The American Chemical Society (ACS) Fall 2025 meeting, held from August 17 to 21 in Washington, DC, has proven to be a monumental event in the scientific community. As the largest international academic event in the field of chemistry, it has drawn researchers, academics, and industry leaders from across the globe, all converging to discuss the latest advancements and challenges in chemistry and its multidisciplinary applications.

Text mining has emerged as a powerful strategy for extracting domain knowledge structure from large amounts of text data. To date, most text mining methods are restricted to specific literature information, resulting in incomplete knowledge graphs. Here, we report a method that combines citation analysis with topic modeling to describe the hidden development patterns in the history of science. Leveraging this method, we construct a knowledge graph in the field of Raman spectroscopy. The traditional Latent DirichletAllocation model is chosen as the baseline model for comparison to validate the performance of our model. Our method improves the topic coherence with a minimum growth rate of 100% compared to the traditional text mining method. It outperforms the traditional text mining method on the diversity, and its growth rate ranges from 0 to 126%. The results show the effectiveness of rule-based tokenizer we designed in solving the word tokenizer problem caused by entity naming rules in the field of chemistry. It is versatile in revealing the distribution of topics, establishing the similarity and inheritance relationships, and identifying the important moments in the history of Raman spectroscopy. Our work provides a comprehensive tool for the science of science research and promises to offer new insights into the historical survey and development forecast of a research field.

Researchers demonstrated the first real-world integration of quantum key distribution (QKD) and high-capacity classical communication over field-deployed multi-core fibers. By optimizing wavelength allocation and transmission directions, they suppressed inter-core Raman noise and achieved secure key generation alongside 110.8 Tbps classical data transmission. A validated theoretical model further guides scalable quantum–classical coexistence. This work paves the way for future secure, high-throughput communication networks integrating quantum and classical systems over shared infrastructure.

In order to improve the diagnostic accuracy of deep-learning AI algorithms, models require larger amounts of high-quality training data, which presents a significant burden for pathologists or radiologists that diagnose disease based on images composed of billions of pixels. Researchers have developed a method to imitate the expertise of pathologists by tracking their eye movements while diagnosing whole slide images. This data helps scientists train AI models to more accurately identify regions of interest and better classify tissue samples based on the behavior of highly experienced and trained professionals with little to no additional burden placed on these providers.

Overcoming the limits between operational bandwidth, aperture size, and numerical aperture, while expanding their potential in advanced applications, has been a main focus of research. At the same time, with growing demand for better light control, metalenses are gradually moving toward system-level designs. If a single metalens is like a skilled solo player performing in specific situations, then a group of metalenses working together is like a well-practiced orchestra, able to achieve more complex and flexible control of light. In this context, recent progress in metalens technology follows two main paths: one is the ongoing improvement and expanded functions of single metalenses; the other is the continuous development and new applications of multi-metalens systems.

Nanoimprint Lithography (NIL), first introduced in the 1990s by Professor Stephen Y. Chou at the University of Minnesota (later Princeton University), is a novel nanofabrication technology noted for its advantages in low cost, high resolution, and high throughput. The working principle involves directly imprinting mold patterns into polymeric materials, which are either cooled before demolding for thermoplastics or UV cured or thermal set for crosslinkable precursors to precisely replicate nanoscale features. With rapid advancements in science and industry, the demand for precise and efficient fabrication of semiconductor devices, optical components, and biomedical devices has significantly increased, making NIL an indispensable manufacturing method. The year 2025 marks the 30th anniversary of NIL. Through three decades of global efforts, NIL has emerged as the primary alternative to extreme ultraviolet (EUV) lithography for deep-nanoscale silicon electronics. Many semiconductor companies have recognized NIL's manufacturing quality and are actively evaluating its capability in producing advanced semiconductor devices. Moreover, with its high throughput and 3D patterning capabilities, NIL is becoming a key technology for emerging applications such as flat optics and augmented reality glasses, opening new avenues for material research and novel applications.

A recent eGastroenterology review by Professor Intissar Anan highlights major breakthroughs in transthyretin amyloidosis (ATTR) management. Once a relentlessly progressive disease with limited treatment options, ATTR now benefits from targeted therapies including TTR stabilisers, RNA-based gene silencers, and emerging CRISPR-Cas9 gene editing. Advances in diagnostics, particularly non-invasive cardiac imaging, have improved early detection, enabling earlier intervention. Novel monoclonal antibodies aim to remove existing amyloid deposits, offering hope for advanced cases. Despite progress, challenges remain—cost, access, optimal therapy sequencing, and long-term safety require attention. The evolving therapeutic landscape signals a transformative shift towards personalised, multi-modality care for ATTR patients.