Hong Chaosheng, an academician of the Chinese Academy of Sciences, was one of the pioneers in China's semiconductor physics research and cryogenic undertakings, as well as a distinguished experimental physicist in the international academic community. Based on detailed historical materials and specific case analyses, this paper explores the profound influence of his family scholarly background and academic mentorship on moral character, professional competence, academic attainment and academic vision. It presents Hong Chaosheng's important academic achievements in the fields including low–temperature impurity conduction in germanium single crystals, and demonstrates his personal charm of being sincere, plain and indifferent to fame, alongside his spirit as a scientist embodied in rigorous scholarship, patriotism and dedication.

Air oxygen oxidation cross coupling is one of the most important and economical coupling reactions, using air as a oxidant replacing external oxidants can construct C—C and C—X (X=N, O, S or P) bonds through oxidative coupling of C−H bonds, which can effectively introduce new functional groups into heterocyclic compounds and have practical value for the development of drugs, agricultural chemicals, and functional materials. This paper reviews the research progress of air oxygen−mediated cross−oxidative coupling reactions of C—H bonds, with a particular focus on the synthetic systems of nitrogen−containing heterocyclic compounds. It elaborates novel strategies for the construction of C—C, C—N, and C—P bonds in heterocyclic compounds under transition−metal catalysis (e.g., nickel, palladium, copper, and iron), as well as metal−free and photocatalytic systems. The reaction characteristics, substrate applicability, and reaction mechanisms of various catalytic systems are summarized, and the research achievements including rare−earth−catalyzed coupling reactions between heterocycles and ethers as well as the novel amination reaction via aldehyde group exchange are also outlined. The environmentally friendly air oxidation reaction is expected to be further applied in industrial production.

With the rapid development of the electric vehicle industry, higher requirements are imposed on the safety, energy density, and service life of power batteries. However, occasional thermal runaway incidents reveal limitations of traditional battery management systems in early fault warning and active protection. Intelligent battery technology therefore emerges as a promising solution. By integrating multi−source sensors, edge computing units, and intelligent algorithms, this technology enables a transition from passive protection to active safety management. This study focuses on thermal runaway in electric vehicles and systematically analyzes key technologies of intelligent batteries from three perspectives, including intrinsic material safety, manufacturing and sensing systems, and intelligent management strategies. The discussion covers the development of high energy density and high−safety materials, long−life material optimization, precision manufacturing processes, and intelligent sensing architectures. In addition, the critical functions of intelligent battery management systems in fault diagnosis, state prediction, and adaptive optimization are clarified. Future development of intelligent battery technology advances toward higher intelligence and enhanced sustainability, providing safe and efficient energy solutions for electric vehicles.

Climate change has intensified extreme rainfall events and increased urban pluvial flooding risks, revealing the inadequacies of traditional drainage infrastructure. Deep tunnel sewer system (DTSS), as a kind of strategic resilient infrastructure, offers an important engineering pathway for enhancing drainage and flood−control capacity in high−density built−up areas of megacities and large cities through rapid conveyance, large−scale storage, peak attenuation, and environmental improvement. International practices indicate that deep tunnels can alleviate shallow drainage network bottlenecks under suitable hydrological, hydraulic, and operational conditions, while generating co−benefits in overflow pollution control, water environment improvement, and intensive land use. Domestic practices in Guangzhou, Shanghai, Wuhan, and other cities further demonstrate the potential of DTSS. Over a review of existing practices, this article analyzes the mechanisms and comprehensive benefits of DTSS, and identifies main issues in planning, construction, and operations of DTSS. It is argued that although DTSS cannot universally replace conventional drainage facilities, they serve as a strategic infrastructure backbone for urban areas featuring high density, intensified flood risk and substantial asset exposure to flood. Policy recommendations are proposed in terms of watershed−scale planning and design, investment and financing mechanisms, as well as construction and operation standards.

With the accelerated process of global industrialization and the widespread use of chemicals, emerging contaminants (ECs), characterized by their great variety, complex environmental behavior, and potential ecological and health risks, have gradually become a critical issue requiring urgent attention in the field of environmental science. However, existing detection technologies still have limitations in terms of coverage, sensitivity, and risk characterization capacity, making it difficult to meet the practical demands for the identification and assessment of ECs. This paper aims to improve the capability for detecting ECs and elucidating their associated risks by systematically reviewing the recent advances and future trends in relevant detection technologies; in particular, it summarizes the latest breakthroughs in key techniques such as pretreatment of complex environmental samples, chromatographic separation, and mass spectrometric detection, reviews data analysis strategies combining targeted analysis, suspect screening, and non−target analysis, and discusses the application of sensor technologies in rapid detection; the important roles of bioeffect−based detection methods and effect−directed analysis in pollutant identification and risk assessment are elaborated, and a novel big−data−driven detection paradigm is also explored; on this basis, this study systematically analyzes the core challenges currently faced in the detection of ECs, focusing on key aspects such as the structural confirmation of unknown pollutants, effect attribution, exposure characterization, and risk assessment, with an emphasis on constructing a comprehensive evidence chain and supporting scientific decision−making; future trends in ECs detection technologies are prospected, particularly toward the integration of multiple techniques, intelligent development, and standardization, with a view to providing a theoretical basis and technical support for the monitoring and scientific management of ECs.

Brain−computer interface (BCI) is a frontier technology that enables bidirectional information exchange between neural tissues and external devices. Broadly speaking, the term "neural tissue" includes not only the human brain itself but also artificially constructed neural tissue models, such as in vitro brain organoids. Through precise sensing and dynamic feedback of neural structural and functional states, BCI is becoming an important technological pathway for advancing experimental paradigms in neuroscience and exploring brain−inspired intelligence. However, current in vitro brain organoid culture still relies on manual operations and therefore suffers from limited stability and consistency; meanwhile, mainstream BCI technologies are still largely restricted to single−modality data acquisition and processing, making it difficult to achieve multidimensional analysis of complex neural network activity. To address these issues, this paper proposes a high−throughput multimodal BCI platform integrating four core modules: closed−loop fully automated brain organoid culture, neuroelectrophysiological interaction, in situ gene detection, and optical imaging. We systematically describe the key technologies and system−integration strategies of this platform, analyze the strengths and bottlenecks of each module, propose feasible paths for technical fusion, and further discuss its potential applications in brain−inspired computing, mechanistic studies of brain diseases, drug screening, and precision medicine.