With climate change posing an unprecedented global challenge, the demand for environmentally friendly solvents in green chemical processes and carbon dioxide capture has surged. Ionic liquids (ILs) have emerged as promising "designer solvents" due to their negligible volatility, broad liquid temperature range, and exceptional thermal stability. However, the immense chemical space of ILs—with theoretically up to 10¹⁸ possible cation-anion combinations—has created a critical bottleneck in efficient screening and design. Traditional experimental methods are costly and time-consuming, while theoretical calculations like molecular dynamics and quantum chemistry remain computationally prohibitive for large-scale screening. This urgent need for accelerated discovery has set the stage for a transformative technological leap.

 A methodology to enhance the energy density of supercapacitors based on a V₄C₃T_Z MXene has been developed via enhancement of the operating electrochemical window using a 17.5 molal LiBr/H₂O highly concentrated electrolyte.

 

A team of Chinese researchers has developed an AI-based modeling approach that revolutionizes the prediction of complex nonlinear dynamics in Kerr resonators. By leveraging recurrent neural networks (RNNs)—specifically gated recurrent units (GRUs)—and a hybrid convolutional neural network (CNN)-GRU model for complex scenarios, the team achieved nearly 20x faster simulations than traditional methods, while maintaining high accuracy. The work paves the way for faster design of next-generation optical systems, from optical memories to all-optical computers.

Fluid–structure interaction (FSI) governs how flowing water and air interact with marine structures—from wind turbines to underwater cables—and is critical for safe renewable energy development. Traditional numerical simulations and experiments require enormous computational resources, yet often fail to capture multiscale turbulence and long-term system behavior. This review highlights how machine learning (ML) is emerging as a powerful solution for analyzing, predicting, and even controlling FSI systems. Key progress spans feature detection, reduced-order modeling, physics-informed neural networks, and reinforcement-based flow control. By leveraging data-driven models to extract hidden patterns and reconstruct flow fields, ML shows promise in improving efficiency, predictive accuracy, and automated control across ocean engineering applications, positioning itself as a transformative tool for next-generation design.

Gust load alleviation is a crucial topic for the practical application of high-altitude long-endurance unmanned aerial vehicles. Passive flexible wingtips mitigate gust loads by naturally adapting their shape to airflow disturbances. Without active control or energy input, they passively relieve aerodynamic peaks, smooth transient loads, and enhance flight stability and structural safety, offering a lightweight, reliable, and energy-efficient gust alleviation solution.

Nitrogen dioxide is one of the most harmful air pollutants in urban and industrial regions, contributing to respiratory disease, smog formation, and climate forcing. As governments tighten air quality standards worldwide, scientists are searching for affordable and efficient ways to remove this toxic gas before it reaches the atmosphere. A new theoretical study reveals that a simple and naturally abundant element, potassium, could dramatically enhance the ability of biochar to capture and chemically transform nitrogen dioxide.

There are over 36,500 pieces of space debris of larger than 10 cm in orbit according to statistical model orbit provided by European Space Agency’s Space Debris Office at European Space Operations Centre in Darmstadt, Germany. Any of those debris can pose a threat to operational satellites. Active debris removal (ADR) is conducted to solve the above problems for stabilizing the space debris environment. Unfortunately, the angular velocity of space debris exceeds the maximum angular velocity of 4 to 5 deg/s that can be directly captured by a robotic arm. Therefore, to stabilize the debris environment, the first task, i.e., de-tumbling tasks, is to reduce the initial angular velocity of the debris by applying an external torque. To ensure the safety of the de-tumbling tasks avoiding collision between the de-tumbling device and the uncooperative space target, many contactless methods have been studied. Among them, the contactless method based on electromagnetic eddy current method has received increasing attention due to the advantages of pollution-free and high safety performance.

Rapid industrialization advancements have grabbed worldwide attention to integrate a very large number of electronic components into a smaller space for performing multifunctional operations. To fulfill the growing computing demand state-of-the-art materials are required for substituting traditional silicon and metal oxide semiconductors frameworks. Two-dimensional (2D) materials have shown their tremendous potential surpassing the limitations of conventional materials for developing smart devices. Despite their ground-breaking progress over the last two decades, systematic studies providing in-depth insights into the exciting physics of 2D materials are still lacking. Therefore, in this review, we discuss the importance of 2D materials in bridging the gap between conventional and advanced technologies due to their distinct statistical and quantum physics. Moreover, the inherent properties of these materials could easily be tailored to meet the specific requirements of smart devices. Hence, we discuss the physics of various 2D materials enabling them to fabricate smart devices. We also shed light on promising opportunities in developing smart devices and identified the formidable challenges that need to be addressed.

Pore-scale mechanisms of drag reduction by micro-blowing have rarely been explored. A direct numerical simulation (DNS) study, published in the Chinese Journal of Aeronautics, is performed to uncover the fundamental physics of single-hole micro-blowing in a supersonic turbulent boundary layer. Results reveal a dual-regime drag-reduction mechanism: upstream reduction driven by adverse pressure gradients and downstream reduction dominated by the formation of a low-speed air film. A detailed vortex-interaction analysis further explains how micro-blowing sustains stable drag-reduction performance under turbulent vortex interference.