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.

Developing environmentalyl friendly and energy-efficient CO₂ adsorbents for post-combustion capture is a critical step toward achieving toward carbon neutrality. While aqueous amines and metal oxides have play pivotal roles in CO₂ capture, their application is limited by issues such as secondary pollution and high energy consumption. In contrast, Zn-based metal-organic frameworks (Zn-based MOFs) have emerged as a green alternative, offering low toxicity reduced regeneration temperatures, and high efficiency in both CO₂ adsorption and catalytic conversion into valuable fuels and chemicals. This mini review begins with a general introduction to MOFs in CO₂ capture and conversion, followed by an overview of early studies on Zn-based MOFs for CO₂ capture. It then summarizes recent research advancements in Zn-based MOFs for integrated CO₂ capture and conversion. Finally, it discusses key challenges and future research directions for post-combustion CO₂ capture and conversion using Zn-based MOFs.

Human cognition relies on the seamless integration of multiple senses, allowing the brain to associate, infer, and even imagine across modalities. Replicating this capability in artificial systems has long remained a challenge, particularly under strict energy constraints. This study presents a bioinspired multisensory framework that integrates vision, touch, hearing, smell, and taste within a self-powered architecture. By enabling cross-modal association and adaptive reconfiguration, the system allows one sensory input—such as touch or sound—to trigger corresponding representations in other sensory domains. Beyond conventional recognition, the framework demonstrates higher-level cognitive functions, including inference and generative pattern creation. These advances point toward a new generation of intelligent machines capable of human-like perception and cognition.

Achieving both high efficiency and high selectivity remains a central challenge in catalytic hydrogenation reactions, as rapid reactant activation often leads to overly strong binding of intermediates and unwanted over-reduction. A new photocatalytic strategy now demonstrates how this long-standing trade-off can be overcome. By harnessing nonequilibrium charge carriers generated under visible light, the system promotes efficient hydrogen dissociation while steering the reaction toward selective semihydrogenation. Active hydrogen species migrate away from their formation sites to neighboring catalytic surfaces, where product formation and desorption are energetically favored. This spatial separation of reaction steps enables near-complete conversion under ambient conditions while maintaining excellent selectivity, offering a powerful new design principle for advanced catalytic systems.