriate spacer thickness (9-35 nm) could broaden the plasmon resonance, narrow the photocurrent suppression range, weaken the suppression amplitude and strengthen the gain at the resonance wavelength, while still providing antireflection effect.

Reflectance, transmittance, and absorption of materials are also known as materials’ Fresnel problem. It is widely accepted that Interface model can be utilized to solve Fresnel problem of two dimensional materials. Here, we question the validity of Interface model. Theoretical and experimental results of two dimensional materials are analyzed, and theoretical optical response of two dimensional materials is derived based on thin film model. A new simple, approximate formula of 4πnkd/λ is proposed for calculation of absorption of two dimensional materials. It is found that, in essence, Interface model is a kind of approximate style of thin film model, the main difference between two models is term of (n² − k²) at normal incidence. A significant error is introduced into reflectance calculation of two dimensional materials when Interface model is utilized. Thus, it is not correct to use Interface model to solve Fresnel problem of two dimensional materials. Thin film model rather than Interface model can be used to universally solve Fresnel problem of two dimensional materials, and exhibit a better agreement with experimental reflectance results than Interface model. Unexpectedly, on contrary to other remarkable, intriguing properties, two dimensional materials exhibit an ordinary Fresnel optical response, which is same with thin film.

Change detection (CD) in high-resolution remote sensing imagery remains challenging due to the complex nature of objects and varying spectral characteristics across different times and locations. Convolutional neural networks (CNNs) have shown promising performance in CD tasks by extracting meaningful semantic features. However, traditional 2D-CNNs may struggle to accurately integrate deep features from multi-temporal images, limiting their ability to improve CD accuracy. This study proposes a Multi-level Feature Cross-Fusion (MFCF) network with 3D-CNNs for remote sensing image change detection. The network aims to effectively extract and fuse deep features from multi-temporal images to identify surface changes. To bridge the semantic gap between high-level and low-level features, a MFCF module is introduced. A channel attention mechanism (CAM) is also integrated to enhance model performance, interpretability, and generalization capabilities. The proposed methodology is validated on the LEVIR construction dataset (LEVIR-CD). The experimental results demonstrate superior performance compared to the current state-of-the-art in evaluation metrics including recall, F1 score, and IOU. The MFCF network, which combines 3D-CNNs and a CAM, effectively utilizes multi-temporal information and deep feature fusion, resulting in precise and reliable change detection in remote sensing imagery. This study significantly contributes to the advancement of change detection methods, facilitating more efficient management and decision making across various domains such as urban planning, natural resource management, and environmental monitoring.

In the far-ultraviolet (UV) region, optical materials are dispersive and absorbing. The previous successful design method utilized in the visible band should be re-examined. We report on the design and fabrication of a far-UV reflective broadband filter based on dielectric materials lanthanum fluoride (LaF ₃ ) and magnesium fluoride (MgF ₂ ). Extended bandwidth technology is utilized in designing this filter. The obtained filter has a high reflectance in the working wavelength range of 140–180 nm, meanwhile, with good suppression in shorter and longer wavelength regions. In 2016, this filter was employed in a wide-angle aurora imager (WAAI) installed in the Feng Yun III satellite that will be launched in China.

Application of π-multilayer technology is extended to high extinction coefficient materials, which is introduced into metal-dielectric filter design. Metal materials often have high extinction coefficients in far ultraviolet (FUV) region, so optical thickness of metal materials should be smaller than that of the dielectric material. A broadband FUV filter of 9-layer non-periodic Al/MgF₂ multilayer was successfully designed and fabricated and it shows high reflectance in 140–180 nm, suppressed reflectance in 120–137 nm and 181–220 nm.