How can traditional coal-fired power plants adapt to the fluctuating nature of renewable energy? A new pre-gasification burner technology offers a solution, enhancing flexibility and stability even at ultra-low loads. Learn how this innovative approach could transform power plant operations and support a more sustainable energy future.

Electrochemical water splitting holds promise for producing clean hydrogen at industrial scales, but current technologies often falter under large current densities. Recent advances in catalyst design and scalable synthesis strategies are bridging this gap, offering materials that maintain high efficiency, stability, and durability under harsh operational conditions. This review synthesizes progress in scalable electrocatalyst production, from electrodeposition and corrosion engineering to thermal treatment approaches, and further to their combinations. By addressing the key challenges of performance degradation, bubble management, and cost limitations, the study highlights emerging solutions that can accelerate the industrial adoption of green hydrogen production technologies.

Organic cathodes have long been sought after for safe, sustainable, and recyclable energy storage, yet their development has been hampered by solubility, low voltage, and poor conductivity. In this study, researchers report a hexaazatriphenylene-based polymer with a three-dimensional (3D) framework that successfully addresses these challenges. The material exhibits remarkable insolubility, strong electronic delocalization, and accessible redox sites, resulting in a zinc–organic battery with an impressive initial discharge voltage of 1.32 V and an extraordinary lifespan of more than 40,000 cycles with over 93% capacity retention. This work provides a new molecular design strategy for creating high-voltage, ultrastable organic batteries.

This paper proposes an intermittent measurement-based attitude tracking control strategy for spacecraft operating in the presence ofsensor-actuator faults. A sampled-data (self-)learning observer is developed to estimate both the spacecraft’s states and lumped disturbances, effectively mitigating the impact of faults. This observer acts as a virtual predictor, reconstructing states and actuator fault deviations using only intermittent measurement data, addressing the limitations imposed by sensor failures. The control scheme incorporates compensation based on the predictor’s estimates, ensuring robust attitude tracking despite the presence of faults. We provide the first proof of bounded stability for this learning observer utilizing intermittent information, expanding its applicability. Numerical simulations demonstrate the effectiveness of this innovative strategy,  highlighting its potential for enhancing spacecraft autonomy and reliability in challenging operational scenarios.

Electrochemical synthesis in aqueous solution has emerged as a sustainable strategy to replace traditional fossil-fuel-driven chemical production, but it is often limited by the sluggish oxygen evolution reaction (OER) that wastes energy and yields low-value products. Recent advances are transforming this challenge into an opportunity by replacing OER with faster, value-added oxidation reactions paired with reduction processes beyond hydrogen production. This review highlights cutting-edge catalysts, hybrid electrolyzers, and in situ characterization tools that enable the simultaneous generation of two valuable products in a single electrochemical process. Such integrated systems not only enhance efficiency and economic viability but also pave the way for greener chemical manufacturing aligned with global net-zero goals.

This study revisits the American option pricing problem by introducing a more realistic exercise assumption: investors may exercise not only when the underlying asset reaches the theoretical optimal boundary but also when it lies within a properly defined ϵ-optimal set. For perpetual options, closed-form pricing formulas are derived. For finite-maturity contracts, a numerical algorithm is developed to approximate the ϵ-optimal exercise strip, and a Crank-Nicolson finite difference scheme is adapted to determine the resulting price interval. This approach provides a flexible pricing framework that better reflects real investor behaviour and risk preferences.