Photothermal Performance of Lignin-Based Nanospheres and Their Applications in Water Surface Actuators.

In this study, the photothermal performance of lignin-based nanospheres was investigated. Subsequently, a photothermal actuator was prepared using lignin-based carbon nanospheres (LCNSs). The results demonstrated that LCNSs exhibited an impressive photothermal conversion efficiency of up to 83.8%. This extreme efficiency significantly surpasses that of lignin nanospheres (LNSs) and covalently stabilized LNSs (HT-LNSs). As a structural material, a hydrophobic coating was effectively engineered by LCNSs on the filter paper, achieving a water contact angle of 151.9° ± 4.6°, while maintaining excellent photothermal effects (with a temperature increment from room temperature to 138 °C in 2 s). When employing hydrophobic filter paper as the substrate for the photothermaldriven actuator, under the influence of a 1.0 W/cm2 power-density NIR laser, the material exhibited outstanding photothermal actuation, achieving speeds up to 16.4 mm/s. In addition, the direction of motion of the actuator can be adjusted in accordance with the location of the NIR light irradiation. This study offers valuable perspectives on the application of LNSs for highvalue applications and the development of innovative photothermal-driven actuators.

Imaging process matched neural network for complex wavefront retrieval with a higher space-bandwidth product.

Recently, deep learning (DL) has shown great potential in complex wavefront retrieval (CWR). However, the application of DL in CWR does not match well with the physical diffraction process. The state-of-the-art DL-based CWR methods crop full-size diffraction patterns down to a smaller size to save computational resources. However, cropping reduces the space-bandwidth product (SBP). In order to solve the trade-off between computational resources and SBP, we propose an imaging process matched neural network (IPMnet). IPMnet accepts full-size diffraction patterns with a larger SBP as inputs and retrieves a higher resolution and a larger field of view of the complex wavefront. We verify the effectiveness of the proposed IPMnet through simulations and experiments.

Extended autofocusing in dual-wavelength digital holography.

In single-wavelength digital holography (DH), the phase wrapping phenomenon limits the total object depth that can be measured due to the requirement for well-resolved phase fringes. To address this limitation, dual-wavelength DH is proposed, enabling measurement of much deeper objects. In single-wavelength DH, because the object depth is limited, the depth of focus (DOF) of DH's optical system at a reconstruction distance is sufficient to cover the object depth. To date, many autofocusing algorithms have been proposed to obtain a correct reconstruction distance. However, in dual-wavelength DH, because the object depth is extended, the DOF at a reconstruction distance cannot cover the extended object depth. The extended object depth can span multiple DOFs, causing partially out of focus object depth. Therefore, in dual-wavelength DH, relying solely on autofocusing algorithms for a single distance is insufficient. But extended autofocusing algorithms, which can autofocus objects through multiple DOFs, are demanded. However, there are no such extended autofocusing algorithms in dual-wavelength DH. Therefore, we propose an extended autofocusing algorithm for dual-wavelength DH based on a correlation coefficient. The proposed algorithm is able to focus the whole object depth when the depth spans multiple DOFs. Through theoretical analysis, simulations, and experiments, the necessity and effectiveness of the proposed algorithm are verified.