Wireless power transfer (WPT) technology uses non-conductive-wire methods to realize power transmission from the power-supply side to the load side, which is an advantageous energy supply method in long-distance, non-contact scenarios. Based on the photovoltaic (PV) effect, traditional laser WPT (LWPT) has the advantage of high transmission power. However, the cooling requirements of PV modules introduce additional structural composition and operating energy. In this paper, an LWPT (BeE-LWPT) technology based on the Seebeck effect is proposed, and a brand-new energy conversion mode is designed. Aiming at the limited hot area of the thermoelectric element periodically heated using the expanded beam laser, the improvement effect of the transient laser grating thermal regulation mechanism on BeE-LWPT is studied. Multiphysics simulations of the temperature response of the hot end of the copper plate modulated by the laser beam spot are carried out with commercial finite-element software. Compared with the traditional beam expansion method for temperature control, the proposed modulation method based on the transient laser grating has a more stable temperature response and a more uniform heating area, which means better thermal regulation effect.

Ultraviolet (UV) photocathodes are indispensable for solar-blind detection in industrial monitoring, astronomy, and biomedical applications. However, the quantum efficiency (QE) of traditional alkali-metal telluride cathodes, such as cesium telluride (Cs2Te), remains constrained by the inherent trade-off between photon absorption depth and electron escape probability. To address this challenge, we introduce plasmonic nanostructures into Cs2Te photocathodes and systematically investigate their effects using finite-difference time-domain simulations. We compare nanoparticle arrays and nanograting structures with a planar reference. The results show that both plasmonic designs significantly enhance UV absorption and local field intensity, while nanogratings yield superior performance in the short-wavelength region. Moreover, the research finds a 38% increase in QE for the nanograting structure compared to the planar structure. These findings establish a theoretical basis for plasmon-assisted UV photocathode design and provide new insights into the development of high-efficiency solar-blind photodetectors.

Extreme noise severely degrades image quality in photon-limited imaging systems and challenges existing denoising methods. Classical total variation (TV) models rely on fixed local gradients and often introduce cross-edge smoothing, while deep learning methods may become unstable under extremely low signal-to-noise ratios. To address these limitations, we propose DSGR-Net, a hybrid denoising framework integrating Deformable Sparse-Gradient Regularization (DSGR) preprocessing with neural network restoration. The proposed DSGR model performs adaptive neighborhood regularization by selecting the eight smallest local gradients within a deformable neighborhood for sparse total variation compensation. This structure-aware strategy effectively suppresses noise while avoiding cross-edge diffusion and preserving fine image details. Experimental results on both simulated and real optical imaging data demonstrate that the proposed DSGR-Net achieves improved structural preservation and noise suppression compared with representative denoising methods.