Nature Materials最新文献

Cells use ‘active’ energy-consuming motor and filament protein networks to control micrometre-scale transport and fluid flows. Biological active materials could be used in dynamically programmable devices that achieve spatial and temporal resolution that exceeds current microfluidic technologies. However, reconstituted motor–microtubule systems generate chaotic flows and cannot be directly harnessed for engineering applications. Here we develop a light-controlled programming strategy for biological active matter to construct micrometre-scale fluid flow fields for transport, separation and mixing. We circumvent nonlinear dynamic effects within the active fluids by limiting hydrodynamic interactions between contracting motor–filament networks patterned with light. Using a predictive model, we design and apply flow fields to accomplish canonical microfluidic tasks such as transporting and separating cell clusters, probing the extensional rheology of polymers and giant lipid vesicles and generating mixing flows at low Reynolds numbers. Our findings provide a framework for programming dynamic flows and demonstrate the potential of active matter systems as an engineering technology.

Machine learning algorithms have proven to be effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of a chiral edge state and a topological surface state. The memristive switching and reading of the giant anomalous Hall effect exhibit high energy efficiency, high stability and low stochasticity. We achieve high accuracy in a proof-of-concept classification task using four magnetic topological memristors. Furthermore, our algorithm-level and circuit-level simulations of large-scale neural networks demonstrate software-level accuracy and lower energy consumption for image recognition and quantum state preparation compared with existing magnetic memristor and complementary metal-oxide-semiconductor technologies. Our results not only showcase a new application of chiral edge states but also may inspire further topological quantum-physics-based novel computing schemes.

Correction to: Nature Materials https://doi.org/10.1038/s41563-024-02064-y, published online 20 January 2025.

Electrocatalysts alter their structure and composition during reaction, which can in turn create new active/selective phases. Identifying these changes is crucial for determining how morphology controls catalytic properties but the mechanisms by which operating conditions shape the catalyst’s working state are not yet fully understood. In this study, we show using correlated operando microscopy and spectroscopy that as well-defined Cu₂O cubes evolve under electrochemical nitrate reduction reaction conditions, distinct catalyst motifs are formed depending on the applied potential and the chemical environment. By further matching the timescales of morphological changes observed via electrochemical liquid cell transmission electron microscopy with time-resolved chemical state information obtained from operando transmission soft X-ray microscopy, hard X-ray absorption spectroscopy and Raman spectroscopy, we reveal that Cu₂O can be kinetically stabilized alongside metallic copper for extended durations under moderately reductive conditions due to surface hydroxide formation. Finally, we rationalize how the interaction between the electrolyte and the catalyst influences the ammonia selectivity.