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.

Artificial intelligence is driving materials research toward a new data−driven paradigm of AI for Materials (AI4M), with data being a key foundation for the success. Both the Genesis Mission program of U.S. and the AI for Science Strategy of U.K. identify the collaborative operation of high−quality research datasets and supercomputing as national strategic infrastructure. China's Overall Construction Plan for the New Materials Big Data Center also clearly aims to integrate national data resources to accelerate materials research using AI. Currently, the lack of high−quality, AI compatible "ready−to−use" data is considered as the biggest bottleneck in AI4M. It should be recognized that datasets are only a component of a larger whole of materials information since any dataset merely reflects the scientific understanding of its creators. Limitations and bias are inevitable. The datasets that are effective today may not necessarily be so in the future. This paper systematically analyzes new requirements that AI imposes on the content and form of materials data, and proposes a future−oriented materials data infrastructure. It should be structured like a "tree" with a comprehensive information data pool as the root and high−quality datasets/corpora as the leaves, which allows for sustainable evolution. By standardizing root resources in advance, raw data can be reused and repurposed to meet the evolving needs of specialized models and general large language models. A modular and assemblable data model is designed to address the standardization challenges caused by the diversity and variability of material data. Building a national−level materials big data infrastructure is a long−term endeavor, crucial to the future development of China's new materials industry. Forward−looking planning is required—not only to meet the current needs of creating quality datasets, but more importantly, to aim on potential future demand on data sources, leaving enough room for development in the years to come. Therefore, material data infrastructures with public welfare and service attributes should have an 'integrated pool and warehouse' structure, catering to both datasets and comprehensive materials information pools, to balance the present with the future.

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.

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.

Bioaerosols contain infectious and pathogenic microorganisms, emitting from hospitals, waste treatment plants, livestock farms and so on, so the workers in these situations face certain health risks due to long−term exposure to high concentrations of bioaerosols. By systematically reviewing recent literatures on the health risk assessment of bioaerosols, this study collates and summarizes the existing assessment methods health risks posed by bioaerosols. Several critical issues are identified, including inconsistent calculation methods, indiscriminate adoption of assessment methods for chemical substances, lack of localized exposure parameters, and absence of reference exposure doses−effect relationships. These deficiencies result in the lack of scientific rationality in bioaerosol health risk assessments, leading to difficult comparisons. Based on the current research, this review proposes that health risk assessment of bioaerosols should unify the calculation methods, adopt local exposure parameters, study the corresponding dose−effect relationship, derive the local reference exposure dose (RfD), and accelerate the formulation of relevant standards, which is helpful to accurately assesses the health risks of bioaerosols.

Air pollution, as the foremost environmental health risk to global sustainable development, continues to exacerbate the associated disease burden amid population aging and rapid urbanization. This paper systematically examines the spatiotemporal evolution patterns, the transformation characteristics of health risks, and the governance paradigm shift required as China’s air pollution control transitions from "rapid improvement" to a "challenging phase". The study finds that since the implementation of the Air Pollution Prevention and Control Action Plan, although the annual average PM_2.5 concentrations in key regions have significantly declined, governance now faces new challenges such as the exacerbation of combined PM_2.5 and ozone pollution, and the limitations of traditional monitoring methods in capturing precise individual exposure. Mechanistic research indicates that ultrafine particles (UFPs) can cross multiple biological barriers and, by activating the hypothalamic−pituitary−adrenal (HPA) axis, induce systemic inflammation and neuroendocrine disorders, leading to a shift in health risks toward more "insidious" and "systemic" forms. Confronted with these challenges, China's governance system urgently needs to transition from the 1.0 stage centered on "pollutant concentration control" to a 2.0 stage centered on "population health risk prevention and control". To this end, this paper proposes the establishment of an Intelligent Health Risk Governance framework (IHRG) integrating "pollution−exposure−disease" early warning and collaborative governance. By incorporating holistic sensing networks, exposomics big data, and AI−driven digital twin mapping technologies, a multi−level smart governance pathway covering "national−regional−individual" dimensions can be established. This aims to provide scientific support for the systematic prevention and control of environment−related diseases, and for the coordinated advancement of the "Beautiful China" and "Healthy China" initiatives, while also offering insights for other countries facing similar challenges.

Wastewater−based epidemiology (WBE) has become a key public health surveillance tool. In China, its use has expanded from illicit drugs to emerging contaminants, pathogen warning, and population health assessment. However, uncertainties and ethics remain challenges. This review synthesizes WBE progress in China concerning drug abuse, emerging contaminants risks, infectious diseases early warming, and population health. Proposes a future framework aligned with the 15th Five−Year Plan: network expansion with standardized sample banks, AI−driven data integration, and enhanced early warning and antimicrobial resistance surveillance. This framework aims to support future application of WBE in China.

Microplastics, as a new pollutant widely present in global aquatic environments, have drawn significant attention for their potential ecological risks. Simultaneously, algae serve as vital primary producers in aquatic ecosystems and readily undergo heterogeneous aggregation with microplastics. This interaction alters the environmental behavior of microplastics, ultimately influencing their ecological effects. This paper provides a systematic review of the research progress on the heteroaggregation between microplastics and algae, focusing on experimental methods, formation mechanisms, migration behaviors, and key influencing factors. Existing studies mainly employ microscopic characterization techniques and theoretical modeling to elucidate the structural features and formation processes of aggregates. The heteroaggregation between microplastics and algae is a dynamic process jointly driven by physical adsorption, chemical bonding, and biological secretion. The formation of aggregates changes the density, surface properties, and occurrence state of microplastics, consequently influencing their vertical sedimentation and horizontal transport in water bodies. The aggregation and migration behaviors of microplastics and algae is primarily influenced by microplastic characteristics, algal properties, and environmental factors. Future research should strengthen the coupled modeling of biological, physical, and chemical processes and develop in−situ, multi−scale observation techniques to deepen the understanding of microplastic–algae interaction mechanisms, thereby providing a scientific basis for risk assessment and ecological management of microplastic pollution in aquatic ecosystems.

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.

Solid−state lithium battery technology is an emerging field expected to overcome the current development bottlenecks of lithium−ion batteries and achieve "revolutionary innovation", which represents a critical area for major nations competing for dominance in "next−generation lithium battery technology". By analyzing global patent trends in solid−state lithium batteries, we can effectively grasp the international competitive landscape, identify key technological barriers and development opportunities, thereby providing support for the high−quality development and high−level security of China's related technologies and industries. Based on retrieval results from the IncoPat patent database, this paper integrates text mining with KeyBERT algorithm, CiteSpace visualization analysis, and Cooperative Patent Classification (CPC) analysis to conduct an in−depth investigation from perspectives of patent quantity, quality, and technology themes. The results indicate that patents in this field primarily originate from China, Japan, the United States, and the republic of Korea. China holds an absolute leading position in the quantity of technological achievements, but there remain deficiencies in technological integration and technological impact. The scope of legal protection remains relatively narrow, and the stability of patent rights needs to be improved. Furthermore, the patent deployment of Chinese entities is largely restricted to the domestic market, with limited adequacy in international patent deployment and constrained influence over the global market. The research focus and development pathways of major countries exhibit distinct priorities. Finally, recommendations for promoting the technological innovation and development of solid-state lithium battery in China are put forward from three aspects: clarify the directions of key technological breakthroughs and secure strategic positions in technological competition; strengthen patent deployment and standard formulation to enhance international discourse power; expand cooperation and exchange to aggregate innovation resources.

The Beijing Spectrometer Ⅲ (BESⅢ) is a world−leading, multi−functional particle detector operating in the τ−charm energy region. Hosted at the Beijing Electron Positron Collider (BEPC) and its upgrade (BEPCⅡ), it serves as the flagship experimental facility for high−energy physics research in China. This study employs bibliometric methods to systematically compare the research output and international collaboration networks of BESⅢ with those of other major international high−energy physics experiments. Data were retrieved from the INSPIRE−HEP and arXiv.org databases, covering publications from 2010 to 2024. The results demonstrate that BESⅢ holds a prominent leading position internationally in the field of τ−charm physics, contributing 61% of the global publications in this area. Research output has exhibited steady growth, increasing from 321 papers in 2010 to 662 papers in 2024. In terms of high−quality journal publications, BESⅢ published 402 papers in Physical Review D (PRD), accounting for 59.7% of its total output, and 122 papers in Physical Review Letters (PRL), a volume that ranks at the forefront of comparable international experiments. Furthermore, BESⅢ published 32 papers in the domestic journal Chinese Physics C, representing 4.75% of its total publications—a proportion significantly higher than that of international experiments. The study concludes that BESⅢ has established a dominant position in τ−charm physics, which aligns closely with China's strategic goals of advancing scientific frontiers and achieving technological autonomy, providing a solid foundation for China's international competitiveness in basic science.

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.

As an emerging digital technology, generative artificial intelligence represented by DeepSeek and ChatGPT has given birth to new scenes, new formats and new markets. However, the uncertainty risk of emerging technologies has also brought the impact of traditional governance structure and paradigm, posing new challenges to governance capacity and governance system. Facing the requirements of "good governance" and combining with the theory of agile governance, this paper puts forward the framework of agile governance of generative artificial intelligence, which can provide reference for the future selection of governance concept and tool mode of generative artificial intelligence in China. Based on the theory of governance, this paper systematically analyzes the international governance practices of generative artificial intelligence in the United States and the European Union, and explores the consensus indicators and differences of international governance concepts of artificial intelligence from four aspects: governance objectives, governance subjects, governance means and governance relations. The optimization ideas of the governance of generative artificial intelligence in China were put forward, namely, following the flexible hierarchical governance principle, building a multi−agent interactive network governance relationship, and adopting proactive governance ideas, so as to adapt to the dynamic characteristics and uncertain risks of new technologies, actively promote the healthy and orderly development of generative artificial intelligence technology and industry, and realize the technological goodness.

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.