Researchers at the Department of Energy’s Oak Ridge National Laboratory have developed an innovative energy storage system design that introduces a safer, more efficient method for electrical charge transfer. This study advances fundamental understanding of how functional groups impact the mechanical and electrochemical performance of highly charged polyelectrolyte membranes — thin, charged polymer sheets that help control the movement of ions in energy devices.

The complex fluid–structure interaction underlying blood flow through vessels has proved challenging to analyze both numerically and experimentally. Addressing this gap, researchers developed a new experimental platform that uses polarized light to directly visualize stress fields in artificial blood vessels and in the flowing blood analogue in real time. Their findings reveal how stress is distributed during pulsating flow and could help design safer devices and improve the diagnosis and treatment of cardiovascular diseases.

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.