Pre-eclampsia is one of the main causes of death in pregnant women. Researchers from China conducted a large multicenter retrospective study involving 53,054 singleton pregnancies in China and identified that elevated blood glucose levels in the first trimester and significant gestational blood glucose fluctuations increase the risk of pre-eclampsia in a U-shaped pattern. These findings provide new evidence for optimizing gestational glucose monitoring strategies, highlighting the need for enhanced blood glucose management.  

With the rapid development of flexible electronics and wearable devices, there is an increasing demand for absorbing materials that exhibit high electromagnetic wave absorption efficiency and mechanical flexibility. In this work, we present a novel and effective strategy for fabricating flexible microwave absorbers via synergistically integrating a polymer framework with conductive fillers. Carbon dots (CDs) functionalized with catechol groups were first synthesized via a bottom-up approach using directed ultrasonication, employing tannic acid dispersed in acetone as the precursor. Owing to the abundant surface functional groups, the as-prepared CDs were uniformly anchored onto MXene nanosheets through chemical bonding, forming a well-dispersed MXene/CDs composite. Subsequently, this hybrid filler was incorporated into a polymer network composed of polyvinyl alcohol (PVA) and acrylamide, with water and glycerol as co-solvents, to fabricate a flexible MXene/CDs organic hydrogel. Notably, glycerol plays an important role in adjusting the polarity of the gel system, thereby effectively optimizing impedance matching. Meanwhile, the abundant heterogeneous interfaces between MXene and CDs significantly enhanced interfacial polarization, and the synergistic coupling of optimized impedance matching with strong dielectric loss endowed the MXene/CDs organic hydrogel with outstanding electromagnetic wave absorption performance. The absorber achieves a minimum reflection loss (RL_min) of − 47.9 dB at 9.46 GHz with a matching thickness of 3.1 mm, along with an effective absorption bandwidth of 3.5 GHz. Furthermore, the synergistic reinforcement of dual-crosslinked polymer chains and the MXene/CDs filler endowed the hydrogel with excellent mechanical robustness, making it suitable for flexible and wearable absorption applications.

About ENGINEERING Energy

  ENGINEERING Energy, formerly known as Frontiers in Energy, is a peer-reviewed English academic journal dedicated to energy research. Jointly sponsored by Higher Education Press, the Chinese Academy of Engineering, and Shanghai Jiao Tong University, and co-published by Higher Education Press and Springer Nature, the journal was launched in 2007 and is the energy-focused sub-journal within the journal cluster of the flagship journal Engineering of the Chinese Academy of Engineering. It focuses on frontier, innovative, and interdisciplinary research in energy science and technology, aiming to build a high-level international academic exchange platform for global researchers, engineers, and policymakers to share high-quality original findings.

  The Editors-in-Chief are Academician Zhen Huang, Academician Shouwei Zhou, Academician Yinao Su, and Academician Suping Peng of the Chinese Academy of Engineering. The Deputy Editors-in-Chief are Academician Jiujun Zhang (Foreign Member of CAE; Fellow of the Royal Society of Canada and the Canadian Academy of Engineering), Professor Radenka Maric (President, University of Connecticut, USA), and Professor Nicolas Alonso-Vante and Professor Yonglin Ju of Shanghai Jiao Tong University.

As intrinsically carbon-free molecules, ammonia and hydrogen are considered as fuels for internal combustion engines, mainly for long-distance or off-road applications. These alternative fuels have different combustion characteristics, reactivity, and exhaust gas compositions compared to conventional fuels, raising questions about the suitability of lubricants in engines operating with them. The impact of ammonia, hydrogen, and their blends on lubricants in internal combustion engines is a relatively new topic, with few reference studies available. However, degradation processes of lubricants have been studied in the context of hydrocarbon fuels, and in compressors using ammonia as a refrigerant, for example. This work presents a review of the literature on engine oil degradation phenomena in relation to ammonia and hydrogen combustion characteristics. In particular, it highlights the current state of knowledge regarding compatibility with unburnt gases, elevated nitrogen oxide levels, and water. Additionally, it summarizes the latest insights into the contribution of lubricants to pollutant emissions.

With the growing emphasis on sustainable development, the demand for environmentally friendly solvents in green chemical processes and carbon dioxide capture is increasing. Ionic liquids (ILs), as promising green solvents, offer significant potential but face considerable challenges, particularly in solvent selection. To overcome the limitations of traditional screening methods, machine learning (ML) techniques have recently been applied, offering a more efficient and data-driven approach. This review provides an overview of key ML methods used in solvent screening and compares them with traditional experimental and theoretical techniques. It examines the role of descriptor selection in structure—property-based methods, such as quantitative structure-activity relationships (QSAR) and quantitative structure—property relationships (QSPR), which are critical for predicting IL properties. The review also explores the application of these methods to screen IL properties, including toxicity, viscosity, density, and CO₂ solubility. Additionally, it discusses challenges in selecting appropriate models based on data scale and task complexity, integrating physical information for model interpretability, and achieving multi-objective optimization to balance key properties in ionic liquid (IL) design. Finally, it summarizes the achievements, limitations, and prospects of ML applications in ILs research, offering insights into how these methods can advance the development of sustainable ILs.