Solar-driven vapor generation is a sustainable and environmentally friendly method for water purification. Despite recent progress on photothermal steam generation, the rate of vapor generation remains low. Here, we enhance the vapor generation rate by combining solar-driven thermal and wind effects on a femtosecond-laser-textured superhydrophilic copper foam surface. Significant solar power can be absorbed and transformed into heat on the treated surface. This solar power can also be converted into electric power to generate wind to further accelerate steam generation. The upper superhydrophilic foam surface facilitates the continuous supply of water. A pre-wetted polyurethane sponge minimizes heat loss by preventing direct contact between the heated foam and bulk water. The as-prepared evaporator achieved a water evaporation rate of ∼7.6 kg m⁻² h⁻¹ under one sun irradiation (1 kW m⁻²) at a wind speed of 3 m s⁻¹. This is a promising technology for enhancing water evaporation rates in seawater desalination and wastewater treatment applications.

Perovskites fabricated by fs-laser on multiple substrates for applications in display, anti-counterfeiting encryption, information storage, and nonlinear optics.

RGBT tracking is rapidly developing due to its complementary advantages of RGB and thermal frames. Existing methods with high accuracy track at a lower speed, and do not make full use of the hierarchical information in the feature extraction and the historical information of the sequences. To address these issues, a novel dual-modality space-time memory (DMSTM) network is proposed for robust RGBT tracking. Specifically, DMSTM is divided into three modules. The first module is the dual-modality backbone that utilizes both shallow and deep information by aggregating feature maps of dimensional changes during downsampling. Another module is the space-time memory reader with bimodal fusion. It aggregates features of historical and current frames to share information in the time domain. The last module is the siamese head network, which computes the predicted loss sum of the two modalities and back-propagates it. This avoids degrading the tracking performance due to sequence frame pairs where the training targets are not perfectly aligned. Extensive experiments on three RGBT benchmark datasets show that the performance and efficiency of the proposed DMSTM exceed that of state-of-the-art methods while running at 27.6 FPS.