Polymerization-induced self-assembly (PISA) offers a versatile platform for designing polymeric nanoparticles. Amphiphilic gradient copolymers, characterized by a gradual transition from hydrophilic to hydrophobic segments, exhibit reduced interfacial tension and enhanced stimulus responsiveness. However, the interplay between polymerization and self-assembly in PISA, influenced by the monomer feed ratio and reactivity, remains ambiguous. Herein, we employ coarse-grained simulations to investigate the role of the effective polymerization bias between monomers. Our results reveal that the relative monomer reactivity plays a key role in determining both the copolymer sequence and the vesicle formation pathway. At low reactivity differences, comparable monomer reactivities facilitate a cooperative polymerization-assembly process that produces numerous small spherical assemblies, which subsequently merge and reorganize into vesicles. In contrast, high reactivity asymmetry favors the formation of anisotropic worm-like micelles that progressively fuse, bend, and enclose into vesicular structures. Microstructural analysis further shows that gradient copolymer vesicles possess internal cavities larger than those formed from block copolymers. These insights provide guidance for tailoring vesicle formation pathways and fine-tuning microstructures for potential applications in drug delivery and materials science.

Electrolyte materials with responsive conductive properties are highly desired in electronic and sensing technologies, which rely on the construction of ion transport channels that combine orderliness with dynamic adjustability. However, achieving such structures remains a significant challenge. In this study, we fabricate a lamellar liquid crystal electrolyte enabling deformation-responsive proton conduction. Polyoxometalate nanoclusters (POMs) and zwitterionic molecules are utilized to construct the electrolytes through a supramolecular eutectic strategy. By balancing electrostatic and hydrogen-bonding interactions, zwitterionic molecules direct lamellar POM assembly while softening the system via hydrogen-bond-induced eutectic effect. This approach ultimately results in a POM-based room-temperature liquid crystal with a unique lamellar superlattice structure. Notably, the integration of proton-conductive POMs with dynamically responsive liquid crystal channels enables a highly sensitive change in proton conductivity under deformation. These findings expand the potential applications of liquid crystal systems and provide valuable insights for the development of responsive electrolyte materials.

The study of intrinsically disordered proteins (IDPs) and their role in biomolecular condensate formation has become a critical area of research, offering insights into fundamental biological processes and therapeutic development. Here, we present IPAMD (Intrinsically disordered Protein Aggregation Molecular Dynamics), a plugin-based software designed to simulate the formation dynamics of biomolecular condensates of IDPs. IPAMD provides a modular, efficient, and customizable simulation platform specifically designed for biomolecular condensate studies. It incorporates advanced force fields, such as HPS-based and Mpipi models, and employs optimization techniques for large-scale simulations. The software features a user-friendly interface and supports batch processing, making it accessible to researchers with varying computational expertise. Benchmarking and case studies demonstrate the ability of IPAMD to accurately simulate and analyze condensate structures and properties.

PyGAMD (Python GPU-accelerated molecular dynamics software) is a molecular simulation platform developed from scratch. It is designed for soft matter, especially for polymer by integrating coarse-grained/multi-scale models, methods, and force fields. It essentially includes an interpreter of molecular dynamics (MD) which supports secondary programming so that users can write their own functions by themselves, such as analytical potential forms for nonbonded, bond, angle, and dihedral interactions in an easy way, greatly extending the flexibility of MD simulations. The interpreter is written by pure Python language, making it easy to be modified and further developed. Some built-in libraries written by other languages that have been compiled for Python are added into PyGAMD to extend it's features, including configuration initialization, property analysis, etc. Machine learning force fields that are trained by DeePMD-kit are supported by PyGAMD for conveniently implementing multi-scale modeling and simulations. By designing an advanced framework of software, graphics processing unit-acceleration achieved by the Numba library of Python and compute unified device architecture reaches a high computing efficiency.

Artificial skin is essential for bionic robotics, facilitating human skin–like functions such as sensation, communication, and protection. However, replicating a skin-matched all-in-one material with excellent mechanical properties, self-healing, adhesion, and multimodal sensing remains a challenge. Herein, we developed a multifunctional hydrogel by establishing a consolidated organic/metal bismuth ion architecture (COMBIA). Benefiting from hierarchical reversible noncovalent interactions, the COMBIA hydrogel exhibits an optimal combination of mechanical and functional properties, particularly its integrated mechanical properties, including unprecedented stretchability, fracture toughness, and resilience. Furthermore, these hydrogels demonstrate superior conductivity, optical transparency, freezing tolerance, adhesion capability, and spontaneous mechanical and electrical self-healing. These unified functions render our hydrogel exceptional properties such as shape adaptability, skin-like perception, and energy harvesting capabilities. To demonstrate its potential applications, an artificial skin using our COMBIA hydrogel was configured for stimulus signal recording, which, as a promising soft electronics platform, could be used for next-generation human-machine interfaces.