ChemSystemsChem最新文献

False knowledge is more misleading than no information, and the rising threats from counterfeitors demand advanced strategies of secure, multilayered encryption. The fluorescence-based routine systems have become recurrent, often requiring complicated synthesis and UV light for decoding. We have countered the common notion that novel applications need newer molecules by employing conventional molecules in unconventional ways. Motivated by nature, we implemented stimuli-responsive organic dyes to develop color-coded encryption—simple, harmless, and UV-free alternatives that preserve crucial security features. pH- or redox-responsive dyes were purposely selected so that time-dependent color changes can be displayed under chemically triggered nonequilibrium conditions. This feature was exploited in designing time-gated, multidimensional, diverse color codes. An alternative decryption method was demonstrated via smart windows—stimuli-sensitive barriers that regulated access across parallel time scales managed by independent triggers. Our strategy affords dual time-locked color codes with hierarchical security operated through orthogonal stimuli, achieving complex encryption with simple, synthesis-free decoding. A highly accurate indigenous decoding strategy is successfully demonstrated.

With the extensive use of nuclear energy, the disposal of radioactive iodine waste has become an issue in the energy industry. Herein, we reported a series of quadruple hydrogen-bonded supramolecular polymers combining 2-ureido-4[1H]-pyrimidinone (UPy) with Tröger's base (TB) for iodine adsorption: TB-UPys, demonstrated a volatile iodine adsorption capacity of 4 g·g⁻¹, with a second-order kinetic rate constant of 2.8 × 10⁻⁴ g·min⁻¹·mg⁻¹ for aqueous iodine adsorption. This work leverages the unique hydrogen bonding properties to construct stable and robust iodine adsorption materials, paving a new way forward for the future development of iodine adsorbents.

Catalyst-free dynamic covalent polyureas (DCPUs) have emerged as a promising class of environmentally friendly and sustainable materials that combine the mechanical robustness of traditional polyureas with the stimuli-responsive properties of dynamic urea bonds (DUBs). This review elaborates on the general concepts that have guided important developments of the recent advances in catalyst-free DUBs and DCPUs, focusing on the dynamic mechanisms of well-designed urea bonds, structure-property relationships, and diverse applications. The rational design of DUBs is discussed, highlighting their unique features in terms of DUBs' reversibility and kinetics. Furthermore, the mechanical properties, self-healing efficiency, thermal stability, reprocessability, and recyclability of DCPUs are analyzed, along with their potential applications in self-healing coatings, recyclable composites, shape-memory materials, 3D printing, and so on. Finally, the current challenges and future perspectives in this fast-growing field of DUBs and DCPUs are discussed.

Synthetic biology has enabled the development of new strategies for creating artificial cells that can sense and respond to external stimuli. This study introduces the bottom-up construction of globular protein vesicles (GPVs) that incorporate elastin-like peptide (ELP) bolaamphiphiles as transmembrane components. To enable this strategy, we devised a Golden Gate-based cloning strategy to streamline the design, expression, and purification of ELP bolaamphiphiles. Three ELP bolamphiphiles with varying structural complexity were developed, incorporating fluorescent proteins to facilitate visualization and characterization. The self-assembly of these bolaamphiphiles into GPVs was optimized by varying the molar ratios of recombinant building blocks. Structural characterization confirmed vesicle formation, dynamic light scattering analysis revealed size distributions dependent on modular complexity, and atomic force microscopy demonstrated that the vesicles exhibited MPa-range Young's moduli, indicative of high mechanical robustness. Our findings demonstrate that multifunctional ELP bolaamphiphiles can be incorporated into GPVs, enabling modular vesicle engineering. This work provides a foundation for designing synthetic cells with customizable bi-functionalities and modularity, advancing compartmentalized systems.

Nature extensively exploits matter and energy to sustain its structures and functions. Encouraged by the understanding of natural principles, researchers have been striving to develop “living” materials with self-sustained and adaptive features under the supply of energy. The past decade has witnessed a variety of fuels to drive such energy-dissipation out-of-equilibrium self-assembly. Recently, gas molecules, featured by their simple polyatomic structure and gaseous properties, have been considered as “green” triggers to guide self-assembly at thermal equilibrium. Nevertheless, whether gases could be used as a new family of fuel form to drive self-assembly operated in a dissipative state remains puzzling. Here, commencing from equilibrium assembly, we systematically introduce a new concept, called dynamic gas bridge (DGB), by which it can allow gases as molecular elements to regulate self-assembly processes and further serves as fuels to underpin the dissipative self-assembled systems. By discussing the emerging examples, we highlight the unique advantages of gas fuel from the perspective of gas dissipation. The rise of such systems would bring new opportunities and pave the way for gas-programmed living materials.

The emergence of life from abiotic chemical systems remains a central and unresolved question in natural sciences. Understanding how simple molecules gave rise to the first biomolecules and self-sustaining reaction networks requires integrating scarce experimental data with advanced computational methods. Machine learning (ML) is rapidly transforming research in prebiotic chemistry. In particular, ML-based interatomic potentials enable the exploration of complex reaction mechanisms at near-quantum accuracy and reduced computational cost. Beyond individual reactions, ML methods can also accelerate the study of chemical reaction networks by predicting transition states, reaction outcomes, and kinetic parameters. Here, we highlight recent methodological and conceptual advances, illustrating how ML complements traditional quantum chemical approaches. We further discuss emerging strategies for integrating data-driven models with experimental and theoretical frameworks, expanding the scope and efficiency of research into the chemical origins of life.