Matter最新文献

Innovative biopolymers emulating natural organisms’ reversible water-induced deformations hold great potential across various domains. Here, we create a biopolymer that unifies actuation, hydrosetting, and shape-memory capabilities through copper-coordinated mercerization of nanocellulose paper. This method transforms the inherently hydrophilic, porous nanocellulose network into a compact amphiphilic membrane, distinguished by Cu²⁺-crosslinked hydrophobic domains acting as tough “net points,” ensuring exceptional water stability and ultrahigh wet mechanical performance (94.9 MPa and 3.50 GPa). Upon hydration, the membrane swiftly establishes reversible hydrogen-bonding “switches,” enabling a rapid plastic-elastic transition. The interplay between the net points and switches resolves the inherent trade-off between rapid, reversible hydrogen-bonding networks and mechanical robustness in cellulosic materials, thereby facilitating remarkable water-induced actuation, hydrosetting, and shape memory. Notably, the membrane demonstrates complex morphing and swift recovery in water, serving as a smart encrypted information carrier. Our study offers a molecular structural engineering paradigm for the rational design of advanced responsive materials.

Foldable structures find diverse applications. Folding of thin structures into compact shapes involves the interplay of nonlinear mechanics and topology. In this study, we employ discrete models, theoretical analysis, and tabletop experiments to systematically investigate the geometrically nonlinear folding process of ring-shape elastic ribbons through in-plane kinks and out-of-plane creases. We find that kinks initiate continuous folding through supercritical bifurcation, while creases trigger abrupt snapping via subcritical bifurcation. Master curves that summarize energy landscapes for ribbons with varying numbers of kinks and creases are obtained. By integrating kinks and creases, a “meta-ribbon” can be created, which shows the tunable folding behavior, transitioning from continuous to snapping, or vice versa, by strategically engineering the in-plane and out-of-plane angles guided by the constructed energy map. As a product of folding, we demonstrate the snapping-induced vibration accomplished with dynamic folding, as well as the multistability of meta-ribbons with saddle-like configurations and their transformation.

Self-driving laboratories (SDLs), which combine automated experimental hardware with computational experiment planning, have emerged as powerful tools for accelerating materials discovery. The intrinsic complexity created by their multitude of components requires an effective orchestration platform to ensure the correct operation of diverse experimental setups. Existing orchestration frameworks, however, are either tailored to specific setups or have not been implemented for real-world synthesis. To address these issues, we introduce ChemOS 2.0, an orchestration architecture that efficiently coordinates communication, data exchange, and instruction management among modular laboratory components. By treating the laboratory as an “operating system,” ChemOS 2.0 combines ab initio calculations, experimental orchestration, and statistical algorithms to guide closed-loop operations. To demonstrate its capabilities, we showcase ChemOS 2.0 in a case study focused on discovering organic laser molecules. The results confirm ChemOS 2.0’s prowess in accelerating materials research and demonstrate its potential as a valuable design for future SDL platforms.

The classical central paradigm of structural biology links a protein’s sequence to its structure and function but overlooks conformational fluctuation that is integral to protein function. We propose a graph neural network model based on gated attention that explicitly incorporates protein dynamics via physics-based models to predict protein crystallization propensity. We compare results to all-atom molecular dynamics simulations of flexible, disordered human tropoelastin and ordered, globular human lysyl oxidase-like protein. Our findings show that fluctuating residues correlate with locally maximal attention scores in the neural network. By methodically truncating the sequences, we establish correlations between dynamical and physicochemical molecular properties and protein crystallization propensity. Accounting for comprehensive biological mechanisms, our tool can facilitate the rational design of protein sequences that lead to diffraction-quality crystals. Our study showcases the integration of physics-based and machine learning models for structure and property prediction, expanding the classical paradigm of structural biology.

A promising mechanism for achieving colossal dielectric constants involves the use of insulating internal barrier layers, such as insulating domain walls in ferroelectrics. A key advantage of domain walls, compared to other stationary interfaces, is their mobility, offering the potential for post-synthesis adjustment of the dielectric constant. In this work, we demonstrate that altering the domain wall density enables the tuning of the dielectric constant in our template material, i.e., hexagonal ErMnO₃ single crystals. Through microscopy and macroscopic dielectric spectroscopy, we quantify changes in domain wall density and correlated these with changes in dielectric constant within a single sample. Analysis of the dielectric data suggests that the insulating domain walls act as “ideal” capacitors connected in series. Our approach to engineering the domain wall density can be readily extended to other control methods, e.g., electric fields or mechanical stresses, providing a degree of flexibility to in situ tune the dielectric constant.