Significance: We describe a physical mechanism capable of achieving simultaneous mixing and focused enrichment in hydrothermal pore microenvironments. Microscale chaotic advection established in response to a temperature gradient paradoxically promotes bulk homogenization of molecular species, while at the same time transporting species to discrete targeted locations on the bounding sidewalls where they become highly enriched. This process delivers an order of magnitude acceleration in surface reaction kinetics under conditions naturally found in subsea hydrothermal microenvironments, suggesting a new avenue to explain prebiotic emergence of macromolecules from dilute organic precursors—a key unanswered question in the origin of life on Earth and elsewhere.

With the development of the information society, display technologies are evolving toward greater flexibility and advanced performance. Dye-based polyvinyl alcohol (PVA) complex films have gained widespread attention for their excellent resistance to high temperature and humidity. This review systematically summarizes the research progress of dye-based polarizers using PVA as the substrate, focusing on their preparation principles, film properties, and impacts on optical performance. Strategies to enhance optical properties and durability are discussed, including dye molecular optimization, formulation design, control of the dyeing process, and PVA substrate film modification. Notably, improving interfacial interactions between dyes and PVA enhances molecular orientation and stability, while PVA modification improves mechanical properties, water resistance, thermal stability, and flame retardancy. By demonstrating these enhanced comprehensive properties, this review highlights the potential of PVA–based films to serve as high-performance platforms for the development of next-generation multifunctional optical and display materials. Finally, the challenges and development directions of dye-based PVA complex films for optical applications in harsh environments are prospected. This review provides a theoretical basis and technical pathway for the design and development of next-generation high-performance composite polarizers.

Recent advances in computational chemistry, machine learning, and large-scale virtual screening have rapidly expanded the accessible chemical space, increasing the need for interpretable molecular representations that capture the hierarchical topological structure of molecules. Existing formats, such as Simplified Molecular Input Line Entry System (SMILES) strings and MOL files, effectively encode molecular graphs but provide limited support for representing the multi-level structural information needed for complex downstream tasks. To address these challenges, we introduce HiFrAMes, a novel graph–theoretic hierarchical molecular fragmentation framework that decomposes molecular graphs into chemically meaningful substructures and organizes them into hierarchical scaffold representations. HiFrAMes is implemented as a four-stage pipeline consisting of leaf and ring chain extraction, ring mesh reduction, ring enumeration, and linker detection, which iteratively transforms raw molecular graphs into interpretable abstract objects. The framework decomposes molecules into chains, rings, linkers, and scaffolds while retaining global topological relationships. We apply HiFrAMes to both complex and drug-like molecules to generate molecular fragments and scaffold representations that capture structural motifs at multiple levels of abstraction. The resulting fragments are evaluated using selection criteria established in the fragment-based drug discovery literature and qualitative case studies to demonstrate their suitability for downstream computational tasks.

The introduction of advanced functional polymer films has been used in wide applications, such as fresh food, electronic devices, and agricultural fields.[1–3] The production of polymer films for d...

In scientific documents, molecule structure information is usually delivered by chemical structure images. Although convenient for human interpretation, the image‐based molecule structure depiction is not a machine‐readable format, blocking productivity improvements in many fields including chemical data mining and drug discovery. Molecule structures can be modeled as graphs with atoms as nodes and bonds as edges. Following this intuitive modeling, optical chemical structure recognition (OCSR) can be achieved by first detecting individual atoms and bonds and then assembling into a graph. However, the challenges in decision ambiguity due to false positives and spatial proximity during graph assembly is rarely explained and explicitly addressed. In this work, we present a rule‐based probabilistic OCSR model to explain and tackle the ambiguity challenges in graph assembly. We developed a novel line detection algorithm for precise bond line identification, and designed a candidate mechanism with probabilistic graph to resolve atom/bond ambiguity. The proposed model is evaluated with popular large image datasets and achieved outperforming recognition accuracy compared to state‐of‐the‐art solutions.