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Self-propelled droplet jumping during condensation has attractive prospects for energy harvesting, water collection and thermal management, but its real-life applications are greatly limited to the challenge of enabling a sustainable control on the entire droplet lifecycle. Herein, we propose a programmable jumping-droplet condensation that evolves along an artificially designed pathway without external stimulations, where the droplets can uniformly form at specific sites, spontaneously migrate and coalesce with their neighboring droplets, and jump off effectively to continuously refresh surface, significantly enhancing the heat transfer performance and durability of condensation. The programmable jumping-droplet condensation is achieved using a wedge-walled rhombus lattice structure surface inspired from the structures and functions of Namib desert beetle skin, shorebird beak and setaria viridis leaf vein. This surface integrates wetting contrast patterns with dual-gradient hierarchical structures, providing persistent and multidimensional droplet rectifications and thus realizing a sustainable control on the entire droplet lifecycle. Furthermore, we systematically investigate the morphology and behavior evolutions of droplets throughout their entire lifecycle, and fully elucidate the programmable control mechanisms of the lattice structure determined by its topology and wettability features. This work not only serves as theoretical foundations and reference framework to realize a durable jumping-droplet condensation and achieve its performance ceiling in a controlled manner, but also promotes the design and fabrication of functional structured surfaces for droplet manipulation and delivery, self-cleaning and anti-fogging/icing.

Over the years, network traffic analysis and generation have advanced significantly. From traditional statistical methods, the field has progressed to sophisticated deep learning techniques. This progress has improved the ability to detect complex patterns and security threats, as well as to test and optimize network performance. However, obstacles persist, such as the dependence on labeled data for analysis and the difficulty of generating traffic samples that follow realistic patterns. Pre-trained deep neural networks have emerged as powerful tools to resolve these issues, offering improved performance by learning robust data representations from large unlabeled datasets. Despite their benefits, existing pre-trained models face challenges like token length limitation, which restricts their usefulness in comprehensive traffic analysis and realistic traffic generation. To address these challenges, we introduce TrafficGPT, a deep learning model that can tackle complex challenges related to long flow classification and generation tasks. This model uses generative pre-training with the linear attention mechanism, which allows for a substantially increased capacity of up to 12,032 tokens from the previous limit of only 512 tokens. TrafficGPT demonstrates superior performance in classification tasks, reaching state-of-the-art levels. In generation tasks, it closely resembles real traffic flows, with low JS divergence and an F1 score close to 0.5 (representing a random guess) in discriminating generated data. These advancements hold promise for future applications in both traffic flow classification and generation tasks.

The jumping-droplet condensation, namely the out-of-plane jumping of condensed droplets upon coalescence, has been a promising technical innovation in the fields of energy harvesting, droplet manipulation, thermal management, etc., yet is limited owing to the challenge of enabling a sustainable and programmable control. Here, we characterized the morphological evolutions and dynamic behaviors of nanoscale condensates on different nanopillar surfaces, and found that there exists an unrevealed domino effect throughout the entire droplet lifecycle and the coalescence is not the only mechanism to access the droplet jumping. The vapor nucleation preferentially occurs in structure intervals, thus the formed liquid embryos incubate and grow in a spatially confined mode, which stores an excess surface energy and simultaneously provides a asymmetric Laplace pressure, stimulating the trapped droplets to undergo a dewetting transition or even a self-jumping, which can be facilitated by the tall and dense nanostructures. Subsequently, the adjacent droplets merge mutually and further trigger more multifarious self-propelled behaviors that are affected by underlying surface nanostructure, including dewetting transition, coalescence-induced jumping and jumping relay. Moreover, an improved energy-based model was developed by considering the nano-physical effects, the theoretical prediction not only extends the coalescence-induced jumping to the nanometer-sized droplets but also correlates the surface nanostructure topology to the jumping velocity. Such a cumulative effect of nucleation-growth-coalescence on the ultimate morphology of droplet may offer a new strategy for designing functional nanostructured surfaces that serve to orientationally manipulate, transport and collect droplets, and motivate surface engineers to achieve the performance ceiling of the jumping-droplet condensation.