Materials Horizons最新文献

Continuous monitoring of pressure and temperature at skin interfaces is essential for preventing tissue damage and circulation-related complications in immobile patients. However, most existing healthcare pressure sensors remain bulky, wired, and battery-powered, which limit their suitability for long term use. Here, we report a battery-free, wireless multimodal sensing platform in which single-layer graphene functions as a high-performance pressure-sensing active layer, achieving high sensitivity (1.75 × 10^-3 kPa^-1, gauge factor = 8.6) and excellent stability (over 1000 operational cycles). The platform enables real-time, reversible detection of pressure and temperature at the skin-device interfaces without external power source. By leveraging deep-learning algorithms, particularly deep neural networks (DNNs), the acquired signals are classified into distinct sitting postures, thereby enabling intelligent and continuous monitoring of patient status. Furthermore, integrated augmented- and virtual-reality (AR/VR) interfaces visualize pressure distributions in real time, enabling immersive and remote healthcare oversight. Collectively, this work introduces a graphene-based smart sensing platform that seamlessly integrates wireless operation, AI-driven analytics, and AR/VR visualization for advanced patient monitoring as a sort of personalized and interactive smart healthcare.

The convergence of laboratory automation, artificial intelligence (AI), and data-driven science has catalyzed the emergence of self-driving laboratories (SDLs), autonomous platforms capable of designing, executing, and analyzing experiments with minimal human input. While early SDLs (SDL 1.0) demonstrated the feasibility of closed-loop discovery, their impact has been constrained by limited scope, poor interoperability, and reliance on human-curated heuristics. This review outlines the vision of SDL 2.0: a new generation of flexible, scalable, and collaborative discovery engines for chemistry and materials science. We discuss recent advances in modular hardware design, AI-driven decision-making including Bayesian optimization, computer vision, and large language models, and orchestration software that integrate scheduling, data management, and safety protocols. Building on these foundations, we propose six defining characteristics for SDL 2.0: interoperable, collaborative, generalizable, orchestrated, safe, and creative. Together, these features establish SDLs as globally networked platforms, enabling reproducible experimentation, accelerated innovation, and democratized access to advanced research infrastructure. By embedding modularity, AI reasoning, and community-driven standards into their core, SDLs 2.0 promise to transform not only how experiments are conducted, but also who can participate in and benefit from the accelerating pace of scientific discovery.

The physicochemical characteristics of nanoparticles (NPs) and the cell type they encounter impact cellular interactions. Yet, which parameters should be precisely controlled to direct endocytosis in specific cell types remains paradoxical. Here, we designed gold-liposome nanohybrids (GLNs) and demonstrated for the first time that the location of gold either inside, outside, or partially in/out of the liposomes enables simultaneous tunability of their physical, molecular, mechanical, and optical properties that go beyond the conventional paradigm of size, shape, and charge. Well-controlled chitosan layers on the liposomes allowed the modulation of the position of gold, generating three distinct GLNs of similar size but varied topology (smooth to uneven to textured), surface molecular composition (lipid-rich to inorganic gold), stiffness (4 to 50.5 MPa), tunable resonances (visible to near-infrared) and photothermal conversion efficiency. These collective properties of GLNs governed cellular interactions in two distinct cell types, dendritic cells (DC2.4) and epithelial cells (MODE-K). Our findings show that (i) endocytosis is cell-type dependent and temporally-controlled varying significantly for the three GLNs, (ii) cells show sensitivity to the endocytosis rate even within the narrow stiffness range studied here, and (iii) the properties of GLNs control their therapeutic function from photothermal heating or mild hyperthermia in MODE-K to optically-driven immunostimulation in DC2.4. Our findings may ultimately establish new mechanisms of NP-cell interactions enabling the development of a family of novel NPs with unexplored properties applicable for a range of biomedical applications.

Negative stiffness mechanical metamaterials have attracted significant attention for their potential in energy dissipation and impact mitigation. However, conventional elastic designs, such as curved beams exhibiting elastic snap-through buckling, suffer from an intrinsic trade-off between recoverable energy dissipation and load-bearing capacity, greatly limiting their engineering applicability. Here, we introduce a pseudoelastic design strategy for negative stiffness curved beam metamaterials by employing a shape memory alloy (SMA) as the base material. The pseudoelasticity of the NiTi SMA enables reversible martensitic transformation at a high-level strain, which couples with structural snap-through instability to achieve recoverable energy dissipation. This synergistic mechanism offers a unique pathway to overcome the dilemma between high strength and recoverable energy dissipation. Experiments reveal that the SMA-based metamaterials exhibit both high strength and reusable, recoverable energy dissipation. Compared to their conventional metallic or polymeric counterparts, the proposed design achieves up to a 28-fold enhancement in strength and a 6-fold improvement in specific energy dissipation. The presented approach establishes a new design approach for recoverable high-strength energy-dissipating metamaterials, promising for applications in vibration control, impact protection, and adaptive structural systems.

The functionalization of methane is regarded as the "holy grail" reaction in chemistry. Photocatalysis is an emerging approach that activates the inert C-H bond in methane under mild conditions, enabling its functionalization to produce sustainable chemicals. However, achieving selective C-H functionalization remains a significant challenge due to methane's "chameleon-like" behavior under diverse reaction conditions. In this review, we provide a comprehensive overview of recent advances in heterogeneous photocatalytic methane functionalization, covering the fundamental principles of photocatalysis and various synthetic systems, with an emphasis on the mechanisms underlying the structure-activity relationships of the microscopic structure of photocatalysts and the reactions. The potential future applications of artificial intelligence and machine learning in developing efficient methane functionalization systems are also highlighted, aiming to guide future research.

Wet-state ball milling of ceramic nanoparticles is analyzed by machine learning and machine-learning-assisted model formulation. A linear model formula is constructed from the high-impact input features revealed in the machine learning. The formula explains the relation between the ball-milling conditions and hydrodynamic size with less precision but better analytical processability compared to the original machine learning.

Microarray biochips offer an advanced platform for the precise spatial control of cell behaviour, enabling the investigation of how geometric constraints influence cell adhesion, morphology, and mechanosensitive signalling. Herein, a microengineered biochip is specifically designed to explore nuclear force-sensing mechanotransduction in human umbilical vein endothelial cells (HUVECs). The patterned substrate facilitates the organized assembly of focal adhesion (FA) nanoarchitectures and cytoskeletal structures by promoting integrin engagement to recruit key scaffolding proteins, including talin, vinculin, and actin filaments, along with myosin. These dynamic interactions between the extracellular matrix (ECM) and cytoskeletal tension form a mechanical interface for promoting efficient signal transduction in nuclei. Moreover, this mechanical interaction enhances the activation of the Piezo1 ion channel, a key sensor of mechanical stress in endothelial cells. Upon activation, Piezo1 induces calcium influx to trigger a cascade of downstream signalling pathways, responsible for cellular responses, such as proliferation, migration, and differentiation. The spatial confinement induced by the microarray-patterned biochips significantly amplifies the integrin-cytoskeleton-Piezo1 signalling axis, suggesting that microtopographical cues are critical for modulating nuclear force-sensing mechanotransduction in endothelial cells. This study provides a foundation for mechanically responsive biomaterials and mechanosensing.

The field of catalysis stands at a pivotal juncture. As societies worldwide grapple with the twin imperatives of sustainability and decarbonisation-from clean energy to carbon recycling, from environmental remediation to sustainable chemical manufacturing-the design of catalysts that are efficient, selective, stable, and scalable has never been more urgent. In this context, the cross-journal themed collection "Nanocatalysis" in Nanoscale Horizons and Materials Horizons offers a timely, high-impact platform for showcasing cutting-edge research at the interface of nanoscience, materials design, and catalysis.

Three-dimensional (3D) cell spheroids provide a powerful model for studying cellular behavior, tissue engineering, and drug screening. However, constructing heterogeneous microtissues from basic spheroids remains challenging, as it requires precise and biocompatible manipulation. Here, we present a method for 3D spheroid manipulation by incorporating microrobots, which, upon laser stimulation, induce thermophoretic fluid flow to actuate spheroid motion. The microrobots are incorporated into spheroids in a reliable manner, relying on cell-driven self-assembly. Locomotion of the microrobot-integrated spheroids is achieved by regulating the laser power (11.7-17.6 mW) and frequency (0.33 Hz), which leads to three characteristic modes of motion: jumping, vectoring, and pulling. The combination of these motions enables robust spheroid assembly with excellent biocompatibility. The system allows for the generation of complex tissue models, where fibrosarcoma (HT1080 cells) spheroids and healthy fibroblast (HDF cells) spheroids are assembled separately and then brought together using the microrobotic locomotion capabilities. The fusion of assembled HT1080 and HDF spheroids reveals cancer-stromal cell interactions and tissue integration, while a cancer-spheroid-centered radial arrangement of fibroblast spheroids demonstrates the construction of spatially sophisticated assembloids. This study establishes a versatile strategy for spheroid manipulation, advancing 3D microtissue biofabrication for in vitro disease modeling.

Bio-inspired cholesteric liquid crystal (CLC) patterns have sparked strong interest in their artificial biomimetic preparation due to the unique functions of helicoidal architectures in nature, such as camouflage protection in chameleons and structural color modulation in cephalopods. In recent years, patterned CLC materials have become a research hotspot in the fields of flexible smart devices, information storage, anti-counterfeiting technology, and smart windows, owing to their controllable optical properties and their ability to respond to various external stimuli. This article systematically reviews the preparation methods of patterned CLCs, including templating techniques and direct writing approaches, as well as the underlying regulatory mechanisms under external stimuli such as light, electric fields, temperature, mechanical force, solvents, humidity, and pH. Their practical applications in information encryption and decryption, optical anti-counterfeiting, and dynamic windows are also discussed. Finally, this article looks forward to the broad prospects of patterned CLCs in the development of intelligent functional materials and highlights the remaining challenges in terms of stability, response rate, and large-scale preparation, thereby laying a solid foundation for future research and technological translation in this field.