A groundbreaking study led by researchers at Huazhong University of Science and Technology (HUST) has developed a high-performance near-infrared (NIR) computational spectrometer using finely-tuned lead sulfide (PbS) quantum dots (QDs). This innovation, published in Nano Research, achieves a spectral resolution of 1.5 nm, making it a powerful tool for applications ranging from qualitative material identification to quantitative alcohol content measurement in liquor. The study highlights the critical role of QD monodispersity and precise synthesis in enhancing spectrometer performance, paving the way for portable, low-cost NIR spectrometers in industrial and consumer applications.

In a groundbreaking study published in Nano Research, researchers from Beijing Normal University (Zhuhai) and the University of Wollongong have developed a novel catalytic system that significantly enhances the efficiency of hydrogen oxidation reactions (HOR) in alkaline media. This advancement could pave the way for more efficient and durable anion exchange membrane fuel cells (AEMFCs), a critical component in the transition to clean energy technologies.

Hydrogen fuel cells are a promising alternative to fossil fuels, offering a clean and renewable energy source. However, the efficiency of these cells is often limited by the sluggish kinetics of the hydrogen oxidation reaction, particularly in alkaline environments. Platinum (Pt) is the most effective catalyst for HOR, but its performance is hindered by high hydrogen adsorption binding energy (HBE) and insufficient hydroxyl adsorption energy (OHBE). This study addresses these challenges by introducing a new catalytic system that balances HBE and OHBE, thereby improving the overall efficiency of the reaction.

Water pollution caused by nitrite (NO₂⁻) from agricultural runoff and industrial discharge presents significant challenges to ecosystem health and human wellbeing. Innovative water treatment technologies are essential for addressing this growing environmental concern. A new cobalt-iron layered double hydroxide decorated on 3D titanium dioxide arrays (TiO₂@CoFe-LDH/TP) shows promise as an effective electrocatalyst for nitrite reduction, offering a practical approach to converting harmful pollutants into valuable ammonia while minimizing unwanted byproducts during the electrochemical process.

Single-atom cobalt catalysts have been recognized as promising alternatives to natural enzymes. However, their relatively low catalytic activity greatly limits their further application. Herein, Single cobalt sites immobilized on defective carbon nanosheets (2D Co-CN(H)) can act as efficient oxidase mimics with high atom utilization efficiency. In particular, the 2D Co-CN(H) catalysts are found to be twice as effective as defect-free Co-CN catalysts. Combined experimental and theoretical analyses reveal that the defects around atomic cobalt sites can rationally regulate the electronic distribution, significantly promoting the cleavage of O-O bonds and thus improving their oxidase-like performance. Taking advantage of the excellent oxidase-like activity of 2D Co-CN(H) catalysts and the good photothermal properties of oxTMB, an innovative dual-mode colorimetric-photothermal sensing platform toward effective discrimination and detection of dihydroxybenzene isomers has been successfully constructed. This study not only highlights the important role of defects on the oxidase-like activity of single-atom nanozymes, but also broadens their potential applications in environmental conservation.

In a leap forward for legume crop research, scientists have assembled a high-quality reference genome for 'D30', an ancient landrace of pigeonpea. 

With its ageing population, the prevalence of dementia in Hong Kong is expected to increase. Healthcare researchers from The Hong Kong Polytechnic University (PolyU) have developed an innovative mobile application-based cognitive remediation programme, Enhancing Memory in Daily Life (E-MinD Life), targeting those who experience memory complaints, mild cognitive impairment (MCI) and mild dementia. The programme, leveraging memory encoding strategies, provides early intervention to delay the functional deterioration associated with cognitive decline, supporting these individuals’ independent living and enhancing their quality of life.

Natural enzymes are highly efficient catalysts with strong substrate specificity, making them ideal for biomedical applications. However, they often face issues such as variability, high costs, challenging preparation processes, and difficulties in large-scale production. This has led to significant efforts in developing effective nanoenzymes and exploring their application potential. In recent years, carbon dots (CDs) have gained attention due to their strong fluorescence, excellent biocompatibility, and low cytotoxicity. Cationic CDs, which possess a positively charged surface, have shown the ability to mimic natural enzyme applications. The positive charge on the surfaces of these nanomaterials significantly influences their fluorescence, biological activity, and interactions with other biomolecules. Therefore, understanding how surface charge affects the performance of CDs is crucial for enhancing their usability. Considerable progress has been made in the design, synthesis, and mechanistic research of enzyme-like cationic CDs, as well as their advanced applications. This article reviews the latest research on the design structure, catalytic mechanisms, biosensing capabilities, and biomedical applications of enzyme-like cationic CDs. First, we review the synthesis strategies for cationic CDs and how surface charge influences their physical and chemical properties. Next, we highlight various applications of these cationic CDs, demonstrating their use in areas such as detection, biomedical applications (including antibacterial agents, gene carriers, and therapeutic agents), catalysis, and more. Finally, we discuss the challenges and obstacles faced in the development of cationic CDs and look forward to exploring new applications in the future.

Researchers from Sun Yat-sen University’s Shenzhen Campus, led by WenYuan Yang and Gege Jianga, have developed a decentralized federated learning framework, DFUN-KDF, to enhance UAV network efficiency. By leveraging federated knowledge distillation, it reduces data transmission by up to 99% while addressing model heterogeneity. A robust filtering mechanism ensures stability by eliminating faulty or malicious data. DFUN-KDF outperforms traditional methods in communication energy efficiency, adaptability, and resilience to node failures and attacks. This scalable solution offers significant potential for large-scale UAV deployments in urban management and logistics.

Accurate and efficient traffic speed prediction is crucial for improving road safety and efficiency. With the emerging deep learning and extensive traffic data, data-driven methods are widely adopted to achieve this task with increasingly complicated structures and progressively deeper layers of neural networks. Despite the design of the models, they aim to optimize the overall average performance without discriminating against different traffic states. However, the fact is that predicting the traffic speed under congestion is normally more important than the one under free flow since the downstream tasks, such as traffic control and optimization, are more interested in congestion rather than free flow. Unfortunately, most of the state-of-the-art (SOTA) models do not differentiate the traffic states during training and evaluation. To this end, we first comprehensively study the performance of the SOTA models under different speed regimes to illustrate the low accuracy of low-speed prediction. We further propose and design a novel Congestion-Aware Sparse Attention transformer (CASAformer) to enhance the prediction performance under low-speed traffic conditions. Specifically, the CASA layer emphasizes the congestion data and reduces the impact of free-flow data. Moreover, we adopt a new congestion adaptive loss function for training to make the model learn more from the congestion data. Extensive experiments on real-world datasets show that our CASAformer outperforms the SOTA models for predicting speed under 40 mph in all prediction horizons.