Combustion instability, which causes dangerous pressure oscillations in combustors, arises from complex feedback between heat release, acoustics, and flow. Now, researchers from Japan have applied network science to spray combustion instability, shedding light on the dynamics of ‘turbulence networks.’ By identifying critical regions, they found a way to suppress combustion instability. This method offers a novel mathematical approach to stabilizing the combustion state in various combustors.

Scientists have unveiled a sunlight-powered catalyst that transforms methane—the main component of natural gas—directly into ethanol, a high-value liquid fuel and chemical feedstock. The new material, described today in Frontiers in Energy, achieves an apparent quantum efficiency of 9.4 %, a record for photocatalytic methane-to-ethanol conversion, while operating under near-ambient conditions.

To achieve effective intraday dispatch of photovoltaic (PV) power generation systems, a reliable ultra-short-term power generation forecasting model is required. Based on a gradient boosting strategy and a dendritic network, this paper proposes a novel ensemble prediction model, named gradient boosting dendritic network (GBDD) model which can reduce the forecast error by learning the relationship between forecast residuals and meteorological factors during the training of sub-models by means of a greedy function approximation. Unlike other machine learning models, the GBDD proposed is able to make fuller use of all meteorological factor data and has a good model interpretation. In addition, based on the structure of GBDD, this paper proposes a strategy that can improve the prediction performance of other types of prediction models. The GBDD is trained by analyzing the relationship between prediction errors and meteorological factors for compensating the prediction results of other prediction models. The experimental results show that the GBDD proposed has the benefit of achieving a higher PV power prediction accuracy for PV power generation and can be used to improve the prediction performance of other prediction models.

The 2024 Nobel Prize in Chemistry was recently granted to David Baker, Demis Hassabis and John M. Jumper, renowned for their pioneering works in protein design.

This study presents a metamaterial-inspired design to develop negative Poisson's ratio (NPR) structural electrodes using a directional freezing 3D printing-assisted strategy. This approach incorporates both macroscopic NPR structures and microscopic directional porous structures, which enhances ion transport, improves compressibility and provides impact resistance, effectively preventing package bulges during compression. Consequently, the electrodes demonstrate a high 50% compressible deformation and recover their original state even after 50 cycles of 25% compression. The 3D-printed lithium iron phosphate cathodes deliver a high average specific capacity of 153 mAh/g over 100 cycles and exhibit outstanding rate capability. Furthermore, the assembled full cell maintains both excellent compressibility and impact-buffered resistance, highlighting its potential applications. This innovative design of NPR metamaterial-structured electrodes provides a universal platform for developing the next generation of impact-buffered, compressible structural batteries.

Researchers from the Faculty of Engineering at The University of Hong Kong (HKU) have made a significant discovery regarding quantum entanglement. This phenomenon, which has long been viewed as a significant obstacle in classical quantum simulations, actually enhances the speed of quantum simulations and acts as a valuable resource. The groundbreaking findings have been featured as the Cover Story in the prestigious journal Nature Physics in an article titled “Entanglement accelerates quantum simulation”.