In a paper published in Polymer Science & Technology, an international team of scientists developed a multifunctional conductive hydrogel (P-EPL/CCT) hydrogel flexible sensor using a physical-chemical dual cross-linking approach involving poly(vinyl alcohol) (PVA), gallic acid grafted chitosan (CS−GA), tannic acid (TA), eggshell membrane (ESM), lysozyme, and 4am-PEG-MAL for emergency cooling and wound healing following fire-works-related skin burns. The organohydrogel sensor exhibits impressive mechanical properties, such as a maximum stress of 2.15 MPa and an elongation of 605%, along with antifreeze resistance down to −39.5 °C, antimicrobial properties with bacterial inhibition exceeding 96.5%, and cytocompatibility. Additionally, serving as strain sensor with high sensitivity (GF = 1.14 at 100% strain) and rapid response times, it can effectively monitor human movement signals. The developed organohydrogel demonstrates the ability to accelerate skin healing, promote angiogenesis, and reduce scarring. Moreover, it is utilized for monitoring finger joint injuries and employing machine learning-assisted electrical signals for intelligent wound healing monitoring and protection. This study introduces a flexible device that combines multiple functionalities, showing promise for diverse applications in the biomedical field. This study is led by Chuang Du (Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China), Weiwei Liu (Stomatological Hospital, Jilin University, Changchun 130021, China) and Lei Wang (Key Laboratory of Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Changchun 130023, China).

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.